Avalanches in the Nordic region in a future climate

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Avalanches in the Nordic region in a future climate

A meteorological study

Master thesis in Meteorology

Reiar Kravik

S S

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E S E

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I TA

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G N N U

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UNIVERSITY OF BERGEN

GEOPHYSICAL INSTITUTE

June 2008

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Photograph on the front- page shows mountains in Overøye, West Norway Photographer: Reiar Kravik

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Acknowledgements

First of all a would like to thank Haraldur Ólafsson for being my supervisor, and for giving me the opportunity of participating in EGU 2008 in Vienna and NMM 2008 in Iceland. Utmerket!

A special thanks to Asgeir Sorteberg for being my co-supervisor, giving constructive feedback and help during the master thesis.

I would like to congratulate the graduating students at the Geophysical Institute this year, for doing a great job. A fantastic bunch of students at ODD during my time here, have given me plenty of laughs and memories during these years at the Geophysical Institute. Thanks to all!

Thanks to, Kalle Kronholm at NGI for the avalanche data from Grasdalen, PRU- DENCE for keeping data easy available on the web, Sigbjørn Grønås for constructive feedback and everyone else supporting me during my studies at the Geophysical In- stitute.

Thanks to my family, especially my mum for good feedback. Thanks Ane, for giving me plenty of laughs and for being a fantastic girl-friend.

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Abstract

In this thesis, the link between meteorological parameters and avalanches is ex- plored, and used together with output from Regional Climate Models (RCMs) to predict avalanche risk in future climate projections in the Nordic countries. A simple snow model is developed and tuned with data from two locations in Norway.

Synoptic scale weather parameters and snow cover are linked to avalanche activity by comparing avalanche observations in W-Norway, downscaled ERA-40 reanalysis and snow cover. The resulting Risk function for avalanches is calculated form output of simulations of current climate and future projections. At low levels (500 m and up to 1000 m in South Norway) there are a general reduction in the Risk function. Far inland areas in Scandinavia and in northeast Iceland the projections give increased values of the Risk function, at high altitudes (1500 m). There are substantial differences between the RCMs, especially in precipitation.

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Chapter 1 Introduction

The Nordic region has areas with steep terrain and potential avalanche risk, and avalanches have indeed been experienced in many coastal and mountain regions.

The documentations of avalanches in the past are often related to loss of lives or destruction of infrastructure, as buildings and roads. In the southern parts of Nor- way, roads and railways passing the mountain range between western and eastern parts of Norway are important for the transport of people and goods, and is often exposed to avalanches.

Avalanches are complex features and depend on local topography, meteorological conditions, snow stability and snow depth. For the Norwegian areas most historical avalanches are located in western and northern parts of Norway. Precipitation, wind and temperature are the most important meteorological factors for generation of avalanches (Kronholm et al. (2006) and Bakkehøi (1987)). However, large variations in small areas of the stability of the snow pack makes the relationship complex. In the seventies, a research cabin, Fonnbu, was established for full scale avalanches studies in Grasdalen, West Norway (Figure 1.1). Since the foundation of the research site the Grasdalen area has a more complete history of avalanches than other locations in Norway, and the avalanche history for this period in the Grasdalen area has good time and date accuracy for most avalanches. This makes it well suited as a testbed for avalanche research.

The internet has become an information source for natural hazards as avalanches and landslides. Avalanche history, risk areas and forecasting of avalanches are easy accessable for Norwegian areas on websites as (www.skrednett.no) and

(www.snoskred.no).

In a future climate, temperatures are expected to rise (e.g. IPCC (2007)). An increase in precipitation is expected in the Scandinavian area, at winter time (Räisä- nen et al. (2004) and Haugen and Iversen (2005). Precipitation and temperature are key elements for the development of snow cover, which again can affect the potential avalanche risk.

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2 Introduction

The main aim of this thesis is to look at how meteorological conditions for avalanches can be expected to change in a future climate. In order to achieve this two following tasks will be carried out first.

• Development and testing of a simple snow model in order to produce snow cover at different heights above sea level.

• To link meteorological parameters and snow cover to avalanches. In order to do this a new method, the Risk function, will be introduced and tested using data from Grasdalen, western Norway.

In order to study individual parameters related to avalanches, Norway, Sweden and Iceland are divided into regions, and changes that are projected by two regional climate models (RCMs) are presented at different heights above sea level for each region. The Risk function will be implemented in the output from two RCMs and a snow model, to study changes in a future climate in avalanche conditions.

The background of the thesis is given in Chapter 2, while the methods are described in Chapter 3. Chapter 4 introduces the data used in this thesis, and Chapter 5 contains a study of the snow model. A link between meteorological parameters, snow cover and avalanches is studied using Grasdalen data, in Chapter 6. A study of future changes in meteorological parameters as precipitation, wind and temperature together with snow is done in Chapter 7. The Risk function is used in output of RCMs to look at the future changes in avalanche risk in the Nordic area in Chapter 7, which includes also a short investigation of systematic errors in the RCMs. Fi- nally a conclusion and discussions on future work are presented in Chapter 8 and 9.

24o W

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Figure 1.1: An overview of the study area, showing the location of Grasdalen, in western Norway.

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Chapter 2 Background

2.1 Avalanches

Avalanches are complex features in nature and are classified into two main categories, loose-snow- and slab-avalanches (e.g. McClung and Schaerer (2006)).

Meteorological parameters are of importance concerning avalanches. The main meteorological parameters for avalanches are wind, precipitation and temperature (Bakkehøi (1987) and Lied and Kristensen (2003)). In addition, good knowledge of the stratigraphy, including the stability of the snowpack is important (e.g. Bakkehøi (1987)).

Temperature decides the phase of the precipitation, and the melting of snow. The amount and phase of precipitation, and temperature are important for snow depth and stability. The snow drift by wind can give large difference in snow depth between different locations because of the transport of snow.

A snow profile can be made to investigate the properties of a snow column. The snow profile includes information on the density, the snow type (size and shape of snow crystals), amount of liquid water, the shear strength of each layer and the hardness of the snow (Lied and Kristensen 2003) .

Snow conditions depends on different parameters as e.g. total amounts of precipitation as snow, radiation, temperature, wind, local topography and vegetation.

Snow conditions can vary strongly in space because all key meteorological parameters are location dependent, particularly in complex terrain.

Information of the topography, the slope angle and the direction of the slope can be related to avalanche risk. Slopes steeper than 25⁰ have potential risk for snow avalanches (McClung and Schaerer 2006). Norwegian avalanche studies show that the slopes of the starting zones are greater than 30⁰ in most cases (Lied and Kristensen 2003).

Areas with dense forrest are not typical as starting zones for avalanches (Lied and 3

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4 Background

Kristensen 2003), and areas with smooth surfaces have a higher risk for avalanche release if snow and meteorological conditions are ideal.

Known historical avalanches in Norway are often related to loss of lives or damage of infrasctucture. In 1973, Fonnbu, a research cabin in Grasdalen, Stryn, western Norway was established, and rebuilt in 2005 after a fire. Fonnbu was built to get a full scale observation site for avalanches (www.ngi.no). Because of the research site in Grasdalen, many of the recorded avalanches are situated here.

Recently, Kronholm et al. (2006) used statistics to look at avalanches and predictors for avalanches in Grasdalen, Norway. Classification tree is a statistical method to find the best predictor to distinguish between an avalanche day and a non-avalanche day using a classification. A classification trees list the best predictors for avalanches in a tree-like structure. Each branch indicates the quality of the split for one predictor. Results from Kronholm et al. (2006) suggested that precipitation amounts was the main predictor in the first split, with 5 days, 3 days and 1 day precipitation amounts. The following predictors were wind and temperature.

2.1.1 Regions with historical avalanches

Historical avalanches in Norway are seen in Figure 2.1, (Jaedicke et al. 2006), together with 13 precipitation regions (Bauer-Hanssen and Førland 1998). The topography in the study area is steep in many regions, as can be seen in the topography map of 1 km resolution in Figure 2.7. The historical avalanches in Norway are located mostly in the western and northern parts of Norway, where there is typically steep terrain (Figure 2.1).

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Figure 2.1: Historical avalanches in Norway (Jaedicke et al. 2006) and precipitation regions for Norway.

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Precipitation and snow climate in Norway 5

The precipitation regions are described in Section 7.1, and will be used in the study of climate change.

Areas with few historical avalanches are located in south-eastern Norway close to the Swedish boarder and on Finnmarksvidda, northern Norway.

2.2 Precipitation and snow climate in Norway

The current climate reference period is from 1961 to 1990. The annual precipitation amounts for Norway during this period is presented in Figure 2.2a). As kan be seen from the Figure, precipitation amounts are largest in the western parts of Norway, with more than 4000 mm a year. Large amounts of precipitation are seen in areas up to and in Nordland county. Eastern Norway has smaller amounts of annual precipitation, mainly because these areas are in the lee of the mountain range of southern Norway during westerly flow. Areas inland in the far northern parts of Norway are experiencing smaller amounts of yearly precipitation compared to western parts of Norway. The phase of the precipitation, depends upon temperature.

a) Precipitation b) Snow Water Equivalent

Figure 2.2: Annual precipitation and annual maximum snow water equivalent in the current climate reference period, 1961-1990, (www.senorge.no).

The temperature discriminating between rain and snow is typically close to 0^o C.

Consequently, snow accumulates primarily in the cold seasons.

For snow water equivalent (SWE) the annual maximum during the 1961-1990 referance period is seen in Figure 2.2b). Snow water equivalent indicates the amount

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6 Background

of water coming from a column of snow if the entire column is melted. For Norwegian areas, the annual maximum SWE in the reference period is located in inland areas in western Norway, mountain areas in southern Norway and Nordland county. The snow water equivalent is in some areas close to 2000 mm. The snow water equivalent depends mainly on temperature and precipitation. Large amounts of precipitation as snow will increase the SWE, while high temperature and melting will decrease the SWE.

2.2.1 Precipitation in the Atmosphere

Precipitation is one of the main meteorological parameter for avalanches together with temperature and wind (Bakkehøi (1987) and Kronholm et al. (2006)). Different types of precipitation are typical for different areas, and some of these precipitation types will be presented below.

Orographic precipitation

Different types of orographic precipitation is presented by Smith (1989), and a description is given below.

• Smooth forced ascent

• Bergeron seeder-feeder cloud mechanism

• Diurnal forced convection

• Triggered convection by forced ascent or blocking

Orographic precipitation is related to the earth’s topography. In western Norway, westerly flow is common, hitting the mountain range of southern Norway. The air is forced to rise because of the mountain range. Air cools and the vapor condensates, and finally precipitation can occur. The wind component impinging the slopes is an important parameter for cooling, because the wind pushes the air towards the mountain range and forces the air to rise over it. Precipitation from smooth forced ascent is an idealized example, Figure 2.3, and are very hard to find in the real world, but can enhance other types of precipitation.

Formation of droplets is described by microphysical processes by Rogers and Yau (1989). Often condensation nucleis are needed to form droplets, at saturation around 100 %, or at supersaturated air. Two mechanisms for futher growth is collision - coalecence, which "pick up" other droplets due to difference in gravitation between droplets, and interaction between water droplets and ice particles.

The Bergeron seeder-feeder cloud mechanism is shown in Figure 2.4, which shows two cloud systems forming close to and above a mountain range.

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Figure 2.3: Orographic precipitaition due to smooth forced acsent, (Smith 1989).

The cloud at the highest altitude called the seeder cloud, is often part of a larger meteorological system. The orographic process forms a lower cloud, called the feeder cloud, located upstream of the mountain range and above the top, and are made due to vertical velocities in the air, cooling and condensation.

Figure 2.4: Orographic precipitaition due to the Bergeron seeder-feeder cloud mechanism, (Smith 1989).

Ice phase mechanism (Rogers and Yau 1989) can lead to precipitation in the seeder cloud. Precipitation falling from the seeder cloud towards the ground can either: 1) Fall all the way towards the ground picking up smaller droplets in the feeder cloud, by collision - coalecense processeses (Rogers and Yau 1989). 2) Or evaporate in the lower areas, increasing the saturation in or close to the feeder cloud, enhacing the formation of droplets.

Diurnal forced convection is mainly associated with high temperatures, mountains and summer season for the higher latitudes. The sun heats the ground in mountain areas leading to rising of the air along the hillside, which could lead to precipitation caused by the convection. As the sun lowers a reverse motion can occur. Sinking of cold air along the hillsides, forcing air at lower levels to rise.

Instabilities in the air column can occur as a air parcel is lifted or blocked by a mountain range. One example is overturning of cold fronts which can happend if the flow is faster at high altitudes. This leads to colder air at higher altitudes because of blocking of lower airmasses by a mountain range (Smith 1982).

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8 Background

Convective precipitation

Precipitation due to convection is often assosiated with showers. Heating of air near the ground can lead to an instable layer in the atmosphere. Rising buoyant air is cooled and clouds can be created, when reaching saturation and condensation.

Convective precipitation is often assosiated with high vertical velocities (w) both positive (rising of warm air) and negative (sinking of cold air). This can be seen as convective cells with clouds where w is positive, and no clouds in between the clouds where w is negative. These clouds are often seen in the afternoon in summertime, when the air has been warmed up for some hours. Condensation and precipitation can occur if the airparcel is lifted high and growth processes of droplets are ideal (Rogers and Yau 1989).

Convective precipitation at winter time is often related to a cold air outbreak.

Frontal precipitation

Frontal precipitation is assosiated with low pressures, which was first disscused by the Bergen school. The Bergen school was started in 1918, and consisted of young scientists as Vilhelm Bjerknes, Jacob Bjerknes, Halvor Solberg and Tor Bergeron (e.g. Wallace and Hobbs (1977)).

Fronts are borders between different air masses, and are assosiated with strong temperature gradients. There are 3 types of fronts; warm-, cold- and occluded- fronts.

There are two types of cold fronts; anafront and katafront (e.g. Godske et al. (1957) and Moore and Smith (1988)). A cold front has an elevation of the frontal surface pointed back and upwards pushing the warm air up above the cold air. Two different cold fronts are shown in Figure 2.5.

Figure 2.5: Cold fronts, anafront (a) and katafront (b), (Godske et al. 1957).

Anafront is a cold front which forces the warm air to rise above the cold air on the frontal surface. Clouds and precipitation behind the surface front are usual.

Warm air gets replaced by cold air as the front passes.

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Katafront is a cold front with descending air along the frontal surface, giving precipitation ahead of the coldfront in a band close to the surface front. Clear weather condition in the warm sector and the cold sector while cloudy conditions in the interaction between them, on the surface front are typical. A classic example is given in Figure 2.5 b).

Warm fronts are assosiated with warm air replacing and rising above cold air.

This leads to saturation, condensation, and precipitation ahead of the surface warm- front together with wind. Temperature rise as the surface front passes an area.

Figure 2.6: Warm front, (Godske et al. 1957).

An occluded front is a warm front which has been overtaken by a cold front.

There are two types of occlusions, warm and cold, depending on where the different air masses are located (Godske et al. 1957). A warm occlusion has a y-shape moving towards the right, with cold air to the left, warm air above and the coldest air at the front of the occlusion. The cold air catches up with the warm air, and the air gets lifted above the coldest air ahead of the occlusion. Precipitation is typically ahead of the surface front.

A cold occlusion has a mirrored shape of the warm occlusion. Colder air is located behind of the warm sector than ahead of warm sector in a typical low pressure system. The coldest air catches up with the warmer air ahead, and forces the warmer air to rise.

Processes that can be classified as orographically enhanced frontal precipitation are definitely of great importance for the precipitation climate and events of heavy precipitation in the mountainous areas in western coastal Norway (Reuder et al.

2007).

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10 Background

2.3 Topography

Topography is the elevation in an area, from fjords to mountains, and is a very complex feature. The western Norway is especially complex, reaching from sea level to around 2000 meters above sea level in very short horizontal distances. The topography is very important in enhancing of precipitation (Section 2.2.1) and for snow avalanches (Section 2.1). Meteorological processes in a climate model can be closely related to the topography and the resolution of the topography in the climate models (Section 4), especially when looking at extreme values. The capacity of the computers limits the resolution of the climate simulations and thereby the representation of the topography. The topography in parts of Scandinavia is presented for 50 km and 1 km solution in Figure 2.7. As can be seen, a horizontal grid of 50 km gives a very smooth representation of the topography compared to the 1 km resolution. The difference in height is in some regions is close to 1000 meters.

a) b)

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Figure 2.7: Topography [m] of Scandinavia with a horizontal grid of a) 50 km (HIRHAM) and b) 1 km resolution (GTOPO30).

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Chapter 3 Method

3.1 Snow model

Snow is essential for avalanches. Large quantities of snow increase the possiblities for avalanches. A snow model is made to get a more realistic snow cover at different heigths H, than in the RCM. The reason for doing this is to get an impression of how the snow cover varies with heigth in the RCMs, since the topography of models in a specific region is very smooth compared to the real topography (Section 2.3).

The snow cover is calculated in a simple snow model depending on temperature and precipitation. The outcome of the snow model is snow water equivalent (SWE) given in mm, and is the amount of water in the snow column.

The density of the snow is variable for different locations. For fresh snow, 1 mm SWE corresponds to 6.7-12.5 mm for fresh snow, while for old snow it corresponds to 2.0-2.5 mm (Eastwood 2000).

There are two processes in the snow model; 1) production of snow and sleet (particles of ice and water) and 2) snow melting by temperature. The first step of the snow model is done by the following equations:

f1 = 0, f or T ≤Tlow (3.1)

f1 = T −T_low

T_up−T_low, f or T_low ≤T ≤T_up (3.2)

f1 = 1, f or T ≥T_up (3.3)

f1 is the fraction of precipitation as rain. f1 is a function depending on the actual temperature T, T_up and T_low. The threshold for T_up and T_low was found for Finnish conditions by Vehvilainen (1992), and are mean values for different areas. Tlow is the temperature indicating precipitation as pure snow and is set to−3^o Celsius. Rainy conditions, with precipitation as rain occurs at temperatures higher thanT_up which

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12 Method

is equal to 1.5^o Celsius. Between the temperatures T_up and T_low, a fraction of the precipitation is snow, and as the temperature gets closer to Tlow larger parts of the precipitation fall as snow. The source of snow is snowfall P_snow

P_snow =P ∗(1−f1), (3.4)

where P is the precipitation observed or modelled. (1 - f1) presents the fraction of precipitation, as snow. The second part of the snow model consists of the melting. The snow melting is a function of temperature, and a melting constant, k [mm/degree day]. The equations presenting snow melting are,

melt =k∗(T −Tmelt), f or T > Tmelt (3.5) melt= 0, f or T ≤T_melt (3.6) T_melt is the temperature where melting occurs, which is set to 0^o Celsius.

The final equation is the mass balance of snow water equivalent (SWE) . dSW E

dt =P_snow−melt (3.7)

The melting constant k will be determined from synoptic observations for Kvam- skogen, West Norway, and Lesjaskog, Central Norway, and is together with the determination of the snow model done in Chapter 5.

3.2 The Risk function

Avalanches are complex features and depend upon different factors, as meteorological conditions, snow conditions and location. A new method, using a Risk function, will be tested in order to link snow cover and several meteorological parameters as temperature, precipitation and wind to historical avalanches in Grasdalen, western Norway.

The Risk function weigths several meteorological parameters and snow cover to find days that are meteorological speaking, optimal for avalanches.

Risk= (

Yn i=1

W_i)^1/n (3.8)

where W_i are weighted functions of different meteorological parameters and snow cover. n is the number of parameters included in the Risk function.

The Risk function presented above, Equation 3.8, consists of weigthed functions, W_i. These weighted function are defined with values between 0 and 1 or 0.5 and 1 (Figure 3.1).

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Temperatures at different heights 13

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

2

1

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B

Figure 3.1: Weighted functions with lower threshold, A and upper threshold, B.

In the weighted functions, the definition of the lower threshold, A (Figure 3.1) is first set, which indicates the value where the weighted functions rise from x_o, in our case 0 or 0.5. Upper threshold B (Figure 3.1) is defined the same way, but this is the upper value, which is where the weighted functions are equal to 1.

A weighted function is built up in four parts:

W_i =x_o, f or x=< A (3.9)

Wi = 2(x−A

B−A)², f or A < x < A+B

2 (3.10)

Wi = 1−2(B−x

B −A)², f or A+B

2 < x < B (3.11) Wi = 1, f or x >=B (3.12) In this thesis we will test the Risk functions based on different meteorological parameters as temperature, precipitation and wind together with snow cover, and different values for x_o, A and B in Chapter 6.

3.3 Temperatures at different heights

Near surface temperatures in RCMs are given at 2 meters above the topography of the models. The topography within a grid point in the models is not representative for extreme values for the topography, but represents a mean value for the grid point area. A simple way of investigating temperatures at different heigths could

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14 Method

be by lifting/lowering the air adiabaticly to different heights from the model height, Hmodel. Temperatures from output of RCMs are lifted/lowered adiabaticly to 4 different heights, sea level, 500 m, 1000 m and 1500 m, to get an impression of how and where the changes in temperatures and snow cover are at these 4 heigths. The lifting of the air is done by the formula

T_H =T_2m+ (H−H_model)∗Γ (3.13)

whereT_H is the temperature at a given height H, T_2m is the model temperature at 2 meters at a given model heigth,Hmodel(at each grid point) and Γis the lapse rate of temperature. Γ is set to 6.5 K/km in this thesis (6-7 K/km, Wallace and Hobbs (1977)).

The temperatures at different heigths (TH) will be used in Section 3.1 and 3.2, to calculate the snow cover (Section 3.1) at different heigths H and later on combing different meteorological parameters. The snow cover can be essential for avalanches, and thresholds in weighted functions for snow cover and temperature for indicating avalanches will be disccused later. Temperatures at different heigths for the RCMs will be presented in Section 7.3.2.

3.4 Percentiles and extremes

Concerning avalanches, extreme values are of great importance especially for the largest avalanches. Percentiles will be used in the study of precipitation and wind in the RCMs. Percentiles give statistical values, when looking at a time series. A sample’s pth % percentile is found, by first arranging a dataset from the smallest to the largest values. The value p for the percentile, indicate that p % of all observations are below this value, and (1-p) % of these observations are above this value in the arranged sample (Bhattacharyya and Johnson 1977). The percentile take into account the distribution of the data, as for the median which correspond to the 50

% percentile. The use of percentiles for precpitation and wind were chosen due to the connection to the distribution of the meteorological data.

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Chapter 4 Data

4.1 Observations and Reanalysis data

Observations from different synoptic stations in Norway are available from the Nor- wegian Meteoroloical Institute (www.eklima.no).

Some stations measure all synoptic parameters, and other only a subset. For the development of the snow model, precipitation, temperature, snow height observations were used from the synoptic stations in Kvamskogen and Lesjaskog.

The synoptic stations closest to Grasdalen are to far away or located at lower levels, which is not representative for Grasdalen. Therefore, when later comparing avalanche data from Grasdalen with meterological data, modelled re-analysis data will be used. The ERA-40 reanalysis data is downscaled using a stretched version of the Arpege climate model with a spectral nudging approach (Barstad et al. 2008), and is used due to lack of a synoptic stations close to or nearby Grasdalen. The reanalysis data is taken from a period from 1961-2001, and there are 3 meteorological parameters used; precipitation, wind and temperature. Daily temperature and wind are the mean values from 00 UTC, 0600 UTC, 1200 UTC and 1800 UTC.

Precipitation amounts is counted from 0600 UTC to 0600 UTC the next day, in the same way as done for Norwegian synoptic observations.

Avalanche data from Grasdalen has time accuracy (Jaedicke et al. 2006), and data where the exact day of the avalanches is unknown, is not used. Avalanche data and downscaled reanalysis ERA-40 data, will be used in Chapter 6.

By looking at historical snow avalanches for Norway (Jaedicke et al. 2006), most of the historical avalanches (95 %) are dated in the period from November to April.

Around 1% of the avalanches are in October, which could be due to, to low amounts of snow cover and to warm temperatures. 1.5 % of the historical avalanches are in May, which could be wet avalanches, caused by rain and/or high temperatures.

Winter time and avalanche season are therefore defined as November to April in the 15

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16 Data

reminder of this thesis.

4.2 Regional climate models

Data from the Prediction of Regional scenarios and Uncertainties for Defining Euro- peaN Climate change risks and Effects (PRUDENCE ) project, is used for studying trends between a control run (1961-1990) and different future projections for the period 2071-2100. There are approximately 20 participants in the PRUDENCE project, which consists of Universities or research institutions. The main aim of the PRUDENCE project was to provide high resolution scenarios for future climate in Europe (Christensen et al. 2007).

The dynamical downscaling of climate data starts with an Atmospheric Ocean General Circulation Model (AOGCM) which normally has a horizontal grid spacing in the order of ∼300 km. Ocean sea surface temperature from the AOGCM is used into a finer resolution, global atmosphere only, model (AGCM). Resolution of the AGCM is in the order of∼150 km. By using the available data from the AGCM as boundary conditions : temperature, wind, and moisture together with sea surface temperatures (SST) from the AOGCM into a Regional Climate Model , the result is high-resolution climate data (∼50km) (Table 4.2).

Precipitation, temperature and wind are the meteorological data from the RCMs investigated in this thesis.

Table 4.1: 3 steps of dynamical downscaling

Step 1 2 3

AOGCM AGCM RCM

4.2.1 The HIRHAM/HADAM3H system

The regional climate model used by the the Norwegian Meteorological Institute is called HIRHAM. A short description is given by Haugen and Iversen (2005). The HIRHAM model is based on the HIRLAM (High Resolution Limited Area model) forecast model, and specifications of the HIRHAM model is described by Bjørge et al. (2000). The resolution of the HIRHAM model is 0.5 x 0.5 degrees, with 19 levels, and uses physical parameterisations from ECHAM4 (Max-Planck Institute, Hamburg). The number of gridpoints in HIRHAM available from PRUDENCE are shown in Figure 4.1, 85*85, covering Greenland, Scandinavia, Iceland and most of Central and Eastern Europa. The driving conditions for HIRHAM is taken from

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Regional climate models 17

HADAM3H which is the global atmospheric model of the Hadley Centre model (Haugen and Iversen (2005) and PRUDENCE ).

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Figure 4.1: HIRHAM gridpoint and area.

4.2.2 The RCAO/ECHAM4-OPYC3 system

The regional climate model used by the Swedish Meteorological and Hydrological (SMHI) Institute is the coupled model RCAO. The RCAO model consist of two different models, an atmospheric model RCA and an oceanic model RCO , which are coupled by a OASIS coupler (Döscher et al. 2002). The RCA was developed from the HIRLAM (High Resolution Limited Area Model) model used in forecasting (Döscher et al. 2002). RCAO is driven by the ECHAM4/OPYC3 from the Max Planck Institute (MPI) (Christensen and Christensen 2007).

The area of the RCAO model does not cover the entire study area. Iceland will therefore not be included in the further study for the RCAO model, because of possible interactions with the boundary conditions in the Icelandic areas.

The RCAO model driven by the ECHAM4/OPYC3, will be referd to as RACO/ECHAM4 in this thesis.

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18 Data

4.2.3 SRES scenarios

The climate models need prescribedCO₂ concentrations. These are taken from the SRES (Special Report of Emission Scenarios) scenarios and are images of different technological, economical, populational, policy and social development (Nakicenovic et al. 2000). The SRES scenarios are divided into 4 main categories, A1, A2, B1 and B2 (Nakicenovic et al. 2000), of which this thesis will use A2 and B2 for the future climate. Only these SRES scenarios were avalible from the PRUDENCE project.

The SRES A2 has a higher increase CO₂ emission from 1990-2100, than SRES B2 (Figure 4.2b). From Nakicenovic et al. (2000) there can be seen an increase inCO₂ emission for the A2 scenario from approximately 7300 MtC/sector in 1990 to around 28500 MtC/sector in 2100. B2 starts up with the same emission ofCO₂ as A2, but gets a much smaller CO₂ emission in 2100 of approximately 13500 MtC/sector .

Another parameter of interest is population growth, which in SRES B2 is expected to be medium, 10.6 billions in 2100. In the SRES A2 has a high population growth (more than 14 billions) and can be seen in Figure 4.2a). Economic growth for the future scenario of A2 and B2 is medium.

a) b)

Figure 4.2: Popullation scenarios(a) and CO2 emission(b), (Nakicenovic et al. 2000).

4.2.4 Uncertainties of Climate model

The confidence of the climate models are justified by their structure of climate models, which consists of physical law and conservations of mass, energy and momentum and the ability of describing current and features of past climates (IPCC 2007). The different models uses different parameterizations which can lead to differences between models.

A wide range of emission scenarios are made to cover uncertainties and to look at differences between them. Other changes in the nature which are not predictable

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Regional climate models 19

and uncertain in a future climate is for example volcanic activity. The PRUDENCE project uses two emission scenarios, A2 and B2. These scenarios were described in Section 4.2.3.

Big differences are seen between different scenarios in Figure 4.2, and the results of the scenarios for each model will be compared in this thesis. A comparison between the two RCMs will also be done to see if the models give similar results.

The internal variability is another source of uncertainty. The time series of the future scenario and the CTRL-run is 30 years for the PRUDENCE data. A 30 year period can be a wet or dry period on a longer cycle. Sorteberg and Kvamstø (2006) investigated this in the BCM model by using two averaging periods of 20 years and 40 years. The 40 year period compared to the 20 year period gave a reduction in variance of temperature change estimates, and spread in precipitation change estimates.

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Chapter 5 The snow model and the

determination of the melting factor k

The simple snow model, presented in Section 3.1, is tested with observed temperature and precipitation data from Kvamskogen, Western Norway and Lesjaskog, Central Norway. The test is done in order to check whether the simple snow model gives a reasonable snow cover, and to investigate the sensitivity of the model to the melting constant, k. These two stations have been chosen for this test because of the quality of the data series that include not only convetional meteorological observations, but snow observations as well. The melting factor (k) was first set to 3.5 mm/(degree day) (based on Eastwood (2000)), and was reduced in steps down to 2 mm/(degree day).

Table 5.1: Modeled SWE versus observed snow cover and different melting factors, k’s, for Kvamskogen. Starting date 02/Jan/1957 and ending date 04/Feb/2004

k Correlation Fraction of Fraction of Total number days without snow days without snow of days

Modeled SWE Observations

3.5 0.831 0.632 0.543 17200

3 0.8546 0.617 0.543 17200

2.5 0.8767 0.599 0.543 17200

2 0.8940 0.572 0.543 17200

Table 5.1 presents the snow model results from Kvamskogen, West Norway. The correlation between the observed snow and the modeled snow water equivalent is close to 0.9, when using a relatively low k. The total number of days with observation was 17200, and the fraction of days without snow cover is closer to the observed

20

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21

days, as k gets smaller. This is in agreement with the increasing correlation, for k decreasing from 3.5 to 2.

For Lesjaskog the best approximation for k is 2 or 2.5 (Table 5.2). The correlation between observed snow and modeled snow water equivalent from the snow model are above 0.93. For k equal to 3, the correlation is high, and the number of days without snow is very close to observed values.

The total number of days for the timeseries from Lesjaskog are 8779 days. Lack of data before 2/Sep/1976 and after 14/Sep/2000 is the reason why the Lesjaskog timeseries is shorter than for the Kvamskogen timeseries.

Table 5.2: Modeled SWE versus observed snow cover and different melting factors, k’s, for Lesjaskog. Starting date 2/Sep/1976 and ending date 14/Sep/2000.

k Correlation Fraction of Fraction of Total number days without snow days without snow of days

Modeled SWE Observations

3.5 0.9118 0.507 0.496 8779

3 0.928 0.495 0.496 8779

2.5 0.938 0.485 0.496 8779

2 0.938 0.467 0.496 8779

A scatter plot for Kvamskogen, Figure 5.1a), shows a linear fit (y = a*x+b) between observed snow depth (cm) and modeled SWE (mm). The gradient of the linear fit for Kvamskogen is close to 0.6, which indicate that 1 mm of snow water equivalent is equal to 6 mm of snow. This relationship between snow depth and SWE correspond to windpacked snow and older surface snow (Eastwood 2000). In the scatter plot, the snow model does not catch up with all days with observed snow cover for Kvamskogen, and other values forT_low and T_up in the simple snow model, could probably improve this result. The constant a, in the linear fit can be expected to be a little lower, especially for high values of SWE (Figure 5.1).

For Lesjaskog, the constant a, is close to 0.5 (Figure 5.1b), indicating that 1 mm of SWE is equal to 5 mm of snow. This relationship between the SWE and observed snow cover correspond to windpacked snow and older surface snow (Eastwood 2000), as for Kvamskogen.

The snow model is made as simple as possible, and can be improved by comparing with data from several synoptic stations, and give the k factor different values for different geographical locations. A study of temperature thresholds (T_low and T_up) for different locations, as done in Finland (Vehvilainen 1992) is also expected to improve the result from the snow model. Including solar radiation and ground conditions could also improve the snow model. The snow model can be improved

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22 The snow model and the determination of the melting factor k

by taking into account densities of snow types, and giving the snow cover in cm as output.

Overall, the setup of the snow cover model as described in Section 3.1 and the testing of the snow model show satisfying results, when comparing to observations.

The simple snow model will be used in the further study of snow and meteorological conditions favourable for avalanches. Following the above results from Kvamskogen and Lesjaskog the kd is set to 2.5 for the reminder of this thesis.

a) Kvamskogen b) Lesjaskog

0 100 200 300 400 500 600

0 50 100 150 200 250 300

Snow water equivalent [mm]

Observed snow depth [cm]

data 1 linear

0 50 100 150 200 250 300 350 400

0 20 40 60 80 100 120 140 160 180 200

Snow water equivalent [mm]

Observed snow depth [cm]

data 2 linear

Figure 5.1: Scatterplot between observed snow depth [cm] and modeled snow water equivalent[mm], for a) Kvamskogen and b) Lesjaskog. kd is equl to 2.5 mm/(degree day).

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Chapter 6 Testing of the Risk function

In 1973, a research cabin for avalanches studies was built in Grasdalen, western Nor- way. The accuracy in time and date for historical avalanche observations improved, especially for the area in Grasdalen.

A comparision of downscaled reanalysis data for 1961-2001 for meteorological parameters were done for historical avalanche data from Grasdalen, Stryn (Section 4.1). The downscaled reanalysis data was used, partly due to lack of weather observations close to or at a representative height for Grasdalen. The Nordic winter months, November - April are defined as avalanche season and are considered in this study.

A dataset of avalanches in Grasdalen together with downscaled reanalysis data were used to link meteorological parameters and snow cover to avalanches. Mete- orological parameters taken from the reanalysis data were wind, precipitation and temperature. The snow cover for the location was calculated from temperature and precipitation with the simple snow model described in Section 3.1 and Chapter 5.

The avalanche data from Grasdalen contain an accuracy file indicating the time and date accuracy of the avalanches. The avalanche data used for this analysis needed a time accuracy within the excat date. Avalanche data with poorer time accuracy is not included in this thesis.

Mean values of meteorological parameters on avalanche days and non- avalanche days is given in Table 6.1. Five days precipitation and one day precipitation are the parameters with the biggest differences in mean value between an avalanche day and a non - avalanche day. Precipitation proved to be the best parameter to distin- guishing between avalanche day and non-avalanche day in a statistical study done by Kronholm et al. (2006). Paranthesis in Table 6.1 present the percentiles corresponding to the mean value of each parameter in the avalanche season. Low values for wind in the reanalysis data, is presumabley due to the use of sub-orographic roughness length in the parameterization of the boundary-layer. The difference in percentile for the mean values in wind for an avalanche day and a non-avalanche

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24 Testing of the Risk function

day can be seen in paranthesis in Table 6.1, and indicate the spread of the wind, even though the mean values from the downscaled reanalysis are very low.

Table 6.1: Mean values of meteorological parameters in Grasdalen on avalanche days and non-avalanche days. In parantesis are the percentiles which correspond to the mean value for each parameter in the avalanche season (1974-2001).

Precip Precip Temperature Wind No. of 1 day[mm] 5 days [mm] [^oC] [m/s] days Avalanche 9.21 (86) 32.56 (80) -3.60 2.20 (69) 316

days

Non-avalanche 3.28 (71) 17.40 (63) -5.06 1.89 (54) 4759 days

A Risk function (Section 3.2) was set up in order to investigate meteorological parameters and snow cover and link them to avalanches.

For each meteorological parameter and snow cover, weighted functions were made with different upper and lower values in the weighted functions (A and B in Figure 3.1). For precipitation and wind, different percentiles were tested as thresholds.

Temperature and snow cover were tested with more specific thresholds. The different weighted functions were combined in different ways to find the best method to indicate avalanche days and to distinguish them from non-avalanche days. The Risk function is obtained by multiplying every weighted function of each meteorological parameter and snow cover, and put it in the power of 1/n, where n is the number of weighted functions included (Equation 3.8). The first Risk function were set up with weigthed functions of downscaled ERA-40 data and snow cover.

Risk(P5, P1, S, T, U) = (P5_weighted∗P1_weighted∗S_weighted∗T_weighted∗U_weighted)^1/5 (6.1) P1_weighted and P5_weighted are weigthed functions of one and five days precipitation.

Sweighted, Tweighted and Uweighted are weighted functions of snow cover, temperature and wind.

The quality of the Risk function was tested using false alarm and hit rates. The hit rate is the fraction between forecasting avalanche and observing one, while false alarm is the fraction between forecasting avalanche and not observing any avalanche.

Thresholds for the meteorological parameters and snow cover, were chosen by re- questing false alarms below 0.3.

The Risk function takes a value between 0 and 1, because of the weighted functions (Equations 3.2-3.2). The threshold in the Risk function to indicate avalanche days and non-avalanche days is set to 0.8, Figure A.1, Appendix A. This is associated with few days of Risk function values in the middle range between 0 and 1, when there are small differences between thresholds (A and B) for the weighted

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25

functions.

The Risk function including 5 different parameters were tested for different thresholds of the weighted functions for each parameter. The results indicated that wind and one day precipitation could be excluded due to no or very small gain in hit rate. The Risk function was made as simple as possible (in number of parameters), because of possible errors by including more meteorological parameters when later using the Risk function in the output of climate models.

Risk(P5, S, T) = (P5_weighted∗S_weighted∗T_weighted)^1/3 (6.2) The final Risk function contains weighted functions of five days precipitation (P5_weighted), snow cover (S_weighted) and temperature (T_weighted). The thresholds A and B (Figure 3.1) were tested by keeping two weigthed functions with constant thresholds and varying the thresholds for the last weighted function. This is shown in Figure 6.1 (blue dots), where threshold in the weigthed functions for snow cover and temperature are constant, while the thresholds for the weighted function for five days precipitation are varied.

The thresholds (A and B) chosen for the weighted functions included in the Risk function in Figure 6.1, are;

• 5 days precipitation: 65 % percentile as upper and 55 % percentile as lower thresholds in the weighted function

• Temperature: 0^oC as the upper and 5^oC as the lower threshold

• Snow cover: snow water equivalent equal to 100 mm as upper threshold and 50 mm as lower threshold

The reason for the lower threshold for temperature being as high as 5^o Celsius is related to the weigthed function of temperature. Threshold for the weighted function equal or higer than 0.8 in the weigthed function for temperature correspond to a temperature of approximatley1.5^o C.

Table 6.2: Hit rate and false alarm for the Risk function consisting of weighted functions of temperature (5^oand0^oCelsius), snow cover (50 mm and 100 mm) and 5 days precipitation (55 % and 65 % percentile), for different periods.

Period False alarm Hit rate

1974-2001 0.29 0.671

1974-1985 0.285 0.558 1986-2001 0.290 0.817

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26 Testing of the Risk function

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

False alarm

Hit rate

1974−1985 1986−2001 1974−2001

Figure 6.1: The Risk function consisting of weighted functions with different upper and lower thresholds (percentiles) of 5 days precipitation , while thresholds for temperature are 0^oC and5^oC and snow cover are 50 mm and 100 mm. Red and black stars, use threshold as listed on the previous page for two different periods.

The values of the Risk function gave hit rates in the order of 0.67 (Table 6.2) for the entire period, 1974-2001. A large gain in hit rate was found by looking at the last 16 years of the period with a hit rate above 0.81, as seen in Figure 6.1.

The difference in hit rate for these different periods could be associated with better reporting routines in the late 80’s and 90’s, than for the previous period. As can be seen from Table 6.1, the upper threshold (A) in the weighted function of five days precipitation is above the percentile for the mean value for non avalanche days.

Weighted functions between 0.5 and 1 for wind and precipitation (Figure 3.1) gave poorer result compared to the weighted functions between 0 and 1, and was therefore not chosen in this study.

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Chapter 7 Regional Climate projections

7.1 Division into regions

A division into region is done in order to make an easy description of climate change of snow cover and the meteorological parameters, precipitation, temperature and wind, for the study area.

6

40

oW

20_o

W 0^o 20^oE

40

oE

55_o N 60o

N 65o

N 70o

N

1 2 4 3 5 8 79

10

11 12 13

14 16 15

17 18 19

20 21

Figure 7.1: Region 1-21.

27

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28 Regional Climate projections

The Nordic region is divided into 21 regions. 13 regions are defined precipitation regions for Norway, found by comparative trend analysis of precipitation (Bauer- Hanssen and Førland 1998). For Sweden and Iceland the remaining 8 of the total 21 regions were chosen based on topography and geographic location, Figure 7.1. The mountain areas of Sweden are divided into two regions (14 and 15) and in Iceland 6 regions are defined (16-21). The following study will be done for all regions in Figure 7.1.

7.2 Statistics

A statistical method, t-test, is applied to look at differences between mean values of meteorological parameters for a CTRL-run (1961-1990) and a future projection (2071-2100) in the avalanche season. The t-test is described in Appendix B.2.

A hypothesis,H₀ is made; the two samples, CTRL-run and future projection, have statistically the same mean value. The outcome the t-test is either rejecting theH₀ hypothesis or not rejecting the H0 hypothesis at 95 % significance level.

Temperature is expected to be close to normal distributed, and as seen in Table 7.1 almost every gridpoint in the HIRHAM model have rejectedH₀. For RCAO H₀ is rejected for all gridpoints. A t-test (Section B.2.1) is calculated for the different meteorological parameters for all grid point in the RCMs, by the ttest2 in MATLAB.

Table 7.1: T-test of different meteorological parameters with significance level 100*α %

= 95%.

Model Scenario Precip Precip Temperature Wind 1 day 5 days

HIRHAM/HADAM3H A2

H0 not rejected 1779 996 9 0

H0 rejected 5446 6229 7216 7225

HIRHAM/HADAM3H B2

H0 rejected 5807 6451 7194 7225

RCAO/ECHAM4 A2

H0 rejected 6857 7309 7740 6692

RCAO/ECHAM4 B2

H0 not rejected 1369 418 0 1155

H0 rejected 6371 6992 7740 6585

The data have to be normally distributed in a t-test. This is not the case for precipitation and wind, which have a Gamma- and a Weibul- distribution. The

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Meterological parameters in the avalanche season 29

t-test was anyway calculated for these parameters even though it do not fulfill the criteria for the test, and the results are not totally statistical correct.

7.3 Meterological parameters in the avalanche sea- son

Meteorological parameters for two future SRES scenarios (see Section 4.2.3), scenario runs and CTRL-run of two different Regional Climate Models (RCM) are compared to get an indication of future climate changes. The RCM used by the Norwegian Meteorological Institute, with boundary conditions from the Hadley Centre, UK, will be referred to as HIRHAM/HADAM3H. The RCM used by the Swedish Mete- orological and Hydrological Institute, with boundary conditions from Max-Planck Institute will be referred to as RCAO/ECHAM4 (for details see Section 4.2).

Avalanche season is chosen to be from November until April (Section 4.1) . In this Section, a study of the differences in the meteorological parameters between a CTRL-run (1961-1990) and the future projections (2071-2100) from the climate models will be presented. The differences between the future scenario and the CTRL-run are presented in mean annual (avalanche season) number of days above a given thresholds for each meteorological parameter.

For RCAO/ECHAM4, the regions in Iceland are excluded because parts of Ice- land is not covered by the RCAO model grid points. The parts in Iceland covered by the RCAO model grid point are too close to the boundary, and could therefore be directly affected by the boundary conditions. The following figures will refer to the different regions in Figure 7.1.

7.3.1 Precipitation

Projected changes in precipitation from the HIRHAM/HADAM3H and the

RCAO/ECHAM4 simulations are studied by the use of percentiles. First a percentile in the CTRL simulation were choosen and the corresponding value were found. The number of days exceeding this value were counted in the future projections.

Following the Grasdalen results, the five days accumulated precipitation is used in this study. Results from one days accumulated precipitation can be found in Appendix A.

Figure 7.2 presents five days precipitation for the 21 regions in Norway, Sweden and Iceland (values in Table 7.2). Annual (avalanche season) number of days with five days precipitation above the 95 % is presented in Figure 7.2 a) and b).

Precipitation from the reference period 1961-1990 for Norway, presented in Fig- ure 2.2, shows large differences in precipitation amounts for different locations.

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30 Regional Climate projections

a) HIRHAM/HADAM3H b) RCAO/ECHAM4

East/South−N West−N Central−N North−N Sweden Iceland 5

10 15 20 25 30 35

Number of days

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

CTRL B2 A2

East/South−N West−N Central−N North−N Sweden 5

10 15 20 25 30 35

Number of days

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

CTRL B2 A2

c) HIRHAM/HADAM3H d) RCAO/ECHAM4

East/South−N West−N Central−N North−N Sweden Iceland 50

60 70 80 90 100 110 120

Number of days

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 CTRL

B2 A2

East/South−N West−N Central−N North−N Sweden 50

60 70 80 90 100 110 120

Number of days

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 CTRL

B2 A2

Figure 7.2: a)Number of winter days with five days precipitation above the 95 % percentile of the CTRL run in HIRHAM/HADAM3H. b) like a), but with output from RCAO/ECHAM4. c) Number of winter days with five days precipitation above the 65

% percentile of the CTRL run in HIRHAM/HADAM3H. d) like c), but with output from RCAO/ECHAM4. The different regions are listed along the upper x-axis, and one bar of each color presents each region.

In HIRHAM/HADAM3H, an increase is indicated especially in the 5 regions covering eastern, southern and south-western parts of Norway. In these regions, B2 has a higher number of extreme days than A2. Both scenarios have nearly a doubling of extreme days compared to the CTRL-run. For region 6 and 7, HIRHAM/HADAM3H simulates a smaller increase in number of days than for the 5 first regions. In central parts of Norway (region 8, 9 and 10) there is an indication of only small changes between the CTRL and the future projections.

For northern parts of Norway (region 11, 12 and 13) and for the Swedish regions, an increase in number of days above the 95 % percentile of the CTRL-run is indicated,

Avalanches in the Nordic region in a future climate