The Impact of Atmospheric River Events in Preserved Stable Water Isotope Signature in the Snow Pack in Finse, Southern Norway

The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway

Evelien van Dijk

Thesis submitted for the degree of Master in Physical Geography, Hydrology and Geomatics 60 credits

Department of Geoscience Faculty of mathematics and natural sciences

UNIVERSITY OF OSLO

Spring 2018

The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway

The impact of atmospheric river events in preserved stable water isotope signature in the snow pack in Finse, Southern Norway http://www.duo.uio.no/

Printed: Reprosentralen, University of Oslo Abstract

Stable water isotopes have become a key element in hydrological and atmospheric research topics. Hydrologists aim to close the water cycle, and the improvement of measurement techniques and models makes that they come closer every time. Especially the atmospheric part of the cycle is not yet fully understood, and this study aims to get an increased understanding about the isotopic signals in the snow pack, and if the snow pack record may be used to derive an improved understanding of the importance of atmospheric river events for the Norwegian snow cover. The Finse area has been chosen as a proxy site for Norway, and 215 samples have been taken to analyze for stable water isotopes. 11 different snow profiles that were computed from snow pits that were excavated during the fieldwork were compared with the isotope values for δ18O and d-excess, and with meteorological data provided by the meteorological institute of Norway. The snow pack from the same winter season (2016-2017) was simulated with the model CROCUS, using forecast data from AROME. One of the precipitation events, an atmospheric river event, was picked out and modeled with FLEXPART, to simulate the moisture uptake areas. This was backed up by satellite data and compared to the isotopic signals from the snow pits. The isotopic signal from this event was found in two of the snow pits, in February and May. Other isotopic signals were connected to other precipitation events, and by comparing the signals with the relative humidity and air temperature at the time of deposition the moisture source conditions were interpreted. Most precipitation events seem to have a southerly source, with the signal from the atmospheric river period having its source furthest south. Two of the signals appear to be from a more local (northern) source, and one isotopic signal was concluded not to have been preserved. Further research is needed to get an understanding about the role of different atmospheric and snow processes altering the isotopic signal, and multi-year studies on the isotope signal in seasonal snow is desirable as well, to get an understanding about different atmospheric circulation patterns.

i List of Names and Abbreviations

AR Atmospheric River AROME numerical weather prediction model CROCUS snow pack model δ18O oxygen isotope 18 δD deuterium d-excess deuterium excess ECMWF European Center for Medium-range Weather Forecast FLEXPART langrangian particle dispersion model IVT vertical integrated horizontal water vapor ﬂux IWV integrated water vapor MET meteorological institute in Norway Metop polar orbiting meteorological satellites MSG Meteosat Second Generation NOAA satellites from the National Oceanic and Atmospheric Administration RH Relative Humidity SEVIRI Spinning Enhanced Visual and Infrared Imager SST Sea Surface Temperature SURFEX land surface model SWE Snow Water Equivalent SWI Stable Water Isotopes TPW Total Precipitable Water WV Water Vapor

ii Contents

1 Introduction 1

2 Background 3 2.1 Stable water isotopes ...... 3 2.2 Stable water isotopes in the atmosphere ...... 5 2.3 Stable water isotopes in snow ...... 6 2.4 Snow metamorphism ...... 7 2.5 Atmospheric rivers ...... 9

3 Study site: The Finse Alpine Research Center and Regional Characteristics 11 3.1 Climate Southern Norway ...... 11 3.2 Finse ...... 12 3.3 Climate ...... 13

4 Methods 17 4.1 Fieldwork ...... 17 4.2 Labwork ...... 18 4.3 Modeling ...... 19 4.3.1 CROCUS ...... 19 4.3.2 FLEXPART ...... 21

5 Results 25 5.1 Snow proﬁle data ...... 25 5.1.1 Meteorological data ...... 34

6 Discussion 41 6.1 Snow pit data ...... 41 6.2 Model sensitivity ...... 52 6.3 Atmospheric river case ...... 53 6.3.1 IWV and IVT ...... 57 6.3.2 TPW ...... 57 6.3.3 Water Vapor imagery ...... 62

iii 6.3.4 Isotopic signals ...... 63 6.3.5 Challenges ...... 67

7 Conclusion 69

8 Acknowledgements 73 8.1 MET data ...... 79 8.2 Isotope- and other ﬁeld data ...... 79

iv List of Figures

2.1 Schematic drawing of stable water isotopes in the hydrological cycle (Yoshimura, 18 2015). H2 O and HDO are heavier than H216O, what makes that the latter will evaporate ﬁrst. The heavier isotopes in turn will condensate ﬁrst (left part). On the right part the mixing and parting of isotopes is shown in the hydrological cycle.4

2.2 Diﬀerent types of snow crystals described and showed by Colbeck (1982); McClung & Schaerer (2006); Dingman (2008)...... 9

2.3 Constructive metamorphism as visualized by McClung & Schaerer (2006). . . . . 10

3.1 The normalized temperature and precipitation for Southern Norway. The values are normalized over the period 1971-2000. Figures adapted from senorge.no . . . 12

3.2 Location of Finse. Modiﬁed from Norgeskart.no ...... 13

3.3 Locations of the samples taken for stable water isotopes. The three main locations are the glacier Middalsbreen, the Thomas station, which is located on a slope and the marshlands, an area around a braided river system. The map used for the foundation was obtained from Kartverket...... 14

3.4 Wind rose with the wind direction at Finse. The colour indicates the wind speed and the size of the bar indicates how often the wind comes from that direction. SOURCE ...... 15

4.1 Example of a snow pit excavation in the ﬁeld...... 18

4.2 Schematic drawing of CROCUS with all its main physical components and variables (Vionnet et al., 2012)...... 21

4.3 a) The diﬀerentiation between dendricity and sphericity of snow crystals. The grain parameter is based on wind speed. b) The density of fresh snow for diﬀerent wind speeds. Figure adapted from Vionnet et al. (2012)...... 22

v 4.4 Sketch of the method for identifying uptakes along a backward trajectory of an air parcel from the Atlantic ocean to Greenland (black line). The time before arrival is given at the top (t). q (dashed line) is the specific humidity in the air parcel [gkg−1]. ∆q° is the changes in specific humidity of an air parcel between two time intervals and BLH is the boundary layer height. The thick blue sections along the trajectory represent sections of moisture increase, where the red arrows are identified evaporation locations (Sodemann, Schwierz & Wernli, 2008). Figure adapted from Sodemann, Schwierz & Wernli (2008)...... 24

5.1 The density-, temperature curves, isotopic values and the stratigraphy for the snow pits from the marsh, Thomas station and Middalsbreen. The density- and temperature curves from the modeled simulation are given for comparison. The numbers represent the precipitation events that were identified and the letters correspond to the warm events, which will be described further down and in the next section...... 30 5.2 δ18O and d-excess values for different snow profiles from December til May. . . . 32 5.3 The a) temperature and b) density profiles from all snow pits taken in the marshlands with normalized depth...... 33 5.4 a) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The highlighted time periods represent warm episodes during the winter season, where ice layers could have formed. Each episode is given a letter for referencing, as will be described in the section Discussion.b) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The beginning of different precipitation events described in the discussion is given by a number. These are derived from the model output and are placed at the time where a significant build up of the snow pack was simulated (figure 6.1). 35 5.5 Meteorological time series for air pressure, air temperature, relative humidity and precipitation for December...... 36 5.6 Meteorological time series for air pressure, air temperature, relative humidity and precipitation for the snow season 2016-2017...... 37 5.7 The normalized temperature and precipitation for Southern Norway and the annual temperature and precipitation for 2017. Figures adapted from senorge.no . . . . . 38 5.8 The precipitation from the forcing data (top) and the meteorological data, both measured (middle) and wind-corrected (bottom). The meteorological data is from eKlima (n.d.). In the lower two plots the wind speed (middle), with the 7m/s indicated by the straight line, and the temperature (bottom), with the 0 °C line, are given. Note that the biggest differences between the non-windcorrected data and the windcorrected data are when the precipitation falls as snow (<0°C) and the wind speed is high (>7m/s)...... 40

vi 6.1 CROCUS output compared with observed precipitation. The different increases in snow height (precipitation events) are each labeled with a number. The legend for the grain type plot uses abbreviations from the international classification for snow on the ground by UNESCO (Fierz et al., 2009). The most important for this study are MF(meltform), FC(faceted crystals) and DH(depth hoar). The average desity was used to calculate the SWE plot...... 43 6.2 Integrated water vapor flux and integrated water vapor for precipitation event 5 on the 30th of December 2016. Especially in the IWV the AR is visible as a long narrow band of water vapor that reaches all the from the south to the west coast of Norway...... 45 6.3 Integrated water vapor flux for precipitation event 6 on the 10th of January 2017. Note how the AR does not reach all the way to Norway, but stays over the landmass of the United Kingdom...... 46 6.4 The air pressure, relative humidity, air temperature and precipitation for Finse from October 2016 to June 2017...... 47 6.5 The snow pack simulated with different precipitation forcing files for snow depth a and for snow water equivalent b. The precipitation is down-scaled to 20%, 40%, 50%, 60%, 70% and 80%. The values for the snow pits are plotted in the figure as dots...... 52 6.6 The snow height in m and the snow water equivalent in mm for the snow season 2016-2017. Note the increase in SWE in January, where at the same time the snow height decreases...... 54 6.7 Precipitation data for December 2016 for Bergen and Finse. The start of the described AR period is given by a black line...... 55 6.8 Visualization of the moisture uptake a. The moisture uptake at 7-12-2016 b. The moisture uptake at 8-12-2016 b. The moisture uptake at 9-12-2016...... 56 6.9 Visualization of the moisture uptake at the troposphere. a. The moisture uptake at 7-12-2016 b. The moisture uptake at 8-12-2016 c. The moisture uptake at 9-12-2016 ...... 58 6.10 Visualization of the integrated water vapor flux (IVT) at 12:00 on the days the AR was active. a. The IVT at 7-12-2016. The AR is at its peak. b. The IVT at 8-12-2016. A zone with a high flux lies west of the UK. A small part still reaches Norway. c. The IVT at 9-12-2016. The flux is broken up in two parts, and the values have dropped considerably...... 59 6.11 Visualization of the integrated water vapor (IWV) at 12:00 on the days the AR was active. a. The IWV at 7-12-2016. The same is visible as for the IVT. b. The IWV at 8-12-2016. As was visible from the IVT, the water vapor is transported around the UK. The northern part however, does not reach Norway. c. The IWV at 9-12-2016. The AR reaches Norway further south then it did on the 7th, resulting in less precipitation in Bergen...... 60

vii 6.12 Visualization of the total precipitable water (TPW) at 12:00 on the days the AR was active. a. The TPW at 7-12-2016. The AR is reaching all the way from subtropical latitudes to the south of Norway. The precipitation is at its peak at 18:00. b. The TPW at 8-12-2016. The heavy precipitation does not reach the westcoast of Norway at this point. A new plume with precipitation is on its way. c. The TPW at 9-12-2016. The new plume has almost reached the westcoast of Norway. Note that the TPW does not have as high values as on the 7th...... 61 6.13 Visualization of the working of the SEVIRI instrument on the MSG (Aminou, 2002). 62 6.14 Water vapor imagery from 07-12-2016 until 09-12-2016. The images are from 01:00 UTC. This time was chosen because the AR period reached its peak at 01:00 on the 8th of December. In a the AR is starting to form. In b the AR is at its peak and in c it has died down again...... 64 6.15 Water vapor imagery from 07-12-2016 until 09-12-2016. The images are from 12:00 UTC, the same time as was chosen for the other satellite images for IVT, TPW and IWV. In a the AR has reached the United Kingdom and stretches on towards Norway. In b the AR is over its peak and has fallen apart slightly and in c it has died down...... 65 6.16 The air pressure, relative humidity, air temperature and precipitation for Finse for December...... 66 6.17 The accumulated precipitation for the forcing data (AROME) and the MET data for the simulated period, 01-10-2016 until 30-06-2017...... 67

7.1 The conclusion about the moisture sources visualized in a figure modified from Sodemann, Schwierz & Wernli (2008). The numbers above the figure represent the isotopic signals from the corresponding precipitation events, where the three above are from southerly moisture sources, and the two at the right side are from northern moisture sources...... 70

8.1 The average air temperature and precipitation for Finse, taken over the last 10 years. 80 8.2 Overview figure of raw data. 2038/2039 is the number of the picarro machine the samples were run on...... 82 8.3 Drift overview for the standard samples GSM1, VATS and DI...... 83 8.4 Calibration overview: measured vs. certified lab standards for δ18O, δD and d-excess. 84 8.5 Overview figure of calibrated data, for δ18O and δD...... 85

viii List of Tables

5.1 Average δ18O, d-excess, density, temperature, snow water equivalent, peak values for δ18O and d-excess and minimum and maximum temperatures for each of the snow pits ...... 33 5.2 Snow depth and density for the snow pits from the marsh and the calculated values for snow water equivalent ...... 34

6.1 The possible melt events and their dates, plus the heights in the simulated snow pack 44 6.2 An attempt was made to connect the diﬀerent melt events to the ice layers for the diﬀerent snow pits. The depths of the ice layers (corresponding to a certain warm event) are given in meters ...... 44 6.3 The height and values for δ18O and d-excess for the isotopic signals, as well as the minimum and maximum values for δ18O and d-excess for the snow pits taken in the marsh. The signals with similar values are given the same color ...... 51 6.4 Overview of the isotopic signals with their values and the relative humidity (RH) and the air temperature (air T). In the last row the concluded moisture sources are given. Warm and cold refers to a warm front or a cold front ...... 51

ix x Chapter 1

Introduction

42% of the land in the northern hemisphere has a seasonal snow cover of significant duration (Dingman, 2008). Understanding how snow behaves is thus of critical importance if we want to understand the full hydrological cycle. The atmospheric part, as well as the storage of water in the form of snow had not been studied exclusively Yoshimura (2015), and these components of the hydrological cycle are therefore not fully understood Dingman (2008). Several different processes play a role in snow, such as advection, metamorphism of snow crystals and diffusion, and understanding which processes play which role and how the snow pack changes as a result of these processes can give better insight in how water storage in the form of snow works. This can be used in the prediction of spring floods, since snow is a water storage, and knowing how much water will be available during the spring melt is important for the prediction of run-off and river discharges (Dingman, 2008). The snow cover is also important for hydro-power purposes, as the available water will end up in reservoirs, and knowing how much water will become available can be used to calculate how much water will be available, when to run it and how the prices will go up or down as a result of it. The aim of this study is to see if the snow pack record may be used to derive an improved understanding of the importance of atmospheric river events for the Norwegian snow pack. Finse was selected as a representative proxy site for Norway. By looking at the stratigraphy of the snow and the stable water isotopes profiles together with the meteorologic data an attempt was made to link meteorologic events such as precipitation and melt events to certain layers in the stratigraphy of the snow and use this to derive a time of deposition for the isotopic signals. The model CROCUS was used to simulate the evolution of the snow pack to connect the different snow pits over time. The model FLEXPART was used to simulate the moisture sources of the meteorologic events that were preserved in the snow. Other studies (Unnikrishna et al., 2002; Taylor et al., 2002; Feng et al., 2002) concluded that towards the end of the season, when the snow becomes isothermal and melt would begin, the snow becomes enriched in δ18O, whereas the melt water is depleted in δ18O. In this study, we therefore expect to find an enrichment in the general isotope value of the snow towards the end of the snow season. The hypothesis is that some major precipitation events can be found in the isotopic signal

1 of the snow, represented by a peak in the stable water isotope values. Expected is, that these peaks will disappear over time due to do snow metamorphic processes and diffusion, as described by Colbeck (1982); Whillans & Grootes (1985); Dominé & Shepson (2002). Modeling the snow pack should provide similar results as the observations, but the representation must be accounted for, as the grid for the model covers an area of 2,5 km2, whereas the snow pits are a point observation. The model can therefore never give an exact duplicate of the stratigraphy as observed in the snow pits. Still, the overall evolution of the snow pack over time should give the same pattern in both the observed and modeled snow pack. The model FLEXPART shows the presence of an atmospheric river period in the beginning of December, which is likely to have left an isotopic signal in the snow pack in Finse. If a signal is found, the values should deviate from the average value quite a lot, as during an atmospheric river event the moisture sources will be in the subtropics. The d-excess value should then be very low, when the signal is preserved in the snow pack. In section 2 the theory of the different aspects to this study, such as snow metamorphism and isotopic fractionation, will be given. Section 3 will describe Southern Norway and the study site and in section 4 the methods used during fieldwork and the lab, as well as the fundamental characteristics of the two models are covered. In section 5 the results from the fieldwork and the meteorological data are presented and everything will be connected together in section 6, where the observations are compared to the model simulations and the possible interpretations are discussed.

2 Chapter 2

Background

2.1 Stable water isotopes

In nature, several stable oxygen and hydrogen isotopes are present. Combining these isotopes to

H2O makes stable water isotopes (Yoshimura, 2015). Stable water isotopes (SWI) have been used particularly in climate reconstruction before, where isotopic compositions in ice cores was studied and used to make the reconstruction (Jouzel et al., 2007). Recent advancements in measurement techniques and models made that the processes playing a role in fractionation are better understood (Feng et al., 2002; Taylor et al., 2002). Because of this, stable water isotopes became useful in other scientific fields besides climatology, such as hydrology and meteorology (Yoshimura, 2015). Stable water isotopes in snow are not completely understood yet, but it has been used in precipitation studies many times before, starting with Dansgaard (1964). Stable water isotopes are usually expressed as a ratio. Ocean water has a fairly uniform isotopic ratio and contains more heavy isotopes than fresh water. For these reasons the isotopic ratio is given as a deviation from ocean water, and this is known as the Vienna Standard Mean Ocean Water (VSMOW) (Dansgaard, 1964). Heavy isotopes (δ18O) require a larger latent energy than light isotopes (δ16O) to change phases due to their larger weight, what makes that every time water evaporates, the water vapor will be depleted in heavy isotopes. Consequently, condensed water will be enriched in δ18O. This process is called fractionation and the process is visualized in 2.1 (Yoshimura, 2015). Besides evaporation and condensation, melting and refreezing will have a similar effect on the isotopic ratio, since a phase change occurs. To be able to use the stable water isotopes in research it is important to know which processes play a role in changing the isotopic ratio. Fractionation based on differences in latent heat (due to different hydrogen bonds) is called equilibrium fractionation. This type of fractionation depends on changes in temperature. Another type is kinetic fractionation, which occurs because of the difference in molecular diffusivity of water molecules (Yoshimura, 2015). If sea water evaporates into unsaturated air, the lighter isotopes evaporate more easily, resulting in a stronger fractionation than under equilibrium conditions. The same goes for condensation from supersaturated vapor. In this case the heavier isotopes condensate

3 Figure 2.1: Schematic drawing of stable water isotopes in the hydrological cycle (Yoshimura, 18 2015). H2 O and HDO are heavier than H216O, what makes that the latter will evaporate first. The heavier isotopes in turn will condensate first (left part). On the right part the mixing and parting of isotopes is shown in the hydrological cycle. more easily, creating a stronger fractionation. If the condensation of the water vapor occurs under Rayleigh conditions, the precipitation will follow the meteoric waterline. Rayleigh conditions are defined as isotopic ratios forming when the water vapor condensates very slowly and the condensate is immediately removed after it was formed (Dansgaard, 1964). The Rayleigh distillation is given by the formula:

α˘1 R = R0 f

Where R0 is the initial isotopic ratio, f is the remaining water after a part is removed by the phase change and α is the equilibrium fractionation factor. The formula for d-excess is deﬁned by Dansgaard (1964) as:

d = δD − 8δ18O

Where δD is the deuterium, and δ18O is the oxygen-18 isotope. The number 8 was derived from the formula for the meteoric water line:

δD = 8δ18O + 10

The meteoric water line equation describes the average relation between the hydrogen and oxygen stable water isotopes in natural terrestrial waters, as a worldwide average (Craig, 1961). Deuterium excess, just as δ18O, in precipitation changes according to the diﬀerence in either vapor saturation pressure or the molecular diﬀusivity of the water particles (Jouzel et al., 2007).

4 However, where δD and δ18O are defined by the temperature difference, or the moisture removal from a cloud, d-excess is influenced by the conditions at the moisture source. δD and δ18O are primarily driven by equilibrium fractionation. Kinetic fractionation takes up a much smaller part, but has to be taken into account in a few cases where the fractionation occurs outside of equilibrium, when the water vapor pressure is under- or over-saturated in regard to solid ice or liquid water. This happens when the air is undersaturated compared to the surface of the ocean water. Other cases that are less important in general, but may be important for this study are when water droplets evaporate under the cloud base or during the formation of snow (Jouzel et al., 2007). Such non-equilibrium conditions occur for example when there is a strong gradient in relative humidity (Pfahl & Sodemann, 2014). So, sea surface conditions are the key parameters for the d-excess signal and this signal thus relates back to the moisture source conditions (Jouzel et al., 2007). In this study isotopes are used to see if the signals from precipitation events are preserved in the snow. It is therefore important to understand how the isotopic ratio changes with processes occurring in the atmosphere, as well as in the snow. This will be described in the next subsections.

2.2 Stable water isotopes in the atmosphere

Fractionation occurs in the atmosphere, where liquid and solid water (or precipitation) interact with water vapor. The atmospheric part of the hydrological cycle connects all other parts and it is therefore very important to understand the processes that play a role in this part of the cycle (Sodemann, Schwierz & Wernli, 2008). Several studies about stable water isotopes in the atmospheric part of the hydrological cycle have been carried out in the last decade or so (Sodemann, Schwierz & Wernli, 2008; Pfahl et al., 2012; Landais et al., 2012; Bonne et al., 2014; H. Steen-Larsen et al., 2015). Models have been used to find the moisture sources of the snow (Sodemann, Schwierz & Wernli, 2008; Landais et al., 2012; Bonne et al., 2014; H. Steen-Larsen et al., 2015) and the isotopic composition of precipitation (Pfahl et al., 2012). Understanding the moisture sources could help to improve climate reconstructions made by looking at the isotopic composition of ice cores. For this study the processes causing isotopic fractionation in the atmosphere are very important, as one of the objectives is to find out if the signals can be traced back to the moisture sources of the precipitation. D-excess is the key to moisture sources, according to several studies (Jouzel et al., 2007; Pfahl & Sodemann, 2014), as the moisture source conditions are preserved during transportation. D-excess is mostly related to the relative humidity (RH), which is related to the sea surface temperature (SST). The moisture source conditions are preserved in the d-excess signal, under the condition that the air parcel does not reach undersaturated water vapor conditions. If the relative humidity is low at the precipitation site this may also influence the d-excess signal, losing the moisture source conditions. When the air is undersaturated for water vapor, the precipitation droplets evaporate again before falling to the ground, changing the isotopic signal with the phase change. The signal will then be from the end location, not from the moisture source. Below cloud processes are

5 therefore important to consider when looking for preserved isotopic signals.

2.3 Stable water isotopes in snow

In the snow pack, water is present in all three phases. These different phases interact with each other. Air pockets within the snow make that water in the solid phase is interacting with water in the vapor phase. Melt water interacts with the snow as it perlocates down the snow pack (Dominé & Shepson, 2002). Water vapor and liquid water have a higher energy then solid water and therefore the water molecules that require less energy to change phase turn into vapor or liquid water first. This makes that the snow pack becomes enriched over time, as the lighter stable water isotopes are removed from it and the heavier stable water isotopes from the air and the melt-water turn into solid water (Unnikrishna et al., 2002). The interaction between melt water and ice in a snow pack has been studied multiple times, for example by Feng et al. (2002); Taylor et al. (2002), who made a physically based one dimensional model to simulate the evolution of snow melt and used laboratory experiments to validate the model, or Unnikrishna et al. (2002), who studied the isotopic variation in the snow pack in Sierra Nevada and how this related to melt water. Dahlke & Lyon (2013) studied the isotopic evolution during the early melt season in Sweden and Dietermann & Weiler (2013) studied the spatial distribution of isotopes in the snow with regard to the elevation. Feng et al. (2002); Taylor et al. (2002) used a one dimensional, physically based model to simulate the isotopic evolution of snow melt. They assumed a homogeneous snow pack with a given height melting at a constant rate from the top. Other assumptions they made are that the transport of isotopes is through advection only and that exchange only happens between perlocating melt water and ice. They found that the isotopic melt curve is mostly controlled by the melt rate. A slow melt gives a curved isotopic trend and a fast melt rate gives a linear isotopic trend. Early in the melt season the melt water is depleted in heavy isotopes, whereas in the end of the melt season the melt water becomes enriched in δ18O. This is due to the exchange of δ18O between the melt water and the ice as the melt water perlocates down through the snow pack. The heavy isotopes stay in solid form, until the end of the snow season, where all the snow melts. Their conclusion is that in hydrographs this effect should be accounted for. For their research, Unnikrishna et al. (2002) frequently sampled the snow pack in a forest clearing at the Central Sierra Snow Laboratory, which has a maritime climate. Weekly samples for snow temperature, density, snow water equivalent (SWE), isotopic composition and liquid water volume were taken at 0.1 m intervals. There were also samples taken for isotopic composition of precipitation by using a bucket collector. To get the mean isotopic composition of the snow pack, the values for SWE were multiplied by the isotopic composition of that layer. All of the values were then added together and divided by the total SWE of the snow pack to get the mean isotopic composition. Note that this method does not account for fractionation processes happening within the snow pack. Unnikrishna et al. (2002) were able to find particular precipitation and melt events in the snow

6 pack based on the isotopic composition of particular layers in the snow pack. By measuring the temperature profile, the change of the snow pack throughout the melt season became clear. During the winter freeze and thaw cycles contributed to a heterogeneity of the snow pack by creating crusts. The real melting of the snow pack began when it became isothermal and at that point the snow pack became enriched in δ18O, due to the removal of depleted melt water. As air parcels rise they can contain less water, resulting in precipitation on the windward side of mountains. Due to the adiabatic rise of the clouds, and the condensation occurring during this process, heavier stable water isotopes will condensate first, what theoretically gives a more depleted snow pack at higher altitudes (Dietermann & Weiler, 2013). Dietermann & Weiler (2013) studied four catchments in Switzerland to determine weather this trend could be found in snow as well. They took samples from north- and south facing slopes in all four catchments covering an altitude difference of approximately 1000m. Samples were taken in April, at the end of the accumulation period and in May, when the melt season had begun. They observed that between the two periods, a slight enrichment of heavy isotopes had occurred within the snow pack, what they concluded was due to the radiation and the consequent melting. A trend in isotopic composition with altitude was only found at a few sites, and they therefore concluded that this trend is very weak, and is quickly overruled by processes that change the isotopic composition in the snow pack after the snow has fallen. It is known that the snow pack isotopes are altered by sublimation, evaporation, metamorphism of snow crystals, perlocating melt water and enriched precipitation, but it is not understood completely how exactly this works and which factors have more influence than others (Dietermann & Weiler, 2013). In their conclusion they address that further research is needed to completely understand all the processes playing a role in changing the isotopic composition of the snow pack. When looking at literature on snow studies, it seems they commonly say it is hard to find concrete evidence for how much other factors, such as snow metamorphism or sublimation, influence isotopic fractionation in the snow. When precipitation falls as snow, only the most outer part of the crystal is in contact with the atmosphere around it. The d-excess signal is therefore preserved. However, if the relative humidity at the precipitation site is low, the air is not saturated and therefore takes up moisture from the snow crystals, where the crystal will sublimate into water vapor. A phase change and thus isotopic fractionation occurs and the d-excess signal from the original water droplet is therefore lost.

2.4 Snow metamorphism

As soon as snow accumulates on the ground, snow metamorphism is set in motion. This process continues until the snow is melted. To understand how the isotopic composition changes and which processes are responsible for this, it is crucial to understand how snow metamorphism works and which processes play a role in this. Colbeck (1982); Dingman (2008) described in great detail the diﬀerent types of snow and how they evolve over time. The metamorphism of snow is dependent on the liquid content and the temperature, or temperature gradient. Colbeck (1982) classiﬁed snow

7 as either wet snow, where the temperature of the snow is at its melting point, or dry snow, where the temperature is below its melting point. The dry snow is divided in two categories, where the crystalline shape is either defined as an equilibrium form or a kinetic growth form. For wet snow, they define the differences based on liquid water content as well as on equilibrium or kinetic growth form. Dingman (2008) describes four mechanisms that are responsible for snow metamorphism. The first is gravitational settling, which occurs at rates that increase with an increasing snow thickness. This mechanism is the principal of snow turning into ice on glaciers. For a more shallow layer of snow however, this mechanism increases the density with 2 to 50kgm−3day−1. The second mechanism described by (Dingman, 2008) is destructive metamorphism. Destructive metamorphism is the breaking down of small dendritical snow crystals into bigger, spherical snow grains. This mechanism is based on the difference in vapor pressure between convex surfaces with a small radius and less convex surfaces. The tips of the snow crystals evaporate, and the vapor gets deposited on nearby less convex surfaces. This process is most rapid in freshly fallen snow. (Colbeck, 1982) describes this as the equilibrium form (figure 2.2a). This mechanism occurs in relatively warm snow with a high liquid content when a high temperature gradient is absent. The third mechanism, constructive metamorphism, is the most important pre-melt densification process in seasonal snow packs (Dingman, 2008). Water molecules are deposited over short distances in concavities where two snow grains lay against each other, creating a bridge between the two adjacent grains (figure 2.3. This process is called sintering. Over longer distances, the water vapor is transferred within the snow pack when a temperature gradient is present. Snow sublimates in warmer parts of the snow pack, and moves towards colder parts, where it is condensated. Colbeck (1982) described this process as kinetic growth forms. Kinetic growth forms are often highly faceted (figure 2.2c). While the faceted crystals grow, the round equilibrium forms evaporate. The kinetic growth forms form in the lower part of the snow pack that is warmest, and where the round crystals are small. Cold air temperatures create an upward-decreasing temperature gradient within the snow (Dingman, 2008). Depth hoar is a well known example of a kinetic growth form and a result of constructive metamorphism, and forms in said warm locations in the snow, often under a restriction or boundary layer such as an ice layer. This happens because the flow restriction causes supersaturation in the warm part of the snow, resulting in deposition of kinetic growth forms (Colbeck, 1982; Dingman, 2008). The fourth and last mechanism Dingman (2008) describes is melt metamorphism, which occurs via two processes. The first is the perlocation of rain or melt water. When the melt water perlocates into the snow pack it may refreeze deeper in the snow pack. Repeated cycles of melt water infiltrating and refreezing leads to the creation of ice layers of greatly reduced porosity and permeability. These ice layers usually form at boundaries between layers that consist of fine grained snow and a coarse grained layer, due to the retentive capacity of the fine grained snow (Colbeck, 1982). The formation of melt layers is of special importance to this study, as isotopic signals diffuse with melting, and ice layers must therefore be taken into account. The second form of melt metamorphism is the disappearance of small snow grains and the consequent growing of

8 (a) Equilibrium form grain as described by (b) Ice crystals gathered into grain clusters as Colbeck (1982). described by Colbeck (1982).

Figure 2.2: Diﬀerent types of snow crystals described and showed by Colbeck (1982); McClung & Schaerer (2006); Dingman (2008). larger snow grains in the presence of liquid water. Actively melting snow is therefore usually made up of grain clusters (ﬁgure 2.2b) and rounded grains with a grain size corresponding that of coarse sand Colbeck (1982); Dingman (2008).

2.5 Atmospheric rivers

Atmospheric rivers are a phenomena that transport a lot of water vapor from (sub)tropical latitudes to higher latitudes. One of the objectives of this project is to study the moisture sources from the isotopic signals. When the origins of the isotopic signal lies in the subtropics, the mechanisms and processes within an atmospheric river have to be understood in order to interpret the isotopic signals and their origin. An atmospheric river (AR) is a narrow band (330 - 500 km wide) of large vertically integrated horizontal water vapor fluxes and was first described by Newell et al. (1992) as ’tropospheric rivers’. These belts have a magnitude similar to the Amazon river, in aspect to both water mass flux and length. AR events occur as a period where the domains are under the influence of air masses that

9 Figure 2.3: Constructive metamorphism as visualized by McClung & Schaerer (2006). have different moisture sources, temperature and precipitation characteristics as during non-AR periods (Sodemann & Stohl, 2013). It is estimated that 90% of the total meridional water vapor transport at the midlatitudes occurs through these atmospheric rivers (Zhu & Newell, 1998) and they are characterized by high water vapor content and strong level winds (Gimeno et al., 2014). To define an AR, two methods can be used. Either the integrated water vapor (IWV) is used, where the boundary values are set at an area with IWV greater then 2 cm, narrower than 1000 km and longer then 2000 km, or the vertically integrated horizontal water vapor flux (IVT) is used, where the AR is defined as a region with a length ≥ 2000 km with an IVT ≥ 250 kg m−1 s−1. When an AR reaches mountainous terrain, the air is forced up. The adiabatic rise of the air will result in rainfall and can produce extreme precipitation events and flooding (Ralph et al., 2006). The west coast of Norway has such an orographic boundary with a north-northeast to south-southwest orientation, perpendicular to the main flow direction of the atmospheric rivers. This causes the high annual precipitation budget at the west coast of Norway (Stohl et al., 2008). Sodemann & Stohl (2013) describe how during an AR event the precipitation does not necessarily have to be extreme. They found a more widespread and intense precipitation on the west coast of Norway during an AR event. They also concluded that the moisture sources originate from lower latitudes during an AR period, as opposed to the more local moisture sources from non-AR periods. Ralph et al. (2004, 2005) describe how a typical AR occurs within the warm conveyor belt of an extratropical cyclone and that its properties include a concentrated band of enhanced low level specific humidity, where frontal convergence is responsible for the vertical expansion of enhanced specific humidity and a pre-cold-frontal low level jet because of the temperature gradient across this cold front. The moisture transported by an atmospheric river has two origins, namely local moisture sources and tropical moisture sources. The former is transported along the cold front of the extratropical cyclone, while the latter is transported by directly poleward transport Bao et al. (2006).

10 Chapter 3

Study site: The Finse Alpine Research Center and Regional Characteristics

The ﬁeld location, Finse, was chosen as a representative proxy for Norway because of its climate and location, as well as because of its research center. In this section the climate in southern Norway is presented, as well as details about the study site Finse.

3.1 Climate Southern Norway

In ﬁgure 3.1 the normalized annual temperature and precipitation are given for Southern Norway. The period over which the annual values are calculated is from 1971-2000. Finse, the location at which the ﬁeld work was carried out is marked on the maps. The temperature in Southern Norway is highest along the southern coast and decreases inland. This is due to the mountains with an orientation north-north-east to south-south-west in the center of the area. The precipitation follows a gradient and is decreasing from west to east. The prevailing wind direction is from the west (eKlima, n.d.), and as a result of orogenic uplift the moisture precipitates at the west coast, leaving dry air on the other side of the mountains. Bergen is located on the west coast and therefore receives a very high annual precipitation, exceeding 3000mm (weather data, n.d.; Stohl et al., 2008). The precipitation gradient described before makes that the equilibrium line altitude (ELA) gradient, the height at which glaciers are at equilibrium, and the lower limit permafrost gradient are crossing each other at Finse. Glaciers are sensitive to winter temperature and precipitation, and because the precipitation is so high at the west coast of Norway the glaciers extend almost all the way to sea level, whereas towards the east they persevere only at higher altitudes. Permafrost however, is dependent on temperature and snow cover, which is why it is present at lower altitudes towards the east and its limit is thus decreasing eastwards (Etzelmüller & Hagen, 2005). Because of Norway’s topography, with high mountains, natural lakes and steep slopes to valleys and fjords the country is perfect for the production of hydropower. During the 19th century hydropower provided the basis for the industrialization in Norway and nowadays 99% of Norway’s power production comes from hydropower. This makes up around one-sixth of the worlds hydro-

11 power supply. At the end of 2016, Norway’s inland waters powered over 31 GW installed capacity, producing 144 TWh of clean power. It marks the highest annual hydropower generation ever recorded in Norway, which has been attributed in large part to very high rainfall throughout the year. As a result of climate change, an increase in average inﬂow feeding the river systems is already observed (IHA et al., 2017).

Figure 3.1: The normalized temperature and precipitation for Southern Norway. The values are normalized over the period 1971-2000. Figures adapted from senorge.no

3.2 Finse

The fieldwork for this project was carried out at the Finse area in Southern Norway, in the community Ulvik. This area was chosen because it has developed into a research area, with a research station and a flux tower, providing accommodation, equipment and meteorological data. As mentioned before, Finse lies at the point where the ELA and the permafrost gradient cross each other. This makes Finse a unique area for geoscience, as both glaciers and permafrost present (figure 3.2). At Finse there is a research station that is in use the whole year. From here researchers can make trips into the field towards the glaciers or into the Finse valley (Finse Alpine Research Center, n.d.). The Finse Alpine Research Center is owned by the mathemathical and natural sciences departments from the University of Bergen and the University of Oslo and is located 1.5km east of the Finse railway station. Finse is located northeast of the Hardangerjøkulen ice-cap on the high-mountain plateau Hardangervidda in Southern Norway (figure 3.2). It is the highest point on the train line between Oslo (4,5 hours) and Bergen (2,5 hours), as the railway station is located at 1222m a.s.l. The valley has a north-west to south-east alignment and lies in the low alpine zone (Finse Alpine Research

12 Center, n.d.). The area belongs to the Hallingskarvet national park. Finse lies just east of the water divide, dividing east and west Norway. The lake Finsevatn and the glacier Middalsbreen feed a river system that flows into south-eastern direction, towards the Oslo fjord. Just east of the lake the river system flows over an area of marshland and here 6 of the 11 snow pits were dug, which will be further elaborated in section 4. The snow pits are represented as red dots on the map in figure 3.3. Finse is located above the treeline and has therefore only low vegetation and mosses (Finse Alpine Research Center, n.d.). Lichens are very common and can be used as an indicator for snow cover. When the boulders that the lichens live on get covered in snow for the winter, the lichens die. Boulders with abundant lichen growth are thus uncovered during the winter. The size of the lichens also indicates the age, so a boulder with small lichen patches has been free from snow or ice for a shorter period of time then a boulder with large patches (Pitman, 1973).

Figure 3.2: Location of Finse. Modiﬁed from Norgeskart.no

3.3 Climate

With data obtained from eKlima.no a mean annual air temperature (MAAT) of -1.2 °C was calculated for the Finse area in the period 1993-2017. The mean annual precipitation is 1027 mm

13 Figure 3.3: Locations of the samples taken for stable water isotopes. The three main locations are the glacier Middalsbreen, the Thomas station, which is located on a slope and the marshlands, an area around a braided river system. The map used for the foundation was obtained from Kartverket.

(Berthling et al., 2001) and is strongly influenced by orographic effects. Mean annual precipitation and temperature from the last 10 years are given in the Appendix. The Hardangerjøkulen ice-cap receives around 3 times more precipitation as Finse in the valley below. The mean annual air temperature in the winter (MAATW) is -2.6 °C(YR, n.d.). The wind direction is influenced by the mountains around, which create a tunnel effect. The main wind direction is therefore mainly from the west/northwest or from the southeast, following the direction of the valley (figure3.4). Wind speeds at Finse can be high, the average wind speed in winter is around 6 m/s, but can get up to around 20 m/s (yr.no). There is sporadic permafrost in the area around the valley, but not in the valley itself. The snow pack in Finse reaches its peak around March - April, with a mean depth of about 160 cm. Snow starts building up in October and disappears again in June or July (Berthling et al., 2001).

14 Figure 3.4: Wind rose with the wind direction at Finse. The colour indicates the wind speed and the size of the bar indicates how often the wind comes from that direction. SOURCE

15 16 Chapter 4

Methods

4.1 Fieldwork

From December 2016 to May 2017 several snow pits were collected, one in December, two in February, two in March and six in May. Therefore several trips had to be made to Finse, usually lasting between three to seven days. Finse is only accessible by train and equipment and supplies had to be carried or pulled on a pulk to the research station, 1.5km from the train station. The pits were excavated in such a way that the stratigraphy of the snow was not disturbed. First, the orientation of the pit wall to be excavated was determined based on the position of the sun and the wind direction. Walking on top of the snow close to the snow pit wall that would be examined was prevented, and in some cases a half circle shaped wall was build around the excavation line to prevent the wind from blowing in as well. Then, after excavating the snow pit wall, the wall from which the stratigraphy would be determined was cut straight down and scraped off an extra time after the excavation was finished, so that the snow was clean and no cross-contamination would occur. Snow pits were excavated all the way down to the ground surface, or in some cases in the marshlands to the water surface, except for the snow pits at Middalsbreen. Because the pits were excavated all the way to the ground (figure 4.1), the snow has been measured with 0 at the bottom of the snow pack, having the snow as a height from the bottom. The stratigraphy, density and temperature were measured, and samples for stable water isotope analysis were taken. The hardness of the stratigraphical layers were measured using hand hardness as described by Pielmeier & Schneebeli (2003). The hand hardness test is a subjective manual penetration test that depends greatly on the observer. The hardness is measured by pushing a fist, four fingers, three fingers, two fingers, one finger, a pencil or a knife with a given force parallel to the layer into the snow, in that way assessing the resistance. The hand hardness captures primarily the hardness differences between the layers of one snow profile, and it can vary greatly between observers, as the pushing force and area are subjective. This method does therefore not give an absolute hardness reference (Pielmeier & Schneebeli, 2003). For the density a square cutter of 5.8cm by 5.8cm by 3cm (100cm3 was used to take snow samples at different depths, ideally from each layer distinguished from the stratigraphy, but this

17 Figure 4.1: Example of a snow pit excavation in the field. was not possible everywhere, as some layers were too small for the cutter. When this was the case the density sample was from multiple stratigraphical layers. The samples were then weighed using a hand-scale. The density can be calculated from the weight and the volume, this will be covered in section 5. The temperature was measured using an Omega temperature probe. The temperature was taken at 10 cm intervals over the whole snow pit by sticking the probe into the snow and wait until the number on the screen was stable before taking it out again and repeating the process all the way down the snow profile. The temperature was measured in the shade to prevent the sun from influencing the measurement on sunny days. Samples for the SWI analysis were taken at different depths, following the stratigraphical layers as much as possible, to cover the whole depth of the snow pit. The same cutter was used to take the samples for SWI as for density. Before cutting into the snow the pit wall was scraped down a bit to get a fresh snow sample. The snow was then transferred to a small plastic bag, opened right before the snow sample was put in, and was closed with metal strips attached to the bag. The samples were kept buried in the snow to prevent any melting of the samples and they were kept frozen until they arrived in the lab in Bergen, where they were thawed under controlled conditions and transferred to glass vials, as described in the next subsection.

4.2 Labwork

Handling and processing of liquid stable isotope samples was done at the Norwegian National Infrastructure project FARLAB (Facility for advanced isotopic research and monitoring of weather, climate, and biogeochemical cycling, Project Nr. 245907) at the University of Bergen, Norway. 215 samples were analyzed in the course of approximately three weeks. 20 samples could be

18 analyzed per session and one session took almost 48 hours to complete. Two weeks was pulled out to work in the lab, but because of some unforeseen circumstances the analysis had some delays. Samples were filtered with 25mm Nylon filters with a 0.24µ m PTFE membrane (part #514- 0066, VWR, USA), and transferred to 1.5ml glass vials with rubber/PTFE septa (part #548-0907, VWR, USA). An autosampler (A0325, Picarro Inc) transferred ca. 2µ l per injection into a high- precision vapourizer (A0211, Picarro Inc, USA) heated to 110 °C. After blending with dry N2 (< 5 ppm H2O) the gas mixture was directed into the measurement cavity of a Cavity-Ring Down Spectrometer (L2140-i, Picarro Inc) for about 7 min with a typical water concentration of 20 000 ppm. Memory effects were reduced by two times measuring a vapour mixture at a mixing ratio of 50 000 ppm, obtained from 2 injections of 2µ l for 5 min at the beginning of each new sample vial. Thereafter, another 12 injections of 2µ l per sample were measured individually as described above, and averages of the last 4 injections were used for further processing. Three standards were measured at the beginning and end of each batch. Batches consisted typically of 20 samples, with drift standard VATS (δD: -127.88 ± 0.09 permil, δ18O: -16.47 ± 0.02 permil, δ17O: -8.62 ± 0.02 permil), measured every 5 samples. For calibration according to IAEA recommendations, the laboratory standards VATS (δD: -127.88 ± 0.09 permil, δ18O: -16.47 ± 0.02 permil, δ17O: -8.62 ± 0.02 permil), DI (δD: -50.38 ± 0.02 permil, δ18O: -7.78 ± 0.01 permil, δ17O: -3.99 ± 0.02 permil), were used, and averaged over the beginning and end of each batch for calibration. Long-term measurement precision is 0.15 permil for δD and 0.02 permil for δ18O, resulting in a measurement precision of 1.0 permil for d-excess. The measurements had to be calibrated after the analyses to correct for humidity dependancy and drift. The water vapor analyzed by the picarro is affected by the water vapor concentration. The humidy response function is established by constantly injecting water vapor with a know isotope composition. To correct the raw data from the concentration effect, a standard value of 20000 ppmv humidity level was used as a reference for this project (H. C. Steen-Larsen et al., 2013; Benetti et al., 2014; H. Steen-Larsen et al., 2015). To account for the drift, sample standards with a known isotopic composition close to the expected values from the snow samples, called standards, are analyzed at a regular interval. Based on these standards, the VSMOW-SLAP scale function can be determined. This scale is then used to calibrate the samples for drift (H. C. Steen-Larsen et al., 2013; H. Steen-Larsen et al., 2015). The calibration process is visualized in the Appendix.

4.3 Modeling

4.3.1 CROCUS

The ﬁrst model that was used for this project is called CROCUS. This is part of the model SURFEX, which is a surface modeling platform developed by météo-France in cooperation with the scientiﬁc community. SURFEX computes the exchange of energy and mass between surfaces

19 and the atmosphere (Vionnet et al., 2012). CROCUS accounts for the snow part of SURFEX and is mostly used in avalanche risk forecasting. Models in this area need to be very detailed on the vertical layering of the snow pack, what makes it also very suitable for this project. CROCUS is the first model that simulates the metamorphism of snow. The lay-out of CROCUS is displayed in figure 4.2 . Crocus is a one-dimensional multilayer physical snow scheme with the vertical snow pack represented on a one-dimensional finite-element grid. The snow pack is described from top to bottom, so the first layer is the surface layer. The density of freshly fallen snow is dependent on the wind (U) and the air temperature (Ta) and is described by the following equation:

1/2 ρnew = aρ + bρ(Ta − Tf us) + cρU

−3 −3 −1 where Tf us is the temperature of the melting point for water, aρ = 109kgm , bρ = 6kgm K −7/2 −1/2 −3 and cρ = 26kgm s . The minimum density for this model is 50 kgm (Vionnet et al., 2012). The density for different wind seed conditions is plotted in figure 4.3. When the wind is very strong the snow is broken up when they hit each other and the snow surface. The relation between dendricity and sphericity is given in figure 4.3. In their paper, Vionnet et al. (2012) describe the equation behind the differentiation between sphericity and dendricity of snow crystals, which is based on the wind speed. The temperature of the new snow corresponds to the temperature of the snow surface layer. If there is no snow layer present on the ground the snow will get the minimum temperature value between the ground temperature and 0°C. New snow falling on existing snow will first be attempted to merge with the existing snow. If the characteristics are too different, the model will create a new layer. When the maximum number of layers is reached, the model will try to merge layers (Vionnet et al., 2012). Snow metamorphism is implemented in the model, as the model will account for wind drift, compaction, snow albedo and the transmission of solar radiation and evaporation of liquid water in the snow and sublimation (within the heat flux element). Snow melt and water perlocation and refreezing are also implemented (Vionnet et al., 2012). The soil temperature is usually provided by the ISBA model that is coupled to SURFEX, but in this study the model was run in offline mode, without the coupling to the ISBA model. In stead, a soil scheme was used as described by Vikhamar-Schuler et al. (2011). The soil temperature was set up with a spin-up period. The input for CROCUS is a NetCDF file with meteorological forecasting data. A very specific format has to be followed, what makes that CROCUS is quite sensitive with respect to the input data. Once the model has ran for a certain time series, the data can be plotted using snowtools. Parameters such as density, temperature, liquid content and grain type are available to be plotted as a time series, or as a ’dateplot’ (e.g. one point in time. When plotting a dateplot, the data is represented as a snow profile similar to the snow profiles made from the observed field data, what makes it fairly easy to compare the simulated snow pack with the observed snow pack. A side

20 Figure 4.2: Schematic drawing of CROCUS with all its main physical components and variables (Vionnet et al., 2012). note to this however, is that the model simulation uses grid cells of 2,5 km2, whereas the observed data is a point observation. The grid size of the model makes that details such as ice layers are almost impossible to simulate and therefore identify in the simulated snow proﬁles. This makes comparing observed and simulated data harder.

4.3.2 FLEXPART

The second model used is FLEXPART. This is a Lagrangian framework model, where air parcels can be traced back in space and time by using trajectories. FLEXPART was developed by Stohl and after adding the deposition code the model was first validated by using three tracer experiments in 1998 (Stohl et al., 1998). Since then the model has been updated with density correction, a convection scheme, backward calculation abilities and improvements in the input/output handling. Lagrangian particle models compute trajectories of a large number of particles, which can be represented by small air parcels) to describe the transport and diffusion of tracers in the atmosphere. Lagrangian models, unlike Eulerian models, do not have numerical diffusion. Also, the Lagrangian models are independent of a computational grid, what makes that they in theory have a infinitesimally small resolution Stohl et al. (2005). FLEXPART can be used forward in time to simulate the dispersion of tracers from their sources, or it can be used to determine potential source contributions by tracing backward in time. For this study the latter has been used. The FLEXPART model is based on model level data of the numerical weather prediction model of the European Centre for Medium-Range Weather Forecasts (ECMWF). The model uses meteorological fields in gridded binary format from the ECMWF numerical weather prediction model on a latitude/longitude grid and on native ECMWF model levels as input Stohl et al.

21 Figure 4.3: a) The diﬀerentiation between dendricity and sphericity of snow crystals. The grain parameter is based on wind speed. b) The density of fresh snow for diﬀerent wind speeds. Figure adapted from Vionnet et al. (2012).

22 (2005). FLEXPART needs five three-dimensional fields: horizontal and vertical wind components, temperature and specific humidity. Running FLEXPART backward in time is more efficient than forward modeling when the number of receptors (the study site) is smaller than the number of potential sources, in this case the moisture sources. Particles are released from the the measurement site and a four-dimensional response function to emission input is calculated Stohl et al. (2005). Sodemann, Schwierz & Wernli (2008); Sodemann, Masson-Delmotte et al. (2008); H. Steen- Larsen et al. (2015) used a Lagrangian particle model to trace the moisture sources related to their measured stable water isotope samples. In figure 4.4 the uptake of moisture along a trajectory (black line) is sketched. The movement of an air parcel through space and time is described as a trajectory. The change in specific humidity of the air parcel will be reflecting the effects of precipitation and evaporation processes. The moisture changes are a result of evaporation into the air parcel and precipitation out of the air parcel. For FLEXPART, it is assumed that either precipitation or evaporation dominate during a time frame and therefore change in moisture (∆q in the figure) allows determination of locations of evaporation or precipitation (Sodemann, Schwierz & Wernli, 2008). Note that the starting point of the back trajectory is not the moisture source, but the precipitation cite. The dashed blue line in the figure is the specific humidity, the ∆q° gives an increase or decrease in the moisture within the air parcel and on the top of the figure the time is given. When the air parcel enters the boundary layer, turbulent fluxes can exchange moisture between the air parcel and the surrounding air. Therefore, if a moisture increase occurs inside the boundary layer, a moisture source is identified at this location Sodemann, Schwierz & Wernli (2008). If a moisture increase occurs outside the boundary layer it is not possible to assign the moisture to a moisture source at the surface. In this case it is assumed that other processes, like convection, evaporation of precipitation or diffusion caused the moisture increase. For the precipitation is assumed that when the relative humidity reaches 80%, clouds exist and precipitation falls (Sodemann, Schwierz & Wernli, 2008).

23 Figure 4.4: Sketch of the method for identifying uptakes along a backward trajectory of an air parcel from the Atlantic ocean to Greenland (black line). The time before arrival is given at the top (t). q (dashed line) is the specific humidity in the air parcel [gkg−1]. ∆q° is the changes in specific humidity of an air parcel between two time intervals and BLH is the boundary layer height. The thick blue sections along the trajectory represent sections of moisture increase, where the red arrows are identified evaporation locations (Sodemann, Schwierz & Wernli, 2008). Figure adapted from Sodemann, Schwierz & Wernli (2008).

24 Chapter 5

Results

In this section a description of the ﬁeld observations, both the snow proﬁles and the isotopic data, and the meteorological data is given. The model results are presented and described in section 6.

5.1 Snow proﬁle data

As described in the section Methods eleven snow pits were excavated over the course of one snow season. In figure 5.1 the temperature, density, isotopic curves and stratigraphy is shown for all individual snow pits. The modeled temperature and density are also shown in figure 5.1, but this will be discussed in the next section. The snow pits in the figure are given as a height above the ground. In table 5.1 the average snow water equivalent (SWE) and the mean isotopic values are given.

25 26

(a) 04-02-2017 (Thomas station)

(b) 05-02-2017 27

(d) 23-03-2017 28

(e) 01-05-2017 (Middalsbreen)

(f) 03-05-2017 29

(g) 03-05-2017

(h) 04-05-2017 30

(i) 04-05-2017

Figure 5.1: The density-, temperature curves, isotopic values and the stratigraphy for the snow pits from the marsh, Thomas station and Middalsbreen. The density- and temperature curves from the modeled simulation are given for comparison. The numbers represent the precipitation events that were identified and the letters correspond to the warm events, which will be described further down and in the next section. Most snow pits (figure 5.1b, 5.1d, 5.1f, 5.1g, 5.1h, 5.1i) were dug in the marsh, and throughout this report the main focus will be on those snow pits. In the snow profiles taken in the marshlands, there are isotopic signals visible in the δ18O (fig5.2). In the snow pit data from December an isotopic signal with a value of -18%can be seen at a height of 0.1 m (fig5.2). The signal in the February snow pit is slightly less depleted, with a value of -16%. There is another depletion visible all the way at the bottom of this snow pit, with a value below -16%. In the snow pits from March and May there are signals with an isotopic value of -18%as well, and in addition to this signal, there are also peaks found with isotopic values of -20 in March, and -16%and -17%in May. The isotopic signals are present in different types of layers, so there is no correlation between the isotopic signals and the stratigraphy. In the next section an attempt is made to link these peaks in isotopic signals to a point in time by using precipitation events, ice layers and the stratigraphy of the snow. When comparing the δ18O curves with d-excess curves for the different snow pits (figure 5.2), not all the δ18O vs d-excess show the same relation. In the isotope curves for the December pit the d-excess has a peak with lower values, whereas for most the other isotopic signals in δ18O the corresponding d-excess values are relatively higher. In table 5.1 the minimum and maximum isotopic values are given.

31 32

Figure 5.2: δ18O and d-excess values for diﬀerent snow proﬁles from December til May. Table 5.1: Average δ18O, d-excess, density, temperature, snow water equivalent, peak values for δ18O and d-excess and minimum and maximum temperatures for each of the snow pits

Pit 16-12 05-02 23-03 03-05 03-05 04-05 04-05 δ18¯O -13.3 -12.5 -14.5 -12.4 -12.6 -11.8 -13.0 d − excess¯ 9.1 8.9 11.0 10.9 11.4 11.3 11.9 min δ18O -18.5 -16.3 -19.7 -17.6 -17.5 -16.1 -17.8 min d-excess 7.3 1.4 8.3 3.1 7.4 7.0 6.6 max δ18O -10.0 -8.8 -8.1 -8.8 -9.3 -9.4 -10.1 max d-excess 12.7 12.2 16.5 17.8 17.0 15.9 21.0 - 172 196 316 339 316 315 ρ¯[gm−3] - 0.26 0.29 0.47 0.51 0.47 0.47 SWE¯ [mm] - 165.3 293.3 757.7 746.4 686.3 846.8 T¯ -2.5 -5.2 -2.8 -1.3 -1.3 -1.3 -1.4 Tmin -0.5 -1.7 -0.2 0.2 0.0 0.0 0.1 Tmax -8.7 -8.8 -6.0 -3.9 -3.3 -3.4 -3.4 ∆T 8.2 7.1 5.8 4.1 3.3 3.4 3.3

In figure 5.3 the density and temperature profiles for all the snow pits from the marshlands are plotted over a normalized depth, so that they can be more easily compared. The curves from December stand out the most, with a shape completely different from the other snow pits. In general the difference in temperature gets less towards the end of the snow season (table 5.1, and the depth at which the temperature has its maximum in the curve moves down over time. Note that the density profile from the pit in March is not complete. The curve from the pit in December shows little variation in density, as opposed to the density in the snow pits from May, where a large variability is visible.

(a) Temperature (b) Density

Figure 5.3: The a) temperature and b) density proﬁles from all snow pits taken in the marshlands with normalized depth.

In table 5.2 the snow water equivalent (SWE) is given for the diﬀerent snow pits from the marsh. The SWE was calculated using the average density in kg m−1 and the snow depth in m. The density from the snow pits goes up towards the end of the season. The second snow pit from the third of May has a slightly higher value than the other pits from May. The snow pits with the highest snow depth also have the highest values for SWE.

33 Table 5.2: Snow depth and density for the snow pits from the marsh and the calculated values for snow water equivalent

snow pit snow depth [m] ρ¯[kgm−3] SWE [mm] 05-02-2017 0.63 258.3 165.3 23-03-2017 1.0 293.3 293.3 03-05-2017 1.6 473.6 757.7 03-05-2017 1.47 507.8 746.4 04-05-2017 1.45 473.3 686.3 04-05-2017 1.79 473.1 846.8

5.1.1 Meteorological data

In figure 5.4 the air temperature, precipitation and wind speed obtained from eKlima are given from October 2016 to June 2017 for Finse. The numbers and letters corresponding to certain events will be covered in the next section. The temperature truly gets below zero degrees Celsius in the end of October and in May the temperature starts to rise above zero degrees again. In between those the season is defined as the snow season. During this snow season there were quite some periods with plus degrees, highlighted in red. As mentioned in the section study site the Finse area is very windblown. Wind speeds above 7 m/s are marked green in the figure. The precipitation data as provided by eKlima (eKlima, n.d.) is not wind corrected, so a wind-correction formula by Wolff et al. (2015) has been used to make wind corrected precipitation, which is described below. A comparison between this data and wind-corrected data is further discussed in the section Discussion. Large precipitation (> 15 mm/day) events happened in the end of October and in December especially. The air pressure and relative humidity are given together with the air temperature and the precipitation in figure 5.5 for Finse for December. This is related to the atmospheric river event, as described in section 6. Air pressure is often used to indicate whether a cyclone is close by. The air temperature and relative humidity are related to each other. If the air temperature drops, the air can no longer hold the same amount of water vapor. This leads to condensation and so the relative humidity will go down as well. The same elements are plotted for the entire winter as well, in figure 5.6. The winter season that was studied for this project was an unusual one, because there was relatively little snow and there were quite some warm periods with plus degrees. In figure 5.7 the normalized (1971-2000) annual temperature and precipitation for southern Norway are given as well as the annual precipitation and temperature for 2017. In 2017 the annual temperature was higher, with a value above zero for Finse, where the normalized value for annual temperature is below zero. There was also more precipitation in 2017 than normal. Since Finse is an area that has high wind speeds a wind-correction formula from Wolff et al. (2015) was used to correct the precipitation data, to compare and see how much difference there is between the measured data and the wind-corrected data. The wind-corrected precipitation is visualized in figure 5.8 together with the uncorrected values for 12-hourly precipitation and the

34 (a)

(b)

Figure 5.4: a) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The highlighted time periods represent warm episodes during the winter season, where ice layers could have formed. Each episode is given a letter for referencing, as will be described in the section Discussion.b) shows meteorological data (precipitation, wind speed and air temperature) from the Finse station. The beginning of different precipitation events described in the discussion is given by a number. These are derived from the model output and are placed at the time where a significant build up of the snow pack was simulated (figure 6.1).

35 Figure 5.5: Meteorological time series for air pressure, air temperature, relative humidity and precipitation for December.

36 Figure 5.6: Meteorological time series for air pressure, air temperature, relative humidity and precipitation for the snow season 2016-2017.

37 Figure 5.7: The normalized temperature and precipitation for Southern Norway and the annual temperature and precipitation for 2017. Figures adapted from senorge.no

38 precipitation data from the forcing data that was used to run the CROCUS model, which will be discussed in the next section. Note that the forcing data is only available until May 2017. The precipitation from the forcing data, which is based on the AROME forecast system data, is a lot higher than the meteorological data from eKlima (eKlima, n.d.). The AROME data is not corrected with observations or representative as an ’analysis’ data set. The highest peaks for the forecasting data are approximately twice as high. Another point that has to be addressed is that not all the precipitation events recorded in the meteorological data are visible in the forcing data. The precipitation event from the 7th-9th of December for instance is a significant event in the meteorological data but gives very low values (>10mm) in the forcing data. In regard to measured precipitation versus wind-corrected, the values become a little lower for precipitation in the form of snow when the wind speed was high. In figure 5.8 the wind speed is given, as well as the temperature. Both these parameters are from the same meteorological data set as the uncorrected precipitation and are resampled as daily mean values to make it easier to read off the values and compare them to the precipitation. The data as provided by eKlima for the study period is visualized in the Appendix. The line in the wind speed is drawn at 7m/s, above which the wind speed is considered high. The 0 °C line is drawn in the temperature plot to make it easier to see when the temperature gets below zero. As mentioned before, the biggest differences between measured and wind-corrected precipitation is for periods where the temperature was below zero, and the precipitation is therefore assumed to fall as snow and when the wind speed is high.

39 Figure 5.8: The precipitation from the forcing data (top) and the meteorological data, both measured (middle) and wind-corrected (bottom). The meteorological data is from eKlima (n.d.). In the lower two plots the wind speed (middle), with the 7m/s indicated by the straight line, and the temperature (bottom), with the 0 °C line, are given. Note that the biggest diﬀerences between the non- windcorrected data and the windcorrected data are when the precipitation falls as snow (<0°C) and the wind speed is high (>7m/s).

40 Chapter 6

Discussion

In this section the results are discussed, as well as the CROCUS model performance and output and the FLEXPART simulations. These are backed up with meteorological forecasts for vertically integrated horizontal water vapor ﬂux (IVT), total precipitable water (TPW) and integrated water vapor (IWV), as well as water vapor imagery data. Everything is then connected together and the ﬁndings and challenges are discussed.

6.1 Snow pit data

As described in the previous section, most snow pits were dug in the marsh. The snow pits from Thomas station and Middalsbreen are not included further in the discussion, because the snow pack at these locations were concluded not to be representative for the whole area. Finse is windblown and together with the micro-topography some places have more or less snow than was deposited from precipitation alone. The site at Thomas station is believed to be a deposition area for wind blown snow, as the snow height is a lot higher than for the snow pits in the marsh. The snow pits from Middalsbreen are located on the glacier and therefore different circumstances as the seasonal snow on the marshlands prevail there. Thus, only the snow pits from the marshlands are chosen to represent the area and will be used for this study. A signal with an isotopic value of around -18 %is visible in several snow profiles, but at different depths (fig 5.2). Through the snow season the signal comes back at highly variable depths, what raises the question if the signal can be from one single event, depending on whether or not the isotope signal can diffuse upwards over time, or if the signals come from different events and happen to have similar values. To answer this question an attempt has been made to place the deposition of the isotopic signal in time, assuming that the signal has not moved significantly up or down through the stratigraphy. The first step is to follow the depth at which the signals occur to the time of formation, using the CROCUS simulation. Then, the signals are linked to a precipitation event, which are described and by using the isotopic values the conditions at the moisture sources are discussed. In figure 5.1 the different snow pits that were dug in the marsh are visualized. The temperature

41 and density curves are compared to the curves from the CROCUS output for that specific day, where UTC 12:00 was chosen, as this is closest to the time of the observations. In table 6.1 the possible melt events are given with the date and the height at which they were identified in the CROCUS simulation. In table 6.2 the ice layers from the snow pits have been linked to the melt events, where the height of the ice layers is given in meters. In figure 5.4a the different melt events (temperature >0°C) are highlighted in the meteorological data and given a letter. When comparing the dates of these events with the simulated stratigraphy (figure 6.1), some of these events are recognizable as the formation of melt forms (red and purple layers). By combining these two figures and comparing the warm events to the ice layers in the snow pits from the march, the ice layers were attempted to be connected to the possible melt events. Melt layers are important for this study, because during a melt event isotopic fractionation occurs and the signal will diffuse. If the isotopic signal is present in an ice layer, the signal has most certainly changed after deposition and can therefore not be used to draw a conclusion about the precipitation event and the moisture source conditions. By comparing the snow layers from the CROCUS simulation with the dates and the height of the isotopic signal in the snow pit, the approximate time of deposition can be derived. In figure 6.1 the occurrence of melt layers have been marked with a red line. The isotopic signal in the December snow pit, which is marked with the first blue line in figure 6.1, has a height of 0.1m. The first melt layer, which lays at a height of approximately 0.2 - 0.4m was formed during precipitation event 2, which was in the end of November. The isotopic signal from the December snow pit therefore has to be from a precipitation event older than that one. Based on the height of the layer in the simulation the isotopic signal in the December snow pit has been assigned to precipitation event 0. The height of the most recent isotopic signal in the February snow pit has been connected to precipitation event 3. The precipitation event is forming the melt layer just mentioned, and the signal is present at a height of 0.4m as well. This specific precipitation event was picked out and will be discussed into further detail in the subsection ’Atmospheric river event’. The second signal in the February snow pit sits at just above 0.2m, which based on the CROCUS simulation corresponds with precipitation event 2 and the lowest signal is believed to be the same signal as found in the December snow pit. The next melt layer was formed during the 23-24th of December 2016. This melt layer lies at approximately 0.6m height in the simulated snow pack. In the isotope curve from the May snow pit there is a peak in δ18O visible just around 0.5m above the ground (figure 5.2. The snow depth in the model is overestimated compared to the observed snow depth, but the melt layer is present just below halfway down the snow column at this point in time (figure 6.1. The isotopic signal is located about half way down the snow column. This leads to the interpretation that this signal is from either this precipitation event or from the one just after, indicated as precipitation events 4 and 5, as the two corresponding heights are relatively close. The lower isotopic signal in the March pit lies slightly below the second signal from the February snow pit and are interpreted as coming from the same precipitation events. Processes in the snow such as diffusion, advection and snow metamorphism can cause the signal to have moved down a little bit over time.

42 Figure 6.1: CROCUS output compared with observed precipitation. The diﬀerent increases in snow height (precipitation events) are each labeled with a number. The legend for the grain type plot uses abbreviations from the international classiﬁcation for snow on the ground by UNESCO (Fierz et al., 2009). The most important for this study are MF(meltform), FC(faceted crystals) and DH(depth hoar). The average desity was used to calculate the SWE plot.

43 Table 6.1: The possible melt events and their dates, plus the heights in the simulated snow pack

Event A B C D E F G Date 27/10 31/10 14/11 24/11 8-11/12 19- 31/12 21/12 Height on formation [m] 0.02 0.10 0.18 0.30 0.40 0.70 0.78 Height in May [m] - 0.10 0.18 0.30 0.35 0.55 0.55 Event H I J K L M N Date 9-21/1 26/1 16/2 27-29/3 31/3- 8-10/4 4-8/5 3/4 Height on formation [m] 1.20 1.20 1.20 1.60* 1.55 1.60 1.65 Height in May [m] 0.90 0.90 0.95 1.30 1.45 1.50 1.65

Table 6.2: An attempt was made to connect the diﬀerent melt events to the ice layers for the diﬀerent snow pits. The depths of the ice layers (corresponding to a certain warm event) are given in meters

Event 05-02 23-03 03-05 03-05 (2) 04-05 04-05 (2) A ------B - 0.10 0.10 - - - C 0.15 - - - - - D 0.35 (E?) 0.2 - 0.2 - - E 0.43 (F?) 0.3 0.35 - 0.4 0.4 F - - - 0.45 0.53 - G - 0.75 0.5 (0.7) - 0.75 0.75 H - - - 0.78 0.8 - I - - 1.0 0.9 0.95 0.9 J - - 1.18 1.05 - 1.18 K - - 1.25 1.23 - - L - - 1.4? - - 1.3 M - - - - - 1.55 N - - - - - 1.65

44 The isotopic signals in the snow pits from May lie around a height of 0.7 - 0.8m. The next melt layer formed in the second half of January and lies at 0.9m in the simulated snow pack at that time. The depth from the simulation and the observed snow depth correspond very well, so the depths are directly compared in this case. As the melt layer is present just above the height of the isotopic signal, the signal has to be from before the date the melt layer was formed and from after the previous melt layer, which has a height of 0.6m. This leaves precipitation events 5 and 6. Precipitation event 5 occurred at the 30th of December 2016 and fits the description of an atmospheric river event. Precipitation event 6 (10-01-2017) also fits the description of an AR period, but based on integrated water vapor flux and integrated water vapor forecasts (see next subsection) this one did not seem to have reached Norway (figure 6.3). Since event 5 was an AR that reached Norway (figure 6.2), this event is more likely to have left the isotopic signal in the δ18O curve of the May snow pits. In the snow pits from May another, smaller bulging peak is visible above the first isotopic signal. This signal is present in the snow pack above the melt layer at 0.9m and therefore interpreted to be from after the 19th of January, the date at which the melt layer is believed to have formed. The signal is not as strong as all the others identified, but it’s still worth mentioning because of the relatively low d-excess values. This signal is interpreted as to be from precipitation event 8, as there is a layer with melt forms (purple) at this height, originating just after precipitation event 8. This signal is present in a melt layer for one of the four snow pits, and thus the d-excess signal has likely changed for this snow pit. The other signals from event 8 are not present in a melt layer, and so these will be used to say something about the moisture source.

(a) IVT (b) IWV

Figure 6.2: Integrated water vapor ﬂux and integrated water vapor for precipitation event 5 on the 30th of December 2016. Especially in the IWV the AR is visible as a long narrow band of water vapor that reaches all the from the south to the west coast of Norway.

The isotopic signatures are not entirely related to the stratigraphy of the snow, as homogenization and diﬀusion occur over time (Unnikrishna et al., 2002; Taylor et al., 2002; Feng et al., 2002; Town et al., 2008; Dahlke & Lyon, 2013; Dietermann & Weiler, 2013), and the signal therefore may become weaker or move up or down the snow column. This is however not studied in detail, and a lot about how this works is still unknown. In solid form the fractionation processes occur a lot slower, and in ice they can normally not even been observed Handbook of environmental isotope geochemistry : 1 : The terrestrial environment. A (1980).

45 (a) IVT (b) IWV

Figure 6.3: Integrated water vapor ﬂux for precipitation event 6 on the 10th of January 2017. Note how the AR does not reach all the way to Norway, but stays over the landmass of the United Kingdom.

When comparing the δ18O curves with the d-excess curves for the same snow pit, the signals from event 2 and 5 show an increased d-excess value at the height of the δ18O peak (ﬁgure 5.2). Pfahl et al. (2012); Bonne et al. (2014); H. Steen-Larsen et al. (2015) coupled d-excess to the condition of the moisture sources, saying that the conditions are preserved in the isotopic signal of d-excess.

Higher d-excess values point towards a moisture source with a RH and SST (RHSST ) more similar to the conditions at the point of precipitation than lower d-excess values. When compared with the relative humidity and air temperature at the time of precipitation moisture source conditions can be proposed. However, d-excess changes as a result of below cloud processes, so one of the criteria for a preserved d-excess signal is that the relative humidity at the time of precipitation is high. If the relative humidity is low, the air is undersaturated with regard to water vapor and the precipitation will then evaporate, thus changing the isotopic signal. In figure 6.4 the relative humidity, air temperature, air pressure and precipitation are plotted for the winter 2016-2017 (October - June). H. Steen-Larsen et al. (2015) conclude that the d-excess increases from south to north, so a lower value for d-excess points towards a moisture source further away. For the isotopic signals found in the δ18O curves the d-excess values differ a lot from snow pit to snow pit. From figure 6.4 can be seen that the relative humidity and the air temperature are related for the time period of the identified precipitation events. When the air temperature is low, the relative humidity drops as well. The cold air contains less vapor due to the limited capacity in the low temperature air. When the cold air (from north) advects to a warm region, the air is warmed up, thus the capacity of holding moisture increases. This results in an air mass of low RH, which favors the moisture uptake (evaporation). In contrast, when the warm air advects to cold regions, the air is cooled down, and with the same principle, the air mass will have a high RH, which favors the moisture release (precipitation). Low air pressures can be interpreted as a low pressure area (the center of a storm) passing by at close range. In table 6.3 the peak values for each of the snow pits from the marsh are given, which gives a little more in detail the different values between the snow pits. The isotopic signals with similar values for δ18O and d-excess are grouped together by color. These signals are interpreted as being

46 Figure 6.4: The air pressure, relative humidity, air temperature and precipitation for Finse from October 2016 to June 2017.

47 fro the same precipitation event, and thus representing the same signal in different snow pits. The isotopic signal with the blue color has d-excess values that are very low compared to the rest of the d-excess values. Lower d-excess values point towards a moisture source further away, so these very low values indicate a moisture source from subtropical regions. The peak around a height of 0.4m corresponds with precipitation event 3, the atmospheric river event. The signals highlighted in green are from precipitation event 5, which is visible in all of the snow pits from May. A second signal is highlighted in May, because of the similar heights and values for δ18O, which are coupled to precipitation event 8. This signal is not visible in the last snow pit taken on the fourth of May. The last snow pit was dug in the afternoon of a relatively warm day (≥ 0 °C), so one may argue that the signal is not as pronounced in this pit due to homogenization of the signal because of melt water perlocating through the snow pack. When comparing the precipitation events connected to the isotopic signals with the relative humidity at time of precipitation the probability of the signals being preserved in the snow can be discussed. In figure 6.4 the precipitation events are given, so it is easier to compare. The relative humidity is only for precipitation event 2 below 70 %, what makes that the isotopic signal from the corresponding event might not be reliable enough to draw a conclusion from in regard to the moisture source conditions. For all the other precipitation events the RH is high, which means that the isotopic signals related to these events are good on this front. However, some events occurred during a warm event, what could have lead to melting. From figure 5.1 the stratigraphy can be compared to see if the isotopic signals are present in an ice layer. During the field work a hardness scale was used based on Pielmeier & Schneebeli (2003). This is not an exact scale, and during the field work the distinction between ice and a hard snow layer has not been very well made. Both ice layers and very hard snow layers have been assigned with ’knife’ on the hardness scale and it is therefore hard to distinguish between actual ice layers and hard snow layers. Based on the observations, the ice layers were a few centimeters thick at maximum. The layers in the stratigraphy that are over a few centimeters thick and have been assigned ’knife’ on the hardness scale are therefore hereby defined as a very hard snow layer, not an ice layer. Because of this, the isotopic signals found in layers wit a hardness ’knife’ are not the most reliable, as it might be present in an ice layer. The highest signal from the February snow pit is found on the boundary between a hard (possible ice) layer and a softer layer. This raises the question if this signal is reliable. However, the signal that was interpreted as being from the same precipitation event in the snow pit from May is not located in an ice layer. The highest isotopic signal from the same snow pit from May, located at 1.30m is present in an ice layer. What is interesting to see is that the same signal in other May pits is not from an ice layer. This signal was tied to precipitation event 8. Looking at figure 6.4 the relative humidity is high at this time, and the temperature is below zero. These are arguments for the preservation of the signal. What could have happened in this case, is that melt water perlocated into the snow pack and refroze at the depth at which the isotopic signal was present. Since this signal is relatively new and it can be found in other pits from the same date we can compare them fairly well. The value for δ18O is almost identical for these signals (table 6.3). The two signals

48 found in a softer snow layer have nearly identical values for d-excess as well. The signal found in the ice layer has a d-excess value that is 2 %lower than the other two signals. The same can be seen from the isotopic signals that are believed to be from precipitation event 3. The δ18O values are nearly identical, whereas the value for d-excess is 2 %less for the isotopic signal found in the ice layer. All the other isotopic signals are found in a layer with softer snow and together with the RH being high enough the signals can be interpreted as been preserved. Two of the signals were present in a hard snow layer or ice layer and might therefore have been subject to melt water. When melt water perlocates down the snow the isotopic ratio changes and these signals might thus not be very useful to draw a conclusion about the moisture source conditions. However, the first signal found in such a layer, the signal from precipitation event 3 in the February snow pack, is present in a hard snow layer that is thicker than 3cm. All the ice layers observed in the field were thinner than 3cm, therefore this signal is not discarded as being diffused. The second signal found in a hard snow layer, from the first snow pit of May and representing precipitation event 8, is found in other snow pits as well, where it was not present in such a layer. Even if the signal is diffused in this one snow pit, the same signal from the other snow pits are not, and this signal can thus still be used to draw a conclusion about the moisture sources. The d-excess values for most isotopic signals show a depletion compared to the average value ( 11%) for d-excess in the snow pits. The relative humidity for all precipitation events but number two was above 80%, in which case we assume the signal has been preserved. For the signal which was assigned to precipitation event 2 the value for d-excess deviates a lot from the other signals, in fact, the d-excess value for this signal is the maximum value for the whole snow pit (12.2%). The relative humidity at the time of the event was below 70%, so this signal is interpreted as having changed as a result of below cloud processes, where the precipitation evaporated due to undersaturated water vapor conditions at the time of precipitation. The d-excess values for the other isotopic signals lie around 7%and around 9%. This is still low enough for the signals to stand out, with the mean value for d-excess being around 11%, but it also shows that the relative humidity wasn’t as high at the moisture sources for these events as with precipitation event 3. This is interpreted as being due to evaporation and precipitation along the way to the measurement site. The original moisture source then becomes less of a contributor. The moisture sources from these signals are interpreted to be from further south, but not from all the way to the subtropics. Thus, to sum up and give some ideas about moisture sources: the moisture source conditions from precipitation event 0 is concluded to have been preserved in the snow, and it’s moisture source is interpreted as being from further away, but not from the subtropics. The air temperature is quite high, what points towards a warm front being associated with a moisture source from the south. The signal connected to precipitation event 2 is concluded not to be preserved, as the value for d-excess is very high and the relative humidity at the time of precipitation was low. The signal from precipitation event 3 is concluded to be from a warm front and will be discussed further below. Precipitation event 4 is believed to be preserved and from a more local moisture source, as the temperature was low, indicating a cold front and therefore a more local moisture source. Event

49 5 has higher temperatures again and is thus connected to a warm front and therefore to a moisture source from the south. D-excess values are a bit higher for this event, so the source is interpreted as being from closer by than the moisture sources from precipitation event 3. Finally the signal from precipitation event 8 is interpreted as having a moisture source with similar conditions as precipitation event 5, based on the values for d-excess. The d-excess values for two of the three signals are lower, around 7%, in stead of 9%, and the δ18O values are higher (-12%), which could be due to the top of the snow pack being around 0 °C and subject to some melting, smoothing out the isotopic curve and bringing the values closer to the average value for the snow pack. An overview is given in table 6.4. Note that the temperature difference for the different precipitation events is not very big, only 7 degrees (table 6.4). In an area where the temperature gets to below -20°C, -5°C is not very cold. This makes sense, for as mentioned before, warmer air can hold more water vapor and can therefore lead to bigger precipitation events, that have a significant increase in snow height. Not a lot of studies have been carried out on the isotopic signals in seasonal snow. Most studies in this area have focused on the change of the isotopic curve in relation to melt. Not a lot about the isotopes in snow related to precipitation has been studied before, and especially not in seasonal snow. The isotopic curves have most probably been influenced by processes playing a role in the snow pack, such as snow metamorphism, the interaction between water vapor present in air-pockets within the snow and the snow itself, diffusion and the perlocation of melt water (Feng et al., 2002; Taylor et al., 2002; Unnikrishna et al., 2002; Dahlke & Lyon, 2013; Dietermann & Weiler, 2013). Previous studies that have been carried out to research the change in isotopic composition of the snow in relation to snow melt are for example Feng et al. (2002); Taylor et al. (2002); Unnikrishna et al. (2002); Dahlke & Lyon (2013). A common conclusion was, that the isotope values increased towards the end of the season due to the snow pack becoming more enriched, because light isotopes melt first and perlocate through and out of the snow pack with the melt water. This is not the case for this study. The δ18O values do not increase later in the season. The trend described by the other studies does show in the values for d-excess. However, from the CROCUS simulation for snow temperature (figure 5.4b) can be seen that the snow pack has not completely reached isothermal conditions yet at the time of the last snow pit excavation in May. Would we have more snow pits from even later in the season, the trend might become visible after the beginning of May, when the snow pack truly becomes isothermal and the melt starts. Since other studies mostly studied the melt season of the seasonal snow, further research on seasonal snow with temperature variations that reach plus degrees temperatures is desirable. The temperature and density curves from the observed data from the snow pits are compared with the density and temperature curves from the CROCUS simulation, where the precipitation was scaled down to 50% (figure 5.1. The density from the observation data seems to be slightly higher than the simulated values for density for the same dates. The observed density did not have as many points down the stratigraphy as the modeled density has, and the curve for observed density is therefore less detailed than the modeled density curve. Some of the variations however, are still captured in the observed density, as can be seen from the plots in figure 5.1 for the pits

50 Table 6.3: The height and values for δ18O and d-excess for the isotopic signals, as well as the minimum and maximum values for δ18O and d-excess for the snow pits taken in the marsh. The signals with similar values are given the same color

Pit 16-12 05-02 23-03 03-05 03-05 04-05 04-05 min δ18O [%] -18.5 -16.3 -19.7 -17.6 -17.5 -16.1 -17.8 max δ18O [%] -10.0 -8.8 -8.1 -8.8 -9.3 -9.4 -10.1 min d-excess [%] 7.3 1.4 8.3 3.1 7.4 7.0 6.6 max d-excess [%] 12.7 12.2 16.5 17.8 17.0 15.9 21.0 peak height [m] 0.09 0.02 0.14 0.35 0.21 0.66 0.78 δ18O [%] -18.5 -16.3 -17.4 -12.8 -12.0 -16.1 -17.0 d-excess [%] 7.6 10.5 10.0 3.1 7.4 9.6 10.7 peak height [m] - 0.23 0.48 0.51 0.62 1.03 1.01 δ18O [%] - -15.4 -19.7 -15.2 -17.5 -12.1 -15.6 d-excess [%] - 12.2 8.9 7.3 9.8 15.0 9.4 peak height [m] - 0.43 - 0.75 1.05 1.3 1.72 δ18O [%] - -12.9 - -17.6 -12.3 -12.0 -17.8 d-excess [%] - 1.4 - 7.2 11.3 7.9 12.0 peak height [m] - - - 1.25 1.31 - - δ18O [%] - - - -12.3 -12.0 - - d-excess [%] - - - 9.8 7.5 - -

Table 6.4: Overview of the isotopic signals with their values and the relative humidity (RH) and the air temperature (air T). In the last row the concluded moisture sources are given. Warm and cold refers to a warm front or a cold front

precipitation event 0 1 2 3 4 5 6 7 8 snow pit 1 16-12-2016 05-02-2017 23-03-2017 23-03-2017 03-05-2017 03-05-2017 snow pit 2 05-02-2017 23-03-2017 03-05-2017 03-05-2017 04-05-2017 snow pit 3 04-05-2017 04-05-2017 snow pit 4 04-05-2017 d18O (1) -18.5 -15.4 -12.9 -19.7 -17.6 -12.3 d18O (2) -16.3 -17.4 -12.8 -17.5 -12 d18O (3) -16.1 -12 d18O (4) -17 d-excess (1) 7.6 12.2 1.4 8.9 7.2 9.8 d-excess (2) 10.5 10 3.1 9.8 7.5 d-excess (3) 9.6 7.9 d-excess (4) 10.7 RH [%] 82 69 90 81 89 88 air T [°C] 1 -5 2 -4 1 -2 moisture source local-south not preserved subtropics north local-south local-north

51 from 05-02-2017, 03-05-2017 and 04-05-2017. The temperature curves for the simulated snow are more straight and show less variation within the stratigraphy than the temperature curves from the observed snow pits. In the observed temperature curves the temperature is most variable at the top of the snow pack, as this is most inﬂuenced by the air temperature. The curve then generally shows lower temperatures downwards, with the minimum temperature at approximately 90% of the snow height, after which the values slowly increase again to about 0°C at the bottom of the snow pack.

6.2 Model sensitivity

Vikhamar-Schuler et al. (2011) compared their input forcing data with meteorological data (MET) and replaced the different components from the forcing data with the MET data to see what variables impact the model the most. They found that CROCUS is most sensitive to precipitation and temperature. When using the forcing data for precipitation as input the thickness of the snow pack is highly overestimated for most of their study sites. In this study the same patterns are visible. CROCUS overestimates the snow depth and gives a depth that is twice that of the observed snow depth where the snow depth reaches its maximum. In figure 6.5 the simulated snow depths and corresponding snow water equivalent (SWE) are given for the different forcing scenarios, where only the precipitation was altered. The scenario where the precipitation was scaled down to 0.5 times the original precipitation gives a snow depth that agrees best with the observed maximum depth. In the beginning of the snow season the snow depth matches well with the snow pit from December. The simulated depth at the time of the snow pits in February and March are still overestimated, but the snow depth at the time of the May snow pits matches again (figure 6.5a). For the corresponding SWE scenarios , calculated with the average density, the February and March snow pit fit best with the 20% scenario. The May snow pits are a best fit with the 40% scenario, which is different from the comparison with snow depth. This could be due to the calculation method, where the average density was used and not the different densities from each layer. There is no dot for the snow pit from December because of a lack of data for the density of this pit.

(a) Snow depth (b) snow water equivalent

Figure 6.5: The snow pack simulated with different precipitation forcing files for snow depth a and for snow water equivalent b. The precipitation is down-scaled to 20%, 40%, 50%, 60%, 70% and 80%. The values for the snow pits are plotted in the figure as dots.

52 As can be seen from figure 5.8, the forcing data gives a much higher precipitation as was observed. The largest peaks are approximately twice as high in the forcing data as the highest peaks for the observed data. Some of the precipitation events that were standing out in the observed precipitation are not very pronounced in the forcing data, the atmospheric river event from the beginning of December being one of them. This could also be a reason why there was not a lot of snow build up simulated by CROCUS during this event. The observed precipitation is not corrected for wind effects, and when comparing the wind-corrected values with the measured values the precipitation events that have changed the most are when the precipitation has likely been falling as snow as well as during periods with high wind speeds, which is as expected. In figure 6.1 the SWE is calculated and plotted for the CROCUS simulation. It was added to this figure so a comparison can be made to the snow depth and the precipitation events contributing to this. From the figure can be seen that the snow depth goes down during precipitation event 3, while the SWE increases a little. This can be explained by the temperature being quite high, melting some of the snow. For some of the other events the same can be seen. For most precipitation events the increase in SWE also shows an increase in snow depth. Note that the snow depth reaches its maximum depth in March, whereas the SWE does not reaches its maximum before the end of May. In figure 6.6 the snow depth and the calculated SWE are plotted. The general trend in build up is the same for both variables, but the details have some differentiation. Around half December there is a drop in SWE visible, while the snow depth increases at the same time. This is a bit strange, but could be a result of the warm period around the 20th of December, after which a lot of precipitation fell, corresponding to events 4 and 5.In the forcing data this precipitation event is very overestimated (figure 6.1), what made that the simulation shows a strong increase in snow depth. The slight shift at the melt season is explained by the snow becoming more compact, therefore showing a decrease in snow depth, before the actual melt starts and the SWE also decreases (Colbeck, 1982; Dingman, 2008).

6.3 Atmospheric river case

From the 7th to the 9th of December 2016 an atmospheric river transported a large amount of precipitation to the west coast of Norway. The atmospheric river lead to a heavy precipitation event on the 7th of December in Bergen and arrived in Finse later that same day (figure 6.7). In the forcing data used to model the snow at Finse the precipitation event is not very pronounced, due to the units used. The forcing data is in kg m−1 s−1, whereas the data from which the forcing file is made consists of hourly data. The data from the meteorological institute did capture this event and shows a peak-precipitation of 19.3mm at the 8th of December (figure 6.7). With FLEXPART the moisture sources are modeled for this specific event and the result is given in figures 6.8 and fig6.9. Figure 6.8 shows the moisture uptake from the boundary layer at the three days of the AR event. The moisture uptake starts at around 30°latitude on the 7th of December and moves towards higher latitudes on the 8th of December, which is also when the AR period has its peak. The moisture uptake in the troposphere (figure 6.9) peaks at the 7th of

53 Figure 6.6: The snow height in m and the snow water equivalent in mm for the snow season 2016- 2017. Note the increase in SWE in January, where at the same time the snow height decreases.

December and shows a moisture uptake over the entire length of the atmospheric river. During the next two days the moisture uptakes becomes less as the AR period comes to an end on the 9th of December. The visualizations from FLEXPART and the satellite data of the atmospheric river event show that a lot of moisture was picked up from both the boundary layer and the troposphere during the event. These moist and warm air masses were transported to the west coast of Norway. Here the water vapor condensated due to the mountains with an orientation perpendicular to the wind direction (Stohl et al., 2008). The mountains force the air parcels to rise and this results precipitation, in this case the precipitation event previously described. Usually due to precipitation along the transect from the source to the final precipitation cite, the earlier sources will contribute less and less to the precipitation at the measurement site (Sodemann, Schwierz & Wernli, 2008). During an atmospheric river event the warm, moist air is transported a lot faster, so perhaps the air reaching the measurement site will still have a strong signal from the original moisture source. The isotopic signal found in the snow pit from February can be linked to the atmospheric river event from the beginning of December. The FLEXPART simulation shows the presence of an atmospheric river, which led to a precipitation event in both Bergen and Finse (figure 6.7). This is backed up by the water vapor image data from SEVIRI, which showed a movement in water vapor from the southern Atlantic to the west coast of Norway (figure 6.14). A low pressure system present over the south Atlantic (figure 6.11) at that time is likely to have created this movement.

54 Figure 6.7: Precipitation data for December 2016 for Bergen and Finse. The start of the described AR period is given by a black line.

55 (a) 07-12-2016

(b) 08-12-2016

Figure 6.8: Visualization of the moisture uptake a. The moisture uptake at 7-12-2016 b. The moisture uptake at 8-12-2016 b. The moisture uptake at 9-12-2016.

56 Sodemann & Stohl (2013) described how the presence of cyclones creates atmospheric rivers carrying large amounts of moisture from the south Atlantic region to the west coast of Norway. The wind direction in Finse during this precipitation event is from the north-west, which means the stable water isotope signal is likely to have come from this meteorologic event. To back up the FLEXPART simulation and the story of the atmospheric river period, additional satellite data was collected for integrated water vapor (IWV), vertically integrated horizontal water vapor ﬂux (IVT), total precipitable water (TPW) and water vapor imagery data.

6.3.1 IWV and IVT

The vertically integrated horizontal water vapor flux (IVT) is one of the markers that can be used to define an AR period. To be counted as an atmospheric river the length has to be ≥ 2000 km and the IVT value has to be ≥ 250 kg m−1 s−1. The other marker that is used to define whether a period is an AR period or not is the integrated water vapor. To qualify as an AR the area has to be narrower than 1000 km and have a length > 2000 km with an IVW of >20 mm (Bao et al., 2006). The AR period from the beginning of December 2016 is > 7000 km long at its peak and from figures 6.10 and 6.11 it becomes clear that the conditions to be called an atmospheric river in this case certainly have been met. The IVT has its maximum value (1250-1500 kg m−1 s−1) at the 6th of December just of the east coast of North America. The front of the AR hits the coast of Norway the 7th of December and has it’s peak precipitation in Bergen on the 7th (28.4 mm) and on the 8th of December in Finse (19.3 mm).

6.3.2 TPW

Total precipitable water is the depth of water in a column in the atmosphere, if all the water would be to precipitate. The TPW is calculated by integration over the whole atmosphere of radiosonde data from NOAA-18, NOAA-19, Metop-A and Metop-B. To calculate TPW air temperature, air pressure and relative humidity are needed. The TPW used for this study is derived by using an algorithm developed by Alishouse et al. (1990). To close the gaps in the data, the data is passed through an algorithm that advects the data backwards and forward in time, creating an overlap that closed the gaps in between satellite sequences, as described by Wimmers & Velden (2011). The TPW for the AR period is given in figure 6.12. The TPW shows the AR reaching all the way to the Norwegian west coast on the 7th of December, at the same time as the peak precipitation in Bergen (figure 6.7. The day after the TPW is not as strong in Norway as the day before. This is reflected in the precipitation graph as well. On the 9th of December the TPW values are higher in Norway than the previous day, but according to the precipitation graph not all the water actually precipitated, as the precipitation peak is not as high for either Bergen or Finse.

57 (a) 07-12-2016

(b) 08-12-2016

Figure 6.9: Visualization of the moisture uptake at the troposphere. a. The moisture uptake at 7-12-2016 b. The moisture uptake at 8-12-2016 c. The moisture uptake at 9-12-2016

58 (a) 07-12-2016

(b) 08-12-2016

Figure 6.10: Visualization of the integrated water vapor flux (IVT) at 12:00 on the days the AR was active. a. The IVT at 7-12-2016. The AR is at its peak. b. The IVT at 8-12-2016. A zone with a high flux lies west of the UK. A small part still reaches Norway. c. The IVT at 9-12-2016. The flux is broken up in two parts, and the values have dropped considerably.

59 (a) 07-12-2016

(b) 08-12-2016

Figure 6.11: Visualization of the integrated water vapor (IWV) at 12:00 on the days the AR was active. a. The IWV at 7-12-2016. The same is visible as for the IVT. b. The IWV at 8-12-2016. As was visible from the IVT, the water vapor is transported around the UK. The northern part however, does not reach Norway. c. The IWV at 9-12-2016. The AR reaches Norway further south then it did on the 7th, resulting in less precipitation in Bergen.

60 (a) 07-12-2016

(b) 08-12-2016

Figure 6.12: Visualization of the total precipitable water (TPW) at 12:00 on the days the AR was active. a. The TPW at 7-12-2016. The AR is reaching all the way from subtropical latitudes to the south of Norway. The precipitation is at its peak at 18:00. b. The TPW at 8-12-2016. The heavy precipitation does not reach the westcoast of Norway at this point. A new plume with precipitation is on its way. c. The TPW at 9-12-2016. The new plume has almost reached the westcoast of Norway. Note that the TPW does not have as high values as on the 7th.

61 6.3.3 Water Vapor imagery

Since the TPW, IVT and IWV are all forecasts, we also looked at water vapor imagery to get an additional observation for water vapor. The water vapor imagery data was obtained by the meteosat second generation satellite (MSG) which carries a spinning enhanced visible and infrared imager (SEVIRI). SEVIRI is a 50cm aperture, line-by-line scanning radiometer that provides image data in visible, near-infrared and infrared channels. It is continuously imaging the earth in 12 spectral channels with a baseline repeat cycle of 15 minutes, which means it takes one picture every 15 minutes. The image sampling distance is 3 kilometer at the sub-satellite point for standard channels (Aminou, 2002). The earth imaging is achieved by using a bi-dimensional earth scan. This scan is based on the spin of the space craft and the scanning mirror. The rapid scan, or line scan, is carried out from east to west due to the rotation of the spacecraft around its spinning axis, which is perpendicular to the orbital plane of the spacecraft and oriented in a north-south position. The slow scan is carried out from south to north by using a scanning mechanism that rotates the scan mirror in steps. The total scan range is 5.5°, which covers the 22°earth imaging range in the north south direction. This corresponds to 1527 scanning lines. 1249 scan lines are needed to cover the whole earth in the baseline repeat cycle. All this is visualized in ﬁgure 6.13 (Aminou, 2002).

Figure 6.13: Visualization of the working of the SEVIRI instrument on the MSG (Aminou, 2002).

The water vapor imagery data shows that during the atmospheric river event from 7-9 December there was a low pressure system present over the south Atlantic, which directed saturated air towards the west coast of Norway (figure 6.15). Water vapor moved from low latitudes towards the west coast of Norway. From the water vapor imagery can be seen that the AR period has its peak at 20:00 UTC at the 7th of December (figure 6.14). Figure 6.15 is from UTC 12:00, just like the TPW, IVT and IWV figures, but because the AR period peaks at 01:00 on the 8th of December the

62 images for this tie are showed as well (figure 6.14. During an AR period the moisture sources are from latitudes further south then during a non- AR period, when the moisture sources are from a more local source Sodemann & Stohl (2013). When the moisture is transported from a longer distance and under different circumstances the isotopic fractionation processes are different as well. This makes that the isotopic signal in the snow from an AR period event stands out, as will be discussed next. From the water vapor imagery and the TPW, IVT and IWV forecasts the same result is seen as the FLEXPART simulation shows. The atmospheric river period starts on the 7th of December and ends at the 9th of December 2016. The moisture uptake takes place from the subtropics and along the entire transect towards the west coast of Norway.

6.3.4 Isotopic signals

In figures 5.4b and 6.1 the precipitation events have been given a number. These events have been picked based on the precipitation from the meteorological data (figure 5.4b) and the snow build up simulated by CROCUS (figure 6.1). The precipitation event that is believed to have been from the AR period described before corresponds to precipitation event with number 3. CROCUS does not simulate snow build up at this time, but factors such as temperature and compaction also influence the snow build up. From figure 5.4b can be seen that the temperature at the time of the AR period was above zero, what (amongst other factors playing a role in snow accumulation) has probably caused the lack of snow build up at this precipitation event. Pfahl et al. (2012); H. C. Steen-Larsen et al. (2013); Bonne et al. (2014); H. Steen-Larsen et al. (2015) found that the moisture source regions are preserved in the d-excess signal. The d-excess value thus gives an insight in the conditions at the source region. According to Sodemann & Stohl (2013) the moisture uptake during an atmospheric river event is from further south than during a non-AR period. In the snow pit from February the δ18O curve shows a peak at about 0.4m (figure 5.2), showing a depletion in the isotopic ratio for both δ18O and d-excess. A depletion in δ18O shows the temperature differences between the source and the precipitation spot were greater than the temperature differences from the other moisture sources and the very low d-excess values point to a moisture source further away. In figure 5.5 the air pressure, air temperature, relative humidity and the precipitation for Finse for December are given. Notice that the air temperature is high during the precipitation event from the 7-9th of December and the relative humidity is high as well. The high temperature shows that it was a warm front that came from the west, which in turn points towards a moisture source from a subtropical region. These arguments all lead to the conclusion that the signal found in the February snow pit is highly likely to have been preserved from the AR period that occurred from the 7-9th of December 2016 and that its moisture sources are from a subtropical origin.

63 (a) 07-12-2016

(b) 08-12-2016

Figure 6.14: Water vapor imagery from 07-12-2016 until 09-12-2016. The images are from 01:00 UTC. This time was chosen because the AR period reached its peak at 01:00 on the 8th of December. In a the AR is starting to form. In b the AR is at its peak and in c it has died down again.

64 (a) 07-12-2016

(b) 08-12-2016

Figure 6.15: Water vapor imagery from 07-12-2016 until 09-12-2016. The images are from 12:00 UTC, the same time as was chosen for the other satellite images for IVT, TPW and IWV. In a the AR has reached the United Kingdom and stretches on towards Norway. In b the AR is over its peak and has fallen apart slightly and in c it has died down.

65 Figure 6.16: The air pressure, relative humidity, air temperature and precipitation for Finse for December.

66 6.3.5 Challenges

Using the model CROCUS and compare the output with the data obtained in the field came with some challenges. First, the grid size of the forcing data is 2.5 km2. This makes that the output has the same resolution. Ideally the grid size would be as small as possible for a proper comparison with point data. The model averages the data over one grid cell, what results in a generalization. Ice layers are therefore impossible to simulate, as they have a high spatial variability. The data obtained from eKlima (eKlima, n.d.) brought some challenges as well. Measuring precipitation in cold and windy environments is very hard, and so far all methods come with quite some measurement errors. In a cold climate the precipitation falls as snow in the winter. As snow is so light it is very prone to wind blowing, which results in the snow being blown over or even out of the precipitation gauges. The position of the MET tower in Finse is not ideal, as it sits on top of a ridge that is known to be wind blown. This leads to an underestimated precipitation in winter and a highly underestimated snow depth. In figure 5.8 the differences between the observed precipitation and the wind-corrected data can be seen, as well as the difference between the observed data and the precipitation data from the forcing file used to run the CROCUS model. The differences between the MET data and the forcing data from AROME are also given in figure 6.17. In this figure the accumulated precipitation is given, so that the differences are better visible. From the figure can be seen that at the start of the snow season in October the forcing data starts to really deviate from the MET data. This can be explained by the fact that the forcing data is a forecast, whereas the MET data is observed data, where the previously discussed problem with intercepting snow plays also a role. Both data sets have their flaws. The observed precipitation has measurement errors due to the high wind speeds at Finse, and the forcing data is a forecast and clearly overestimates at least the precipitation that falls as snow (see sensitivity).

Figure 6.17: The accumulated precipitation for the forcing data (AROME) and the MET data for the simulated period, 01-10-2016 until 30-06-2017.

Another thing worth discussing is the certainty of this study. As the processes altering the

67 isotopic signal in the snow have not been extensively studied yet, they are not taking into account here. The interpretations from the moisture sources could completely change when we learn more about what kind of influence for example the different snow metamorphism processes have on the isotopic signal in the seasonal snow pack. Also, the isotopic signals are represented by only a few samples as mostly single samples were taken from each depth in the snow profile. So, the chance of the isotopic signal being an outlier is present. The four snow pits from May show that some signals can be found in multiple snow pits, which gives a good argument against that, but this does not go for all of the signals found, as for the other months only one snow pit was taken in the marsh. For the next study it would be advised to take cluster samples, so that a statistical analysis can be carried out over the data. Finally, this study was carried out only for one snow season, and so the different atmospheric circulation patterns are not accounted for. A multi-year study could take this into account.

68 Chapter 7

Conclusion

The aim of this study was to get an understanding of whether the snow pack record may be used to derive an improved understanding of the importance of atmospheric river events for the Norwegian snow pack. An attempt has been made to link the precipitation events to the isotopic signals (figure 5.2). The AR period during 7-9 December, which was modeled with FLEXPART and backed up with water vapor imagery and other satellite data, is visible as an isotopic signal in the snow pits from February and May. The other isotopic signals have been linked to other precipitation events and were then compared to the stratigraphy and the relative humidity to see if the signal could have been preserved from the moisture source. Two of the signals were present in a hard snow layer or ice layer and might therefore have been subject to melt water. When melt water perlocates down the snow the isotopic ratio changes and these signals might thus not be very useful to draw a conclusion about the moisture source conditions. The isotopic signal from the AR period described in this thesis clearly stands out because of its low values, especially for d-excess. D-excess is related to relative humidity and a low value indicates a moisture source with a high relative humidity. The signal is also found at the right height in the snow to have been deposited around the time of the event. This makes that this signal is interpreted as being from the AR period. The air in the subtropics is warm and wet, what agrees well with both the δ18O and the d-excess values. Depleted δ18O show that the temperature differences between source and destination were high, and very low d-excess values mean that the relative humidity was very high. Usually, due to precipitation along the transect from the source to the final precipitation site, the earlier moisture sources will contribute less and less to the precipitation at the measurement site (Sodemann, Schwierz & Wernli, 2008). However, during an atmospheric river event the warm, moist air is transported much faster, what makes that the air reaching the measurement site still has a strong signal from the original moisture source. The moisture source conditions from precipitation events 0 and 5 are concluded to have been preserved in the snow, and their moisture source is interpreted as being from the south, but not from the subtropics. The signal connected to precipitation event 2 is concluded not to be preserved, as the value for

69 Figure 7.1: The conclusion about the moisture sources visualized in a figure modified from Sodemann, Schwierz & Wernli (2008). The numbers above the figure represent the isotopic signals from the corresponding precipitation events, where the three above are from southerly moisture sources, and the two at the right side are from northern moisture sources. d-excess is very high and the relative humidity at the time of precipitation was low. Precipitation event 4 is believed to be preserved and from a northern moisture source, as the temperature was low, indicating a cold front and therefore a more local moisture source. Finally, the signal from precipitation event 8 is interpreted as being from similar conditions as precipitation event 4. The isotopic values are however quite average, which could be due to the top of the snow pack being around 0 °C and subject to some melting, smoothing out the isotopic curve and bringing the values closer to the average value for the snow pack. An overview of all the signals and their concluded moisture sources is given in table 6.4 and in figure 7.1 the conclusion about the moisture sources is visualized. The mean isotopic value changes towards the end of the season only for d-excess, not for δ18O. The trend of enrichment of the snow pack that other studies found was thus not found during this study, which is possibly due to the study not extending far enough into the melt season. Due to the high spatial variability in the snow pack and the insufficient knowledge about every process playing a role in the alteration of the isotopic signals, further research in snow- and atmospheric processes would help improve models and increase the understanding of isotopic

70 signals. Further research would be desirable for 1) the role of diﬀerent processes in altering the isotopic signal in seasonal snow, 2) multi-year studies on the preservation of isotopic signals in the snow pack in regard to the diﬀerent atmospheric circulation patterns and 3), to carry out a statistical analysis over cluster samples from the snow, to see the distribution of isotopic values for one point in the snow stratigraphy. Isotopic signals can help with connecting the separate parts of the hydrological cycle and help with understanding the hydrological cycle as a whole, but there is a lot yet to be understood about the processes involved and how they interact.

71 72 Chapter 8

Acknowledgements

First of all, thanks to my supervisor John F. Burkhart for letting this project really be my own and for giving me a nudge in the right direction when needed. Secondly I want to thank Sven Dekker and Simon Filhol, who showed me how things work in the ﬁeld and helped me with visualizing my data. Simon was also a great help when I was stuck again with plotting some snow data in Python and showed me patiently how to work with this and get better with scripting. Special thanks to Anne Claire Fouilleux for helping me with my programming and everything IT related, I could not have done this without her. I hereby acknowledge the FARLAB at the university of Bergen for the measurements and data processing. Many thanks to Yongbiao Weng, for a very warm welcome in Bergen and who was a great help in the lab and helped me whenever he could. He was also very eager to help me whenever I had questions about the workings of isotopes in the atmosphere and took the time to read through my thesis eventhough he was very busy. Thanks to Harald Sodemann for running FLEXPART and for arranging that I could work in the lab, and for giving me the opportunity to present my thesis to the ’water cycle and water isotopes’ group in Bergen. I also want to thank everyone else who has helped me when I got stuck in Unix, Python or LaTeX, with reading through my thesis and giving feedback, or just for simply hearing me out and giving me advice on all sorts of manners. It could be a little frustrating at times, but there was always someone who was willing to help. Thanks also to the amazing people in room 214, who made writing this thesis a social event as well (with a lot of ice cream!).

73 74 References

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78 Appendix

All the raw data and the calibrated data for the stable water isotope samples, the data from the ﬁeld on which the snow proﬁle plots are based, the MET data and forcing data, the satellite data and the model input and output are all available via

DOI:10.5281/zenodo.1290678

8.1 MET data

8.2 Isotope- and other ﬁeld data

Presented here are plots to visualize the calibration of the stable water isotope samples.

79 (a) Temperature

(b) Precipitation

Figure 8.1: The average air temperature and precipitation for Finse, taken over the last 10 years.

80 (a) 2038

(b) 2039

(d) 2039

Figure 8.2: Overview ﬁgure of raw data. 2038/2039 is the number of the picarro machine the samples were run on.

82 (a) 2038

(b) 2039

Figure 8.3: Drift overview for the standard samples GSM1, VATS and DI.

83 (a) 2038

(b) 2039

Figure 8.4: Calibration overview: measured vs. certiﬁed lab standards for δ18O, δD and d-excess.

84 (a) 2038

(b) 2039

Figure 8.5: Overview ﬁgure of calibrated data, for δ18O and δD.