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

The impact of atmospheric river events in preserved stable water isotope signature

in the snow pack in Finse, Southern Norway

Evelien van Dijk

© 2018 Evelien van Dijk

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.

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

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

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

List of Figures

2.1 Schematic drawing of stable water isotopes in the hydrological cycle (Yoshimura, 2015). H₂¹⁸Oand 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

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 δ¹⁸Oand 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 °Cline, 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

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

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 forδ¹⁸O,δDand d-excess. 84 8.5 Overview figure of calibrated data, forδ¹⁸OandδD. . . 85

List of Tables

5.1 Averageδ¹⁸O, d-excess, density, temperature, snow water equivalent, peak values forδ¹⁸Oand 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 forδ¹⁸Oand d-excess for the isotopic signals, as well as the

minimum and maximum values forδ¹⁸Oand 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

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 inδ¹⁸O, whereas the melt water is depleted inδ¹⁸O. 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

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.

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 inδ¹⁸O. 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 wa- ter 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

Figure 2.1: Schematic drawing of stable water isotopes in the hydrological cycle (Yoshimura, 2015). H₂¹⁸Oand 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:

R= R₀f^α˘1

WhereR₀is the initial isotopic ratio, f is the remaining water after a part is removed by the phase change andαis the equilibrium fractionation factor. The formula for d-excess is defined by Dansgaard (1964) as:

d =δD−8δ¹⁸O

WhereδDis the deuterium, andδ¹⁸Ois 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 asδ¹⁸O, in precipitation changes according to the difference in either vapor saturation pressure or the molecular diffusivity of the water particles (Jouzel et al., 2007).

However, whereδDandδ¹⁸Oare 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

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 inδ¹⁸O. This is due to the exchange ofδ¹⁸Obetween 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

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 inδ¹⁸O, 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

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

(a) Equilibrium form grain as described by Colbeck (1982).

(b) Ice crystals gathered into grain clusters as described by Colbeck (1982).

(c) Kinetic growth form of dry snow. It is distinctly faceted and appears at faster growth rates (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

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⁻¹s⁻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).

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-

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

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

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).

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

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

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

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 0.24µm 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. 2µl 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 2µl for 5 min at the beginning of each new sample vial. Thereafter, another 12 injections of 2µl 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 forδDand 0.02 permil forδ¹⁸O, 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

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 (T_a) and is described by the following equation:

ρnew=a_ρ+b_ρ(T_a−T_{f us})+c_ρU^1/2

whereT_{f us}is the temperature of the melting point for water,a_ρ=109kgm⁻³,b_ρ=6kgm⁻³K⁻¹ andc_ρ=26kgm^−7/2s^−1/2. The minimum density for this model is 50kgm⁻³(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

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 2,5km², 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.

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).

(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 (∆qin 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).

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).

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.

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

26

(c) 22-03-2017 (Thomas station)

27

(e) 01-05-2017 (Middalsbreen)

28

(g) 03-05-2017

29

(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.

30

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 theδ¹⁸Ocurves with d-excess curves for the different snow pits (figure 5.2), not all theδ¹⁸Ovs 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 inδ¹⁸O the corresponding d-excess values are relatively higher. In table 5.1 the minimum and maximum isotopic values are given.

Figure 5.2:δ¹⁸Oand d-excess values for different snow profiles from December til May.

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Table 5.1: Averageδ¹⁸O, d-excess, density, temperature, snow water equivalent, peak values for δ¹⁸Oand 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

minδ¹⁸O -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

maxδ¹⁸O -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 SW E[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 T_min -0.5 -1.7 -0.2 0.2 0.0 0.0 0.1 T_max -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.