Climatology and Scenarios up to the 2050S

Extreme weather impacts on European networks of transport Grant Nr 233919

Co-funded by the European Commission under the 7th Framework Programme, Transport, Horizontal activities

D2.1 Probabilities of adverse weather affecting transport in Europe: climatology and scenarios up to the 2050s

Project: EWENT Document Number and Title: D2.1 (In DoW: Geographical mapping and final report the probabilities of weather extremes and harmful events and scenarios in Europe) Work-Package: WP2 Deliverable Type: Report Contractual Date of Delivery: 31.1.2011 Actual Date of Delivery: 19.8.2011 Author/Responsible(s): FMI Contributors: FMI: Andrea Vajda, Heikki Tuomenvirta, Pauli, Jokinen, Anna Luomaranta VTT: Lasse Makkonen, Maria Tikanmäki ESSL: Pieter Groenemeijer Foreca: Pirkko Saarikivi CYMET: Silas Michaelides, Matheos Papada- kis, Filippos Tymvios, Spyros Athanasatos Approval of this report: by the EC Summary of this report: Keyword List: WP3, D2, EWENT, extreme weather, transport system, consequences Dissemination level: Public (PU)

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Version history

Version Date Status Author (Partner) Description 1 01.11.2010 1st draft Heikki Tuomenvirta, Andrea outline Vajda, Pauli Jokinen (FMI) 2 13.04.2011 2nd draft Andrea Vajda, Heikki Tuo- first version menvirta, Pauli Jokinen (FMI) 3 10.05.2011 3rd draft Andrea Vajda, Heikki Tuo- nearly complete versi- menvirta, Pauli Jokinen (FMI) on 4 14.05.2011 4th draft Andrea Vajda, Heikki Tuo- complete menvirta, Pauli Jokinen (FMI) 5 01.07.2011 final Andrea Vajda, Heikki Tuo- complete with partners menvirta, Pauli Jokinen (FMI) comments included

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Website http://ewent.vtt.fi

This publication has been produced with the assistance of the European Union. The contents of this publication are the sole responsibil- ity of EWENT Consortium and can in no way be taken to reflect the views of the European Union.

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Executive summary

Adverse and extreme weather events, such as heavy rain, heavy snowfall, strong winds, extreme heat and cold, drought and reduced visibility can have negative impact on the transport sector, causing injuries and damages as well as other economic losses. Frequency and intensity of weather and climate extremes are likely to continue to change in the future due to the projected climate change; consequences of changes will be both negative and positive for transportation.

This Deliverable frames the findings of Work Package 2 (WP 2) of the EWENT project. The WP 2 focuses firstly on providing a comprehensive climatology of the adverse and extreme weather events affecting the European transport system by estimating the frequency (or probability) of phenomena for the present climate focusing mostly on the time period 1971-2000. Secondly, it provides an overview of projected changes in the adverse and extreme weather events that are most likely to affect the European transport network in the future climate until the 2050s.

The analysis of the relevant weather phenomena takes into account the ranking and impact threshold values defined from the viewpoint of different transport modes and infrastructure within WP1. Three impact threshold indices were studied for each of the following phenomenon: wind, snow, blizzard, heavy precipitation, cold spell and heat wave. In addition, visibility conditions determined by fog and dust events, small-scale phenomena affecting transport system, such as thunderstorms, lightning, large hail and tornadoes and events damaging infrastructure of the transport system have been analysed.

In order to estimate the frequency and probability of past extreme weather phenomena we have applied the following data sets: E-OBS European high resolution land-only gridded dataset, the ECMWF provided ERA-Interim re-analysis dataset, the European Severe Weather Database (ESWD), the NOAA National Climatic Data Centre database, the METAR aviation weather report data, observational data from meteorological services and road weather network.

Future changes in the probability of adverse and extreme weather phenomena are assessed based on six high resolution (ca. 25x25 km2) Regional Climate Model (RCM) simulations produced in the ENSEMBLES project. The studied time horizons are the 2020s (2011-2040) and the 2050s (2041-2070).

The analysis of the observational data draws a regional distinction in the probability of extreme weather events over the European continent. Northern Europe and the Alpine region are the most impacted by winter extremes, such as snowfall, cold spell and winter storms, while the probability of extreme heat spells is the highest in Southern Europe. Extreme winds and blizzards are the most common over the Atlantic and along its shores. Although heavy rainfall may impact on yearly basis the whole continent, the very extreme rainfall events (over 100 mm/24 h) are relative sparse. Visibility conditions indicate a pan-European improvement over the studied decades, severe fog conditions becoming almost non-existent at some of the main European airports.

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The multimodel mean climate projections indicate robust changes in temperature extreme, but less coherent in precipitation and wind extremes. Both cold extremes and snow events are likely to become rarer, especially in the north where the extreme cold might shorten by 30-40 days/year by the 2050s. This change involves a positive impact on road, rail and air transportation reducing the cost of maintenance. On the other hand, heavy snowfalls (>10 cm/day) are not expected decrease, instead models project 1-5 days/year increase over Scandinavia. Extreme heat is likely to intensify over the entire continent, being more accentuated in the south (by 30-40 days/year). Due to this intensification, by 2050s the Central European countries are expected to experience as many heat spells as currently in the Mediterranean region. Heavy rainfall events indicate less significant change (1-5 days/year) or no change (e.g. in Mediterranean) for the future climate scenarios. Changes in wind extreme were more difficult to assess, since the models displayed somewhat con- flicting results. The analysis of the Baltic Sea ice cover indicates a probable decrease in the frequency of severe ice winters. The average maximum fast ice thickness becomes thinner everywhere in the area, in the southern parts the sea ice will possibly disappear totally by 2060. Al- though the changes in probabilities of thunderstorm-related hazards in the future climate have not been approached in the present project, earlier studies were not able to indicate changes in thunderstorm occurrence over Europe. However, the fraction of thunderstorms that may develop hazardous phenomena such as hail, severe wind gusts and tornadoes may increase.

Due to the various climatic patterns, different regions of Europe are impacted by different extremes. In order to facilitate the assessment of impacts and consequences of extremes phenomena on European levels we created a map of the European climate zones. Based on the frequency and probability analysis we differentiated six main climate regions. Future changes in the frequency of weather extremes for the major European transportation nodes (ports, airports, road, rail and inland transportation) and corridors are also derived based on six RCMs. The RCM projections provide plausible scenarios of detailed regional change, consistent with larger-scale changes lying within the wider uncertainty envelope defined by the three GCM projections driving the simulations of six RCMs used in this project. The low number of climate models used did not allow probabilistic quantification of uncertainty, however, the range of projected changes is shown.

Our study suggests that changes in adverse and extreme weather events affecting the European transportation network are of both signs. Extreme heat events are likely to increase in many regions, most accentuated in south, on the other hand extreme cold and snow events are expected to decline. However due to variability from year to year, winter extremes are still expected to hazard the transportation.

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Table of contents

1. INTRODUCTION...... 7

2. CLIMATOLOGY OF ADVERSE WEATHER CONDITIONS AFFECTING TRANSPORT...... 9

2.1. Description of gridded observational data sets ...... 9

2.2. Description of the European Severe Weather Database...... 12

2.3. The extreme value analysis method used...... 13 2.3.1. The General Extreme Value Function GEV...... 13 2.3.2. Plotting positions ...... 14 2.3.3. Inference by GEV...... 15 2.3.4. Selection of number of extremes included in the analysis ...... 16 2.3.5. The fitting method...... 16

2.4. Wind...... 18

2.5. Snow ...... 19

2.6. Blizzard ...... 22

2.7. Ice formed by freezing precipitation ...... 23

2.8. Heavy precipitation ...... 24

2.9. Heat spells ...... 26

2.10. Cold waves...... 28

2.11. Poor visibility...... 29 2.11.1. Fog...... 29 2.11.2. Dust transport episodes...... 32

2.12. Small scale phenomena – thunderstorm and associated phenomena ...... 38 2.12.1. Lightning...... 38 2.12.2. Thunderstorm-related gusts ...... 39 2.12.3. Large hail and tornadoes...... 40

2.13. Events damaging infrastructure ...... 42

3. SCENARIOS OF ADVERSE WEATHER CONDITIONS...... 46

3.1. Description of model simulations...... 46

3.2. Description of scenario construction ...... 48

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3.3. Other methods of downscaling to road network...... 49

3.4. Scenario maps (2011-2040) ...... 51 3.4.1. Wind ...... 51 3.4.2. Snow...... 52 3.4.3. Blizzard...... 52 3.4.4. Heavy rainfall ...... 54 3.4.5. Heat spells ...... 55 3.4.6. Cold spells...... 56

3.5. Scenario maps (2041-2070) ...... 57 3.5.1. Wind ...... 57 3.5.2. Snow...... 58 3.5.3. Blizzard...... 60 3.5.4. Heavy precipitation...... 61 3.5.5. Heat spells ...... 62 3.5.6. Cold spells...... 63

3.6. Further scenarios for focused purposes ...... 64 3.6.1. Events damaging infrastructure ...... 64 3.6.2. Climate projections of thunderstorm-related hazards...... 66 3.6.3. Changes in the Baltic Sea ice cover by 2060...... 67 3.6.4. Scenarios on road networks: a Nordic view ...... 72

4. DISCUSSION AND SUMMARY...... 76

ACKNOWLEDGEMENTS...... 84

REFERENCES ...... 85

Appendix A: Classification of weather patterns 90

Appendix B: Projected changes for selected transport routes and nodes 94

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1. Introduction

The EWENT project (Extreme Weather impacts on European Networks of Transport) funded by the European Commission under the 7th Framework Programme (Transport, Horizontal Activities) has the objective of assessing extreme weather impacts on the European transport system. The goal of EWENT is to estimate and quantify in monetary terms the disruptive effect of extreme weather events and to identify strategies to make the transport system more resilient against extreme weather phenomena in a changing climate.

This report contains the main results of the Work Package 2 (WP2) of the EWENT project. The ob- jectives of EWENT WP2 were: (1) to develop a first comprehensive European climatology of extreme weather events relevant to transport system to support socio-economic and technical research and decision making, and (2) to estimate the changes in extreme weather event intensities and frequencies of occurrence relevant for the transport system.

The analyses attempt to cover the time period from the 1970s up to the 2050s, however, there are limitations in the availability of data. For some weather elements and phenomena studies cover only the past ten to twenty years. The selection of weather events to be analyzed and their defini- tions are based on the results of the preceding EWENT WP1, which identified the extreme weather events relevant within the European transport system context. WP1 D1 report “Extreme Weather Impacts on Transport Systems” defines threshold values for critical weather parameters (Leviäkangas et al., 2011). A three level category for thresholds was developed for many of the adverse weather phenomena. The qualitative description being: 1. threshold: Adverse impacts to the transport system may start to occur. For example, if the resilience of the transport system against the phenomena in-question is at low level. 2. threshold: Some adverse impacts are likely. Their severity depends on the resilience of the transport system. 3. threshold: Weather phenomena is so severe that it is virtually certain that some adverse impacts will occur.

In order to present EWENT work and get feedback from the atmospheric sciences community EWENT WP2 organized a “Workshop on Estimation of Probabilities of Extreme and Harmful Weather Events in a Changing Climate” at the 10th EMS Annual Meeting/8th European Confer- ence on Applied Climatology (ECAC) on 13 – 17 September 2010 in Zürich, Switzerland. The presentations of the workshop are available at the EWENT web-site (ewent.vtt.fi). This workshop activity constitutes the deliverable D2.2 of WP2.

A parallel running ECCONET project (EU FP7 Transport, Horizontal Activities) assesses the effect of climate change on the inland waterway transport network. ECCONET project produces scenarios utilizing climate and hydrological models. Low water levels are of special concern to inland water transport and ECCONET scenarios are specifically constructed to analyze these low flow, drought-like conditions. Therefore, this EWENT does not address drought events.

The structure of this report is the following. Chapter 2 describes observational data used and methodology development in the analyses and presents climatology of weather conditions affecting

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Grant No. 233919 transport. Chapter 3 starts with description climate model simulations used in the construction of scenarios of harmful weather events. Scenario maps are presented as changes in frequencies or probabilities. Chapter also depicts the changes in selected events affecting the infrastructure; syn- opsis of an earlier study on assessment of changes in thunderstorms in climate model simulation; projected changes in the Baltic Sea ice cover and climate scenarios for Nordic road network. In the final chapter the results are discussed in a broader context of related studies. A summary of results is presented for six climate regions covering Europe.

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2. Climatology of adverse weather conditions affecting transport

The analysis of the relevant adverse and extreme weather phenomena takes into account the ranking and impact threshold values defined from the viewpoint of different transport modes, such as road, rail, aviation, waterways, and infrastructure within WP1. Threshold indices are defined as number of days on which a variable falls above or below a fixed threshold. In the present study three impact threshold indices were defined for each of the following phenomenon (see WP1): wind, snow, blizzard, heavy precipitation, cold spell and heat wave, in such a way that those can be applied for the entire European continent.

The estimation of the recent and past (1971-2000) adverse weather events is based on the observed data available from the meteorological services, from the E-OBS dataset and the ERA- Interim re-analysis dataset. A range of statistical methods are applied to define the features of these extremes, such as their probability, changes in the spatial extension, intensity and temporal duration.

2.1. Description of gridded observational data sets

In order to assess the spatial and temporal variation of adverse weather conditions over the Euro- pean continent two gridded datasets were used: the E-OBS dataset (Haylock et al., 2008) produced through spatial interpolation of daily station data and the reanalysis dataset produced within the ERA project carried out by the European Center for Medium range Weather Forecasting (ECMWF) which through a multisource data assimilation system describe the state of the atmosphere, land and ocean-wave condition (Uppala et al., 2005).

The E-OBS European high resolution land-only gridded dataset of daily surface temperature and precipitation has been developed within the EU-funded ENSEMBLES project. The mean, maximum, minimum temperature and precipitation was derived through interpolation of the ECA&D (European Climate Assessment and Data) station data described by Klok and Klein Tank (2008). The station data set comprises a network of over 2300 stations, with the highest station density in Ireland, the Netherlands and Switzerland, and lowest density in Spain, Northern Africa, the Balkans and Northern Scandinavia. The number of stations used for the interpolation differs through time and by variable. The full period of record used for interpolation is 1950–2009, but the period 1961– 1990 has the highest density (Hofstra et al., 2009). The total number of series is higher for precipitation than for temperature. Inhomogeneities in the station time series have been flagged, but potentially inhomogeneous stations have been used for the interpolation, for reasons noted above.

The E-OBS data set has been derived through a three stage process (Haylock et al., 2008): Inter- polating monthly means (totals) of temperature (precipitation) to a rotated pole 0.1 degree latitude by longitude grid using three-dimensional (latitude, longitude, elevation) thin-plate splines to define the spatial trend of data. Daily anomalies, defined as the departure from the monthly mean (total), were interpolated to the same 0.1 degree grid, and applied to the monthly mean grid. For temperature, daily anomalies were interpolated using kriging with elevation as an external drift factor. For precipitation indicator kriging has been used to interpolate daily anomalies, where the state

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(wet/dry) of precipitation was first interpolated, after which the magnitude at “wet” 0.1 degree grid points was interpolated using universal kriging (Haylock et al., 2008, Hofstra et al., 2009). Finally, the 0.1 degree points have been used to compute area-average values at the four E-OBS grid resolutions (0.25° and 0.5° regular latitude-longitude grid and 0.22° and 0.44° latitude-longitude rotated pole grids). Details on the interpolation methods, their implementation and the calculation of uncertainties are available from Haylock et al. (2008).

In this study, we have been used the 0.25° regular latitude-longitude grid for further evaluation, as results for the other grids are essentially on a similar grid resolution. Since the interpolation methodology has smoothed the magnitudes of extremes in the variation of variables, we have applied correction factors for precipitation (0.66) and maximum temperature data (-1.1 °C) indicated by the cross-validation with station observation (Haylock et al., 2008).

The ERA-40 and ERA-Interim datasets cover the period of 1957-2002 and from 1989 to present day respectively. The ERA-40 (Uppala et al., 2005) dataset uses 3D-variational analysis method on a spectral grid with triangular truncation of 159 waves (corresponds approximately to 125 km) and a hybrid vertical coordinate system with 60 levels. ERA-Interim (Simmons et al., 2006) uses 4D-variational analysis on a spectral grid with triangular truncation of 255 waves (corresponds to approximately 80 km) and a hybrid vertical coordinate system with 60 levels.

The ERA-40 system produces four analyses per day (00, 06, 12 and 18 UTC), two forecast per day to 6 hour ahead (06 and 18 UTC) and two forecast per day to 36 hours ahead (00 and 12 UTC). The fields are saved on the T159 reduced Gaussian grid with spacing of 1.125°. The ERA-Interim data assimilation and forecast system produces four analyses per day (00, 06, 12 and 18 UTC) and two 10-day forecast per day, initialized from analysis at 00 and 12 UTC. Fields from the atmospheric model are archived on the N128 reduced Gaussian grid with a spacing of about 0.703125° (Berrisford et al., 2009).

The two ERA product sets include analyses of temperature and dew point at a height of 2 m. These analyses are produced six hourly as part of the data assimilation, but are not provided directly by the primary variational analysis of atmospheric fields. Instead, separate analyses of near- surface temperature and relative humidity are made using optimal interpolation (OI) of data from screen-level measurements. In a first step, reported values of temperature and dew point from the synoptic network are converted into values of relative humidity. The corresponding background values are provided by the background forecasts of the full variation data assimilation, which em- ployed 6 hourly cycling in ERA-40 and uses 12 hourly cycling in ERA-Interim. The optimal interpolation analysis for near-surface temperature is described by Simmons et al. [2004], and the scheme for relative humidity is generally similar. The background and observation errors are set at 5% and 10%, respectively, for relative humidity, and observations are used only if the derived relative humidity is in the range from 2 to 100%. The resulting relative-humidity analysis is likewise bounded between 2 and 100%, and values are converted to dew point temperature for archiving.

The 2 m temperature and humidity analyses are not used to modify the model-level atmospheric fields from which the background forecast for the next analysis in the data assimilation sequence is initiated. They are, however, used as input to an analysis of soil moisture and temperature (Douville et al., 2000). They thus influence the background forecast through the resulting adjust- ments to the model's initial soil moisture and soil temperature fields.

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Screen-level observations of temperature are used only in the analysis of 2 m temperature, and not directly in the variational analysis of temperature higher in the boundary layer. In contrast, the screen-level observations of humidity are used also in the variational analysis of specific humidity at higher levels. The ERA-40 3D-Var used the screen-level humidities at all times of the day, whereas the ERA-Interim 4D-Var uses them only in daytime, to avoid an inappropriate upward spreading of information above shallow nocturnal boundary layers. The variational boundary layer humidity analysis over land is also influenced significantly by humidity measurements from ra- diosondes (Andersson et al., 2007). This use of surface and radiosonde measurements in the variational analysis influences the background fields at 2 m height used in the separate OI analysis of screen-level humidity measurements.

The sea surface temperature (SST) analysis for ERA-40 was based on the monthly HadISST1 data set (Rayner et al., 2003) up to November 1981, and the weekly U.S. National Centers for Envi- ronmental Prediction (NCEP) two-dimensional variational data assimilation (2D-Var) data set (Reynolds et al., 2002) thereafter until June 2001. Interpolation was used to produce daily values from each data set. Daily operational NCEP products were used after June 2001. ERA-Interim used the same SSTs as ERA-40 in the period of overlap, and continued to use the daily operational NCEP data stream over the additional period from 2002 to 2008 considered in this paper. Further information on the ECMWF data assimilation system can be found in the online documen- tation available from http://www.ecmwf.int. The analyses themselves are also available from the same website: they can be downloaded directly with resolutions of 1.125° for ERA-40 and 0.7° for ERA-Interim.

Although the ERA-Interim data does not cover the entire study period (1971-2000), the enhanced data assimilation system and the better spatial resolution provided in this reanalysis dataset in comparison with the ERA-40 data justified the use of the ERA-Interim data in the present study. Furthermore a comparison between the wind gust data forecasted by the two reanalyses datasets indicated a more accurate estimation for the strong wind gust frequency in the ERA-Interim. Similar results were indicated by earlier analysis of the wind storm frequency in the two reanalyses (Kåll- berg, 2008).

The calculation of probabilities and frequencies of adverse weather phenomena utilizes the two datasets as follows: - 2-m daily mean temperature values from E-OBS data set for cold spell, - 2-m daily maximum temperature values from E-OBS data set for heat wave, - total daily precipitation from E-OBS data set for heavy rainfall, - 2-m daily mean temperature (T ≤ 0 °C) and total daily precipitation from E-OBS data set for snowfall calculation - 6-hour forecasted 10 m wind gust available from ERA-Interim dataset for wind gust - 6-hour forecasted 10 m wind gust and precipitation sum, 6-hour reanalysed 2-m mean temperature from ERA-Interim dataset for calculation of blizzard frequency.

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Figure 2.1. Observed gusts and gusts in the ERA-INTERIM dataset for Stuttgart and Helsinki for each day in the period 1990-2009 (Stuttgart) and 1997-2009 (Helsinki). Blue crosses occurred in the months Sep-Apr, red crosses are May-Aug.

The representation of some extreme wind gusts caused by convective storms is problematic in the ERA datasets. This is not surprising, because ERA six-hour gust fields are derived from a grid (N128 Gaussian truncation) that is much too coarse to resolve the thunderstorms producing them. Fig. 2.1 (left) shows that in Stuttgart (southern Germany) a sizeable fraction of the summertime gusts (in red) tend to be more intense than forecast, whereas this is much less the case in winter. Stuttgart is known for experiencing many heavy thunderstorms in summer. For Helsinki, in which thunderstorms are rarer and less intense on average, the ERA-INTERIM appears to represent summertime gusts much better, and not evidently worse than those in winter. The above illustrates that data from ERA nor any other reanalysis or climate model with similar resolution can be used directly to estimate the occurrence of gusts that occur with convective storms. Instead, advanced methods are required to do so, but have, as far as the authors are aware, not yet been developed.

The coordinates of area covered by the two datasets in the present study are: 32 °N, 25 °W and 72 °N, 45 °E for the E-OBS data, respectively 30.937 °N, 26.018 °W and 73.124 °N, 45.7 °E for the ERA-Interim reanalyses data.

In addition for calculation of the frequency of freezing precipitation, the 16-year NOAA National Climatic Data Centre database of surface observations at airports covering the period 1982-1997 was used (Lott, 2000). Further observation data has been used in the evaluation of fog and dust occurrence, all of them being described in Chapter 2.10.

2.2. Description of the European Severe Weather Database

The European Severe Weather Database (Dotzek et al., 2009) contains an ever-growing collection of reports of individual severe weather events and is managed by the European Severe Storms Laboratory (ESSL). The events contained in the database are typically of a local and short- lived nature, and many of them are associated with severe thunderstorms. They include hail (2 cm

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Grant No. 233919 or larger), tornadoes, severe wind gusts (25 m/s or stronger), flash floods and lesser whirlwinds (dust devils, gustnadoes). As a part of EWENT, the scope of the database is expanded to include damaging lightning, heavy snow, freezing rain and avalanches. The data in the database originates from various sources, including reports by the general public, by media, by several National Mete- orological Services (NMS) and by Voluntary Observing Networks (VON). Most data is not measured by conventional measurement stations, but actually complements these networks by providing additional data that is focused on extreme events.

The nature of the dataset and its sources requires that a thorough system of quality control be in place. Indeed, this process is carried out by ESSL and its NMS and VON partners. The aim of the process is to assign any of four quality levels to each report, the lowest of which results in immedi- ate deletion of the report. These QC-levels are:

 QC0: “as received” (new report, only retained if at least plausibility can be ascertained);  QC0+: “plausibility checked” (assigned by VON, NMS or ESSL);  QC1: “report confirmed” by reliable sources (assigned by VON, NMS or ESSL);  QC2: “event fully verified” i.e. all information about this event is verified, consistent and comes from reliable sources (assigned by NMS or ESSL).

Figure 2.2. All severe weather events contained in the ESWD as of March 2011.

As of March 2011, the ESWD dataset, which is being expanded by two EWENT partners (FMI and CYMET), contains 34423 severe weather events (Fig. 2.2). The data set is used for climate moni- toring, climatological studies, development of detection algorithms using remote-sensing data, operational forecasting, and forecast verification.

2.3. The extreme value analysis method used

2.3.1. The General Extreme Value Function GEV In most design standards and other engineering codes, as well in hydrological and meteorological literature the measure of the expected frequency of rare events is the return period R (in years).

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Typically, in standards, such as the Eurocode, the 50-year return value x50 is utilized (e.g. Anon, 2001). This is the value of the variable x that is exceeded by an annual maximum once in 50 years in the mean. The return period R of an event is related to the probability P of not exceeding this event in one year by 1 R  (1) 1 P Hence, the 50-year return period corresponds to the annual probability of exceedance of 0.02. De- termining R for some value of x or, vice versa, the return value x50 from measured or simulated climate data requires statistical analysis of the cumulative distribution function F(x) of the extremes. In this, the extreme value theory (Fisher and Tippett, 1928, Gumbel, 1958) has been commonly utilized. The advantage of the extreme value theory is that, ideally, the original parent distribution and its cumulative distribution function need not to be known, because the distribution of the extremes asymptotically approaches a known distribution.

In the case of annual extremes, considered here, this distribution is the generalized extreme value distribution GEV

xF  1exp x   1   (2) where α > 0 and 1 + γ(x-μ)/α > 0. GEV is a three parameter distribution, the limiting value of which at γ = 0 is the Gumbel distribution

xF expexp  y  (3) where y is the reduced variate

xy   / (4)

Here μ is the location parameter and α is the scale parameter. The GEV distribution has a finite upper tail for the shape parameter γ < 0 (Weibull), whereas for γ > 0 (Frechet) and γ = 0 (Gumbel) there is no upper bound.

2.3.2. Plotting positions The cumulative distribution function of n number of annual maxima is estimated from the order statistics so that each maximum is assigned a rank m in ascending value 1  m  N. For any analysis of the order ranked extremes one needs to associate them to some probability, the so called plotting position. This is done here by the formula

m NmP  1 (5) as we have shown (Makkonen 2006, 2008a,b) that the only correct plotting position when estimating return periods is that given by eq. (5) for any distribution of extremes. In the earlier literature and related software, the issues of plotting and fitting are often combined erroneously. This is due to the widely spread wrong belief, that the CDF of the data should some- how affect the correct plotting positions, so that linearity is achieved. For example, the modified Gumbel method, the probability weighted moments, the alternative probability weighted moments,

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Grant No. 233919 the maximum likelihood method and the method of penultimate extreme value distributions all produce incorrect probability estimates in their published forms due to adopting a wrong plotting formula.

2.3.3. Inference by GEV The fit to estimate the parameters μ and α is traditionally performed on a Gumbel probability graph where the ordinate is the reduced variate from the estimated F(x) in Eq. (3), plotted as y = -ln(- ln(F(x))) and the abscissa is the variate value x. This plot transforms the Gumbel distribution model in Eq. (3) into a line with a slope of 1/α and intercept μ, as shown in eq. (4).

There are a number of ways to fit a distribution to the plotted data. In addition to the simple method of least squares, methods that weigh each extreme according to its theoretical statistical confidence have been proposed (Lieblein, 1974, Landwehr, et al., 1979, Hosking, 1985, Hosking et al., 1985, Wang, 1991, Castillo, 1988, Katz et al., 2002). In addition to the different statistical confidence of different ranks, the problem with weighing them is related to the possibility that the highest extremes may not belong to the same population as the more moderate extremes.

Once the fit is made and the parameters α and μ are determined, one can calculate x that corresponds to any probability F(x) = P from and the corresponding return period R. Often, one will need to estimate extreme values of X that are higher than those included in the data. Graphically this corresponds to extrapolation along the fit on the graph. Here, such an extrapolation is made from the 30-year simulation data to the 50-year return value.

Commonly, the Gumbel method, eq. (3) has been used solely. The background of this is in the theory of extremes, which implies that an unbounded cumulative distribution function of the annual extremes is eq. (3). This has often been taken too literally in spite of many words of warning (Gumbel, 1958, Cook, 1982, Galambos and Macri, 1999, Makkonen, 2008a). The theory only shows that the distribution of extremes asymptotically approaches the theoretical extreme value distribution when the number N of independent observations in each observation period from which the extreme is abstracted increases. Acknowledging that the two-parameter Gumbel distribution may not always be a good model of the observed extremes, similar fitting methods may be applied to estimating the three parameters of a GEV distribution, Eq. (2) as well. When the data show that the shape parameter deviates significantly from zero, then, on a Gumbel-plot, the order ranked points form a curve that is concave up or concave down depending on the sign of γ. When the data show γ < 0, this has often been inter- preted to indicate that the tail of the cumulative distribution function is bounded. However, as discussed above, and previously by Galambos and Macri (1998), Cook et al. (2003) and Harris (2004), this interpretation is incorrect when the number of observations per each extreme is small, and the points may thus be positioned nonlinearly on also for an unbounded distribution.

Accordingly, we use here GEV, but not for reasons related to the extreme value theory only. We simply use GEV to make a convenient empirical three-parameter fit to the simulated order ranked annual extremes. Additional support for this procedure comes from the finding (Furrer and Katz 2008) that GEV fits well also to extreme data are not converging fully to an asymptotic extreme value distribution, i.e. in cases of so-called penultimate distributions. Since our method aims at an empirical fit, all three parameters of GEV are allowed to vary freely at every grid point and for all the data analysed.

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2.3.4. Selection of number of extremes included in the analysis The selection of the number of biggest extremes used in the fitting is subjective and comparable to the problem of selecting the threshold in the peaks-over-threshold method that utilizes the General- ized Pareto Distribution (Brabson and Palutikof, 2000). The choice may be based on the degree of prior belief on some specific distribution or on physical understanding on which of the highest extremes belong to the same population. In fitting the GEV to the order ranked extremes from the 30- year simulations we use the 15 biggest annual extremes. This is a sufficient number to allow fitting of a three parameter GEV while limiting the analysis to those extremes that are most relevant to extrapolating to the 50 year return value.

However, in some cases the variable X may have an absolute lower limit according to the fitted GEV. This can sometimes be seen also in real data, for example, for snow amount the limiting value is zero. Since, in the extreme value analysis, we are interested in the upper tail of the distribution, values at the lower limit should not be used. Therefore, we apply a criterion on how close to the theoretical lower limit the smallest observed extremes are and reject them from the analysis accordingly. This is explained in detail at the end of the next section.

2.3.5. The fitting method There is significant controversy related to the methods of fitting a distribution to extreme value data (Katz et al., 2002, Makkonen, 2008a). For example, the performance of the maximum likelihood method for the GEV distribution may be erratic for samples as small as here (Martins and Steding- er, 2000) and the method of probability weighed moments is not optimal for the Gumbel- distributed samples (Rasmussen and Gautam, 2003). We have developed a new method which deviates from the conventional methods and is explained below.

In all conventional fitting methods applied to the extreme value analysis the error, i.e. the differences between the observed and modelled values, is minimized with respect to the probability P. In contrast, we minimize the error between the fitted GEV and the data points with respect to the observed variable X. We do this because we have previously shown that, in order ranked data P, is not a dependent variable but known (Makkonen, 2008a,b, 2011), as discussed above.

It is noteworthy that all curve fitting techniques implicitly involve giving weights to the data points. In the extreme value analysis the issue of the weight is particularly important and controversial. Giv- ing larger weights to the most extreme observations may be supported by the possibility that the highest extremes do not belong to the same population with more moderate extremes. An example of this is a mixed climate where the bulk of the wind speed distribution is a result of normal low- pressure systems, but the highest extremes are results of hurricanes. Thus, putting more weight on the points with a low probability would seem to be necessary for the phenomena whose formation mechanisms are poorly understood. However, this line of thinking contradicts the commonly used approach (Harris, 1996, Landwehr et al., 1979, Hosking et al., 1985) in which the points are weighted according to their theoretical statistical confidence, thus effectively neglecting the data at the tail of the distribution.

We use the weighted least squares method in making the fit to GEV and solve the problem of the subjective weighing of the data as follows.

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As mentioned above, in our fitting method, we minimize errors with respect to the random variable X, and not P.

For this purpose we solve  m from the GEV distribution as

  m   ln1 F m  (6) 

Here ξm is the value of X given by the estimate of the cumulative distribution function F at the probability corresponding to the observation xm. In other words,

1  m  NmF  .1/ (7)

We give weights wm for the data points according to the inverse of the variance of ξm. The variance can be calculated when the cumulative distribution function at F(ξm) is known. This is explained below.

Consider the stochastic variable Xm from the order ranked data.

21 ...  xxx N (8)

There are m-1 independent stochastic variables less than and n-m larger than Xm. Hence the probability density function f  mm  of Xm can be determined by (e.g. Castillo 1988) N! f   F m11 mN fF   (9) mm !1  mNm m m m

where m is rank number, N is the total number of variables, F m  is the cumulative distribution function and f  m  is the density function. For the GEV-distribution

1    x  1     exF     (10) and 1  1   x 1 1     1   x        xf 1     e (11)     

Hence, mean of the variable  m can be calculated from the distribution function by  m  df  mmmm (12)   and the variance σ2 by 2  2 m mm  df  mmm (13)  

The weights wm are then determined by equation 1 wm  2 (14)  m

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Grant No. 233919 and are normalized so that N  wm  1 (15) km

Using these weights we minimize the sum N 2  xw   mmm  (16) km

Thus, in our method the estimate of the cumulative distribution function F(ξ) needs to be applied when determining the weights. On the other hand, the weights are required in determining an estimate F(ξ). This situation is solved by considering both the weights and the fitted function simultaneously in an iteration process as follows.

First, equal weights are given for the 15 biggest extremes in order to obtain the first estimate. Then, if the fitted GEV distribution is such that |γ| < 0.1, the iteration is stopped and a Gumbel-fit is made by finding μ and α for Eqs. (3) and (4). This is done because fitting by Eq. (2) can never result in a Gumbel distribution, eq. (3), i.e. γ = 0.

When at the first iteration |γ| > 0.1, the iteration continues and also γ is solved at each step. At each iteration step, the weights are adjusted according to Eqs. (13)-(16) and the three parameters of GEV are found by minimizing the sum in Eq. (16). When the estimated cumulative distribution function F(ξ) and the weights wm do not change anymore in iteration, the CDF F(ξ) is accepted as a final result if it fills the following criterion.

As discussed in the previous section, the smallest extremes are excluded in our analysis, i.e. they are given zero weights, if they lay very close to the lower asymptotic limit given by the estimated F(ξ). This is done based on the criterion which compares the difference between the smallest and the second smallest  m value,  k and  k1 respectively, and the difference between the largest value  N and the smallest value  k . If    1 kk  01.0 (17)   kN

then the smallest observation is eliminated from the analysis and only observations from xk 1 to xN are used in the new iteration process. The elimination process continues to the next smallest extreme until the criterion in Eq. (17) is not met or there are no more than four observations left for the analysis.

2.4. Wind

The impact of wind storms on transport network is considerable, air, sea and land transportation being all affected. Wind can impede transport operation or damage vehicles and infrastructure leading to significant economic impacts and injuries. Three thresholds have been considered in the wind gust analysis: 17 m/s, 25 m/s and 32 m/s.

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As indicated in Fig. 2.3., extreme wind gust are more frequent over the Atlantic, the most affected land areas are the British Isles, Iceland and the coastal area (40-80 days/year with wind gust over 17m/s). Most of the continent experiences 10-20 days per year with strong wind gust (17 m/s). Very extreme wind gust events (> 25 m/s) occur rarely and sporadically over Europe.

Figure 2.3. Average number of days per year with wind gust exceeding 17 m/s, 25 m/s and 32 m/s during the period 1989-2010 based on ERA-Interim data.

2.5. Snow

Snow represents a great challenge for the transport system operations. Heavy snowfall results in increased travel time and delays and increased accident risk. Dense snowfall causes poor grip between the road surface and tyres and reduces the visibility, resulting in a possible reduction of road capacity by 19-27% and the traffic speed by 11-15% (Agarwal et al. 2005). Keeping roads free from snow is a major part of winter road maintenance in many European countries. Snowfall have

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Snow events impact almost the entire continent (Fig. 2.4) with an increase in probability toward Northern, Eastern Europe and the Alpine region, where the frequency of snowy days varies between 100-140 days/year. However the chosen 1 cm snowfall is a relatively low threshold value, even a thin snow cover may cause many disruptions, particularly in the regions where its probability is reduced.

Figure 2.4. Frequency-based probability of daily snowfall exceeding 1 cm (in percent) during the period 1971-2000 based on E-OBS data.

A) B)

Figure 2.5. Average number of days per year with snowfall exceeding (A) 10 cm and (B) 20 cm during the period 1971-2000 based on E-OBS data.

Dense snowfall (≥10 cm/24 h) occurs only sporadically over Western, Southern and most of Cen- tral Europe (max. 5 days/year). In Scandinavia its frequency varies between 5-15 days/year, while in Eastern Europe and Balkan Peninsula rarely exceeds 5 days/year (Fig. 2.5). Heavy snowfall

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(≥20 cm/24 h) is frequent over Northern Europe (Norway and Iceland) and the Alps: 10-25 cases/year. Nevertheless the analyses indicates 30-40 cases for the rest of Scandinavia, Eastern Eu- rope and some parts of Balkan during 1971-2000. The temporal variation of snowfall events indicates predominant positive trend over Eastern Europe and the western part of Scandinavia, and a negative trend in central Europe, Alps and locally in Scandinavia Fig. 2.6.

. A) B)

-1 -0.5 -0.1 0.1 0.5 1 1.5 -0.5 -0.1 0.1 0.5

Figure 2.6. Linear trend for annual series of (A) 1 cm and (B) 10 cm of snowfall events (events/year) during the time period 1971-2000.

We also analysed the severity of very rare extreme snow fall events determined as the 50-year return period value of mm of water equivalent precipitated in 24 hours. This was possible based on the SMHIRCA-ECHAM5 simulations, discussed in Section 4.1 and the methodology presented in Section 2.2. The result is shown in Fig. 2.7 and illustrates a large range of daily extreme snowfall rate within Europe. An interesting feature that may not be very clear in observed data (Figs. 2.4 and 2.5) is that the most extreme snow storms are only weakly correlated with latitude and occur on the mountain slope areas facing warm and moist average air flow. In these regions the extreme snow accumulation is typically 2-3 times as high as in other areas of Europe. RCM has problems in handling the eastern border of the regional climate model domain. There is an artificial maximum along the eastern border of the regional climate model domain stretching from Turkey to Russia (for RCM boundary treatment, see e.g. Rummukainen (2010)).

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Figure 2.7. Simulated fifty-year return value of the annual maximum snow fall in 24 hours (in mm of water) in the present climate. N.B. Disregard the artificial maximum along the eastern border of the regional climate model domain stretching from Turkey to Russia that is due to lateral boundary condition problems (see e.g. Rummukainen (2010)).

2.6. Blizzard

A blizzard is a severe storm condition defined by low temperature, sustained wind or frequent wind gust and considerable falling or blowing snow causing e.g. damage to structures, failures in transport control systems as well as reduced friction and visibility. A blizzard condition may generate delays and cancellations in all the transport modes and increase the rate of accidents. In the present study we have applied the thresholds defined in WP1 for blizzard: snowfall exceeding 10 cm/24 hours, wind gust ≥ 17 m/s and daily mean temperature below 0°C.

The analysis of blizzard indicated relatively low frequency during the studied period (Fig. 2.8.). Blizzard conditions occur predominantly over the Alps and Northern Europe (30-40 cases in 30 years). The most affected regions are the western cost of Scandinavia and Iceland, with more than 140 cases in 1989-2010 (~10 cases/year). However, due to the difficulties in wind gust prediction and the relatively coarse resolution of ERA-Interim data, the frequency of blizzard events might be underestimated.

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Figure 2.8. Total number of blizzard events during the period 1989-2010 based on ERA-Interim data.

2.7. Ice formed by freezing precipitation

One of the most harmful and dangerous phenomena for transport is freezing precipitation which causes ice on the roads and consequent extreme slipperiness. Ground based icing is a hazard to air transport as well due to ice on runways and icing of aircraft on ground. Even a very thin ice layer causes a slippery and aerodynamically rough surface, so that the severity of this phenomenon can be estimated by its frequency alone.

We have made an analysis of the frequency of freezing precipitation based on the re-analysis data for airports in Europe. We used a 16-year NOAA National Climatic Data Centre database of surface observations at airports covering the period 1982-1997. From these data we filtered out cases where liquid precipitation was reported in the present weather code and simultaneously the wet bulb temperature was below 0 oC.

The results are shown in Figure 2.9. Although the data is limited to airports, the results provide a general view on the frequency of ice on ground. There are more than 144 hours of freezing precipitation per year in many locations in Central and Eastern Europe while the number is less than 72 hours in most of Western Europe. As one would expect the variation within areas is large, which is due to the effect of the local terrain on temperature inversions that are required for the freezing precipitation to occur. The phenomenon is most typical to isolated valley areas.

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glazeGlaze (hours/year20 (Hours/Year]‐1 ) 0-0-11 ‐12 1-1212‐24 12-2424‐72 24-7272‐144 72-144>144 >144

Figure 2.9. Total duration of freezing precipitation (hours/year) derived from NOAA data.

2.8. Heavy precipitation

Precipitation frequency, duration and intensity impact all transportation sectors and the infrastructure. Heavy precipitation is one of the most costly weather situation, resulting in road submersion, flooding that additionally causes road washout, rail damages and destroy structures in most transportation sectors (Peterson et al., 2008). The probability of heavy precipitation had been analyzed using three thresholds, e.g. 30 mm, 100 mm and 150 mm/day, defined based on their impact level and consequences. Also intense precipitation events lasting only few hours or even less can cause adverse impacts to the transport system. However, due to limited amount of high frequency precipitation measurements it is difficult to perform European wide analyses, therefore, we focus on widely available daily precipitation data.

The probability of heavy rainfall exceeding 30 mm/24 hour over the European continent is 2% (Fig. 2.10.). The frequency of days with 30 mm is higher in the Alps and on the western cost of the Brit- ish Isles, of Iberian Peninsula (up to 30-35 days/year) and of Scandinavia (45 days/year). The less affected European regions seem to be the eastern part of Scandinavia (<5 days/year).

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Figure 2.10. Frequency-based probability of daily rainfall exceeding 30 mm (in percent) during the period 1971-2000 based on E-OBS data.

Extreme rainfall events (≥ 100 mm/day) are rare (Fig. 2.11.), in general with only 10-20 cases reg- istered in 30 years over Western Norway, parts of Mediterranean, Alps and sporadically over East- ern Europe. There are stations that have occasionally measured precipitation amounts exceeding 150 mm per day in practically all European countries, but these measurements may not always be part of E-OBS data archive. Nevertheless, daily precipitation amounts >150 mm are very rare; therefore, these very high values are mostly levelled off in the E-OBS gridded data (Fig. 2.11B).

(A) (B)

Figure 2.11. Total numbers of days with rainfall exceeding (A) 100 mm and (B) 150 mm during the period 1971-2000 based on E-OBS data.

Considering the monthly variation, heavy rainfall events (30 mm) are more frequent in winter season, 72% of the cases occurring between October and January (Fig 2.12.).

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Figure 2.12. Monthly variation heavy rainfall days exceeding 30 mm.

2.9. Heat spells

Hot days and heat spells are harmful for land (road and rail), air transportation and also for infrastructure. High temperature may cause fatigue among the drivers and dilapidation on pavement, while extreme high temperature, such as 43 °C causes buckling in road pavement, airport runways and rail tracks and equipment failure, thus increasing the accident rates, the probability of delays and diversion. The thresholds used in the analysis of heat waves were: 25 °C, 32 °C and 43°C.

(A) (B)

Figure 2.13. Frequency-based probability of daily maximum temperature exceeding (A) 25 °C and (B) 32 °C (in percent) during the period 1971-2000 based on E-OBS data.

There is at least about 5% probability of having high temperatures (≥ 25 °C) over the European continent (Fig 2.13). The frequency of hot days varies between the annual average of 90-150 days (for the 25 °C threshold) and 30-80 days (32 °C) in southern Europe, while in central and eastern part of the continent between 15-60 days (25 °C) and 10-20 days (32 °C).

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Figure 2.14. Total number of days with maximum temperature exceeding 43 °C during the period 1971-2000 based on E-OBS data.

Very hot days, with daily maximum temperature above 43 °C are very rare with a maximum of 25 days over the Iberian Peninsula and the Balkan (Fig. 2.14). However, it must be kept in mind that although an individual station may record temperatures above 43 °C due to gridding procedure the E-OBS data may not have a grid value above the threshold. The trend analysis of heat waves indicates that number of days with high temperature has been increasing by 1-2 days/year during the time 1970-2000 over most of the continent except for Scandinavia and some parts of Russia (Fig. 2.15.).

A) B)

-2 -1.5 -1 -0.5 -0.1 0.1 0.5 1 1.5 2 Figure 2.15. Linear trend for annual series of heat spells (events/year) with (A) 25 °C and (B) 32 °C during the time period 1971-2000.

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The low temperature is rather a modifier of hazardous conditions for transportation than a main cause. Low temperature contributes to the development of slippery conditions; combined with precipitation and wind can have disruptive effects on traffic. Air temperature at the cold extremes may cause disruption in the railway sector, inland waterway transport and may severely limit the ground crew work at airports.

The spatial and temporal variation of low temperature was studied using three thresholds: 0 °C, -7 °C and -20 °C.

(A) (B)

(C)

Figure 2.16. Average number of days per year with daily mean temperature under (A) 0 °C, (B) -7 °C and -20 °C during the period 1971-2000 based on E-OBS data.

The frequency of frost days varies from 100 to 200 per year in Scandinavia, with the highest values in Scandes (220 days), and decreases toward south, where it records about 20 days per year (Fig. 2.16). Most of the continent is free of very extreme cold spell (< 20 °C), except Scandinavia and

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NE part of Europe, with 5-30 days/year. The trend analysis of probability of cold spells (Fig. 2.17.) implies a predominant decrease during the 30 year period especially over Northern Europe, Russia and the Alpine region.

A) B)

-1.5 -1 -0.5 -0.1 0.1 0.5 1 1.5 Figure 2.17. Linear trend for annual series of cold spells (events/year) with (A) 0 °C and (B) -7 °C during the time period 1971-2000.

2.11. Poor visibility

Low visibility is in general caused by particles suspended in the air. These particles may be dust or sand that has been lifted up from the earth's surface in sand or dust storms. These will be discussed below. Most frequently, however, limited visibility is caused by water or ice particles. This may be the case during heavy rain- or snowfall, but it is more typical that very small suspended droplets (or ice crystals) are responsible for the reduction of visibility. If the resulting visibility is smaller than 1 km, this by convention is called fog.

2.11.1. Fog Fog occurrence is a problem for many modes of transport, most importantly aviation and shipping. For shipping, it is necessary that the vessels reduce their speed to ensure safe operation. Simi- larly, for aviation, an increasing separation of aircraft is required as visibility reduces. In addition to horizontal visibility, vertical visibility is another factor of importance. Vertical visibility is operation- ally measured by ceilometers that measure the height of the bottom of a cloud deck, called the ceil- ing.

The horizontal and vertical visibility conditions at airports are relevant to the requirements to the Instrument Landing System (ILS) of the aircraft. These are, with increasing demands on that system, CAT I, CAT II, CAT IIIa, CATIIIb and CAT IIIc. In what follows, we will focus on the first three of these thresholds.

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Table 1. List of ILS categories.

horizontal vertical visibility visibility CAT I ≥ 550 m > 60 m CAT II ≥ 300 m ≥ 30 m CAT IIIa ≥ 200 m CAT IIIb/c < 200 m

Using hourly weather reports (known as METAR reports) originating from major European airports through the period 1975-2009, we have assessed the number of hours that these criteria are not met. Particularly, we have studied the temporal change of these hours.

Figure 2.18. Annual number of hours that CATII (a.), CATIIIa (b.) and CATIIIb/c (c.) conditions occurred at selected in the period 2000-2009 at major European airports.

The occurrence of low visibility at 24 major European airports during the period 2000-2009 is depicted in Fig. 2.18. The severity of the fog problem is strongly related to the climate zone. In the Mediterranean zone, visibility problems are very rare, with the notable exception of Milan, located in the fog-prone Po-Valley. This particular airport has more than double the hours of less than 200m visibility (requiring CATIIIb/c) than any other European airport. Other airports with a notable

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Grant No. 233919 problem (> 20 hours) with such dense fog are Manchester, London Gatwick, Copenhagen, Brus- sels, Geneva, Zurich, Munich and St. Petersburg. It is noted that some airports have many hours with moderate visibility problems (CATII, CATIIIa) like Oslo and Stockholm, yet the most severe CATIIIb/c situations are rare at these locations.

Figure 2.19. Annual numbers of hours with visibility less than 200 m at the airports of Zurich (green), London Gatwick (blue) and Oslo (red). The thin lines are the actual observed values; the thick lines are 7-year moving averages.

Recently, a number of scientists (Vautard et al, 2009: Van Oldenborgh et al. 2010) have considered the temporal trend of visibility during a number of decades across Europe and found the in- triguing result that the occurrence of low visibility has strongly declined. Van Oldenborgh et al. (2010) found that this decline is due to the decrease in aerosol emissions over Europe, and not changes in flow patterns, which can be predicted by climate models. For that reason, in order to predict the occurrence of visibility over the next decades, climate models, which currently do not or poorly represent aerosol emissions and chemistry, are of little help.

The METAR aviation weather report data analysed here confirm their conclusion that the low visibility problem is becoming smaller very rapidly (Fig. 2.19). Although yearly variations of hours with less than 200 m visibility, the overall trend is clearly and significantly downward at all 24 airports displayed in Fig. 2.13. These very poor visibility conditions have diminished at some airports to being almost non-existent. For example, Oslo Gardemoen airport, where, in the four years of 2006, 2007, 2008 and 2009 combined visibility below 200 m occurred at less than 10 hours. It must be recognized, however, that the same airport were annually still 121 hours of visibility between 200 and 500 metres in the same time period. At other airports, periods of low visibility have become rarer, but do still occur.

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Figure 2.20. Annual numbers of hours with particular visibility conditions averaged over the 24 airports (Instrument Landing System categories, see Table 1).

The pan-European improvement in visibility conditions can be seen from Fig. 2.20, which shows that the average hours of CATII or worse conditions has reduced by about a factor four. When considering the individual categories, it can be seen that the duration of CATII has decreased much less (by a factor of 2.5) compared with that of the CATIIIb/c conditions (by a factor 5). This means that the lower aerosol content of the air is more effective at reducing the occurrence of very dense fog, compared with that of less dense fog. These CATII conditions may additionally be caused in part by heavy snowfall or very heavy rain, which are probably less sensitive to changes in aerosol content. Poor visibility caused by the dust episodes are discussed in the following section 2.11.2.

For traffic, and air traffic in particular, the above results are naturally good news. We may expect that the trend of improving visibility conditions will continue into the future, provided that actions improving air quality continue, although the reduction may level off soon. However, at particularly fog-prone airports, like Milan Malpensa, frequent reduced visibility episodes due to fog still exist. At other European airports this is, and will be the case in the coming decades, albeit to a lesser extent.

2.11.2. Dust transport episodes The occurrence of high level concentrations of dust originating from deserts is quite common even at locations quite distant from the source region. The mechanisms for lifting the particulates within the source region, the conditions leading to their suspension in the atmospheric air, their transportation to great distances and the eventual deposition (either dry or wet) on the ground comprise a highly complex phenomenon affecting human health and several activities, including air- transportation. Below, a novel approach is presented, namely the application of Artificial Neural Networks (ANN) for diagnosing and predicting atmospheric pollutant levels over the island of Cy- prus, in the eastern Mediterranean, due to the transportation of dust from the adjacent deserts.

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Synoptic pattern classification One of the major inherent problems in an endeavour to classify synoptic patterns is the a priori determination of the number of different classes that one can expect from such a classification. In other words, the number of distinctive synoptic patterns over any particular geographical region is by no means fixed. Traditionally, such a synoptic classification was performed by a qualitative in- spection of synoptic maps. Professional meteorologists examined a series of plotted synoptic maps and picked out geometric similarities (e.g., Prezerakos et al. 1991). Nevertheless, it seems that in an attempt to perform a classification with an unknown number of classes, experimentation with various possibilities is a practical procedure, which can lead to some useful considerations. For this reason, it was decided to run a number of experiments and build classification models with different numbers of output nodes (i.e., classes). For the present analysis 35 output nodes are presented, as described in the Appendix 1.

The ability of ANN to group synoptic patterns into seasonally dependent clusters was noted by Michaelides et al. (2007). This seasonal discretization of classes should be an essential attribute of a classification technique.

Figure 2.21. Number of days per class, for the 35 classes in the synoptic classification, during the time period 2003-2005.

Figure 2.21 shows the frequency of appearance of the synoptic patterns in the 35 classification for the three-year period 2003-2005. Apparently, class 35 is most frequently encountered, followed by classes 1 and 25. Figure 2.22 is a graphical representation of the assignment of a class to each day in the three year period. It is clear that there is a seasonal “quasi-cyclic” behavior. Certain classes occur, almost exclusively, during summer or winter; during the (Mediterranean) transitional periods of spring and autumn, both the summertime and wintertime patterns can occur. This graphical representation is proposed as a practical visual tool to identify the level of seasonal discretization pursued in adopting a synoptic classification technique.

Synoptic classes and dust deposition events During 2003-2005, 85 dust transport events were observed in Cyprus. Using the existing dust collection network, a dust transport episode is considered as a day when the average PM10 measurement exceeds the threshold of 50mg/m3 (for more details see Michaelides et al., 2011). Figure 2.23 shows how the 85 dust transport events are distributed among the 35 classes in the synoptic classification. There appears to be a certain preference of classes associated with these events: most prone to dust events is class 1, followed by class 31; representative synoptic situations for these two classes are shown in Figure 2.23. Figure 2.24(a) refers to 1200 UTC 1 February 2003,

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Grant No. 233919 and Fig. 2.24(b) at 1200 UTC 10 May 2004: in the former, a central Mediterranean upper trough extends well into the north African desert; in the later, the trough axis extends southwards from the Iberian peninsula. In both cases, typical patterns are identified favouring dust raising and its trans- fer eastwards with the resulting south-westerly airflow over the eastern Mediterranean.

Figure 2.22. Daily distribution of 35 classes in the three-year period 2003-2005.

Figure 2.23. Distribution of dust deposition events per class, for the 35 classes in the synoptic classification, during 2003-2005.

(a) (b) Figure 2.24. Representative synoptic situations corresponding to: (a) class 1; (b) class 31. Isolines are drawn for every 60 geopotential meters.

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Synoptic classes and dust deposition event trends Since a period of 3 consecutive years (2003-2005) was examined in order to identify and classify the synoptic conditions which lead to dust transport episodes in the eastern Mediterranean, a time- line of 25 years (1980-2005) is also examined in order to identify trends of those synoptic conditions which favor dust transport episodes for this period, using the results from the 3 years period. For each year, the total of days belonging to each of the 35 classes were counted and, using linear regression, an attempt in order to identify evolution patters which prevail or preceed within the selected time frame was made. The period of three years is quite small, but we operate under the assumption that it is characteristic of a much wider period.

Figure 2.25 shows the frequency of appearance of the synoptic patterns in the 35 classification for the period 1980-2005. Again class 35 is most frequently encountered, followed by classes 30 and 25.

Figure 2.25. Number of days per class, for the 35 classes in the synoptic classification, during 1980-2005.

Using the results from the distribution of dust deposition events per class, for the 35 classes in the synoptic classification, during 2003-2005 (see Fig. 2.23.) we were able to identify classes which favor dust deposition events and classes that present no dust events at all. Table 2 shows how the classes are distributed in those 3 distinct categories

Table 2: Classification of Classes according to dust deposition events during the period 2003-2005 Category Classes Classes with dust deposit events 1,31,11,15,20,23,6,7,10,32, 16,17,35,13,14,26,2,8,18,19,22,24,29,30,33,34 Classes with no dust deposit events 3,4,5,9,12,21,25,27,28

Figure 2.26 below presents the evolution of Classes 1, 31 and 11 within the 25-year period. The first two present an overall negative trend, while the third presents a positive one. These classes represent the synoptic conditions that favour dust deposition within the 3-year period.

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Figure 2.26. The evolution of classes 31, 1 and 11 within the 25 year period (1980-2005).

In Figure 2.27, the trends of the “no dust deposition” classes are presented. It is evident that for all these classes (except Class 25) the trend is negative for the 25 year period.

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Figure 2.27. The evolution of the “no dust deposition” classes within the 25 year period (1980- 2005).

The trend of each of the 35 classes is presented in the table below. From Table 3 it is evident that while in both categories there is an overall prevailing of negative trends, this is more severe in the no-dust ones: 7 out of 8 of the no-dust trends present a negative trend as opposed to 14 out of 26 for the one where dust is present. Furthermore, from the latest category, if we consider only the classes where more than two dust events were present, then 6 out of 10 classes present positive trends. We can assume that there is an enhanced presence of classes that favour the presence of dust; however this assumption surely needs to be investigated further using a larger period that the one used for this preliminary work.

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Table 3. Classes and trends for the period of 25 years (1980-2005)

Class Slope Trend Class Slope Trend 1 -0,1019 NEG 19 0,0131 POS 2 -0,0588 NEG 20 0,0499 POS 3 -0,0537 NEG 21 -0,0048 NEG 4 -0,1255 NEG 22 0,012 POS 5 -0,3484 NEG 23 0,0961 POS 6 0,1036 POS 24 -0,121 NEG 7 -0,0803 NEG 25 0,0834 POS 8 0,0581 POS 26 0,0602 POS 9 -0,0058 NEG 27 -0,0048 NEG 10 0,147 POS 28 -0,1959 NEG 11 0,2574 POS 29 -0,0178 NEG 12 -0,1015 NEG 30 0,1084 POS 13 -0,0147 NEG 31 -0,1364 NEG 14 -0,0291 NEG 32 0,026 POS 15 -0,0472 NEG 33 -0,1159 NEG 16 -0,0817 NEG 34 -0,1338 NEG 17 -0,0574 NEG 35 0,6376 POS 18 -0,0229 NEG

2.12. Small scale phenomena – thunderstorm and associated phenomena

The occurrence of thunderstorms affects transport systems in various ways, e.g. via strong winds, lightning, intense precipitation, and hail.

2.12.1. Lightning By their definition, thunderstorms are associated with lightning, which poses a hazard for people in the open. This requires that airport aprons needing to be cleared as soon as a thunderstorm is detected. Furthermore, infrastructural damage may occur, for example to electrified railways.

Sensors have been installed on several satellites that give an impression of the lightning coverage across Europe (Fig. 2.28). Lightning-prone areas are the entire Mediterranean region, the Balkans and, to a lesser extent, parts of east-central Europe such as Poland, Ukraine and Belarus. Else- where, lightning is less frequent. However, except for far northern Scandinavia and parts of the northern Atlantic Ocean and Iceland, lightning does regularly occur across all of Europe. The lightning activity has a peak in early summer (June-July) across northern and central parts of Europe, and in late summer and autumn (August-Oct) across the Mediterranean Sea and adjacent coastal areas.

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Figure 2.28. Annual lightning frequency in Europe (strikes per km2 per year) in the period 1995- 2005. Data source: NOAA Earth System Research Laboratory.

2.12.2. Thunderstorm-related gusts In addition to lightning, some thunderstorms produce very strong wind gusts. These may be caused by the evaporation of rain and small hail (graupel) in the dry air below the bottom of the storm cloud, causing a so-called downburst, which is a very local phenomenon. Besides, large thunderstorm complexes can develop local wind systems that can produce large swaths of damaging gusts.

The climatological occurrence of such thunderstorm gusts across Europe is not very well known. An important reason for this is that it is often hard to reconstruct whether a recorded wind gusts was associated with thunderstorms. Yet it is important to treat them separate from gusts occurring with large-scale windstorms. Thunderstorm gusts occur much more locally and are generally not well represented in climate models (cf. discussion in Section 2.1).

In many regions in Europe, large scale storm systems are responsible for the strongest gusts. These systems occur primarily in the winter season from October through March. This is the case, for example, in southern Germany (Fig. 2.29, top), where gusts stronger than 25 m/s (or about 90 km/h) are concentrated in those months. In this area however, a secondary maximum occurs in the summer season from May through August, which is attributable to gusts resulting from thunderstorms that have their highest frequency in that season. Many of those gusts (those in red) were more than twice as strong as the reanalysis dataset indicates. In other areas, like Finland (Fig. 2.29, bottom), strong gusts occurring with thunderstorms are much rarer.

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Figure 2.29. Maximum daily wind gusts at measurement stations across southern Germany (Augs- burg, Bamberg, Constance, Hof, Munich, Nuremberg, Stuttgart-Echterdingen, Stuttgart- Schnarrenberg) (top) and across Finland (Helsinki, Kaarina, Maaninka, Niinisalo, Rovaniemi, Seinajoki, Siikajoki, Vihti) (bottom). German gusts are from the period 1990-2009, Finnish gusts from 2000-2009. Gusts for which the ERA values were more than 50% lower than the observed value are depicted with red crosses, all others with blue crosses.

2.12.3. Large hail and tornadoes Large hail is a thunderstorm-related threat that occurs throughout Europe. It affects transport primarily in the sense that it is a safety concern for air traffic. Large hail occurs with the most powerful thunderstorms, which have extremely strong updraughts that can easily exceed 30 m/s (108 km/h). Moreover, the storm must have a long enough lifetime for large hailstones to form. Both conditions are given in situations when the temperature drops quickly with height, the air close the earth's surface is humid, and strong vertical wind shear is present (i.e. the wind speed and direction changes with height).

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Figure 2.30. Large hail reports collected in the European Severe Weather Database. It is sus- pected that hail across Eastern Europe, (Belarus, Baltic States, Russia, Ukraine) is strongly under- reported.

Large hail is most frequent in central and eastern parts of Europe (Fig.2.30.). Across western and north-western Europe its occurrence is much rarer, but all European regions that regularly experience thunderstorms are occasionally affected by large hail.

Yet another thunderstorm-related hazard is tornadoes. A tornado is an intense vortex typically between a few metres to a few kilometres in diameter, extending between a convective cloud and the earth's surface that may be visible by condensation of water and/or by material that is lifted off the earth's surface.

Figure 2.31. Annual frequency of tornadoes across Europe. Source: ESWD.

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Tornadoes occur relatively infrequently, but do occur all across Europe (Fig. 2.31.). Tornadoes occurring over open water are often called waterspouts. Waterspouts occur more frequently than tornadoes over land, and are weaker on average. Still, many of them reach wind speeds well in ex- cess of hurricane force (32 m/s) and cause extremely dangerous conditions for shipping. More- over, one case is known of an aircraft encountering a tornado in the Netherlands, resulting in a se- rious accident in which all 16 passengers and one person on the ground died (Roach and Find- later, 1983).

Figure 2.32. Distribution of tornadoes over the year. Source: European Severe Weather Database.

Tornadoes in Europe are most common in the summer months, although they can happen year- round. This is true as well for the most destructive tornadoes, classified as F2 or higher on the Fu- jita scale.

2.13. Events damaging infrastructure

Some extreme weather events which may, or may not, disturb transport itself can be disastrous to the infrastructure of the transport system. These events have been identified in Work Package 1 of EWENT. It was shown that of particular concern are extreme temperature which causes bending of railway tracks and softening of asphalt pavements, extreme long-term precipitation which causes floods, landslides and road erosion and extreme wind speed which may cause damage to electric power supply networks and block roads and railways by falling trees, debris etc. Extreme winds also prevent using some large suspension bridges in Europe and operation of aircraft.

For these variables, extreme value analysis has been made for 48 000 grid points in Europe based on the E-OBS (1971-2000), ERA-Interim (1989-2009) observational data as well as SMHIRCA- ECHAM5 climate model simulations for the control period 1971-2000 all three data sets representing present climate. Climate change projections for the extremes are based on SMHIRCA- ECHAM5 climate model simulations for the time period 2041-2070. Analysis results describing the present climate are shown below. The fifty-year return value of air temperature in Fig. 2.33 and the probabilities of exceeding 35 oC and 40 oC (Figs. 2.34 and 2.35) show big differences within Europe and point out that the highest extreme temperatures occur in the Mediterranean Europe.

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Figure 2.33. Fifty-year return value of the annual maximum air temperature (in oC) based on E- OBS data covering the time period 1971-2000.

Figure 2.34. Probability of the annual maximum air temperature to exceed 35 oC during the period 1971-2000 based on E-OBS data.

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Figure 2.35. Probability of the annual maximum air temperature to exceed 40 oC during the period 1971-2000 based on E-OBS data.

The fifty-year return value of the five-day precipitation is shown Fig. 2.36 and the probability of the annual maximum precipitation to exceed 100 mm in five days in Fig. 2.37. As one would expect, the extreme precipitation is much higher in mountainous regions and on the West coast than else- where in Europe.

Figure 2.36. Fifty-year return value of the annual maximum of five-day precipitation (in mm) based on E-OBS data covering the time period 1971-2000.

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Figure 2.37. Probability of the five-day precipitation amount to exceed 100 during the period 1971- 2000 based on E-OBS data.

The fifty-year return mean wind speed is shown in Fig. 2.38 illustrating that, in general, the imme- diate coastline regions are much more vulnerable to extreme wind speeds than inland regions. In general, the extreme wind speeds are lower in colder regions of Europe. This is due to the fact that the extreme geostrophic wind over Europe occurs in winter when the boundary-layer is very stable in its cold regions.

Figure 2.38. Fifty-year return value of the 10 minute mean wind speed (m/s) during the period 1989-2009 based on ERA-Interim data.

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3. Scenarios of adverse weather conditions

3.1. Description of model simulations

The EWENT project Description of Work proposed the assessment of changes in the probability of magnitude and frequency of adverse an extreme weather conditions in Europe by 2050s using the outputs of the first multi-model system of regional climate models available in the ENSEMBLE project. For this purpose we have used six high-resolution (ca. 25x25 km2) Regional Climate Model (RCM) simulations produced in the ENSEMBLES project.

The EU-funded project ENSEMBLE is a collaborative effort of different European institutes to provide a reliable quantitative risk assessment of long term climate change and impact. In particular, dynamical downscaling of five different General Circulation Models (GCMs) was performed using different RCM runs by fifteen institutes. The RCMs were run over a common area covering the entire continental European region and with a spatial resolution of 25 km and 50 km, the highest resolution available at the moment. All the GCMs used the A1B (medium, non-mitigation) emission scenario (van der Linden P., and J.F.B. Mitchell, 2009).

Within ENSEMBLES, an initial RCM verification experiment was carried out using the reanalyses from the ECMWF (ERA-40) as boundary conditions for the RCMs, running the RCMs over the common period of 1961-1990. In another experiment, regional climate change scenarios over Eu- rope with different weighting techniques were produce by nesting the RCMs into different GCM simulations for a control climate and future projections. More details of ensemble model experiments are given in the ENSEMBLE project final report (van der Linden P. and J.F.B. Mitchell, 2009).

For our analysis, we have selected six models that provide all the variables required for our analyses and cover the time horizon studied: the near-future period from 2011 to 2040 and the far-future period from 2041 to 2070. The chosen regional climate change projections are:  SMHIRCA-ECHAM5-r3  SMHIRCA-BCM  SMHIRCA-HadCM3Q3  KNMI-RACMO2-ECHAM5-r3  MPI-M-REMO-ECHAM5-r3  C4IRCA3-HadCM3Q16 The analyses presented in this report are based on daily data of 2–m mean temperature, maximum temperature, total precipitation and wind gust. The area covered by the models is shown in Figure 3.1.

There are three main uncertainties in climate projections: internal variability of the climate system that exists even in the absence of any external forcing; uncertainty in radiative forcing due to future emissions of greenhouse gases and aerosols; and model uncertainty (Hawkins and Sutton 2009). We decided to neglect the uncertainty due to emissions and focus on A1B emission scenario, because the emission scenario rivals model uncertainty as source of uncertainty only in the latter half of the 21st century.

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The use of regional climate model provides advantages in spatial and temporal resolution but also some additional sources of uncertainties: the initial and boundary conditions from the GCM used as driving force, the downscaling method used, parameterization and resolution of the regional climate model (Foley 2010, Kjellström et al. 2011). Since models represent the aspects of climate in slightly different ways, using different parametrizations, the intermodal variability can be considerable. The changes can be better assessed using an ensemble mean and combining multiple predictions from six regional climate models, than by the individual simulations; still intermodal variability is represented in the spread of the projection (Foley 2010).

Even though regional climate models are run at high resolution, the evaluation of many extremes might be complicated, since the gridded data provided by RCMs is more homogenous in space than observations, thus extremes are attenuated (Rummukainen, 2010). Internal variability is a natural characteristic of the climate system. GCMs do simulate natural variability but in the climate change experiments it is considered noise obscuring the signal due to external forcings. The use of three different GCMs reduces somewhat the noise due to internal variability in the multimodel mean scenarios presented in this study.

Figure 3.1. Spatial coverage of ENSEMBLE regional climate models used in the study.

During the project a new methodology for extreme value analysis was developed (Ch. 2.3). Due to limited resources these methods have tentatively been applied with thresholds relevant for the transport infrastructure (Ch. 2.13 and 3.6.1) and some other parameters (Figs. 2.7 and 3.12) based on SMHIRCA-ECHAM5 simulation only. SMHIRCA-ECHAM5 simulation was selected mainly for the following two reasons: SMHI regional climate model simulations have been used in earlier work by Makkonen et al. (2007) and ECHAM5 simulation is used also in the statistical downscaling (see 3.3 and 3.7).

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Grant No. 233919 3.2. Description of scenario construction

Regional climate models have the potential of better represent local climate variability and extremes by providing more realistic climate data (Rummukainen, 2010). However one problematic issue related to the models are the systematic biases of the outputs. One of the most common approaches of handling the biases is the delta-change method, by which differences in climate variables are extracted from the control and scenario simulations of the model and transferred on to observed dataset. Although the method has its benefits, it has also the possible drawback of get- ting locked within a past course of variability and extremes (Rummukainen, 2010). Another disad- vantage of the delta-change method is that the use of the observed climate as a baseline implies that the number of rainy days does not change for the future climate and the extreme values are modified by the same factor as the mean values. Another alternative is to rescale and use time series directly from the model.

In order to compare the outcome of these two approaches we have calculated the change in annual 17 m/s wind gust days using the delta-change method and the direct model simulations (Fig. 3.2). The changes in the frequency of strong wind gust differ by the order of maximum 5 days over the continent in the two analyses. Taking into account that the wind variables are among more difficult to model, we anticipated an even stronger correlation for the other climate variables. Thus we have used the direct model outputs in order to assess the changes in the future adverse and extreme weather events.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 -50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50

Figure 3.2. Comparison of two methods to calculate change is annual number of days with wind gusts exceeding 17 m/s. On the left time series undergone bias removal applying delta-change approach; on the right time series applied directly from the model.

Based on the calculations of frequencies using the six RCM, the multi-model mean of the change compared to the control period (1971-2000), furthermore the upper and lower limits of the change has been defined and presented for each threshold of the adverse and extreme phenomena. The multi-model mean is the average change indicated by the six models giving each model equal

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Grant No. 233919 weight. The range of changes is also indicated for each grid that describes well the intermodal variability, i.e. upper and lower limit. The upper limit (maximum) shows the “most positive” change of any model, while the lower limit (minimum) indicates the “most negative” change.

3.3. Other methods of downscaling to road network

Neither global nor regional climate models are sufficiently accurate or detailed to allow road weather conditions to be simulated directly. Instead, road-weather time-series must be derived from GCM or RCM outputs using statistical relationships between large-scale atmospheric patterns (which GCMs simulate well) and the observed road weather, a process known as empirical statistical downscaling. The relationships between the large-scale atmosphere and the road weather must be determined by analyzing historical atmospheric patterns and road weather time-series. A simple statistical downscaling technique, which can and has been confidently applied for is the analogue model. This method involves iterating over each day in the GCM future scenario, and for each day finding the day in the historical record for which the large-scale atmospheric patterns match most closely. The future road-weather for each future day is taken to be the historical road- weather on the most closely matched historical day.

ERA-NET Road project IRWIN (2008-2010) used this method with longest possible archived road weather information system (RWIS) observation time series in Finland and Sweden to assess the effects of climate change to winter road maintenance. The analogue model could be utilized quite successfully, even though the existing RWIS data archives date back only for about 10-15 years. However, this was long enough to find analogues for present and future road climatology for scenarios 30-50 years onwards.

The resulting downscaled time series provided realistic climate scenarios for road network as revealed by RWIS observations, giving also estimates for road surface temperature which is missing from general weather observations. It is worth noting that road climatology may indeed be in some locations substantially different from general climatology because the RWIS stations have been placed adjacent to the roads and not with the principle to give best estimate of average conditions on the area. In particular temperature may differ several degrees from the average and wind directions may be blocked from some directions due to road constructions.

To compare the differences between global and downscaled climate scenarios, Fig. 3.3a shows a three-month long example of GCM daily air temperature for Helsinki Finland, and Fig. 3.3b corresponding downscaled time series for several parameters. The temperature profile in b) shows the same basic pattern as the GCM time-series, with cold spells around 21-Jan, 14-Feb and 23-Feb, but there is much more realistic detail. And, of course, the downscaled time-series also contain data for road temperature, wind and precipitation. In particular, wind and precipitation time series show extreme values which are not resolved in global models due to their coarse resolution.

Similar assessments as were performed in Scandinavia could be repeated relatively easily in other parts of Europe too, if enough road weather information was archived and available. Taken into account the seriousness of climate change and its implications, it is urgent to manage European- wide operational archival and quality control of all road weather observations.

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2-Jan-2029 22-Jan-2029 11-Feb-2029 03-Mar-2029 23-Mar-2029 Figure 3.3. a) Mean daily air temperature for Helsinki, from the Global Climate Model CCSM3.

2-Jan-2029 22-Jan-2029 11-Feb-2029 03-Mar-2029 23-Mar-2029

Figure 3.3. b) The same section of the downscaled data for Helsinki. Parameters from top down are: Tair (°C), Troad (°C), Wind (m/s), and Precipitation (mm).

Results and conclusions of the IRWIN project on downscaled climate scenarios projected on the Nordic road network are further discussed in Chapters 3.6.4.

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Grant No. 233919 3.4. Scenario maps (2011-2040)

3.4.1. Wind Figure 3.4 presents the spatial variation of the projected changes in wind gust by 2040 for the three selected wind gust thresholds: 17 m/s, 25 m/s and 32 m/s. The six RCMs indicate in average a decrease in the severe gust events over the Atlantic and the Mediterranean Sea, with a magnitude of change between 1-5 days/year. The sea areas with a projected positive change in storm events during the near future are the Baltic Sea, Barents Sea, north of Iceland and partially around the British Isles, especially in the terms of wind gusts over 25 m/s.

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Figure 3.4. Multi-model mean, upper and lower limit of changes in annual wind gust days from 1989-2009 to 2011-2040 exceeding the (A) 17 m/s, (B) 25 m/s and (C) 32 m/s thresholds based on six RCM simulations.

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The multi-model means indicate an increase by 5 days/year for the grid cells of these regions, with higher values (10-20 days/year) over the Barents Sea, while the most positive change indicated measures up to 30 days for all three thresholds (as shown in the upper limit maps). Over the continent, most of the countries situated in Central, Western and Southern Europe experience in general an increase by 1-5 days/year in the frequency of wind gusts higher than 17 m/s; however the sign of the change is not uniform. Over the Iberian Peninsula the change is positive over the coast for gusts exceeding 17 m/s and 25 m/s and mainly negative over the land. In Northern and most of Eastern Europe is indicated a slight decrease for the 17 m/s gust.

Wind gust is one of the most difficult variables for numerical models to predict and there are large differences in how they are parameterized in the RCMs. Therefore it is a rather anticipated result that there is a large range of outcomes depicted by the models as shown by the opposite signs of change in the upper and lower limits.

3.4.2. Snow As the number of freezing days decreases in the future climate, there is a decreasing tendency also in the frequency of snowfall events and a shift from snow to liquid or mixed precipitation, as shown in Fig. 3.5.

The multi-model mean of the RCMs exhibits a decrease in the frequency of snow events (1cm) for the whole continent. The magnitude of change increases progressively northward, from 1-5 days in Southern Europe, 5 days over most of the continent to 10 days over Northern Europe. Although winter precipitation is projected to increase in northern and central Europe in the future (Christen- sen and Christensen, 2007), more is expected to occur as liquid precipitation because of the reduced frequency of frost days. On the other hand, the intensity of heavy snowfall (10 cm/day and 20cm/day) is expected to increase partially over Scandinavia, especially at high elevation (Scan- des) and Eastern Europe. Over the coastal region of Northern Europe and the rest of mainland is indicated a slight decrease in the frequency of heavy snowfall, with higher magnitude in the mountainous areas (Alps, Pyrenees and Carpathians). The models have fairly similar view on the sign of change in terms of snowfall, however some of them indicated an increase by 5-10 days in the frequency of heavy snow events over most of the continent.

3.4.3. Blizzard Due to its combined nature, the changes in the frequency and intensity of blizzards are defined by the changes in heavy snowfall, strong winds and cold days.

As the storminess is projected to decrease over most of the Atlantic region by 2040, the intensity of blizzards is also expected to decrease by 1-5 days over Northern Atlantic, Barents Sea, Gulf of Bothnia, western cost of Norway, Iceland and isolated over the mainland (Fig. 3.6.). However, for more of the blizzard affected regions in current climate, no significant change is anticipated in the near future. Enhanced blizzards are expected only isolated over the Icelandic glaciers and in Nor- way.

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-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 -1 5 10 20 30 40 50 Figure 3.5. Multi-model mean, upper and lower limit of changes in annual snowfall days from 1971- 2000 to 2011-2040 exceeding (A) 1 cm, (B) 10 cm and (C) 20 cm based on six RCM simulations.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.6. Multi-model mean, upper and lower limit of changes in annual blizzard days from 1989- 2010 to 2011-2040 based on six RCM simulations.

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3.4.4. Heavy rainfall The near-future projections indicate a positive change (1 day/year) in the frequency of heavy rainfall (≥30 mm) compared to the control period (1971-2000) over the continent (Fig. 3.7.). Heavy rainfall events are indicated to become less isolated in southern Europe. The upper- and lower limit maps indicate large intermodal variation; the dominant pattern suggested by the most positive model is a 1-5 day decrease in extreme precipitation, while the lower limit maps indicate slight decrease or no change over the continent. RCMs produce quite few very heavy precipitation events (≥100 mm) in the present and future climate simulations. Therefore, no major changes could be detected. Current RCMs still show problems especially simulating high intensity precipitation (e.g., Boberg et al., 2009).

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3.4.5. Heat spells The positive trend on the frequency of heat spells indicated by the observed data is likely to continue, inasmuch as the RCMs indicate an increase in the occurrence and intensity of high temperature.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.8. Multi-model mean, upper- and lower limit of changes in annual heat-wave days from 1971-2000 to 2011-2040 exceeding (A) 25 °C, (B) 32 °C and (C) 45 °C based on six RCM simulations.

The multi-model mean maps (Fig. 3.8.) depict a significant positive change in the frequency and intensity of heat spells in the near future. The mean number of days with maximum temperature above 25 °C increases by 20-30 days/year in Southern Europe and by 5-20 days/year in mid latitude compared to the current climate. The change is less evident in Scandinavia: 1 day/year in the southern part of it, while most of Norway, Sweden and Iceland expect no increase in heat waves. The highest magnitude of change indicated by the model (shown in the upper-limit maps) varies between 40-50 days/year over the Mediterranean Sea and Black Sea and 30-40 days/year over southern Europe.

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The frequency of hot days, with daily maximum temperature above 32 °C exhibits the greatest change (by 10 days/year) over the Iberian Peninsula, Italy and the Balkan Peninsula, while the frequency of extreme hot days (43 °C) is predicted to increase by 1-5 days locally over Portugal, Spain, Italy, Balkan, Romania and Hungary.

3.4.6. Cold spells Multi-model mean indicates a significant decrease in the frequency and intensity of cold spells (Fig. 3.9.) for the near future climate, the change being more intense over high latitudes. The dominant pattern suggests a decrease by 20 days/year in the frequency of frost days over Northern Europe, Russia, Alps and the Carpathians and by 50 days/year over the Arctic. Over the mid-latitude regions 5-10 days/year less frost days are expected by 2040, while over the southern European countries 1-5, isolated 10 days less frost days per year.

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Extreme cold events will also decrease in frequency: 10-20 fewer cold days (with daily mean temperature below -7 °C) over Scandinavia, the Baltic, Russia, Scotland and the Alps, predominantly by 5 days over Central, Easter Europe and Balkan Peninsula and by 1 day over France, Italy and the British Isles. Scandinavia, the Baltic and Eastern Europe will also experience less very extreme cold spells (-20 °C) compared to the current climate. However according to the most positive projection the frequency of these events may slightly increase in most of the affected regions (shown on the Upper limit maps). These increases are very likely resulting from the fact that climate change signal is still comparable in size with the natural climate variability at regional scale.

3.5. Scenario maps (2041-2070)

3.5.1. Wind

Figure 3.10 shows the expected mean changes by the 2050s for the three wind gust thresholds. Over ocean and sea areas the most notable change is the decrease of severe wind gust frequencies over most of the Northern Atlantic as well as the Mediterranean Sea. The multi-model mean shows on average 1-20 fewer days per year with 17 m/s wind gusts in these areas.

From the Northern Atlantic and the Mediterranean Sea can also be found the most distinctive areas where all six used models agreed on the sign of the change (decrease in the number of events exceeding 17 m/s threshold). On the other hand, the strongest increases over sea areas can be found north of Iceland, over the Barents Sea and the Baltic Sea.

The multi-model mean indicates that over the Baltic Sea the 17 m/s wind gust threshold is exceeded on 1-10 more days per year in the 2050s compared to the control 1989-2009 timeframe. Similarly the increase of the 25 m/s threshold is 0.1-5 days per year. Over land the changes in severe wind gust days are less evident. A slight increase is shown in parts of Central and Eastern Europe for the 17 m/s gusts, but overall in Europe there is a tendency for a slight decrease (17 m/s) or no clear change in either direction for the higher thresholds (25 m/s and 32 m/s)

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-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.10. Multi-model mean, upper and lower limit of changes in annual wind gust days from 1989-2009 to 2041-2070 exceeding the (A) 17 m/s, (B) 25 m/s and (C) 32 m/s thresholds based on six RCM simulations.

3.5.2. Snow The frequency of snowfall events continues to decrease. The signal is clearer during the 2050s than in the 2020s.

The multi-model mean (Fig. 3.11.) shows 1-5 fewer snowy days in the southern Europe, the frequency of snowy days decreasing progressively northward to 10-20 days in Scandinavia. The sign of change is consistent among the six RCM except the Mediterranean and the western part of the continent. In contrast to the general decrease of snowy days, the occurrence of extreme snowfall (>10 cm) increases over large areas in Scandinavia and north-eastern Russia (1-5 days/year). This increase is resulted partly by the anticipated increase of precipitation amount in the future but also by warming, since heavy snowfall occurs mainly in near zero temperature. Some of the models, as shown in the upper limit maps, indicate a more robust increase in the frequency of 10 cm and especially 20 cm snowfall/day for several central and eastern European countries. A decrease in

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Grant No. 233919 snowfall and frozen precipitation would have a positive impact on road, rail and air transportation reducing the cost of maintenance.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.11. Multi-model mean, upper and lower limit of changes in annual snowfall days from 1971-2000 to 2041-2070 exceeding (A) 1 cm, (B) 10 cm and (C) 20 cm based on six RCM simulations.

We also analysed the projected change in extremely severe snow fall events by the SMHIRCA- ECHAM climate model simulation data. We chose here the 50-year return event (mm of water in 24 hours) as the parameter of interest. These changes can be compared to the present climate simulations for the same parameter, shown in Fig. 2.6. The results for the change, shown in Fig. 3.12 show that the 50-year return daily snow precipitation will increase significantly in Northern England and Southern Scotland, on the coastal area of Netherlands and in some mountainous regions, particularly in Eastern Europe. The exact geographical distribution of areas with increase and decrease partly result from random variability.

The results in Fig. 3.12 are based on one simulation, but they are supported by multi-model mean results for 10 cm snowfall (Fig 3.11.),thus illustrating the remarkable possibility of increasing ex-

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Grant No. 233919 tremely severe snow-related disturbances of the European transport system, while snow problems are clearly reducing in general.

Figure 3.12. Simulated change in the fifty-year return value of the annual maximum snow fall in 24 hours (in percent of water equivalent) based on SMHIRCA-ECHAM simulation.

3.5.3. Blizzard Blizzard are expected to become sparser (Fig. 3.13.) in the 2050s by 1-5 days compared to the present climate over the North-Atlantic, Iceland, Baltic Sea and its coastal area, the fjord coast of Norway, in the Alps and Pyrenees. Increasing tendencies (by 1 day/year) are sparse and less coherent. Nevertheless the sign of change is not consistence among the models, the indicated patterns varying between an increase of blizzards by 5 days (upper limit maps) and a decrease by 5 days (lower limit) over Northern and Eastern Europe.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.13. Multi-model mean, upper and lower limit of changes in annual blizzard days from 1989-2010 to 2041-2070 based on six RCM simulations.

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3.5.4. Heavy precipitation The European scale pattern of change in precipitation extremes (>30 mm/day) shows an increase by 1-5 days, except over the Mediterranean Sea and the coastal Mediterranean, where no or a small changes is projected (Fig. 3.14.). Concerning the variation of very extreme precipitation events (>100 mm/day), no significant change has been predicted by the selected RCMs. Further- more the sign of the change is not consistent among the models indicating large intermodal differences due to internal climate variability present in simulations, but possibly also due to model differences in description of deep convection. There is no clear signal for very extreme precipitation events (100 mm/day) that is presumably resulted by the limitations of the RCMs. Even if the climate models used are run at high resolution, the gridded data generated is more homogenous and the extremes are typically attenuated compared to the observational data (Rummukainen 2010). The thresholds used in the present study were defined based on station observations and the extreme values are most probably underestimated by the models in both control and future climate runs.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.14. Multi-model mean, upper and lower limit of changes in annual heavy rainfall days from 1971-2000 to 2041-2070 exceeding (A) 30 mm, (B) 100 mm and (C) 150 mm based on six RCM simulations.

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3.5.5. Heat spells As Figure 3.15 shows, warm days (mean temperature above 25 °C) become more frequent by the 2050s. Scandinavia will experience 5 more days/year and Southern Europe with 30-40 more days/year. In western and central part of the continent warm days will be more frequent by 20-30 days/year. The spatial variation of hot days (maximum temperature above 32 °C) suggests a substantial increase (up to 40 days/year) in the southern part of the continent and by 5-20 days/year on mid-latitudes. This change implies that mid-latitudinal regions may experience as many days with heat waves by year 2070 as the Mediterranean countries in the present climate. As for the frequency and spatial variation of days with 43 °C, more countries will be affected than nowadays; most of S and SE Europe will experience extreme heat waves, their number increasing with 5 days. Some of the climate models indicate an increase by 20 days for the Mediterranean countries.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.15. Multi-model mean, upper and lower limit of changes in annual heat-spells days from 1971-2000 to 2041-2070 exceeding (A) 25 °C, (B) 32 °C and (C) 45 °C based on six RCM simulations.

The projected increase in the duration and intensity of hot days will have a negative impact on transportation and infrastructure during the summer months, especially in those countries which already experience high temperatures.

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3.5.6. Cold spells The simulated cold extremes decline substantially by 2070 over the whole continent (Fig. 3.16), most strongly over Northern Europe. The decrease in the frequency of frost days varies between 20-30 days in Northern Europe and decreases gradually towards Southern Europe, where it implies a decrease by 1-5 days. Most of the six models agree on the amplitude of change over the land. This implies that Finland, Sweden and Norway are likely to experience as many frost days in the 2050s as some mid-latitude countries (such as the Baltic countries, Poland and Ukraine) in the current climate. The spatial and temporal analysis of the extreme cold spells also indicates fewer days with temperature under -20 °C for the affected areas: by 10-20 days in Scandinavia, Alps and northeastern part of the continent. The RCMs agree on the variation of upper and lower limit; the most negative change for the extreme cold spell is 1-5 days/year for Northern and Northeastern Europe. The largest differences compared to the current climate are 30-40 frost days/year in Scandinavia and Alps, 50 days in the Arctic and Baltic Sea and 10-20 days in Southern Europe. The magnitude of change in the extreme cold spells (daily mean temperature below -20 °C) varies between 1-40 days according to the most negative projection (see Lower limit map), with the highest values at high latitudes.

-50 -40 -30 -20 -10 -5 -1 -0.1 0.1 1 5 10 20 30 40 50 Figure 3.16. Multi-model mean, upper and lower limit of changes in annual cold-spell days from 1971- 2000 to 2041-2070 exceeding (A) 0 °C, (B) -7 °C and (C) -20 °C based on six RCM simulations.

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Grant No. 233919 3.6. Further scenarios for focused purposes

3.6.1. Events damaging infrastructure Results for the fifty-year return values of those extreme weather events that are likely to cause damage to the transport infrastructure in Europe were given in Section 2.12. Here the projected changes of these variables from the present climate (1970-2000) to the period of 2041-2070 are provided based on the SMHIRCA-ECHAM5 simulations and the extreme analysis methods of Sec- tion 2.2.

Figure 3.17 shows an increase of several degrees in fifty-year return maximum air temperature in many parts of Europe, but the pattern is scattered and shows a decrease at some isolated regions, e.g. in England. There is a significant increase in many areas of Europe that are already hot (see Fig. 2.24).

Figure 3.17. Simulated changes in the fifty-year return value of the annual maximum air temperature (in oC) based on SMHIRCA-ECHAM simulations.

The change in the fifty-year return value in precipitation amount in five days is shown in Fig. 3.18. These data include large stochastic variation and provide little evidence for regional variations. It appears, however, that on average there will be more severe flood-inducing precipitation events in Europe, except perhaps in England, and the Mediterranean area. The most significant increase is likely to occur in Central Europe.

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Figure 3.18. Simulated changes in the fifty-year return value of the annual maximum precipitation amount in five days (in percent) based on SMHIRCA-ECHAM simulations.

The change in the 50-year return wind speed is shown in Figure 3.19. These extreme wind speeds, potentially damaging the infrastructure of transport systems, appear to be changing little with climate change, except for the coastal regions of the North Sea and Baltic Sea. In some parts of these regions a remarkable increase, up to 50%, is projected by the simulations. However, data include large stochastic variation.

Figure 3.19. Simulated changes in the fifty-year return value of the 10 minute mean wind speed (in percent) based on SMHIRCA-ECHAM simulations.

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3.6.2. Climate projections of thunderstorm-related hazards The study of the connection between wind gusts, large hail and tornadoes and climate change is presently being pursued only by a few research groups worldwide. Here, we will briefly describe some of the work and their results. Because of the small size Brooks et al. (2003) have presented a concept to use reanalysis data to diagnose the global severe convective storm climatology by using so-called proxies. These proxies are developed through a statistical analysis of the "atmospheric environment" of severe convective storm events, as resolved by 4-dimensional atmospheric datasets. This approach has its foundations in studies of the storm environment that identified relevant physical quantities using radiosonde measurements in the proximity of extreme convective events (e.g. Rasmussen and Blanchard, 1998; Groenemeijer and van Delden, 2007; Brooks, 2009).

The most well-known proxy to characterize severe storm environments was developed by Brooks et al. (2003) that has since been used by a few groups: a product of convective available potential energy (CAPE) and the vertical wind shear across the surface-to-6 km layer. Only two studies have used these methods on climate model data while addressing Europe: Marsh et al. (2009), and Sander (2011), see figure 3.20.

These studies have split the problem into to two sub-questions: 1. What will be the climatic trend of thunderstorm occurrence? 2. What will be the climatic trend in the probability of thunderstorms to produce hazardous weather?

Figure 3.20. Increase in thunderstorm severity potential (TSP) as predicted by a regional climate model. From Sander (2011). Courtesy of Julia Sander.

The consensus of these two studies is that no indications of an increase in thunderstorm occurrence were found, and that it may even slightly decrease across most of Europe. However, the fraction of thunderstorms that may develop hazardous phenomena such as hail, severe wind gusts and tornadoes will likely increase. Whether this increase will compensate for the potentially reduced occurrence of storms is not clear rendering these studies inconclusive in this respect.

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3.6.3. Changes in the Baltic Sea ice cover by 2060 Climate change will have many consequences on society and nature in the Baltic Sea. Also the Baltic Sea ice cover will face changes that affect for example transportation and ecosystems. Large scale atmospheric pressure systems affect strongly the climate in the Baltic Sea area. These include The Icelandic Low, the Azores High and the winter high and summer low over Russia. Hu- mid and mild air flows with westerly winds into the Baltic Sea Basin. Because of warm ocean cur- rents that bring heat through the Gulf Stream and the North Atlantic Drift, the mean air temperature in the Baltic Sea Basin is usually higher than in other areas at the same latitudes. Since the late 19th century the annual mean temperature has increased by about 0.07-0.10°C per decade in the Baltic Sea area. The precipitation in wintertime has also increased (BACC 2008).

Ice winters in the Baltic Sea have been classified into five different severity classes according to Seinä and Palosuo (1996). This classification is based on the observations of annual maximum sea ice cover of the period 1720-1995 (Table 4). In this classification the extreme categories (extremely mild and extremely severe) both contain about 10 % of the winters.

Table 4. Severity classification of ice winters according to Seinä and Palosuo (1996). Severity Ice cover extent (km2) Extremely mild 52000-81000 Mild 81001-139000 Average 139001-279000 Severe 279001-383000 Extremely average 383001-420000

The sea ice extent varies a lot from year to year (BACC 2008). In extremely mild winters, only parts of the Gulf of Finland and Bothnian Sea and the Bothnian Bay are ice-covered. The maximum ice coverage in those winters is only about 12 % of the total Baltic Sea area. In extremely severe winters almost all of the Baltic Sea freezes. Since the late 19th century, the maximum sea ice extent has decreased. The frequency distribution of annual maximum Baltic Sea ice cover in 1951-2009 is shown in Fig. 3.21. During this period the amount of average ice winters was largest, and mild and extremely mild ice winters were more common than severe and extremely severe ice winters.

According to BACC (2008), first ice typically forms in November and the freezing begins in the coastal areas of the northernmost Bothnian Bay. The ice cover reaches its maximum value usually in February or March. According to observations, the trend in the duration of the ice season shows a tendency towards milder conditions. The length of the ice season has decreased about 14-44 days during the 20th century. The changes are mainly due to the earlier ice break-up.

In this study, the future changes in the probability of annual maximum ice cover extent and average maximum sea ice thickness are assessed using observations of air temperature, sea ice cover in the Baltic Sea and the climate model data. In winter 2011 the countries around the Baltic Sea have agreed upon a new severity classification of ice winters in the Baltic Sea, because the old classification did not coincide with the situation in the changing climate anymore. This new classification is based on observations in 1961-2010. However, in this study the old classification (Seinä and Palosuo 1996) is still used.

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Figure 3.21. The frequency distribution of annual maximum ice cover extent in the Baltic Sea based on observations in 1951-2009.

Regression model for estimating change in maximum ice cover extent A regression model was fitted between the observed maximum ice cover extent and coastal No- vember-March mean temperature in 1951-2009 (Fig. 3.22). The temperature observations were monthly mean values from E-OBS data set (Haylock et al. 2008). The regression model can be presented as an equation:

 81307ey  2695.0 x (18) where y is the maximum ice cover extent given as km2 and x is the November-March mean temperature in coastal grid points given as degrees Celsius. The coefficient of determination is R2=0.8439 which means that the model can explain about 84 % of the observed variation. The RMS-error is 31964.15 km2. Using the regression model the future maximum ice cover extent was assessed for each decade until 2060.

The information of the future temperatures was obtained using delta-change method (Agresti 2002). The estimate of the change in future temperature was obtained as a mean value of the output of 19 global climate models. The model data was originally downloaded from the World Climate Research Programme (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) multi- model dataset (Meehl et al. 2007). After downloading, the model dataset was interpolated onto the same 0.25 degrees grid as the dataset of observed temperature. The emission scenario A1B was used for future changes.

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Figure 3.22. A regression model (black curve) fitted to the observed maximum ice cover extent and the coastal Nov-Mar temperatures (blue symbols) in 1951-2009.

The average temperature change from the output of the global climate models was calculated for five 30-year periods, so that the change in each period represented each of the five future decades: 2011-2020, 2021-2030, 2031-2040, 2041-2050, and 2051-2060. These values were added to the observed temperature of each winter in the period 1951-2000, so that the interannual variation remained for the future. This was done separately for each of the future decades. As a result a distribution of possible coastal winter temperatures was obtained for each decade. Using the regression model (18) and these temperature distributions the distributions of maximum ice cover extent for each decade was achieved.

Average maximum fast ice thickness using Stefan’s law The average maximum fast ice thickness was assessed using Stefan’s law (Zubov 1945):

22  ddSah (19) where h is the average maximum fast ice thickness, S is frost sum, a=3 cm(°C·d)-1/2 and d is insu- lation efficiency of the surface layer of air over ice cover (about 10 cm). Stefan’s law slightly over- estimates the ice thickness because it does not take into account the insulating effect of the snow layer on top of the ice.

The frost sum for the future decades needed in the equation (19) was obtained using delta-change method. Mean temperature change for each month and each of the five future decades 2011-2020, 2021-2030, 2031-2040, 2041-2050, 2051-2060 based on the output of 19 global climate models was added to the observed 30-year mean temperature of each month in 1971-2000 so that a mean temperature for each month in each future decade was obtained. The frost sum was then calcu-

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Grant No. 233919 lated by multiplying the mean temperature of each month by the number of days in that month and then summing the negative values. Using this frost sum and Stefan’s law an average maximum fast ice thickness was obtained for each decade. As the temperature observations were available only on land areas the results can represent the average maximum fast ice thickness only in sea areas next to the coast line.

Maximum ice cover extent As the warming proceeds the maximum sea ice extent will decrease in the coming decades. The cumulative probability distributions of the maximum sea ice extent for each decade until 2060 are shown in Fig. 3.23 when it is assumed that the warming proceeds according to the average model results and emission scenario A1B. In the period 2011-2020 the probability for severe ice winters (ice cover extent more than 279 000 km2) is less than 10 %, in the period 2021-2030 the probability is about 5 %. Finally in 2041-2050 and 2051-2060 the severe ice winters have practically disap- peared. At the same time the probability for mild and extremely mild ice winters increases.

The probability curve in the Fig.3.23 shifts towards the upper left corner, which means that the portion of mild and extremely mild winters increases and the portion of severe winters decreases. For example in 2011-2020 the probability that maximum sea ice extent reaches 150·103 km2 at the most is about 65 %, and in 2021-2030 the probability is about 70 %. In 2031-2040 this probability is 80 %, in 2041-2050 it is almost 90 % and finally in 2051-2060 it is over 90 %. The winters when ice cover extent is more than 150·103 km2 become rarer all the time.

A1B: Change in max. ice cover extent 2011−2060 0 52 139 279 383 Cumulative probability Cumulative 2011−2020 2021−2030 2031−2040 2041−2050 2051−2060 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0 50 100 150 200 250 300 350 400

Ice cover extent (1000 km^2)

Figure 3.23. The change in the cumulative probability distribution of the maximum sea ice extent during the five future decades. The curves with steps represent empirical cumulative probability distributions and the smooth curves represent theoretical cumulative distributions (generalized extreme value distribution, GEV) fitted to empirical distributions. On the upper x-axis the severity classification limits for ice winters are shown.

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Average maximum fast ice thickness As the temperature observations were available only in land areas, the fast ice thickness was assessed only in sea areas next to the coastline. The assessed average maximum fast ice thickness for each future decade according to average model results is shown in Fig. 3.24. In 2011-2020 the average maximum fast ice thickness in the Bay of Bothnia coast areas is about 90 cm, in southern Baltic Sea the thickness is about 0-20 cm. In the Gulf of Finland the average maximum fast ice thickness varies mainly between 20 and 60 cm. In the next decade (2021-2030) the average maximum fast ice thickness is still over 80 cm in the coast of the Bay of Bothnia whereas in the southern Baltic Sea the coast areas remain partly iceless. In the next two decades in the Bay of Bothnia the average maximum fast ice thickness does not reach values over 80 cm anymore, in the Gulf of Finland the ice thickness is about 10-40 cm. The southern Baltic Sea and the west coast of the Northern Baltic become iceless in normal winter. In the last decade (2051-2060) in the northern parts of the Bay of Bothnia the average maximum fast ice thickness is about 50-60 cm and the southern areas are largely ice-free.

The ice thickness in future decades was compared to the average maximum fast ice thickness in 1971-2000 (not shown). The average maximum fast ice thickness for period 1971-2000 was calculated in a same way as for the future decades: using Stefan’s law and observed temperatures. In 2011-2020 the average maximum fast ice thickness was mainly 10-15 cm thinner than in 1971- 2000, in 2021-2030 the difference was about 15-20cm, in 2031-2040, 20-25 cm and in 2041-2050 the average maximum fast ice thickness was at least 25 cm thinner than in 1971-2000. Finally in 2051-2060 the difference to the value in 1971-2000 was about 30-40 cm.

Conclusions on the Baltic Sea ice cover The Baltic Sea maximum ice cover extent and the average maximum fast ice thickness were assessed in the five future decades. According to the results, maximum ice cover extent and probability for severe ice winters will decrease. Severe ice winters become rare after 2030. The probability for mild and extremely mild ice winters will increase.

The average maximum fast ice thickness was assessed only in the coastal sea areas. According to these results, the average maximum fast ice thickness becomes thinner everywhere in the area, in the southern parts the sea ice will probably disappear totally. It is however important to remember that Stefan’s law which was used in assessing the average maximum fast ice thickness does not take into account the snow layer on top of the fast ice. The results shown here are thus about 10- 20 cm too high (Leppäranta 1993). On the other hand, when climate is warming also the snow layer on top of the ice becomes thinner.

The change in the heat storage of the sea and its possible effect on the ice thickness is not taken into account either. It is presumable that the warming of summer and autumn increases the heat storage of the surface layer of the sea in the future. This would cause delay to the cooling of the sea water and thus decrease the maximum fast ice thickness. The change in the heat storage is an important factor especially in the open sea areas.

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a) b)

c) d)

Figure 3.24. The average maximum fast ice thickness (cm) for each future decade. a) 2011-2020, b) 2021-2030, c) 2031-2040, d) 2041-2050, e) 2051-2060.

3.6.4. Scenarios on road networks: a Nordic view Studies on climate change impacts on roads have already been concluded around the world, e.g. Galbraith et al. (2005) in Scotland, Thordarson (2008) in Iceland, Youman (2007) in Australia, Nat- ural Resources in Canada (2007) and most recently a comprehensive study in the USA by the Na- tional Science and Technology Council (2008). A feature common to all climate scenarios is that

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Grant No. 233919 the changes will be most dramatic near the poles and during the winter, due to the positive feedback effects of the shrinking snow and ice coverage.

The results of project IRWIN introduced in Chapter 3.3 indicated however that no dramatic changes are expected before 2050 according to the most established climate models. In Northernmost Europe, mean air and road temperature will rise by 0.9-2.5 degrees by 2050, the rise being larger towards the North. Rainfall amounts have a positive trend in Northern Europe, but naturally accu- mulated snowfall amounts will be less. Results indicate increase of extreme precipitation events. Average of maximum wind increased by 0.11-0.34 m/s, the larger values occurring in coastal areas or in the North. All these results quite well in line with the results of this report.

To illustrate the results in more detail, some examples of the downscaled scenarios are shown. Figure 3.25 shows the scenario for precipitation falling either as snow or rain in Sundsvall in the middle parts of Sweden. Figure 3.26 displays the number of days in Stockholm in the winter season when temperature falls below zero. In Figure 3.27 scenario for number of wind gusts exceeding 100 km/h (28 m/s) on a bridge in Gothenburg in Southern Sweden is shown. The previously mentioned trends can be clearly seen, as well as large annual variations.

Figure 3.25. Average November-to-March rain- and snowfalls for all stations in the Sundsvall region for the ECHAM-5 model based scenario. Thin lines are annual means, thick lines are 10-year averages. Under this scenario, the amount of snow declines by nearly 50% by year 2100, whereas the rainfall increases by over 100%.

Figure 3.26. Number of days in the winter season (November-to-March) in the Stockholm region where the air temperature falls below 0ºC. The thin lines are the time-series for individual stations; the thick line is the 10-year regional average. Under this ECHAM-5 based scenario the number of days where temperature falls below freezing will decrease by ~30% by 2100.

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Figure 3.27. Number of 30-minute periods where the maximum wind gust exceeds 100km/h on the Tjörnbron, Gothenburg region, for the ECHAM-5 based scenario. The thin line are annual counts, the thick line is the 10-year count. The number of wind gusts over 100km/h increase significantly in this scenario.

Even though expected average changes are relatively modest in the coming 30-40 decades, the need for road maintenance operations will change in the different regions as the climate changes. As an example, in Northern or mountainous parts of Europe, warmer climate can both mean more needs for salting due to more slippery roads but at the same time less snow removal events when precipitation falls as rain instead of snow. The future will bring more rainy days on a cold surface in the north due to the milder climate and more rainy days in the north instead of precipitation falling as snow. The northern areas will also experience more slipperiness due to frost days when the surface temperature is low and the dew point is larger than the surface temperature. These frost days will occur less frequently in the future in the more southerly areas, due to fewer days with minus degrees.

Effects on road networks in Northern Europe The effects of climate change on road networks are a matter of great concern to road owners, as many decisions especially on investments in the infrastructure may have implications over decades. A change in the impacts may cause a need to change road structures. Improvements of drainage, erosion control and raising the road surface levels may be needed. Other adaptation and proactive measures are control of design criteria and improvement of current roads to assure the service level. Services for road users may need changes as well, in order to deliver appropriate warnings and other valid information through the most efficient channels.

Several results from climate models for road network have been shown in this report. Even though many of the trends seem to be slow and not affecting the road network seriously, some signals are alarming. This is true in particular for larger and heavier rainfall amounts which are predicted in the future climate. For example according to the study by Saarelainen and Makkonen (2005) in Fin- land, risks for heavy rainfall showers and flash flooding will increase. The probability of very heavy showers may increase even by 25-50% in certain areas, most likely in parts of the Central and Northern Europe. Even though there are no clear indications that the frequency of destructive storms will increase, trees will fall more easily during Northern wintertime storms. The reason is because the layer of frost will be thinner in the future. As a specific Northern problem, it will be also

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Grant No. 233919 more and more difficult to maintain the present ice roads, which provide temporary wintertime transport of goods and people to remote areas with no permanent road access.

It is also worth reminding that the progress of climate change is depending crucially on the level of global greenhouse emissions, and most of the scenarios (also in this report) are based on a relatively modest and optimistic emission scenario A1B. Some recent statistics indicate that the true anthropogenic emissions may be considerably larger and, if continued, will accelerate the warming as compared to the A1B model results.

Even though the warming trend of European winters is evident, the large variability from year to year is not disappearing. We have recently experienced the risk inherent in long successions of warm winters, followed by few colder “good old-time” winters. Road management resources and equipment have been optimised to a warmer climate minimum due to economic reasons, resulting in inadequate resources on harder winters. This has caused enormous economic losses for Euro- pean transport during winters of 2009-2010 and 2010-2011.

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4. Discussion and summary

The present report provides an overview of changes in the extreme and adverse weather events that are most likely to affect the European transport network focusing on the present climate (1971- 2000) and the possible future climate (2011-2070) under the SRES A1B emission scenario using a set of various threshold indices.

Present climate according to observational data  The analysis of the observed changes in winter and summer temperature, precipitation and wind extremes revealed some common patterns on European level: high frequency of winter extremes (snowfall, cold spell and winter storms) in Northern Europe and Alpine region, and strong continentality effect in the frequency of cold spells. Nevertheless there has been observed a decline in the probability of frost days and cold spell during the studied period and a tendency toward wetter winters in Scandinavia and Eastern Europe.  Extreme heat spells (25 °C and 32 °C) have been most intense in Southern Europe, although the frequency of very extreme heat spells (above 43 °C) has been reduced even in the south. The trend of heat spells indicates an increase, especially in the Mediterranean region.  It is remarkable the dissimilar warming degree observed in the changes of cold and heat spells, the latter one being more accentuated. Similar asymmetric warming patterns of cold and warm tails were indicated also in the previous works. Klein Tank and Können (2003) found a symmetric warming for the first half of their study period (1946-75) and an asymmetry for the 1976-99 subperiod. Moberg et al. (2006) reported a tendency for asymmetry in European warming for the entire century. However the pattern broke down on regional level, since regional differences were quite large.  Although adverse rainfall events may occur on yearly basis over the continent, the very extreme rain amounts are relatively rare. Most common they are in the Alps and on the western coastline of the continent.  Considering the trend of heavy precipitation events, it indicates moderate increase over the Alps and sporadically over the western part of the continent, but no significant trend has been detected. This is at some extend in contrast with the findings from earlier studies that reported significant increase in the number of heavy precipitation days (Alexander et al. 2006, Grossman et al. 2005, Klein Tank and Können 2003) for the second part of the twen- tieth century. At the same time we have to notice that unlike our analyses, the mentioned studies did not consider the state of the precipitation events (snow and rain) separately. Moberg et al. (2006) found an average increase of winter precipitation and a weak increasing tendency for summer precipitation, but not an overall trend.  Extreme wind events and blizzards are the most common over the Atlantic and along its shores and sea areas. Limited by the availability of data (22 years) no trend has been computed.  Low visibility due to fog at the main European airports is strongly related to the climate zone; the most affected airport being Milan due to its location in the fog-prone Po Valley. Other major airports with high frequency of low visibility occurrence are Manchester, Lon- don Gatwick, Copenhagen, Brussels, Geneva, Zurich, Munich and St. Petersburg. Accord- ing to observations the occurrence of low visibility events has strongly declined. Very dense fog conditions have diminished all over Europe even becoming non-existent at some of the studied airports.

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Future climate based on model projections  On the basis of the multimodel mean climate projections robust changes in temperature extremes but less coherent in precipitation extremes has been detected.  For wind extremes, changes are fairly uncertain, with discrepancies among the models.  Cold extremes will become rarer, with a substantial decrease in the north and less accentuated over southern Europe in moderate cold extremes.  Snow events in general tend to decrease over the continent. However, extreme snowfall events do not decrease with similar phase. Heavy snowfall events (>10 cm/day) are expected to become more frequent over Scandinavia by 1-5 days by the 2050s.  Extreme warm events intensify over the entire continent by 2070. The magnitude of increase is larger in Southern Europe, about 30-40 days/year for days with 32°C, 10-20 days in Cen- tral Europe and 1-5 days in northern Europe for the same threshold. This implies a shift of the warm extremes northwards,.The central European countries will experience as many cold spells by 2070 as Mediterranean areas in the current climate. Similar findings were reported by Nikulin et al. (2011) and Beniston et al. (2007) for changes in temperature extremes. Al- though we have not analysed the duration indices in our study, an increase in the intensity of heat waves might be also expected. For example, Dankers and Hiederer (2008) indicated an increase in the 7-day heat wave duration index over most of the continent.  Considering the magnitude of changes in precipitation extremes, we found somewhat less clear change for the applied threshold indices than the earlier studies. An increase by 1-5 days/year magnitude over Europe except the Mediterranean, where no significant change or a sporadical decrease is expected. Nikulin et al. (2011) indicated an increase by 10-30% over Scandinavia, Poland and Baltic countries in 20-yr return levels and a significant decrease (10-40%) in south-eastern part of Iberian Peninsula for summer precipitation as well as increase in winter precipitation over the whole continent. Beniston et al. (2007) found a decreasing tendency of winter precipitation in the south and an increase in central and northern Europe. Similar results were given by Frei et al. (2006) for winter precipitation, indicating an increase in extreme precipitation north of about 45 °N and a decrease south of it, but a more complex character of change for the summer precipitation. Nevertheless the variance in patterns of changes in all studies relate to the number of regional model used as well as the different methods and indices applied.  Changes in wind extremes are more difficult to assess, since the multi-mean indicates clear decrease over the Atlantic and Mediterranean Sea but a slight decrease or no significant change in either direction over the continent. Additionally, there is a large variation in the results among the models.

Summary of regional characteristics The nature and the extent of adverse and extreme weather events essentially depend on the climate region they eventuate in. The geographical location and topography of the location considerably determines the impact and consequences of the phenomena, e.g. a snowfall event resulting in a few cm of snow cover or daily mean temperature being around zero degrees can be either considered a common event, e.g. in the northern Europe and in the mountainous regions, or very exceptional and extreme event in the Mediterranean. Different regions of Europe are impacted by different type of extreme weather phenomena. In order to provide an extensive climatology of adverse phenomena and extremes over Europe, a classification of regions affected by similar adverse and extreme events is a beneficial platform in the risk assessment for the European transport system. The regions described follow the classification used in the EWENT project (Leviäkan- gas et al., 2011, Fig.4) with a modification of dividing the Temperate region into two parts.

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Based on the frequency and probability maps of extremes six climate regions with similarities in the most common phenomena were defined: - Northern European (sub-arctic) region: NE - Maritime (oceanic) region: O - Mediterranean region: M - Temperate Central European region: Tc - Temperate Eastern European region: Te - Alpine region: A

These regions are not strictly delimited from each other; rather they are connected through transitional sectors. Most of the European countries are characterised by several extreme climate regions (Table 5), thus when identifying extreme weather related hazards for a certain country, not only phenomena relevant to the representative geographical zones but preferably also to their adjacent zone should be considered.

Figure 5.1. Classification of climate regions based on adverse and extreme phenomena for the Eu- ropean countries.

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Table 5. The European countries with the dominating climate regions COUNTRY CLIMATOLOGICAL REGION NE O Tc Te M A ٭ ٭ Albania ٭ Andorra ٭ ٭ Austria ٭ Belarus ٭ Belgium ٭ Bosnia and Herzegovina ٭ Bulgaria ٭ ٭ Croatia ٭ Cyprus ٭ Czech Republic ٭ ٭ Denmark ٭ Estonia ٭ Finland ٭ Former Yugoslav Rep. of Macedonia ٭ ٭ ٭ France ٭ ٭ Germany ٭ Greece ٭ Hungary ٭ Iceland ٭ Ireland ٭ ٭ Italy ٭ Latvia ٭ Lithuania ٭ Luxemburg ٭ Malta ٭ Moldova ٭ Monaco ٭ Montenegro ٭ Netherlands ٭ ٭ Norway ٭ ٭ Poland ٭ Portugal ٭ ٭ Romania ٭ ٭ Russia ٭ Serbia ٭ ٭ Slovakia ٭ ٭ ٭ Slovenia ٭ ٭ ٭ Spain ٭ ٭ Sweden ٭ Switzerland ٭ ٭ Turkey ٭ Ukraine ٭ United Kingdom

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In the following we describe the characteristic phenomena of each climate region, focusing on their probabilities, intensities and spatial extensions in the present climate as much as the changes in the projected future climate.

The Northern European region (NE), primarily located in Fennoscandia, Russia, north of 55° latitude, Iceland and the northern part of Denmark (Fig. 5.1) is typically dominated by extreme winter phenomena. The frequency of cold spells, heavy snowfalls and blizzards is the highest in this geographical zone. Within this zone the most impacted by winter extremes are the areas located north of the 65° latitude, e.g. Lapland and Iceland, recording the highest probability of extreme cold spells (20-35 days/year for daily mean temperature under -20 °C in Lapland), of heavy snowfall (40-50 days/year with 10 cm/day on the western coast of Norway and Iceland) and of blizzards (locally over 140 cases between 1971-2000) and of extreme wind (especially over Iceland). Heavy rainfalls are frequent mainly over the fjord coast and westerly exposed mountain ranges of Norway. On the other hand, the frequency of hot spells is the lowest within the Northern European zone (typically 5-15 days/year with maximum temperature over 25 °C).

In terms of projected future climate the winter extremes are predicted to moderate by 2050s in the Northern European zone compared to their present range, with a substantial decrease in the frequency of cold spell (with 20-30 days per year), blizzard and snowfall events, while heavy snowfalls (over 10 cm/24 hour) indicate a mixed behaviour marked by decrease (5 days/24 hour) along the shore and a very slight increase over the land (up to 1 day). Maximum ice cover extent on Bal- tic Sea and probability for severe ice winters will decrease. The average maximum fast ice thickness is likely to decrease by 30-40 cm by 2060 compared to the control period 1971-2000, letting the southern areas of Baltic Sea coast largely ice-free.

As anticipated in a warming climate, heat spells (>25 °C) are expected to impact more frequently (ca. 5 days/year) Scandinavia and northern Russia; however, their increase is not as robust as in Southern Europe. Precipitation extremes record (>30 mm/day) a slight intensification (1-2 days). Wind gusts show a tendency of strengthening over the Baltic Sea and weakening over the land areas.

The maritime (oceanic) region (O), located over the western part of the continent – British Isles, France, Spain, Belgium, Luxemburg and the Netherlands, is featured by relatively moderate frequency of extremes winter phenomena, such as blizzards, extreme cold spells (on average less than 10 days/year with -7 °C and practically no cases with -20 °C) and heavy snowfall (in general 3% probability of 1 cm snow). On the other hand, as a consequence of the reduced probability of extreme winter events, most of the affected countries have a more reduced level of preparedness against these phenomena. This implies that once an extreme weather event eventuate the severity of disruption and the damages caused on transport system, such as the adverse snowfall events during winter 2009 and 2010, are quite remarkable. The probability of heat waves is higher, especially over the mainland (5% for daily maximum temperature over 32 °C), while heavy rainfall and extreme wind gusts are more common over the British Isles (80 cases/year with 17m/s wind gust).

By the 2050s this climatic region is likely to become more impacted by warm extremes, with an increase by 5-10 days/year for the applied threshold indices, particularly over the land. Heavy precipitation events are expected to increase slightly, on average by 1 day for the 30 mm threshold index. The intensity of cold temperature, extreme snowfall and blizzard events are predicted to de-

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The Mediterranean Sea and its coastal areas, the so called Mediterranean zone (M) is affected by the summer extreme phenomena, characterised by the highest frequency of heat spells in Europe (locally 25 % probability of daily maximum temperature over 32 °C) and heavy rainfall (locally more than 100 mm/24 h). However frost days and snowfalls may occur on annual bases, extreme winter events are not typical.

Dust events also impact the region occasionally, especially the countries in the southeast Mediter- ranean. The trend analysis of synoptic classes with no-dust and dust deposition events indicate a decreasing tendency, however the classes with frequent dust deposition seem to become more frequent.

The multimodel mean indicates a robust intensification of warm extremes over the Mediterranean zone: by 30-40 days/year of daily maximum temperature above 32 °C and by 5-10 days of those above 43 °C. According to the applied RCM projections the frequency and intensity of warm extremes increase more rapidly in this zone than over the rest of Europe. The spatial variation of changes in heavy rainfall events indicates mixed patterns, with no significant change over most of the Mediterranean basin and sporadical decrease by 1 day/year. Cold temperatures and snowfalls decrease on average by 5-10 days over this region. Extreme winds tend to weaken except the eastern part of the region where it locally alternates with strengthening.

The temperate Central European region (Tc) is less affected by the very extreme weather events; however, the adverse weather events might impact the area: 5% probability of heat spells (Tmax≥35 °C) and 2% for heavy rainfall (30 mm/24 h) and on average 15-20 days/year with 17 m/s wind gusts. Winters might bring blizzards, sporadically severe snowfall (5 events/year), especially over the southern part of this region, and cold spells (up to 20 days).

An intensification of warm extremes and decline in cold extremes are expected for this zone by 2050s. The intensification is more robust heading southward, by 20 days/year for days with 32 °C. Accordingly, by 2070 the central European region is expected to experience as many heat spells as currently the Mediterranean region. Extreme wind gusts indicate mixed patterns of changes, with no general trend. Heavy rainfalls tend to decrease in frequency over most of the area, except locally over the Balkan Peninsula. Snowfalls are also expected to rarefy due mainly to a shift from snow to rain in the warming climate.

The temperate Eastern European climate region (Te) differs from the Central European one by more pronounced effect of continentality. Accordingly, cold spells are more frequent and intense during winter, 30 days/year with -7 °C over the western part of the zone, increasing eastward to 70-80 days. Very extreme cold spells (under -20 °C) are frequent over the eastern part. Blizzards and heavy snowfall events (10 cm/24 h) impact the eastern European zone about 5 times/year; very heavy snowfall events may also occur sporadically. Days with 32 °C eventuate with a 5% probability every year, being more frequent over the southern part of the region (~10%). The spatial variation of heavy rainfalls does not differ from the general European patterns, although very heavy events (≥ 100 mm/24 h) may locally occur.

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Regarding the changes in extremes there is expected a decrease in cold waves (by 5-10 days for - 7 °C), snowfall (by 1-5 days for 10 cm/day snow) and blizzard events and a significant increase in warm extremes (up to 20 days for 32 °C) by the 2050s. Wind gusts and heavy rainfall show mixed patterns over this region, however, the frequency of heavy rainfall tends to increase over most of the region expect in the southern part.

The Alpine region (A) implies in our regionalization not only the Alps but also the Carpathians, the Pyrenees and in many aspects also the Scandinavian Mountains. These regions due their topography can have remarkably different extreme phenomena than their surroundings. The most characteristic extreme phenomena affecting this zone are the cold spells (on average 50-60 days with - 7 °C), heavy snowfall (40-45 days with 10 cm and 20 days with 20 cm of snow in 24 hours), blizzards, especially in the Scandinavian mountains (over 120 cases during 1989-2010) and Alps (20 cases) and heavy rainfall (about 20 cases/year with 100 mm/24 h) on the slopes. The frequency and intensity of these phenomena is somewhat moderated in the Pyrenees by its southern location. As for the future changes expected by 2070 in the Alpine zone extremes are predicted to abate, especially the cold and snow related phenomena, while heavy rainfall and warm related extremes will become more intense.

In addition to the regionalization map, we provide a detailed description of future changes of severe weather phenomena for the major European ports, airports, road, rail and inland water transportation nodes and corridors (see Appendix B). The annex consists of table indicating the climate region where the transportation nodes and corridors belong to (Appendix B, Table 1) and of the tables of future change in the number of days that a certain weather phenomenon occurs (Appen- dix B, Table 1 and 2). The change is calculated by comparing average annual frequencies of the past/current climate (1989-2009 for wind gusts and blizzard, 1971-2000 for other parameters) and two future time periods (2011-2040 and 2041-2070). Point values of the changes are given for locations across Europe. These values are not statistically downscaled for each location, but rather the multimodel mean/upper/lower changes of the nearest grid points have been used.

As an example the number of days per year when the daily maximum temperature exceeds 25 °C is expected to increase in Athens by a mean of 12.6 days from 1971-2000 to 2011-2040. However based on the six models the possible range of the change is between 9.7 and 16.6 days (lower and upper limits).

Concerning the effect of changes in extremes on the transportation system it is of both signs: decrease in cold spells, extreme snowfalls and frozen precipitation have a positive impact on road, rail and air transportation, reducing the cost of maintenance and producing side benefits. On the other hand even if there is a general decreasing trend in the winter extremes, due to variability from year to year, winter extremes are still expected to hazard the transportation, thus, they still need to be considered in maintenance and investment in preparedness for many European countries. Intensifications of warm extremes and heavy precipitation events are likely to have a negative impact on transportation and infrastructure. During summer, especially in those countries which already experience high temperatures, further warming implies needs for improvements in the heat tolerance of the transport system.

All the changes anticipated in the RCM simulations applied follow the A1B emission scenario that lies in the central range of emission scenarios considered by IPCC.. The present trends of green-

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Acknowledgements

We acknowledge the E-OBS dataset from the EU FP6 project ENSEMBLES (http://ensembles- eu.metoffice.com) and the data providers in the ECA&D project (http://eca.knmi.nl). ERA-Interim data used in this study have been provided by ECMWF, and icing and visibility data by NOAA National Climatic Data Centre.

We acknowledge the climate modelling community. The ENSEMBLES and Coupled Model Inter- comparision Project (CMIP3) multi-model data were widely used.

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Uppala S.M., Kållberg P.W., Simmons A.J., Andrae U., da Costa Bechtold V., Fiorino M., Gibson J.K., Haseler J., Hernandez,A., Kelly G.A., Li X., Onogi K., Saarinen S., Sokka N., Allan R.P., Andersson E., Arpe K., Balmaseda M.A., Beljaars A.C.M., van de Berg L., Bidlot J., Bormann N., Caires S., Chevallier F., Dethof A., Dragosavac M., Fisher M., Fuentes M., Hagemann S., Hólm E., Hoskins B.J., Isaksen L., Janssen P.A.E.M., Jenne R., McNally A.P., Mahfouf J.-F., Morcrette J.-J., Rayner N.A., Saunders R.W., Simon P., Sterl A., Trenberth K.E., Untch A., Vasiljevic D., Viterbo P., and Woollen J. 2005: The ERA-40 re-analysis. Quart. J. R. Meteorol. Soc., 131, 2961-3012.doi:10.1256/qj.04.176 van der Linden P., and J.F.B. Mitchell (eds.) 2009: ENSEMBLES: Climate Change and its Impacts: Summary of research and results from the ENSEMBLES project. Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. 160 pp.

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APPENDIX A: Classification of weather patterns

Classification of synoptic patterns with Artificial Neural Networks The systematic use of synoptic weather charts dates back to the beginnings of modern meteorological practices. Synoptic weather stations, scattered all over world, supply meteorological services with observations of specific parameters at regular and fixed times. Upper-air observing stations report, generally, geodynamic height, speed and direction of wind, as well as temperature and humidity. Conventionally, for the analysis of the prevailing synoptic situation, charts of the geopotential height at selected levels are used. These charts depict the geopotential height at which a given pressure value is found and are usually called isobaric charts (hPa is the pressure unit used); quite commonly, the 500hPa level is analyzed. This level possesses several characteristics that make it distinct from others: it is well above the friction layer and therefore is not much affected by factors related with the Earth’s surface, at least in the short term; it represents the middle troposphere, that is the layer in which most of the weather phenomena take place; it is the level at which roughly half of the mass of the entire atmosphere is found below and half is found above; at this level, on average, divergence of the atmospheric air is negligible, in mid-latitudes. Meteorologists can identify on such isobaric charts discrete geometric patterns that characterize a synoptic situation of the atmosphere. There is a strong association between large scale atmospheric circulation patterns and regional meteorological phenomena that are observed at the Earth’s surface. As a consequence, synoptic upper air charts at certain levels provide a valuable tool for the operational weather forecaster to predict qualitatively occurrences of certain weather phenomena over particular areas (see Tymvios et al. 2010). One such typical example is the close association between the atmospheric circulation and the onset and maintenance of desert dust transport episodes, which is elaborated in this study. There are several techniques for weather type classification, developed for different regions and for different purposes. Many of them are based on automatic, objective and consistent methodologies. However, none of the proposed methods in the literature is accepted as universal and applicable for all problems. Each method has its strong and weak points. The method to be selected and its parameters are usually defined by the application itself. An initial effort for categorization of synoptic situations was made by Lamb (1950), while in literature there exists an abundance of methods of classification (see Hewitson and Crane 1996). In order to take advantage of these semi-empirical methods and to simplify the statistical processing, stochastic downscaling methods are often applied to the actual weather patterns in order to generate clusters of synoptic cases with similar characteristics. Weather type classifications are simple, discrete characterizations of the current atmospheric conditions and they are commonly used in atmospheric sciences. For a review of various classifications, including their applications, the reader is referred to Key and Crane (1986), El-Kadi and Smithoson (1992), Hewitson and Crane (1996) and Cannon and Whitfield (2002). Recently, a wide ongoing European effort in evaluating different classification methods within the framework of COST Action 733, which is entitled “Harmonization of weather type classifications in Europe” (http://www.cost733.org/) was completed (Philipp et al. 2010). The main objective of this Action is to “achieve a general numerical method for assessing, comparing and classifying typical weather situations in European regions, scalable to any European sub-region with time scales between 12 h and 3 days and spatial scales of ca. 200 to 2000 km. The technique described below was partly developed and expanded within this framework. In the following we present a relatively new methodology for the classification of synoptic situations with the use of ANN. More specifically, Kohonen’s Self-Organised Features maps (Kohonen 1990,

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1997) were used for the classification of distribution of isobaric height on charts of the 500hPa. As result of this classification, synoptic prototypes can be formulated which will be related to dust events. While ANN proved to be valuable tools for forecasting purposes, Kohonen’s Self Organizing Maps (SOM) technique is a relatively new method for climate research (Main 1997). Maheras et al. (2000) used a combination of an ANN classifier and semi-empirical methods for the classification of weather types in Greece, while Cavazos (1999) utilized a mixed architecture of a SOM network in line with a feed-forward ANN (FF-ANN) to study extreme precipitation events in Mexico and Texas. The same methodology was successfully applied for winter-time precipitation in the Balkans (Cavazos 2000). For a research on forecasting the risk of extreme rainfall events, a neural classification was recently implemented (Tymvios et al. 2010). A neural classification was also used in an attempt to identify possible climatic trends in atmospheric patterns (Tymvios et al. 2010). The method exploited in the present work for the classification of synoptic patterns is the Kohonen’ Self Organizing Maps architecture which is a neural networks method with unsupervised learning (Kohonen, 1990). A detailed description of the method and procedures used is provided below (see also Michaelides et al. 2007).

Kohonen’s Self Organizing Maps ANN are constructions of artificial neurons (algorithms that mimic the properties of biological neurons); they are commonly used to solve artificial intelligence problems, to simulate and predict the evolution of complex physical systems, to discover hidden structures inside data groups and they are ideal for the classification of individuals into groups of similar properties. All of these are achieved according to the network’s architecture and parameter tuning. Details of the classification method are discussed in Tymvios et al. (2010) and Michaelides et al. (2010), hence, a short description is sufficient here. As mentioned above, the neural network architecture used in this research is the Kohonen’ Self Organizing Maps (SOM) (Kohonen 1990). These networks provide a way of representing multidi- mensional data in much lower dimensional spaces, usually one or two dimensions. An advantage of the SOM networks over other neural network classification techniques is that the Kohonen technique creates a network that stores information in such a way that any topological relationships within the training set are maintained; for example, even if the Kohonen network associates weather patterns with dust events inaccurately, the error obtained will not be of great amplitude, since the result will be a class with similar characteristics. The process of reducing the dimension- ality of height vectors is essential in order to investigate productively the relationship among weather patterns and heavy rainfall. For a recent review of the advantages in using SOM as a tool in synoptic climatology, the reader is referred to Sheridan and Lee (2011). The geographical area studied is bounded by latitude circles 20oN and 60oN and meridians 20oW and 40oE; it covers Europe, North Africa and the Middle East. The data that were exploited comprise the 500hPa field at 1200 UTC (Universal Time Coordinated) for each day, from 1 January 1980 to 31 December 2005 (26 years), retrieved from the online data base of NCEP (National Cen- tres for Environmental Prediction, USA). The grid distance is 2.5οx2.5ο, thus the area in study is covered by 7x25=425 grid points The Kohonen SOM algorithm in its unsupervised mode was chosen for building the neural network models, because neither the number of output classes (synoptic classes) nor the desired output are known a priori. This is a typical example where unsupervised learning is more appropriate, since the domain expert (in this case a professional meteorologist) will be given the chance to see the results and decide which model gives the best results. The expert’s guidance can help to decide the number of output classes that better represent the system (Pattichis et al. 1995).

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In unsupervised learning, there are no target values, as in the case of other methods of ANN. Given a training set, Χ(k), k=1, 2, … , p, the objective is to discover significant features or regulari- ties in the training data (input data). In our case, the input data were 9497 vectors (i.e., the number of consecutive days in the 26 years 1980-2005). The neural network attempts to map the input feature vectors onto an array of neurons (usually one or two-dimensional), thereby compresses information while preserving the most important topological and metric relationships of the primary data items on the display. By doing so, the input feature vectors can be clustered into c-clusters, where c is less or equal to the number of neurons used (Charalambous et al. 2001). Input vectors are presented sequentially in time without specifying the desired output (see Schnorrenberg et al. 1996, Michaelides et al. 2001). The two-dimensional rectangular grid architecture of Kohonen’s SOM was adopted in the present research.

The input vector X is connected with each unit of the network through weights wj, where j = 1, 2,…, M; M equals to 425 grid points, for the area in study. The training procedure utilizes competitive learning. When a training example is fed to the network, its Euclidean distance to all weight vectors is computed. The neuron whose weight vector is closest to the input vector X (in terms of Euclid- ean distance) is the winner. This neuron is represented with I and is the winner neuron to input X if

l Xw min ii  Xw , i = 1, …, M.

Note that

1 2 2 2 2 l i 11 i xwxwXw 22 ...  xw NiN  (1)

is the Euclidean distance between weight vector wi and input vector X (see Charalambous et al. 2001). N refers to the number of output classes. The weight vectors of the winner neuron, as well as its neighbourhood neurons, are updated in such a way that they become closer to the input pattern. Learning follows the following rule:

 old old  new i i  ,, RINiwXw w  (2) i  old  i  ,, RINiw where, the neighbourhood set N(I,R) of neuron I with radius R consists of neurons 1, I±1,…, I±R, assuming these neurons exist, with maximum value being around the winner I, in order for a larger number of neighbourhood units to share the experience of learning with the winner unit, and it becomes zero as the distance between the neighbourhood units and I increases. The coefficient α in the above relationship is called the learning factor and decreases to zero as the learning pro- gresses. For simplicity, R is considered to have the shape of a geometric area, such as a rectangle or hexagon. The radius of the neighbourhood around the winner unit is relatively large to start with, in order to include all neurons. As the learning process continues, the neighbourhood is consecutively shrunk down to the point where only the winner unit is updated (Patterson, 1995). As more input vectors are represented to the network, the size of the neighbourhood decreases until it includes only the winning unit or the winning unit and some of its neighbours. Initially, the values of the weights are selected at random. The method which was used for the SOM to work can be described as follows:

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1. The initial value of the weights is set to small random numbers, as well as the learning rate and the neighbourhood. Steps 2 to 4 are repeated until the weights of the network are stabilized. 2. One vector X is chosen from the dataset as an input to the network. 3. The table for unit I with weight vector closest to X is determined by calculating

l Xw min ii  Xw . 4. The weight vector in (t + 1) iteration is updated according to:

i 1 i   i twXtatwtw , for units that belong in set N(I,R)

i  i twtw ,1 for units that do not belong in set N(I,R). 5. The neighbourhood and the learning rate of the parameters are decreased (Charalam- bous et al. 2001). When all vectors in the training set were presented once at the input, the procedure is repeated many times with the vectors presented in order each time. This part of the algorithm at the end or- ganizes the weights of the one-dimensional map, such that topologically close nodes become sensitive to input that is physically similar. Nodes are ordered in a natural manner, reflecting the different classes of the training set (Michaelides et al. 2001). The number of outputs is not a priori determined, but an “optimum” can be adopted by experimentation, in relation to the specific application under study. For several applications, it appears that the optimum number of outputs is around 30, as this exhibits the level of discretization required for the synoptic scale phenomena examined (see Tymvios et al. 2010). Although several experiments were carried out with varying output nodes, for the association of weather type patterns and dust transportation over the eastern Mediterranean 35 classes were considered.

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APPENDIX B: Projected changes for selected transport routes and nodes

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Table 1. Transportation locations for which severe weather changes were calculated (Table 2 and 3). The locations are divided into different climate regions (color coded) based on the most suitable choice. For more information see chapter 4 in the EWENT deliverable D2.1.

Cities Road corridors Airports Inland waterways Copenhagen Copenhagen - Stockholm Copenhagen Kastrup Kuopio Helsinki Oslo - Stockholm Helsinki Vantaa Jyväskylä Oslo Turku - St. Peterburg Oslo Gardemoen Lappeenranta St. Petersburg Cork - Dublin St. Peterburg St. Petersburg Stockholm Liverpool - Glasgow Stockholm Arlanda Savonlinna Birmingham London - Liverpool Amsterdam Schiphol Nurmes Glasgow Berlin - Prague Brussels National Iisalmi Leeds Gdansk - Vienna Dublin Viitasaari London Münich - Budapest London Gatwick Hämeenlinna Paris Tallinn - Warsaw London Heathrow Tampere Sheffield Athens - Patras Manchester Virrat Turin Lisbon - Madrid Paris Charles de Gaulle Rotterdam Belgrad Milan - Naples Zürich London Berlin Thessaloniki - Igoumenitsa Düsseldorf Manchester Budapest Copenhagen - Vienna (NE - Tc) Frankfurt am Main Birgmingham Cologne Lisbon - Bilbao (M - O) Münich Leeds Hamburg Paris - Marseilles (O - M) Vienna Schwechat Glasgow Münich Rotterdam - Berlin (O - Tc) Athens E. Venizelos Edinburgh Vienna Vienna - Milan (Tc - M) Barcelona Basel Bucharest Warsaw - Berlin (Te - Tc) Istanbul Ataturk Kaub Warsaw Madrid Barajas Hamburg Barcelona Milan Malpensa Magdeburg Larnaka Rome Fiumicino Budapest Madrid Geneva Cointrin (A/Tc) Pfelling Milan Wildungsmauer Rome Belgrade Achleiten Railway routes Sea routes Ports Kienstock Copenhagen S-train (S-tog) Bergen - Reykjavik Bergen Duisburg Helsinki commuter rail Primorsk - Skagen Gothenburg Koblenz Turku - Helsinki - Lahti - St. Petersburg Amsterdam - Antwerp Primorsk Mainz Cork - Dublin - Belfast Dublin - Liverpool Reykjavik Kehlheim Glasgow - Manchester - London - Lille Rotterdam - Hull Amsterdam Trier Liverpool Mersey rail Bilbao - Southampton Antwerp Neuves-Maisons London commuter rail Trelleborg - Travemünde Dublin Hostenbach Paris RER Algeciras - Messina Grimsby & Immingham Bratislava Zürich S-Bahn Barcelona - Genova Le Havre Mohács Berlin S-Bahn Marseilles - Algiers London Braunschweig Dresden - Praha - Budapest Port Said (Suez) - Gibraltar Milford Haven Szczecin Hamburg - Hannover - Nürnberg - München Antwerp - Rauma (O - NE) Rotterdam Kaunas Lisbon - Porto Immingham - Bergen (O - NE) Hamburg Vidin Madrid Cercanías Le Havre - Hamburg (O - Tc) Odessa Giurgiu Verona - Naples Messina - Odessa (M - Te) Riga Cernavoda Berlin - Warsaw (Tc - Te) Riga - Stockholm (Te - NE) Tallinn Constanta Lyon - Verona (O - M) Rotterdam - Gibraltar (O - M) Algeciras Klaipèda Mannheim - Basel - Milan - Genova (Tc-A-M-M) Rotterdam - Primorsk (O - NE) Genova Reni München - Verona (Tc - M) Umeå - Kiel Kanal (NE - Tc) Limassol Sulina Rotterdam - Cologne - Mannheim (O-Tc-Tc) Marseilles Izmail Stockholm - Malmö - Hamburg (NE-NE-Tc) Valencia Kilia Valencia - Lyon (M - O) Legend NE Northern European (sub-arctic) region O Maritime (oceanic) region A Alpine region Tc Temperate Central European region Te Temperate Eastern European region M Mediterranean region Multiple regions Table 2. Change in the number of days per year for various weather phenomena from the period 1971-2000 to 2011-2040. The changes are color coded according to the legend at the end of the list. In the "method" column the term "mean" refers to the multi-model mean change, "upper" refers to the most 'positive' change and "lower" refers to the most 'negative' change (in terms of numerical values). For more information see chapter 3 in the EWENT deliverable D2.1.

Severe weather phenomena changes from 1971-2000 (1989-2009 for wind gusts and blizzard) to 2011-2040 (unit = days per year) Wind gusts Snowfall Blizzard Heavy precipitation Heat waves Cold waves Location Method Latitude Longitude 17m/s 25m/s 32m/s 1cm/d 10 cm/d 20 cm/d 30mm/d 100mm/d 150mm/d ≥25°C ≥32°C ≥43°C ≤0°C ≤-7°C ≤-20°C Achleiten mean 48.625 13.375 0,5 0,0 0,0 -4,1 -0,1 0,0 0,1 0,5 0,0 0,0 5,9 1,0 0,0 -12,0 -2,7 0,0 Achleiten upper 48.625 13.375 1,8 0,1 0,0 -0,4 0,4 0,1 0,2 1,5 0,0 0,0 10,2 3,9 0,0 -6,7 -0,3 0,0 Achleiten lower 48.625 13.375 -0,9 0,0 0,0 -8,7 -0,9 0,0 0,0 0,2 0,0 0,0 2,5 0,0 0,0 -20,3 -9,8 -0,1 Algeciras mean 36.125 -5.625 1,2 0,3 0,2 0 0 0 0 -0,7 0 0 13,9 15,1 0,7 0 0 0 Algeciras upper 36.125 -5.625 2,6 1 0,8 0 0 0 0 0,1 0,1 0 23,1 19,7 3,9 0 0 0 Algeciras lower 36.125 -5.625 -0,2 -0,1 0 0 0 0 0 -2,8 0 0 9,1 7,7 0 -0,1 0 0 Amsterdam mean 52.375 4.875 1,2 0,2 0 -1,8 0 0 0 0,2 0 0 0,9 0 0 -4,9 -0,4 0 Amsterdam upper 52.375 4.875 3,9 0,8 0,3 0,6 0,2 0 0,2 0,3 0 0 2,3 0 0 -0,5 0,1 0 Amsterdam lower 52.375 4.875 -2,1 -0,2 -0,1 -6,5 -0,1 0 -0,1 0 0 0 -0,2 0 0 -13 -2 0 Antwerp mean 51.375 4.375 0,6 0 0 -1,5 -0,1 0 0 0 0 0 3,6 0,3 0 -5,4 -0,9 0 Antwerp upper 51.375 4.375 2,9 0,4 0 -0,2 0 0 0 0,3 0 0 6,7 0,7 0 -2,4 0,1 0 Antwerp lower 51.375 4.375 -1,9 -0,3 0 -4,9 -0,1 0 0 -0,3 0 0 -1,4 0 0 -12 -3,9 0 Athens mean 38.125 23.875 1,6 0,1 0 0 0 0 0 0,1 0 0 12,6 17,8 0,1 -0,6 0 0 Athens upper 38.125 23.875 4,2 0,5 0,1 0 0 0 0 0,5 0,1 0 16,6 31,4 0,6 0,1 0 0 Athens lower 38.125 23.875 -1,3 0 0 -0,3 0 0 0 -0,4 -0,1 0 9,7 11,7 -0,2 -2 -0,1 0 Barcelona mean 41.375 2.125 0,4 0 0 0 0 0 0 0,2 0 0 17,7 4,6 0 -0,1 0 0 Barcelona upper 41.375 2.125 1,5 0,3 0 0 0 0 0 0,6 0,1 0 29,1 16,7 0 0,1 0 0 Barcelona lower 41.375 2.125 -2,3 -0,1 -0,2 -0,1 0 0 0 -0,2 -0,1 0 3,3 0 0 -1,1 0 0 Basel mean 47.625 7.625 0,8 0,1 0 -1,7 -0,1 0 0 0,7 0 0 7,6 1,5 0 -8,1 -1,3 0 Basel upper 47.625 7.625 3 0,4 0 -0,1 0,1 0 0 1,7 0 0 12,7 5,1 0 -3,4 0,1 0 Basel lower 47.625 7.625 -1,4 -0,1 0 -4 -0,4 -0,1 0 0,1 0 0 0,3 0 0 -13 -5 0 Belfast mean 54.625 -5.875 0,1 0,4 0 -0,8 0 0 0 0,1 0 0 0 0 0 -3 -0,1 0 Belfast upper 54.625 -5.875 4,3 1 0,3 0 0 0 0 0,3 0 0 0,1 0 0 -0,1 0 0 Belfast lower 54.625 -5.875 -3,1 -0,3 -0,1 -2,7 -0,2 0 0 -0,1 0 0 0 0 0 -11 -0,3 0 Belgrad mean 44.625 20.375 0,2 0,1 0 -1,2 -0,1 0 0 0,2 0 0 13,4 8,9 0,6 -5,3 -1,2 0 Belgrad upper 44.625 20.375 0,9 0,3 0 0,1 0,1 0,1 0 0,7 0 0 21,6 13,4 2,7 -1,8 -0,4 0 Belgrad lower 44.625 20.375 -0,7 0 0 -3,1 -0,5 -0,1 -0,1 0 0 0 9,5 2,1 0 -11 -4,5 -0,1 Bergen mean 60.375 5.375 -1,5 -0,4 0 -4 -0,7 -0,1 -0,3 1,2 0 0 0 0 0 -9 -1,9 0 Bergen upper 60.375 5.375 1,1 0,1 0,1 -0,2 0 0 0,1 2,9 0,2 0 0 0 0 0,7 0,4 0 Bergen lower 60.375 5.375 -6,4 -1,3 -0,3 -9,8 -1,8 -0,2 -0,7 -0,1 0 0 -0,1 0 0 -26 -8,1 -0,1 Berlin mean 52.625 13.375 0 0,1 0 -2,4 -0,1 0 0 0,1 0 0 3,9 0,2 0 -8,4 -1,8 -0,1 Berlin upper 52.625 13.375 1,9 0,3 0 -0,1 0,2 0 0,1 0,3 0 0 7,9 0,7 0 -2,2 0,3 0 Berlin lower 52.625 13.375 -1,8 -0,2 0 -4,4 -0,2 -0,1 -0,1 -0,2 -0,1 0 -2,5 -0,7 0 -15 -8,3 -0,3 Bilbao mean 43.375 -3.125 0,3 0,2 0 -0,2 -0,1 0 0 0,1 0 0 9,9 2,1 0 -0,7 0 0 Bilbao upper 43.375 -3.125 1,8 1,8 0,1 0,3 0 0 0 1 0 0 19,6 7 0 0,3 0 0 Bilbao lower 43.375 -3.125 -3,4 -0,3 -0,2 -0,9 -0,2 0 0 -0,7 0 0 0,7 0 0 -3 -0,2 0 Birmingham mean 52.375 -1.875 -0,2 0,2 0 -1,4 0 0 0 0,1 0 0 2,1 0 0 -5,5 -0,5 0 Birmingham upper 52.375 -1.875 3,1 0,8 0 0,2 0,2 0,1 0,1 0,2 0 0 5,1 0,2 0 -0,8 0 0 Birmingham lower 52.375 -1.875 -4,6 -0,1 -0,1 -5,3 -0,3 0 0 -0,1 0 0 -0,1 0 0 -16 -1,7 0 Bratislava mean 48.125 17.125 0 0 0 -1,5 0 0 0 0,1 0 0 11 2,6 0 -6,6 -1,8 0 Bratislava upper 48.125 17.125 1,7 0,1 0,1 0,3 0,2 0,1 0,1 0,3 0 0 17,5 8,2 0 -1,5 -0,3 0 Bratislava lower 48.125 17.125 -2,1 -0,2 0 -4,6 -0,3 -0,2 -0,1 -0,2 0 0 3,9 0,3 0 -13 -7,7 -0,1 Braunschweig mean 52.375 10.625 0,5 0,2 0 -2,1 -0,1 0 0 0,2 0 0 3,3 0,3 0 -7,7 -2 0 Braunschweig upper 52.375 10.625 1,9 0,8 0 -0,6 0,1 0,1 0,1 0,3 0 0 7,3 0,8 0 -2,8 0,2 0 Braunschweig lower 52.375 10.625 -0,6 0 -0,1 -3,8 -0,3 -0,1 -0,1 0 0 0 -1,4 -0,1 0 -12 -8,7 -0,2 Brussels mean 50.875 4.375 0,1 -0,1 0 -1,8 0 0 0 0,2 0 0 4,4 0,4 0 -6,1 -1,1 0 Brussels upper 50.875 4.375 3,6 0,2 0 -0,1 0,1 0 0 0,3 0,1 0 8 1,2 0 -2,4 0 0 Brussels lower 50.875 4.375 -2,8 -0,4 0 -6,3 -0,2 0 0 0,1 0 0 -0,9 0 0 -13 -4,9 0 Bucharest mean 44.375 26.125 -0,6 0 0 -1,3 -0,1 0 0 0 0 0 12,7 12,2 0,3 -7,7 -1,4 0 Bucharest upper 44.375 26.125 0,5 0,2 0 0,1 0,1 0,1 0,1 0,3 0 0 15,3 22,7 1,8 -2 0,4 0 Bucharest lower 44.375 26.125 -2 -0,1 0 -3,9 -0,3 -0,1 -0,3 -0,5 -0,1 0 11,1 0,8 -0,1 -13 -4,9 -0,1 Budapest mean 47.625 19.125 0,2 0 0 -1,7 0 0 0 0,3 0 0 11,3 3,3 0 -7,2 -1,9 0 Budapest upper 47.625 19.125 1,3 0,1 0 1,5 0,2 0 0,1 0,9 0 0 21,4 9,7 0 -2 -0,3 0 Budapest lower 47.625 19.125 -1,4 0 0 -5,1 -0,4 -0,1 -0,1 0,1 0 0 3,1 -0,1 0 -15 -7,6 -0,1 Cernavoda mean 44.375 28.125 -0,6 0,0 0,0 -1,0 -0,1 0,0 0,0 0,1 0,0 0,0 13,6 11,7 0,1 -7,0 -1,2 0,0 Cernavoda upper 44.375 28.125 0,6 0,2 0,0 -0,3 0,0 0,1 0,1 0,4 0,0 0,0 15,5 23,5 0,5 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30,1 to 40 o) Change 40,1 ≤ Table 3. Change in the number of days per year for various weather phenomena from 1971-2000 to 2041-2070. The changes are color coded according to the legend at the end of the list. In the "method" column the term "mean" refers to the multi-model mean change, "upper" refers to the most 'positive' change and "lower" refers to the most 'negative' change (in terms of numerical values). For more information see chapter 3 in the EWENT delivable D2.1.

Severe weather phenomena changes from 1971-2000 (1989-2009 for wind gusts and blizzard) to 2041-2070 (unit = days per year) Wind gusts Snowfall Blizzard Heavy precipitation Heat waves Cold waves Location Method Latitude Longitude 17m/s 25m/s 32m/s 1cm/d 10 cm/d 20 cm/d 30mm/d 100mm/d 150mm/d ≥25°C ≥32°C ≥43°C ≤0°C ≤-7°C ≤-20°C Achleiten mean 48.625 13.375 0,6 0,1 0,0 -10,2 -0,5 0,0 -0,1 0,4 0,0 0,0 14,2 3,8 0,0 -25,4 -5,4 0,0 Achleiten upper 48.625 13.375 1,6 0,2 0,0 -7,4 0,0 0,0 0,0 0,8 0,0 0,0 25,9 15,8 0,0 -21,1 -1,6 0,1 Achleiten lower 48.625 13.375 -0,9 -0,1 0,0 -14,6 -1,3 -0,1 -0,3 0,0 0,0 0,0 7,3 0,3 0,0 -32,9 -15,3 -0,3 Algeciras mean 36.125 -5.625 4,1 0,4 0 0 0 0 0 -0,6 0 0 30,8 29 2,4 0 0 0 Algeciras upper 36.125 -5.625 8,8 2,1 0,1 0 0 0 0 0,1 0,1 0 41 34 13,7 0 0 0 Algeciras lower 36.125 -5.625 -0,9 -0,2 0 0 0 0 0 -1,1 0 0 21,9 22 0 -0,1 0 0 Amsterdam mean 52.375 4.875 -0,3 0,1 0 -3 -0,1 0 0 0,4 0 0 3,8 0,1 0 -9,3 -0,6 0 Amsterdam upper 52.375 4.875 5,6 1,1 0,2 -0,8 0 0 0 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Change (number of days per year) a) Change ≤ -40,1 b) Change -40 to -30,1 c) Change -30 to -20,1 d) Change -20 to -10,1 e) Change -10 to -5,1 f) Change -5 to -1,1 g) Change -1 to -0,2 h) Change -0,1 to 0,1 i) Change 0,2 to 1 j) Change 1,1 to 5 k) Change 5,1 to 10 l) Change 10,1 to 20 m) Change 20,1 to 30 n) Change 30,1 to 40 o) Change 40,1 ≤