Master’s thesisPhysical Geography and Quaternary Geology, 60 Credits

Department of Physical Geography

An investigation into the relative importance of different

climatic and oceanographic factors for the frontal ablation

rate of Kronebreen, Svalbard

Felicity Holmes

NKA 2042018

Preface

This Master’s thesis is Felicity Holmes' degree project in Physical Geography and Quaternary

Geology at the Department of Physical Geography, Stockholm University. The Master’s

thesis comprises 60 credits (two terms of full-time studies).

Supervisor has been Nina Kirchner at the Department of Physical Geography, Stockholm

University. Examiner has been Peter Jansson at the Department of Physical Geography,

Stockholm University.

The author is responsible for the contents of this thesis.

Stockholm, 7 June 2018

Lars-Ove Westerberg

Vice Director of studies

2

Abstract

Ice-ocean interactions are an important area of glaciological research today, in light of evidencethat accelerating levels of global mass loss are being driven by submarine melt and calving, asopposed to surface melt (Khazendar et al., 2016). Mass losses at tidewater glaciers are related toa complex set of processes involving atmospheric circulation, ocean circulation, bathymetry, andglaciological processes. The fact that so many processes are involved, as well as a lack of in situobservational data, has made it hard to distinguish long term directional trends from short termnatural variability. However, increasing knowledge about these processes is vital for the creationof better estimates of sea level rise and so has societal implications. This thesis uses observationaldata collected using LoTUS buoys from close to the calving front of Kronebreen, Svalbard, toinvestigate ice-ocean interactions in this locality. Frontal ablation rates are determined fromthe use of high resolution ground range detected Sentinel 1 radar images and then analysed inconjunction with meteorological and oceanographic variables, as well as compared to a physicallybased submarine melt rate.

3

4

Contents1 Introduction 9

1.1 Subject Area and Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91.2 Study Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.3 Thesis Aims and Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

2 Methods 142.1 Summary of Data and Software Used . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Externally acquired data used in this thesis . . . . . . . . . . . . . . . . . . . . . . . . 15

2.2.1 LoTUS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.2.2 Sound Velocity Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

2.3 Data acquired in the process of the thesis . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.1 Meteorological Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.2 Sea Surface Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.3 Sea Ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.3.4 Frontal Ablation Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222.5 Physically based melt rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 232.6 Anti-fouling Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

3 Results 273.1 LoTUS Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273.2 Spatial Variations in Frontal Ablation Rate . . . . . . . . . . . . . . . . . . . . . . . . 293.3 Temporal Variations in Frontal Ablation Rate . . . . . . . . . . . . . . . . . . . . . . . 313.4 Regression Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333.5 Sound Velocity Profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.6 Physically Based Melt Rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363.7 Anti-fouling Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

4 Discussion 404.1 LoTUS data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.2 Frontal ablation rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 404.3 Regression analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.4 Sound velocity profiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 424.5 Physically based melt rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 434.6 Anti-fouling experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

5 Conclusions and Further Research 44

6 Acknowledgements 45

7 Appendix: R code extract 49

5

6

List of Figures1 Conceptual diagram of a fjord, showing circulation patterns.Red indicates buoyancy

driven circulation, purple indicates estuarine circulation, blue indicates intermediarybaroclinic circulation, and pink indicates circulation driven by dense overflow at thesill. Information and figure from Straneo and Cenedese (2015). . . . . . . . . . . . . . 10

2 Location of main currents around Svalbard, with red indicating the West SpitsbergenCurrent, and blue denoting Arctic coastal waters. The black dashed line signifies thefront between the two water masses. Figure from Sundfjord et al. (2017). . . . . . . . 11

3 Map of study area, with key sites labelled. Close-up of Kronebreen is a Landsat TMimage. Image of Svalbard is from Google Earth. . . . . . . . . . . . . . . . . . . . . . . 13

4 Left: Location of LoTUS buoys overlain on Kongfjorden bathymetry, showing theplacement of the buoys in two distinct basins. Kronebreen is located to the right ofthe bathymetry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5 Location of Sound Velocity Profiles (green) overlain on a Landsat image. Kronebreencan be seen on the right hand side of the image. The two transects used to visualisethe presence of water masses within Kongsfjorden are shown. The location of SVP 245is only shown on the small image, as it is not located in close proximity to Kronebreen. 17

6 Sea Surface Temperatures around Svalbard, 30th May 2017. Data from GHRSST Level4 MUR Global Foundation Sea Surface Temperature Analysis, JPL MUR MEaSUREsProject . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

7 Sea Ice Classification Chart (From www.vedur.is) . . . . . . . . . . . . . . . . . . . . . 208 Velocity of Kronebreen 28th Feb to 12th March from Offset Tracking . . . . . . . . . . 219 Digitised glacier fronts overlain on a Sentinel 1 image. . . . . . . . . . . . . . . . . . . 2210 Left: Single Layer of Copper Mesh over open end of Rubber Housing. Right: Three

Star-Oddi CTDs with different experimental set ups. All CTDs shown are in the rubberhousing, with one also covered in copper netting, and one also covered in Planktonnetting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

11 Map of Askö and surrounding area, with the location of the mooring and path takenby the boat superimposed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

12 Map of Kronebreen and Kongsfjorden, with boxes showing the locations where figurespresented in the results section are based. The white circle represents the location ofthe mooring providing the data from the (Luckman et al., 2015), and the blue circleshowing the location of the LoTUS 8 buoy. . . . . . . . . . . . . . . . . . . . . . . . . 27

13 LoTUS 8 temperature data, alongside temperature and salinity data from the Star-Oddi CTD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

14 LoTUS 8 Temperature Data with Hourly and Daily Moving Averages . . . . . . . . . . 2815 Kongsfjorden tides and temperatures for one week in January (1st January until 7th

January) and one week in July (10th July until 16th July). . . . . . . . . . . . . . . . 2816 Temperature data from LoTUS 8, 11, and 12. LoTUS 12 data is from the 2nd Septem-

ber 2016 until the 9th October 2016, and LoTUS 11 data is from 2nd September 2016until 21st December 2016. LoTUS 8 data corresponding to the time period of LoTUS11 is also shown for comparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

17 Fjord bathymetry along the flowline where the highest frontal ablation rate was mea-sured. Starting and ending positions of Kronebreen from the study period are indi-cated. Bathymetry data from J. Kohler . . . . . . . . . . . . . . . . . . . . . . . . . . 29

18 Fjord bathymetry of the frontal area of Kronebreen compared with the mean frontalablation rate, as well as with both the mean retreat rate, and mean velocity (whichwere used to calculate the frontal ablation rate). The dots indicate the location of theflowlines. A dashed line highlights the location of the flowline with the highest frontalablation rate, when taken as a mean over the the study period. . . . . . . . . . . . . 30

19 Temporal variation of all four possible frontal ablation rates Aug 2016-Sept 2017. Seetable 4 for explanation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

20 Temporal variation of Frontal Velocity August 2016 until September 2017 . . . . . . . 3121 Comparison of frontal ablation rate, retreat rate, and velocity time series August 2016

to September 2017 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

7

22 Left: Summary graphs for 2016-2017 study Right: Summary graphs for 2013-2014study, taken from (Luckman et al., 2015) . . . . . . . . . . . . . . . . . . . . . . . . . 32

23 Temperature with depth at each of the twelve SVP locations. See figure 5 for locations. 3424 Salinity with depth at each of the twelve SVP locations. See figure 5 for locations. . . 3425 Water masses along transect 1, with the SVP each profile is derived from indicated by

the numbers above the graph. See figure 5 for locations. . . . . . . . . . . . . . . . . . 3526 Water masses along transect 2, with the SVP each profile is derived from indicated by

the numbers above the graph. See figure 5 for locations. . . . . . . . . . . . . . . . . . 3627 Water mass classifications for Kongsfjorden in different years, adapted from (Promińska

et al., 2017) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3628 Physical submarine melt rate and Frontal ablation rate Plotted Over Time with . . . 3729 Melt rates along the front of Kronbreen, calculated using salinity and temperature

data from sound velocity profiles taken on the 24th August 2016. . . . . . . . . . . . . 3830 Left: Temperature measurements from the three Star-Oddi CTD sensors from the test

experiment Right: Salinity measurements from the three Star-Oddi CTD sensors fromthe test experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

31 Top: Depth measurements from the three Star-Oddi CTD sensors and MicroCat Mid-dle:Temperature measurements from the three Star-Oddi CTD sensors and MicroCatBottom: Salinity measurements from the three Star-Oddi CTD sensors and MicroCat.The dashed vertical line delineates the point at which the mooring and sensors weretaken up to remove the rubber housing and replace the netting. . . . . . . . . . . . . . 39

List of Tables1 Summary of data used in the study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142 Summary of software used in the study . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 Water mass classification table for Kongsfjorden, adapted from Promińska et al. (2017) 184 Summary of frontal ablation rates used in this study . . . . . . . . . . . . . . . . . . . 215 Constants used in the physically based melt rate, as in (Beckmann and Goosse, 2003) 246 Anti-fouling experimental set up . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257 Multivariate regression analysis summary . . . . . . . . . . . . . . . . . . . . . . . . . 338 Univariate regression analysis summary . . . . . . . . . . . . . . . . . . . . . . . . . . 339 Water mass classification summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3510 Summary of melt rate calculations from SVP and LoTUS data . . . . . . . . . . . . . 37

8

1 Introduction

1.1 Subject Area and Background

Ice-ocean interactions are an area of increasing research due to their importance for the issue of glacialmass balance and, as a consequence, sea level rise. This is because elevated mass losses at a marineterminating glacier can have impacts on glacier dynamics and set in motion a positive feedbackwhich leads to further mass loss (Favier et al., 2014). It is known that mass loss through calvingand submarine melting are significant for glacial mass balance around the globe, and that thesemass losses are increasing (Khazendar et al., 2016). For example, mass losses in West Antarcticaincreased by 70% between 2003 and 2013 as a result of elevated levels of submarine melting andcalving (Paolo et al., 2015). Ice-ocean interactions are of importance wherever a glacier terminates inthe water, and so impact ice sheets and smaller tidewater glaciers alike (Truffer and Motyka, 2016).Due to the implications of these processes for the aforementioned issue of sea level rise, increasing theknowledge that we have about the interactions between the oceans and the cryosphere is of societalimportance. This is because an increased understanding of these interactions could help to betterconstrain estimates of sea level rise and so help prepare societies for future changes.

Speed-up, thinning, and retreat of marine terminating glaciers are all inherently interlinked; a changein one of these leads to a change in the others, and often it is difficult to decipher which factor wasoriginally perturbed. For example, the retreat of a tidewater glacier terminus leads to accelerationthrough a reduction in buttressing/resistance to the upstream flow of ice. This speed up then causesdynamic thinning as a result of longitudinal stretching. In turn, this increases the surface slopeof the glacier and thus the driving stress, causing further velocity increases. Through this process,thinning and speed up are propagated upstream (although the magnitude of the change decreaseswith distance inland). Therefore a small perturbation can have a significant impact on a glacier, withpositive feedbacks meaning even a seemingly inconsequential modification to the boundary conditionsof a glacier can be of considerable importance when it comes to glacier dynamics and retreat.

When the trends of speed up, thinning, and retreat were first observed at the marine terminatingglaciers, the key research aim was to determine what caused the dynamical changes. Possibilitieswere ocean temperatures leading to terminus retreat, atmospheric warming leading to increasedsurface melt and thinning, or some combination of factors. The presence of warm, subtropicalAtlantic water in fjords can elevate melt (and calving) rates at the terminus by a considerable extent,as relationships between water temperature and melt rates are non-linear. To exemplify this, astudy focusing on Pine Island glacier in Antarctica by Payne et al. (2007) found that plume watertemperatures of 1.25oC generate melt rates of 10ma-1, whilst plume temperatures of 2oC generatemelt rates of more than 100ma-1. Warmer waters also prevent or delay fjord freeze up (in conjunctionwith elevated atmospheric temperatures), which reduces buttressing and leads to increased glacialvelocity (Christoffersen et al., 2011).

Modelling studies and observational data have added weight to the argument that ocean temperaturesare of great importance for glacial retreat rates (Christoffersen et al., 2011; Favier et al., 2014; Chauchéet al., 2014; Payne et al., 2007; Holland et al., 2008; Seale et al., 2011). With relation to Greenlandand Svalbard, the intrusion of warm and saline Atlantic water into fjords is thought to be a keydriver of retreat (Holland et al., 2008; Luckman et al., 2015). However, more observational data isneeded; data points are scarce and are often not collected in the immediate vicinity of the glacierfront.

This thesis will primarily focus on the interactions between fjord waters and glacial retreat, butit is of importance to acknowledge the role of atmospheric circulation and wind patterns. This isbecause there is evidence from Greenland, Antarctica, and Svalbard that winds are a key driver ofcirculation within fjords and can drive different water masses in or out of the fjord (Straneo et al.,2010; Sundfjord et al., 2017). These local wind patterns relate to larger scale atmospheric processes(Straneo et al., 2010; Sundfjord et al., 2017). Understanding these patterns is an important area ofresearch as the warm Atlantic water hypothesised to be of great importance to the frontal ablationrate of Kronebreen is dense and so found at depth. Thus, it is hard for this water to pass over thesill at the fjord entrance and make contact with the glacier without the aid of wind driven fjord

9

Figure 1: Conceptual diagram of a fjord, showing circulation patterns.Red indicates buoyancy drivencirculation, purple indicates estuarine circulation, blue indicates intermediary baroclinic circulation,and pink indicates circulation driven by dense overflow at the sill. Information and figure fromStraneo and Cenedese (2015).

xcd

circulation (see figure 1 for conceptualisation of a fjord and general circulation patterns). However,as will be discussed in the next section, Kongsfjorden does not have a pronounced sill meaning thebarrier to warm water intrusion is less prominent here than in other locations.

A number of different mechanisms have been suggested to account for the intrusion of water, andwinds play a key part in this. The front between the West Spitsbergen current (WSC), which carrieswarm water northwards along the coast of Svalbard, and cold coastal currents forms a barrier to theintrusion of the warm WSC waters into the fjords of Svalbard, such as Kongsfjorden (see figure 2).However, sufficiently strong northerly winds lead to the movement of surface waters offshore which inturn causes coastal up-welling and the intrusion of Atlantic water into the fjord systems (Sundfjordet al., 2017). Intrusions which occur in this way are episodic events, such as occurred during thewinter of 2006 (Sundfjord et al., 2017).

As well as episodic events such as this, barotropic instabilities can result in the exchange of wateracross the WSC/coastal water boundary (Teigen et al., 2010), and these instabilities have beendirectly observed in Kongsfjorden (Saloranta and Svendsen, 2001). The premise for this method ofintrusion is that the barotropic branch of the West Spitsbergen current (the part of the current thatlies over the upper continental shelf) has an asymmetric current profile, which leads to sharp shearon the shelf side of the current. The stability of this system is related to the location over which theWest Spitsbergen current is centered, with a more shoreward location leading to instabilities (Teigenet al., 2010). When these instabilities occur, vorticity waves and eddies are formed, which permitthe movement of Atlantic water into fjord systems (Teigen et al., 2010).

There is also a seasonal aspect to the intrusion of Atlantic water. For example, Cottier et al. (2005)suggest a geostrophic control mechanism for intrusion, whereby a density front at the mouth of thefjord is reduced gradually during spring as the fjord water is modified. This leads to a situationwhere Atlantic water is able to intrude.

There is also evidence from a modelling study that larger scale atmospheric conditions in recentyears may have led to a separate branch of the WSC forming, the so-called Spitsbergen Trough

10

Figure 2: Location of main currents around Svalbard, with red indicating the West SpitsbergenCurrent, and blue denoting Arctic coastal waters. The black dashed line signifies the front betweenthe two water masses. Figure from Sundfjord et al. (2017).

Current, which is found at shallower depths and so can more easily intrude onto the continentalshelf and into Svalbard fjords (Nilsen et al., 2016). This study focused primarily on Isfjorden andfinds that Atlantic water penetrates Isfjorden more easily than other fjords due to its greater depth,but also presents evidence for the intrusion of Atlantic water and Transformed Atlantic water intoKongsfjorden (Nilsen et al., 2016). The atmospheric conditions held responsible for this phenomenonare the presence of sustained southerly winds as well as northward cyclones centered on the westcoast of Svalbard (Nilsen et al., 2016).

In addition, internal fjord processes are also of importance. For example, run-off from glaciersor rivers, vertical stratification of waters, and the seasonal warming of surface waters (related tostratification) all have an influence on fjord circulation (Cottier et al., 2005). Modelling by Sundfjordet al. (2017) finds that ocean circulation in Kongsfjorden leads to intrusion of warm waters in bothsummer and winter, with all the aforementioned mechanisms being present, but with glacial run-offand wind patterns being particularly significant. It is also of consequence to note that the intrusionof warm water in winter can act to reduce sea ice cover, which reduces the level of buttressing (Toddand Christoffersen, 2014).

How a glacier responds to an influx of warm water is site specific and is not uniform through spaceor time. Spatially, frontal ablation is often centered on the location of subglacial plumes, which areplaces where meltwater is ejected from the base of the glacier. These areas exhibit elevated melt ratesas a result of the fact that freshwater coming out of the glacier is less dense than the surroundingwater and so rises through the water column. As it does so, the deep and warm Atlantic wateris turbulently entrained into the water column. This means that warm water is transported up theglacier front, coming into contact with more ice than it would have without the presence of the plumeand so resulting in higher melt levels. This can lead to a crenulated frontal morphology of the glacier,

11

reflecting the spatially differential melt rates (Sundfjord et al., 2017). The location of plumes canchange over time, showing there is also a temporal aspect to this phenomenon.

Another site specific characteristic is bathymetry. Narrow and shallow parts of a fjord (’pinningpoints’) exert a greater resistance to glacier flow, and are associated with reduced retreat rates.Wide and deep parts of a fjord are, conversely, associated with elevated retreat rates (Carr et al.,2015). Once a glacier retreats past a pinning point, retreat rates may accelerate drastically and thuslead to temporal variations in retreat rate unrelated to ocean temperatures. Variations in bathymetryacross a glacier front may also lead to spatial variations in retreat rate.

The above introduction to ice-ocean interactions highlights the complex web of processes which occurat glacier terminal locations, and explains why there are still gaps in our knowledge. Compoundingthis, the response time of glaciers can mean that larger glaciers and ice sheets are still respondingto climatic changes from the past. The result of this is that, even if climatic and oceanic changeswere to stop now, sea level rise would still continue (Price et al., 2011). Thus, trying to betterunderstand the interactions between the oceans, cryosphere, and atmosphere is vital for improvingsea level rise estimates. However, conditions change temporally and the timescale of observationscan make it difficult to separate natural variability from directional long term trends. Due to thesecomplications, studies are conducted on different spatio-temporal scales and in a variety of locations,with the resulting body of information being used to refine current understandings. This studyfocuses on a small area (Kongsfjorden and Kronebreen, Svalbard) over a short time period (376days), but the results will be situated within a wider context.

1.2 Study Area

The study area for this research is the glacier Kronebreen and adjacent fjord Kongsfjorden, which areboth located near the research town of Ny Ålesund on the western side of the island of Spitsbergen,Svalbard (see figure 3).

Kronebreen is a very fast flowing polythermal glacier, with a study by Kääb et al. (2005) usingoptical satellite imagery to yield a mean flow velocity of 600myr−1 for the summer periods of 1999to 2002. Kronebreen is also a surging glacier, with the last surge taking place in 1869 (Błaszczyket al., 2009). The flow field in the frontal zone of Kronebreen is relatively variable year on year as aresult of the near-frontal confluence with the neighbouring glacier Kongsvegen (Kääb et al., 2005). Inrecent years, Kronebreen has widened at the expense of Konsvegen (Trusel et al., 2010). Kongsvegenis, like Kronebreen, a polythermal surging glacier which is currently in a stagnant phase. However,it has a much lower velocity than Kronebreen at only 2.6myr−1 (Trusel et al., 2010). According toBłaszczyk et al. (2009), Kronebreen has a length of around 40,000 m, an area of 302.9 km2, and acalving flux of 0.0856 km3 yr−1. The length of the melt season of both Kronebreen and Kongsvegenis not precisely known (Trusel et al., 2010).

Kongsfjorden, the fjord which Kronebreen calves into, is is 22km long and between 4 and 12km wide(Trusel et al., 2010). It is therefore characterised as a wide fjord, in which circulation is impacted bythe coriolis effect (Svendsen et al., 2002). The bathymetry of the fjord is highly variable, reachingdepths of 400m in some areas compared to less than 60m in others. This leads to a situation inwhich the fjord is divided up into a number of distinct basins. Kongsfjorden does not have a welldefined sill at its mouth, instead having a trench which decreases in depth as one moves towards theshallow shelf, meaning it is susceptible to intrusions of water from outside the fjord (Svendsen et al.,2002).

A number of different water masses can be found along the west coast of Svalbard (Nilsen et al., 2008),and it is notable that this region is impacted by warm water originating from the West SpitsbergenCurrent (WSC) (Cokelet et al., 2008). The different water masses can be defined by considering boththe temperature and salinity of the water, as was done by Nilsen et al. (2008). In this classification,Atlantic water (AW) is the warmest at over 3◦C compared to local and winter cooled waters whichhave a temperature below 1◦C. The physical environment of Kongsfjorden is seasonally controlledby the balance of Atlantic waters, Arctic waters, and glacial melt (Cottier et al., 2005). The fjordcan be conceptualised as having two different modes; a ’cold’ and ’warm’ mode relating to the levelof Atlantic water intrusions (Cottier et al., 2005). Freshwater inputs from glacial run-off and iceberg

12

Figure 3: Map of study area, with key sites labelled. Close-up of Kronebreen is a Landsat TM image.Image of Svalbard is from Google Earth.

melt occur year round, but are subdued during the winter months (Svendsen et al., 2002). A numberof factors are thought to be important in determining the state of the fjord, including tides, glacialmelt, and wind patterns (Svendsen et al., 2002).

The area often experiences unstable weather conditions due to its location on the boundary of thecold, polar basin circulation and warm air masses arriving from lower latitudes (Ingólfsson, 2004).Wind conditions are of importance to fjord circulation and the dominant direction of the windvaries seasonally. In winter the wind direction is predominantly down-fjord, whereas in summer theprevailing direction is up-fjord (Svendsen et al., 2002). The climate can be described as coastal duringsummer periods and continental during winter periods, as the freeze-up of the fjord during winterdecreases the influence of the ocean (Ingólfsson, 2004).

This site is thought to be particularly sensitive to climatic changes as both glaciological and fjordprocesses would be impacted, and the interconnections between the two could amplify the magnitudeof the impacts (Svendsen et al., 2002). Some changes are already apparent, for example a decreasein sea ice extent and a warming of the West Spitsbergen current (Svendsen et al., 2002).The causeof these changes in complex, and is thought to be related to atmospheric processes- in particular anincreased North Atlantic Oscillation index (Svendsen et al., 2002).

The proximity of the site to Ny Ålesund means that both the glacier and fjord have been extensivelystudied, allowing for the data collected for use in this report to be compared to oceanographic andglaciological data from previous years. This helps to give a sense of the inter-annual variability andprovides a more comprehensive view of processes that occur.

13

1.3 Thesis Aims and Outline

The overarching aim of this study is to use an annual time series of fjord temperatures collected inclose proximity to the calving front of Kronebreen, in order to determine the relative importance ofdifferent factors for the frontal ablation rate of Kronebreen. The specific factors to be considered arethe fjord temperature at depth, sea surface temperature, atmospheric temperature, precipitation, andsea ice presence. The unique contribution of this thesis is the use of fjord temperature observationsfrom so close to the calving front of Kronebreen, as previous studies such as that by Luckman et al.(2015) use observations from much further away. Aside from this primary aim, there are the followingsubsidiary aims:

(i) To investigate the presence of different water masses within Kongsfjorden

(ii) To create a conceptual model of the composition of Kongsfjorden at the beginning of theresearch period in August 2016

(iii) To compare a subglacial melt rate obtained through calculation from physical principles to afrontal ablation rate obtained from remotely-sensed data

(iv) To investigate the behaviour of Kronebreen over an annual timescale, and compare this toanalysis from previous years

(v) To explore the effectiveness of different anti-fouling methods for oceanographic sensing equip-ment

In the following, the methods employed for both data collection and analysis will be described. Theresults will then be presented, and subsequently discussed. Finally, conclusions will be stated alongwith suggestions for further research.

2 Methods

2.1 Summary of Data and Software Used

Table 1: Summary of data used in the studyData Source Personal role

LoTUS data Kirchner and Noormets AnalysisSea surface temperature NASA’s MEaSUREs program Extraction and analysisMeteorological variables Norwegian Meteorological Institute Extraction and analysisSea ice Norwegian ice service Extraction and analysisGlacial velocity Sentinel 1 images Extraction, processing,

and analysisGlacial retreat Sentinel 1 images Extraction, processing,

and analysisSound velocity profiles Kirchner and Noormets AnalysisSalinity data ESSO-NCAOR AnalysisSatellite Imagery for graphics Landsat GISKongsfjorden bathymetry Noormets and Kirchner AnalysisKronebreen sub-surface topography Kohler Analysis

14

Table 2: Summary of software used in the studySoftware Use

Sentinel Application Platform (SNAP) Sentinel image correction, and offset tracking for glacial velocityQGIS Digitisation of glacier terminus positionsValeport Datalog X2 Processing of sound velocity profilesR and R Studio Regression analysis and figure creationMatlab Data analysis and reading of netCDF files

2.2 Externally acquired data used in this thesis

2.2.1 LoTUS

In order to collect water temperature at depth, two LoTUS buoys, which are developed at KTHand not commercially available, were deployed near to the calving front of Kronebreen in August2016. Deployment was done prior to this MSc thesis in the framework of the LoTUS Seven Islandand Kongsfjorden project1. One buoy was located in the northern part of the fjord, and one in thesouthern part, but they were both placed in close proximity to the calving front (approx. 1km). Theselocations were chosen as they correspond to two bathymetric basins which have been observed in frontof Kronebreen (see figure 4). Having two buoys was also of use in allowing for an investigation of fjordcirculation, as the coriolis effect is known to play a role in wide fjords such as Kongsfjorden.

For this study, only the buoy located in the northern end of Kongsfjorden was used, as the otherbuoy failed to send data and is currently missing. The successful buoy was anchored underwaterbetween the 25th August 2016 and the 5th September 2017, collecting temperature data every 10minutes. A commercially available Star-Oddi CTD sensor (unrelated to the LoTUS technology) hadalso been attached to the buoy, providing a second record of temperature over the time period aswell as depth measurements. Salinity was also recorded, although an unknown error occurred whichled to a shorter time series extending until only the beginning of October 2016 being recorded. Thefjord depth at the deployment location is 82m, and the buoy is suspended 15m above the sea floor.The average depth of the buoy is thus 67m although this varies with tidal cycles.

The LoTUS buoys are pre-programmed to surface at a given time, and use electronically acceleratedcorrosion to detach themselves from the anchor. Temperature sensing is stopped at a pre-definedtime, after which the buoy enters communication mode. The temperature data collected is thentransmitted via satellite along with GPS positions, thereby negating the need for a return trip toretrieve the buoy. However due to the presence of the CTD sensors on the buoys in Kongsfjorden,which do not send their data via satellite, a field trip to Ny Ålesund was undertaken between the5th and 12th September 2017 in order to collect the buoys, and this marked the beginning of theMSc thesis. As previously mentioned, only one buoy was located as no signal was received from theother.

The functioning buoy from Kongsfjorden was found on the island of Gerdoya, having been washed upthere. The CTD sensor was still attached, and all the data from it was readable. The recovery wasconducted via boat, with a small rib being used to make the final approach to Gerdoya. The searchtook a number of days as the GPS transmission did not begin until all the recorded data had beensent, and so only an estimate of the location was known up until this point. Wind patterns fromthe days following the scheduled surfacing were consulted to help identify possible areas to focus thesearch.

The resulting oceanographic data from Kongsfjorden was composed of a 376 day record of Kongs-fjorden temperature at depth from one LoTUS buoy, as well as temperature and depth informationfrom the CTD sensor. Additionally, there was a shorter time period of salinity data, also from theCTD sensor.

1Kirchner, N., and Noormets, R., personal communication

15

Figure 4: Left: Location of LoTUS buoys overlain on Kongfjorden bathymetry, showing the placementof the buoys in two distinct basins. Kronebreen is located to the right of the bathymetry.

Two buoys had also been placed in another Svalbard fjord, namely Billefjorden, in the proximity ofthe glacier Nordenskjöldbreen. These surfaced as planned on the 5th October and transmitted partof the annual time series of data before the battery terminated and transmission ceased. This datais not yet related to any glaciological phenomena but, as the buoys were arranged in a vertical arrayat depths of 22m and 37m, this data will be used to examine the depth at which the warmest fjordtemperatures may be found in Billefjorden. The depth of warm water intrusion is site specific andso the results from this array cannot inform the depth at which moorings should be placed in otherfjords, but may prove the need for a vertical array.

2.2.2 Sound Velocity Profiles

Sound velocity profiles (SVP) were taken in various locations (see figure 5) in the framework of theLoTUS Seven Islands and Kongsfjorden project on the 24th August 2016, the day before LoTUSbuoy data acquisition began.

Temperature, salinity, and depth was extracted from the SVPs with the use of ’Valeport DatalogX2’ software. The downward profiles were chosen, as the water is disturbed by the downwardsmotion of the profiler, meaning the upward profile may not be an accurate representation of thewater properties. However, approximately the top 70cm of the downward profiles were discarded, asmeasurements prior this point are taken before the instrument acclimatises to being in the water and

16

Figure 5: Location of Sound Velocity Profiles (green) overlain on a Landsat image. Kronebreen canbe seen on the right hand side of the image. The two transects used to visualise the presence of watermasses within Kongsfjorden are shown. The location of SVP 245 is only shown on the small image,as it is not located in close proximity to Kronebreen.

so show erroneous results. Once these complexities had been taken into account, temperature andsalinity could then be graphed with respect to depth, to provide an insight into the vertical variationof water properties exhibited in Kongsfjorden.

Taking both the salinity and temperature data into account, the data from the SVPs could be usedin order to identify the presence of different water masses in the fjord, using the criteria shown intable 3.

In order to visualise the spatial variation of the different water masses within the fjord, two transectswere created, as shown in figure 5. The bathymetry of the fjord along these transects was extractedfrom data collected in the framework of the LoTUS Seven Island and Kongsfjorden project. Verticalprofiles showing water mass classifications were plotted alongside the bathymetry, using the samecolour scheme as in Promińska et al. (2017). Although the SVPs are not located exactly on the twotransects chosen, the data was plotted according to only their longitude (transect 1) or their latitude(transect 2) for these visualisations, in effect moving them onto the transects. This was done in orderto maximise the effectiveness of the illustration, allowing for the ’state of the fjord’ at the beginningof the study period to be visualised.

As the top 70cm of the SVPs were not used, the sea surface temperature measurement from the24th August 2016 was taken into account when classifying the water masses (more information onsea surface temperature measurements is given in section 2.3.2) . This was done in order to give anindication of whether the surface temperature of the fjord waters was below −0.5oC or above 3oC,

17

as this would indicate the water mass present at the surface.

Table 3: Water mass classification table for Kongsfjorden, adapted from Promińska et al. (2017)Water Mass Abbreviation Temp Salinity

Atlantic water AW > 3.0 > 34.9Transformed Atlantic Water TAW > 1.0 > 34.7Surface Water SW > 1.0 < 34Intermediate Water IW > 1.0 34 to 34.7Local Water LW < 1.0Winter Cooled Water WCW < -0.5 > 34.4

2.3 Data acquired in the process of the thesis

2.3.1 Meteorological Data

A number of different meteorological data sets were required for this study. Specifically, atmospherictemperature (daily mean), precipitation (daily totals), and wind (direction and speed) were required.Atmospheric temperature and precipitation were used as potential explanatory factors for the frontalablation rate of Kronebreen within the regression analysis. The wind data was instead used to explainsome of the patterns seen within the data as wind is thought to be important for the intrusion ofAtlantic water (Cottier et al., 2005).

All the meteorological variables were harvested from yr.no, which displays data from the NorwegianMeteorological Service.

2.3.2 Sea Surface Temperature

Sea Surface Temperature (SST) was collected as a daily product from the Group for High ResolutionSea Surface Temperature (GHRSST) at NASA’s jet propulsion laboratory. Specifically, the productused is named ’GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)’and has a spatial resolution of 0.01 degrees longitude by 0.01 degrees latitude.

The temperatures from this product are based upon observations from a number of sources includingthe NASA Advanced Microwave Scanning Radiometer-EOS (AMSRE), the Moderate ResolutionImaging Spectroradiometer (MODIS), the US Navy microwave WindSat radiometer, and AdvancedVery High Resolution Radiometer (AVHRR). Measurements from platforms with infra-red sensorssuch as AVHRR are used where possible as they have good spatial resolution. However, infra-redcan be obstructed by clouds and microwave sensors are then used to fill in the gaps.

The data, which comes as a global data set including many other parameters, was downloaded as anumber of NetCDF files and processed in Matlab to retrieve a daily figure for sea surface temperaturein front of Kronebreen.

2.3.3 Sea Ice

In order to include the state of sea ice in Kongsfjorden, data was retrieved from the archives ofthe Norwegian Ice Service (http://polarview.met.no/). This provided a daily measurement of theice fraction in front of Kronebreen. The data is available in a visual format, with the colours usedcorresponding to the ice cover. The classifications used are internationally recognised (See Figure 7for explanation)

As a quantitative approach was needed in order for sea ice concentration to be included in theregression analysis, a number scale ranging from 0 for ’ice free’ to 6 for ’fast ice’ was employed.

18

2.3.4 Frontal Ablation Rate

The frontal ablation rate (a) for this study was calculated using the equation 1, as was the methodof Luckman et al. (2015).

a = UT − ∂l

∂t(1)

where UT is terminus ice speed (m/day) and ∂l∂t is the change in ice front position with time (m/day).

Due to the fact that the glacier terminus is crenulated, the velocity measurements were taken at afluxgate, which was upstream of the terminus at a longitude of 12.65◦E, which was as close to theterminus as possible whilst still having a good velocity field that spanned the entire width of theglacier. Surface velocity values as measured by the offset tracking procedure were used, as opposedto calculating depth averaged velocities. For this study, surface mass balance was not taken intoaccount when calculating the frontal ablation rate. The frontal ablation rate does not explicitlyinclude submarine melt at the glacier terminus, but only the retreat of the part of the glacier thatis visible in satellite imagery (i.e. above the water line). This retreat rate is, however, related tosubmarine melt as melt beneath the water line leads to undercutting and so calving. It is this calvingthat produces the visible retreat at the glacier terminus. Thus, the frontal ablation rate calculatedhere is not a direct measurement, but a proxy. This use of a proxy is widespread, for example in thepaper by Luckman et al. (2015).

In order to retrieve the terminus ice speed, the velocity of the frontal area of Kronebreen was cal-culated using pairs of GRDH (high resolution ground range detected) Sentinel 1 images. These areradar images, which were chosen as optical images are not available for winter periods as a result ofthe 24 hour darkness. The chosen images were all from the same track and were orbitally correctedwith the most up to date orbit file available before being used. Once the images were orbitally cor-rected, pairs of images were placed into a stack using DEM assisted co-registration. The ESA sentineltoolbox software was used in order to perform offset tracking on the image stacks, with a maximumvelocity and pixel dimensions of the grid spacings stipulated. A series of pilot studies to determinethe optimal grid spacing were conducted. Offset tracking was chosen instead of interferometry as therepeat time between the images was too long during most of the study period, meaning the coherencewas too low for interferometry to yield results. An example of the result from this process can be

Figure 6: Sea Surface Temperatures around Svalbard, 30th May 2017. Data from GHRSST Level 4MUR Global Foundation Sea Surface Temperature Analysis, JPL MUR MEaSUREs Project

19

Figure 7: Sea Ice Classification Chart (From www.vedur.is)

seen in figure 8. The steps followed are those detailed on the ESA website for the calculation ofglacial velocity using offset tracking (STEP, 2016).

After the offset tracking had been conducted, a set of flowlines spaced at 250m intervals across theglacier were used to determine the points at which velocity measurements were taken. The meanof these measurements was taken to provide a value for velocity over the time period between theacquisition of the two images. The time period between the images varied, with a average spanof 8.35 days. Some error is apparent in these images, as a very low velocity is shown for ice-free,rocky areas. However, this magnitude is stable between images and very low ( 0.01 m/day). Thevelocity vectors can be viewed within the ESA software to check that the movement is in the expecteddirection, helping to check that the results are reliable. The velocity vectors in all the images showeda downslope direction, meaning confidence can be had in the offset tracking procedure.

Sentinel 1 images were also used to track and quantify the retreat rate of the glacier terminus. Thesame GRDH images were used, but the pre-processing was different; the images were terrain correctedin the ESA software using the range-doppler method. The corrected images were then imported intothe GIS software ’QGIS’, where the position of the glacier terminus in each image was manuallydigitised. Once all the terminus positions had been digitised, the same 250m spaced flowlines weresuperimposed. The distance between the glacier terminus in each two sequential images was thenmeasured. The mean frontal position change was then calculated from the measurements at eachflowline. Some issues were identified with the digitisation of terminus positions at flowline 15 (atthe most southerly end of Kronebreen, at the confluence with Kongsvegen). This was a result of thebuild up of sea ice in this area, which looks similar to glacier ice and so makes it hard to distinguishthe position of the terminus. As a result, two separate retreat rates were recorded; one that includesall flowlines (1 to 15) and one that excludes flowline 15.

The mean frontal velocity ("UT ") and the mean frontal retreat rate (" ∂l∂t") were then fed into equation

1, providing a daily value for the frontal ablation rate across the whole glacier. As measurementswere taken at flowlines, it is also possible to compare frontal ablation rates across the length of theglacier terminus, but it is the daily average figure for the whole calving front that is used as the inputto the regression analysis.

A close inspection of the two frontal ablation rates led to the identification of potentially anomalousdata in February 2017. This was identified as the jump in the frontal ablation rate value was

20

Figure 8: Velocity of Kronebreen 28th Feb to 12th March from Offset Tracking

conspicuously high and, when compared to the frontal ablation rate time series from 2013 to 2014by Luckman et al. (2015), the jump was bigger than anything recorded for this time period. Thus,two additional frontal ablation rates were created which excluded the potentially anomalous data.The difference between these two frontal ablation rates was that one included flowline 15, and oneexcluded it. All frontal ablation rates used are summarised in table 4.

Without having a multi-year time series of data, it is difficult to determine which frontal ablationrate is the best to use. However, rates 2 and 4 are preferred as the digitisation of the glacier terminiaround flowline 15 are likely to have contained a lot of error; there was a lot of sea ice where the glacierfronts of Kongsvegen and Kronebreen merged, which looks similar to glacier ice on the radar imagesand so can lead to misidentification of the glacier front. Repeating this analysis for a multi-year timeseries would be beneficial as it appears the R2 values are sensitive to the frontal ablation rate used,and anomalies could be better identified with the collection of many annual cycles. Between thesetwo frontal ablation rates, rate 4 is preferred. This is because, although it is not certain, rate 2 isassumed to include erroneous results. Thus, where only one frontal ablation rate is displayed, rate 4will be chosen.

Table 4: Summary of frontal ablation rates used in this studyFrontal ablation rate number Flowlines included Data excluded

1 1 to 15 None2 1 to 14 None3 1 to 15 Anomalous February data4 1 to 14 Anomalous February data

21

Figure 9: Digitised glacier fronts overlain on a Sentinel 1 image.

2.4 Regression Analysis

In order to determine the relative importance of different factors for the frontal ablation rate of Kro-nebreen, both multivariate and univariate regression analysis was undertaken. This was done usingthe software package ’R’. Prior to doing the statistical analysis, Stockholm University’s StatisticalResearch Group was consulted for advice.

All the variables (LoTUS temperature, sea surface temperature, atmospheric temperature, sea icecover, frontal ablation rate) were re-sampled to provide daily values. For the LoTUS temperaturedata, this meant that averages were taken as the temperature had been recorded every ten minutes.For the frontal ablation rate, the same value was input for all the days between the two datesfrom which the value had been calculated. For days where there was missing data, such as in theatmospheric temperature time series, these values were left blank.

Before any regression analysis was performed, all the explanatory variables (LoTUS temperature, seasurface temperature, atmospheric temperature, sea ice cover) were graphed against frontal ablationrate, to get a sense of the relationships between the time series. Explanatory variables were alsoplotted against each other, to give an impression of whether they may be related.

After inspecting the graphs, a multivariate regression including all the possible explanatory variableswas conducted. Then, the VIF (Variance Inflation Factor) values of every explanatory variable in thisregression model were examined, so that any variables with too high a VIF value could be excluded.After this, a regression using all remaining explanatory values was performed. The summary statisticsof the regression were then consulted, so that any variables with too high a p value (> 0.05) could beidentified. The regression was then re-run, including only those variables with satisfactory VIF and

22

p values. Once this regression had been performed, the R2 values could be consulted. The summarystatistics display both the R2 and R2adj, with the latter giving a lower figure which compensates forthe fact that the more variables one includes in the analysis, the higher the R2 value will be. Thus,it was the R2adj value that was of primary interest for the multivariate regression.

Once this simple regression model had been done, an attempt was made to investigate whether laggedvariables were of importance. This was a key question as it is plausible that, for example, an increasein oceanic temperatures may not have a immediate impact on frontal ablation rates. To do this,the CCF (cross correlation function) values of each explanatory variable were consulted. A graphof the CCF values was used to identify the possible number of lags that could be important. Asthis study uses time series data, only negative lags (e.g. those going back in time) are of interest.The regression was then re-performed including the possible lagged variables as well as the originalexplanatory variables. Once again, any variables that did not have satisfactory VIF and p values wererejected. Unfortunately, no lagged variables had satisfactory p values, and so could not be includedin the regression model.

Once the final multivariate regression model had been determined, the R2 value could be extracted.As will be shown in the results section, these values provide information on the relative importanceof the different explanatory factors, and can be compared to other studies who have performed asimilar analysis, such as (Luckman et al., 2015).

The multivariate regression was done for the four different frontal ablation rates summarised in table4. This allowed for a sensitivity analysis experiment, where the effect of removing the potentiallyanomalous variables could be quantified.

The univariate regression followed similar steps, but only regressed one explanatory variable againsta frontal ablation rate at a time. As only one explanatory variable was included, it was the R2 valueand not the R2adj value that was of interest.

An extract of the R code used to perform the regression analysis is presented in the appendix.

2.5 Physically based melt rate

As previously mentioned, the frontal ablation rate equation used in this study, does not explicitlymeasure frontal ablation, but provides a proxy. This means that submarine melt is not explicitlyincluded. Thus, it is not possible to delineate the contributions of calving and submarine melting tothe frontal ablation rate. As a result of this, it was decided to calculate the submarine melt rate fromphysical principles and observations in order to garner an idea of how much of the frontal ablationrate can be explained by submarine melt alone. A paper by Beckmann and Goosse (2003) providesequations which allow for the determination of the melt rate of glacial ice as a function of the heatflux from ocean waters, as shown below.

Qm =Qh

(ρiLi), (2)

Qh = ρwCpwγT (Tocean − Tf )Aeff (3)

Tf = 0.0939− 0.057S + 7.64 ∗ 10−4z , (4)

where Qm is melt rate (m3s−1), Qh is heat flux (Js−1), ρi is the density of ice, Li is the latent heatof fusion for ice, ρw is the density of freshwater, Cpw is the specific heat capacity of water, γT is thethermal conductivity of water, Tocean is ocean temperature, Tf is freezing point temperature at agiven depth (z) and salinity (S), and Aeff is the area over which melting occurs.

These calculations are a paramaterization, but were found to be valid and represent the process withreasonable accuracy (Beckmann and Goosse, 2003).

23

Table 5: Constants used in the physically based melt rate, as in (Beckmann and Goosse, 2003)Constant Notation Value Units

Density of ice ρi 920 kg m−3

Latent heat of fusion for ice Li 334 000 J kg−1

Density of freshwater ρw 1000 kg m−3

Specific heat capacity of water Cpw 4000 J (kg◦C)−1

Thermal conductivity of water γT 10−4 m s−1

Many of the constant input values used were the same as used by Beckmann and Goosse (2003) andare as shown in table 5:

The area over which melt occurs was calculated as the length of the glacier (3600m) measured usinggoogle maps, multiplied by a representative value for the underwater height of the glacier (60m)which was chosen after consulting the bathymetry of Kongsfjorden from as close to the glacier frontas possible. This gave an area of 216000m2, which was split up into two. This is because theexperiment was conducted for the ’top’ and ’bottom’ halves of the glacier front, using the sea surfaceand LoTUS water temperatures respectively (both in ◦C). Salinity (psu) was harvested from valuesfrom a mooring further away from the glacier front, with the data kindly provided by ESSO-NCAOR.The length of this time series is not the same as the LoTUS time series, and the salinities were notseen to vary much; the maximum value was 35.0469psu and the minimum was 34.6923psu. Thus, themean value from this time series was used throughout calculations. The value for z corresponded towhere the water temperature measurement was taken from, and so was 67m when the LoTUS datawas used and 0m when the sea surface temperature was used.

The resulting time series could be compared to the time series for the frontal ablation rate. Thisallowed the differing patterns of the two time series to be compared and, when taking into accountthe geometry of Kronebreen, both time series can be given the same units. This allows for a directcomparison in terms of magnitude.

In addition to this time series, the salinity and temperature data from the sound velocity profiles(located in close proximity to the calving front of Kronebreen) was used to enable a visualisation ofhow melt rates vary with depth and across the front of Kronebreen. All the SVPs were used apartfrom number 245, as this is not located in the vicinity of Kronebreen’s terminus. The same methodwas used, with a melt rate calculated for each data point separately using a Aeff value of 1. Inaddition, the data used could be extrapolated over the entire glacier front, in order to give a singlemelt rate value for the whole day. This melt rate can then be compared with the melt rate for thefollowing data calculated using LoTUS data to give an impression of how well the melt rate fromLoTUS data represents reality. This is done under the assumption that the melt rate from the SVPdata is more accurate as it utilises many more data points and includes proximal salinity data. Thefigure for the whole glacier front is created by multiplying the average melt rate figure from the datapoints by the Aeff value used for the LoTUS melt rate calculations of 216000m. As the melt ratefrom the SVP data unfortunately does not correspond to any date included in the LoTUS melt ratetime series, subsequent days (24th August 2016 for SVP melt rate, and the 25th August 2016 for theLoTUS melt rate) are instead compared.

2.6 Anti-fouling Experiment

Due to the issues with the salinity data from the Star-Oddi CTD sensor, an experiment to comparevarious anti-fouling methods was devised. This is because Star-Oddi suggested that biofouling waslikely to be the cause of the lack of salinity measurements, but that they had only investigatedbiofouling in tropical waters. Bio-fouling was a likely candidate as the salinity sensor on the CTDbegan working again after being washed up on the island of Gerdoya, where it is hypothesisedthat wave action may have cleaned the sensor. Another possible explanation was a change in theenvironment in which the CTD was placed, for example an increase in sea ice cover. However, the

24

period when the CTD failed shows no directional or drastic change in any of the environmental orclimatic variables collected for this study. This seeming lack of an alternate explanation adds credenceto the idea that it was biofouling which led to the failure of the salinity sensor. For the purpose ofthis experiment, three Star-Oddi CTD sensors were obtained, as well as a Sea-bird Scientific SBE37- SMP MicroCAT which was to be used as a reference.

Before beginning the experiment, a pilot study using the sensors was conducted in controlled condi-tions to check for any discrepancies between the readings from the different sensors. To this end, thethree CTDs were, without any anti-fouling protection or rubber housing, placed in a measuring jugwith 5dl of water. The test was run over a couple of days, with the temperature and salinity of thewater being artificially manipulated during this time. For instance, the jug of water was placed inthe fridge, salt was added, or warm water was added. The times when these changes were made wasrecorded, so that it was possible to check if the CTDs measured the changes at the correct times.The readings from this short experiment could then be viewed to see if the sensors were in agreementwith each other.

A set of six different ’set-ups’ for the CTD were decided upon, meaning that two experimental periodswere needed to test them all out. These were as follows;

Table 6: Anti-fouling experimental set upSet-Up Number Rubber Housing (Y/N) Additional Protection

1 Y Copper Mesh2 Y Plankton Net3 Y None4 N Copper Mesh5 N Plankton Net6 N None

Figure 10: Left: Single Layer of Copper Mesh over open end of Rubber Housing. Right: ThreeStar-Oddi CTDs with different experimental set ups. All CTDs shown are in the rubber housing,with one also covered in copper netting, and one also covered in Plankton netting

The rubber housing is also manufactured by Star-Oddi as a way to protect the sensors. The set-upwith only the rubber housing is the same as that which the sensor placed in Kongsfjorden had. Coppermesh was chosen as this is the anti-fouling method that Star-Oddi provides upon request (although

25

Figure 11: Map of Askö and surrounding area, with the location of the mooring and path taken bythe boat superimposed

only recommended by them for tropical waters). Plankton netting is a third alternative that we hopemay prevent small organisms from gaining access to the sensor, so allowing measurements to be takenunhindered. Star-Oddi provided instructions on how to attach the copper netting to a CTD sensorencased within rubber housing, and these were followed. The same method was used for attachingthe plankton netting to the rubber-housing encased CTD. The key part of this method was that therewas only a single layer of netting over the end of the rubber housing nearest the sensors, to allow for agood water flow (See Figure 10). All three CTD sensors were then attached to the mooring rope 2mabove the MicroCAT. The buoys visible in Figure 10 are there to keep the MicroCAT upright.

The equipment was deployed from a boat in the vicinity of the island of Askö (see figure 11), usinginfrastructure provided by Stockholm University’s Baltic Sea Centre which runs the Askö laboratoryresearch station on the island. The MicroCAT was deployed at a depth of around ten metres fromthe sea floor, with the Star-Oddis located above this.

After a 6 week period, all the equipment was collected. The readings could be compared to eachother (and in particular to the MicroCAT as the reference) to determine if any of the sensors hadnot functioned properly. Additionally, the sensors were cleaned and the material upon each onekept. This material was then filtered out from the water which was used for cleaning, in order tocompare the degree of biofouling between experimental set ups. As there were two separate runsof the experiment to include all six set-ups, the cleaned sensors were re-covered in the appropriateprotections before being replaced underwater.

26

3 Results

The location of many of the figures in this section is summarised in figure 12, with the bathymetryof Kongsfjorden and a Landsat image of Kronebreen also shown.

Figure 12: Map of Kronebreen and Kongsfjorden, with boxes showing the locations where figurespresented in the results section are based. The white circle represents the location of the mooringproviding the data from the (Luckman et al., 2015), and the blue circle showing the location of theLoTUS 8 buoy.

3.1 LoTUS Data

The temperature data from the LoTUS 8 buoy (from Kongsfjorden) was of great interest by itself,as it provided an insight into how the water temperature varied during the year long period. Ideally,salinity measurements would allow for the water mass measured to be classified, such as done by(Cottier et al., 2005; Aliani et al., 2016), but due to a malfunction of the CTD sensor, this is notpossible. The LoTUS data was time corrected to account for drift, but was otherwise unaltered. Thetemperature sensor from the Star-Oddi CTD sensor worked for the entire period, and corroboratedextremely well with the LoTUS data (see figure 13), allowing for a great deal of confidence to beplaced in the validity of the LoTUS data. This confidence in the temperature readings allowed for theconclusion that the salinity sensor was faulty to be formulated with high certainty. This is because,at the salinities recorded by the Star-Oddi CTD, the temperatures measured by the LoTUS buoywould have meant that the water was frozen which is known to be incorrect. It is also of note thatthe salinities recorded by the CTD flat line from around the 1st October 2016, and show no variationuntil the sensor is washed up on Gerdoya at the beginning of September 2017 (figure 13). This patternhelps to give credibility to the notion that the sensor stopped functioning due to bio-fouling, andstarted working again when the sensor was washed by wave action on the shore of Gerdoya.

Figure 14 shows a range in temperature of around 6◦C during the time period, with minimum watertemperatures almost reaching the freezing point of −1.8◦C for saline water. The lowest tempera-tures were reached around March/April 2017, with the highest temperatures being recorded at thebeginning of the time period in September 2016. Although there is a general trend of cooling fromAugust 2016 until April 2017, followed by warming temperatures until the end of the time series inSeptember 2017, some variability is apparent.

Something of particular interest is that there is larger magnitude and more seemingly stochastic

27

Figure 13: LoTUS 8 temperature data, alongside temperature and salinity data from the Star-OddiCTD

Figure 14: LoTUS 8 Temperature Data with Hourly and Daily Moving Averages

variability in the first half of the time series than in the second half, where lower magnitude buthigher frequency fluctuations are apparent. A consideration of how the time series looks when insteadplotting hourly and daily averages shows that these high frequency/low magnitude fluctuations occuron a sub-daily time scale (see figure 14).

Temperatures and depths from the Star-Oddi CTD were compared for a week in January 2017 anda week in July 2017, as shown in figure 15. The temperatures in January do not seem to show anyrelation to the water depth fluctuations. However, the water temperatures in July do appear to showa similar pattern to the water depth fluctuations, although there is some lag between the peaks andtroughs of the two time series.

Figure 15: Kongsfjorden tides and temperatures for one week in January (1st January until 7thJanuary) and one week in July (10th July until 16th July).

The data from the two LoTUS buoys in Billefjorden (11 and 12) covered different time periods. Theyboth began on the 2nd September 2016, with LoTUS 11 ending on the 21st December 2016 andLoTUS 12 on the 9th October 2016. This data, along with that from the buoy in Kongsfjorden, is

28

shown in figure 16. It is apparent that the water temperatures in Billefjorden varied by a much greatermagnitude than in Kongsfjorden over this period. Over the whole period, the average temperaturefrom LoTUS 8 was 4.53◦C, from LoTUS 11 was 2.93◦C, and from LoTUS 12 was 4.29◦C. Thus,with reference to the time time series from Billefjorden, it appears that fjord water temperatures aregreater at the shallower depth in the water column, rather than near the base of the fjord. This islikely to be site specific, but highlights how the experimental set-up is improved through the use ofa vertical array.

Figure 16: Temperature data from LoTUS 8, 11, and 12. LoTUS 12 data is from the 2nd September2016 until the 9th October 2016, and LoTUS 11 data is from 2nd September 2016 until 21st December2016. LoTUS 8 data corresponding to the time period of LoTUS 11 is also shown for comparison.

3.2 Spatial Variations in Frontal Ablation Rate

Although there was only one LoTUS time series, the velocity and frontal retreat data was collected atflowlines located every 250m along the glacier front, thus allowing for an investigation of the extentof spatial variation in the frontal ablation rate.

Figure 17: Fjord bathymetry along the flowline where the highest frontal ablation rate was measured.Starting and ending positions of Kronebreen from the study period are indicated. Bathymetry datafrom J. Kohler

Before comparing the variation in frontal ablation rates with reference to the bathymetry of Kongs-fjorden, it is important to consider how the fjord bathymetry changes with distance along a flowline,as is shown in figure 17. It is noticeable from this visualisation that the bathymetry along a flowline

29

is changeable. Thus, the relationships and patterns shown in figure 18 may be distorted by the factthat the frontal ablation rate, retreat rate, and velocity are averages from the entire period, whilstthe bathymetry represents only a brief snapshot in time.

Figure 18: Fjord bathymetry of the frontal area of Kronebreen compared with the mean frontalablation rate, as well as with both the mean retreat rate, and mean velocity (which were used tocalculate the frontal ablation rate). The dots indicate the location of the flowlines. A dashed linehighlights the location of the flowline with the highest frontal ablation rate, when taken as a meanover the the study period.

When looking specifically at velocity in figure 18, there is a clear pattern of higher velocities observedtowards the centre of the glacier. When instead considering retreat rate and frontal ablation rate,the spatial pattern is more complicated but it appears that, on the whole, both of these rates weregreater towards the northern end of Kronebreen. The location where the frontal ablation rate peakscorresponds to an area where the bathysmetry is reasonably shallow compared to the rest of theprofile.

30

3.3 Temporal Variations in Frontal Ablation Rate

As well as looking at the variation in frontal ablation rates across the front of the glacier, it isinteresting to take a closer look at how the frontal ablation rates (which are averaged across all theflowlines) vary during the course of the study period. As summarised in table 4, four different frontalablation rates were calculated. How each of these frontal ablation rates vary from August 2016 untilSeptember 2017 is shown in figure 19.

Figure 19: Temporal variation of all four possible frontal ablation rates Aug 2016-Sept 2017. Seetable 4 for explanation.

It is apparent that for the majority of the time period, all the frontal ablation rates were positive.However, accumulation was instead observed during certain periods of time, most notably in February.The magnitude of this accumulation is dependant on which frontal accumulation rate is chosen, as isevident when viewing figure 19. Ablation is highest during the summer months, in particular August,and lowest during winter and early spring.

Figure 20: Temporal variation of Frontal Velocity August 2016 until September 2017

Figure 20 displays the temporal variation in velocity. This shows that velocities, on the whole,increase from a low in late summer 2016 to a high in early/mid summer 2017.

Figure 21 shows the temporal variation of the frontal ablation rate, retreat rate, and terminus ve-locity. The frontal ablation rate, calculated using equation 1, is derived from both retreat rate andvelocity. From figure 21, however, it appears to resemble the pattern of the retreat rate to a greaterextent

It is also of interest to consider how many metres the glacier retreated over the course of the study

31

period. Based on the analysis of satellite imagery, this figure is 399.9m when averaged over all theflowlines, and 396.8m when instead averaged over flowlines 1 through 14. This corresponds to aretreat rate of 1.04m/day (all flowlines) or 1.03m/day (flowlines 1 through 14 inclusive).

Figure 21: Comparison of frontal ablation rate, retreat rate, and velocity time series August 2016 toSeptember 2017

The temporal variations seen in this study can be compared to those presented by (Luckman et al.,2015), to give an impression of inter-annual variability as well as to promote discussion over how

Figure 22: Left: Summary graphs for 2016-2017 study Right: Summary graphs for 2013-2014 study,taken from (Luckman et al., 2015)

32

differences in methodology may have yielded different results (see figure 22). It is most pertinent tofocus comparison on similar time periods, that is August 2016 to July 2017 with August 2013 to July2014. When considering these sub-sections of the data, a number of observations can be made. Inthe 2016/2017 data, a readvance of the glacier front is made in late winter/ early spring, which is notapparent in the 2013/2014 data set. Retreat rates seem to have a similar range between the two datasets. In terms of velocity, those recorded in during 2013/2014 are higher (up to 4m/day) than thosefrom 2016/2017, where velocity peaks at only around 2m/day. Correspondingly, the frontal ablationrates during the 2013 to 2014 study peak at higher magnitudes than during 2016 to 2017, howeverthe difference is not as pronounced as for velocity. With relation to atmospheric temperatures, datafrom the Norwegian Meteorological institute can be used to compare mean values. For the period 1stSeptember 2013 until the 31st July 2014, the mean atmospheric temperature was −3.96◦C, comparedto a higher temperature of −3.5◦C for the same time period during 2016 to 2017. When consideringocean temperatures, both at depth and on the surface, the data from 2016 to 2017 is observed to becooler, although maximum sea surface temperatures are similar.

3.4 Regression Analysis

A summary of the results from the multivariate regression experiment is shown in table 7 below.

Table 7: Multivariate regression analysis summaryFrontal Ablation Rate R2 Value Adjusted R2 value Variables Included

1 0.5991 0.5956 Air Temp, LoTUS, SST2 0.6226 0.6182 Air Temp, LoTUS, SST, Precipitation3 0.6736 0.6708 Air Temp, LoTUS, SST4 0.6699 0.667 Air Temp, LoTUS, SST

Sea ice never had a satisfactory p value to be included in the regression, and so is not included.Precipitation was also for the most part excluded, but remained in the regression with FrontalAblation Rate 2. It is clear that, as potentially anomalous data was removed, the R2 values increased.The R2 values suggest that the frontal ablation rate of Kronebreen is at least partially explained bythe variables chosen, but the values are not so high as to suggest that there are not other factorsat play. For example, the bathymetry of the fjord and interactions with the neighbouring glacierKongsvegen were not taken into account in this analysis but are likely to be important.

The R2 values obtained from the univariate regression experiments are shown below in table 8:

Table 8: Univariate regression analysis summaryVariable R2 Rate 1 R2 Rate 2 R2 Rate 3 R2 Rate 4

LoTUS Temperature 0.4269 0.4394 0.4652 0.4659Sea Surface Temperature (SST) 0.4014 0.4289 0.4427 0.4592Air Temperature 0.306 0.3047 0.3947 0.372Precipitation n/a n/a n/a n/aSea Ice 0.08411 0.09376 0.09813 0.1054

When conducting univariate regression, sea ice does not get excluded, but precipitation always does.It is clear from the R2 values that oceanic temperatures (both at the surface and at depth) areof the greatest importance. Temperatures at depth explain more of the frontal ablation rate thansea surface temperatures when using all the frontal ablation rates, showing that it to be the mostsignificant variable. This relative lack of variation in absolute or relative magnitude of the differentvariables between different frontal ablation rates suggests a low sensitivity to the choice of frontalablation rate.

33

3.5 Sound Velocity Profiles

After extracting depth, temperature, and salinity information from the sound velocity profiles,they can be graphed to look at variation in water properties with depth as shown in figures 23and 24.

Figure 23: Temperature with depth at each of the twelve SVP locations. See figure 5 for locations.

Figure 24: Salinity with depth at each of the twelve SVP locations. See figure 5 for locations.

The SVPs are taken from different locations, shown in figure 5, and so represent the water propertiesat different distances from the calving front. For example, SVP 245 (shown in red) is the SVP located

34

furthest from the calving front and displays higher temperatures than many of the other SVPs.

When focusing on figure 23, it appears that the warmest temperatures occur at a depth of around20m, much shallower than the location of the LoTUS buoy at approximately 67m. The absolutedepth of the LoTUS buoy is dependant on the tide and so varies on a sub-diurnal time scale.

In terms of salinity (figure 24), all the SVPs shown a general trend of lower salinities near the surface,and higher salinities at depth. However, this is not a linear pattern and the salinity values appearto oscillate as they follow this general trend. These oscillations become smaller in magnitude withdepth.

The cross sections of the fjord showing water mass classifications according to the criteria in table3 are shown below in figures 25 and 26. Only four water masses are present in the data, with theproportions of each shown in table 9.

Table 9: Water mass classification summaryWater mass Percentage of observations

Atlantic water 55Surface water 22Intermediate water 15Transformed atlantic water 8

Figure 25: Water masses along transect 1, with the SVP each profile is derived from indicated bythe numbers above the graph. See figure 5 for locations.

Atlantic water which, at temperatures of greater than 3◦C, is the warmest of the water masses andis present at a high proportion within the fjord.

The cross section of the fjord shown in figure 25 can be compared to the similar transects made by(Promińska et al., 2017) for a number of different years. The water masses present in Kongsfjordenappear to vary considerably between years, with there being no Atlantic water in 2012, but a largeproportion of Atlantic water in 2014. The data used to make these figures was collected duringsummer time, and so is comparable with the SVP data used in this thesis. The transects created by(Promińska et al., 2017) cover a greater distance than those created for the thesis, and so it is only asmall section that can be directly compared. When considering which of Prominska’s transects the2016 data in figure 25 most resembles, it is apparent that the high proportion of Atlantic water is

35

Figure 26: Water masses along transect 2, with the SVP each profile is derived from indicated bythe numbers above the graph. See figure 5 for locations.

Figure 27: Water mass classifications for Kongsfjorden in different years, adapted from (Promińskaet al., 2017)

only reflected in the data from 2014 Promińska et al. (2017). This corresponds to the end of theperiod investigated by (Luckman et al., 2015), suggesting a similarity in oceanographic conditionsbetween both this and Luckman et al. (2015)’s studies.

3.6 Physically Based Melt Rate

The submarine physically based melt rate follows a similar pattern to the frontal ablation rate (seefigure 28, with lower melt rates corresponding to periods with lower frontal ablation rates. Thephysically based melt rate is, by definition, related to oceanic temperature (see equations 2, 3 and4). Oceanic temperatures are also of importance to the frontal ablation rate, yet not as explicitly as

36

for the physically based melt rate.

Figure 28: Physical submarine melt rate and Frontal ablation rate Plotted Over Time with

There is a big increase in the melt rate in July 2017, corresponding to a sudden increase in sea surfacetemperatures. It must be noted that the melt rate calculation is based on only two temperatures;surface temperatures and LoTUS temperatures (approx. 67m).

In terms of magnitude, both the submarine melt rate and the frontal ablation rate can be given thesame units if the geometry of Kronebreen is taken into account. This is done by taking the frontalablation rate and multiplying it by the length of the calving front from Google Earth (3600m) and arepresentative figure for the height of the glacier above sea level (50m) to give a figure in m3d−1. Itis likely that calving also occurs below the water line, and it is also known that calving is not uniformacross the glacier front. However, this simple calculation allows for a comparison of the melt ratewith the frontal ablation rate used in this study. Finally, to convert the frontal ablation rate intom3s−1, it was divided by the number of seconds in a day (86400). After this was done, the two ratescould be directly compared. On average, the physically based melt rate was 25.6% of the frontalablation rate, showing it to be of considerable significance.

The melt rate values calculated using the sound velocity profile data are shown in figure 29. Here,the lowest melt rates are found at the top of the water column, and the highest melt rates at depthsof around 20m. The melt rates vary between 0.28m3d−1 and 1.01m3d−1, with a mean value of0.68m3d−1. This means the highest value is 357% of the lowest value, showing there to be considerablevariation.

In terms of a melt rate figure for the whole glacier, the figures from the SVP and LoTUS dataare summarised in table 10 and show the melt rate calculated from the SVP data to be noticeablyhigher.

Table 10: Summary of melt rate calculations from SVP and LoTUS dataDate figure valid for Data used Melt rate (m3d−1)

24th August 2016 SVP data (temperature and salinity) 14688025th August 2016 LoTUS (temperature), ESSO-NCAOR (salinity) 119751

37

Figure 29: Melt rates along the front of Kronbreen, calculated using salinity and temperature datafrom sound velocity profiles taken on the 24th August 2016.

3.7 Anti-fouling Experiment

After conducting the test study under controlled conditions, the data from the three Star-Oddi CTDsensors was compared, and the temperature and salinity data is shown in figure 30.

When considering temperatures, all the CTDs correspond well and recorded the imposed changes intemperature at the correct times. The depth data is not shown as all CTDs just recorded a depthof 0m for the entire period, which makes sense when considering the sensors were submerged in only5dl of water and that depth variations were not imposed.

However, the salinity data suggests that one of the CTD sensors (7839) has a fault, as it does notcorrespond with the data from the other sensors. A second test experiment was conducted afterthis discovery, and the same sensor showed the same discrepancy. The sensor in question is brandnew and has not been used previously, suggesting it is a manufacturing fault. Despite this, timeconstraints meant there was no opportunity to decipher the specific problem or rectify it. Thus, thedeployment in Askö of all three CTDs was carried out as planned, but with the knowledge that one ofthe salinity sensors is faulty. Although the salinity sensor exhibits this flaw, the temperature sensorsseemed functional both in Kongsfjorden and in the preliminary experiments. Ergo, the temperaturedata from the CTD sensors can be used for comparative purposes.

Figure 30: Left: Temperature measurements from the three Star-Oddi CTD sensors from the testexperiment Right: Salinity measurements from the three Star-Oddi CTD sensors from the test ex-periment

During the first experimental time period at Askö, the mooring was found to have moved. Thiswas likely due to the anchor not being heavy enough in combination with the ice cover dragging themooring. This disturbance is visible from the depth data (see figure 31), where the mooring appearsto almost surface around the 19th March. This was remedied by increasing the weight of the anchor

38

Figure 31: Top: Depth measurements from the three Star-Oddi CTD sensors and MicroCat Mid-dle:Temperature measurements from the three Star-Oddi CTD sensors and MicroCat Bottom: Salin-ity measurements from the three Star-Oddi CTD sensors and MicroCat. The dashed vertical linedelineates the point at which the mooring and sensors were taken up to remove the rubber housingand replace the netting.

chains before replacing the CTDs for the second experimental period. The mooring was placed backnear the original location, although it is apparent from the depth measurements that this was not inthe exact same place as where the original experiment began.

When looking at correspondence between the depth measurements of the different sensors, Star-OddiCTD S8739 shows a consistent offset from both the other Star-Oddi sensors and the MicroCat duringthe entirety of both experimental periods and so it is probable that the depth sesnor is faulty. Thisis the same CTD that did not function properly during the test experiments. The other two Star-Oddi CTDs correspond well with eachother, but differ from the readings from the MicroCat. As theStar-Oddi sesnors were moored two metres above the top of Micro-cat, this offset can be explainedby the experimental set-up.

When comparing the temperature data (figure 30), all the Star-Oddi CTD sensors show good cor-respondence. The data from the MicroCat is generally similar, but in some parts diverges from theStar-Oddi data, noticeably around the 23rd March where the MicroCat shows higher temperatures.These divergences seem to occur during the periods when temperature oscillations are greatest.

39

The salinity data (figure 30) shows CTD sensor 7839 (which was not covered in any anti-foulingprotection) to have recorded vastly different salinity readings than the other Star-Oddi sensors andthe MicroCat. As this behaviour was noted during the test experiments, it is assumed to be anerror with the sensor and not the result of bio-fouling. The other two star-Oddi CTD sensors do notdiverge much from each other but consistently record slightly higher salinities than the MicroCat,with the difference in salinity measurements being greater after the removal of the rubber housingfrom the Star-Oddi CTDs.

The attempts to filter out material from the different coverings did not yield any results. This maybe due to very limited biofouling, or due to the fact the motion of pulling the sensors out of the watermay have washed away any biofouling that was present.

4 Discussion

4.1 LoTUS data

It is likely that the high frequency, low magnitude fluctuations in temperature seen in the LoTUS 8data correspond to tidal cycles, with the depth of the buoy below the sea surface varying continuouslywith the tides. Tidal cycles occur year round, so the question of interest is why do we only see theseclear fluctuations in the temperature during the summer months. One possible explanation is thatis relates to the stratification of the fjord.

Noufal et al. (2017) found that, in Kongsfjorden, there was stratification of water masses duringsummer as a result of freshwater influxes, but that mixing occurred during winter. Therefore, thechange in depth of the LoTUS buoy during tidal cycles in summer could lead to the temperaturesensor being in contact with different water masses throughout a day. In winter, the mixing wouldmean that the temperature sensor remained in the same water mass. The graphs of depth andtemperature displayed in figure 15 support this pattern, as temperature fluctuations seem linked towater depth in the summer time series, but not in the winter one. However, the high frequencyfluctuations are not so clearly seen at the beginning of the whole LoTUS time series, which is alsoduring summer, and this may be a result of natural variability. It is not clear what causes the lowfrequency high magnitude temperature fluctuations apparent during the first half of the time series,but this may well relate to intrusions of water masses from outside the fjord, or as a result of processessuch as changing sea ice cover or wind patterns (all of which are connected).

From consideration of the LoTUS 11 and 12 data, it appears that warmer waters are found atshallower depths, rather than from bottom of the fjord. This suggests that, in order to capture thevariation within the water column and record the warmest water masses, a vertical array is necessary.If only one buoy is available, placing it mid-way up the water column may provide the best chanceat capturing the intrusions of warm water. However, as evident from figure 16, conditions varyconsiderably between different fjords. This means that conclusions about the depth that warm waterintrudes to based upon around 3 months of data from one fjord is unlikely to be applicable to othersettings.

4.2 Frontal ablation rates

It has been shown in many previous studies that bathymetry plays a significant role in frontal ablationrates. Glacier retreat can often be halted, at least temporarily, by pinning points. These are areaswhere the sea bed is shallow and/or the fjord walls are narrow. This leads to increased frictionalstresses which act to hinder and modulate glacial retreat. For example, a study by Favier et al. (2014)linked the retreat of Pine Island glacier in Antarctica to the retrograde bedrock slope that the glacieris currently retreating over. This highlights that the slope over which a glacier is retreating is animportant factor, with the possibility of retreat over retrograde slopes leading to unhindered retreatbeing a key component of marine ice sheet instability (Weertman, 1974). However, the relationshipbetween bathymetry and ablation is not simple, with factors such as lateral drag and buttressingfrom sea ice also being of importance (Favier et al., 2014).

40

Thus, it was anticipated that the shallower parts of the fjord would act as pinning points and correlatewith areas of lower frontal retreat, velocity, and so frontal ablation rates. However, this pattern is notevident in the data from Kronebreen/ Kongsfjorden. In figure 18, almost the antithetical relationshipis instead seen. This could be the result of other factors at play, for instance basal conditions orhydrology, which could be explored further with the use of numerical modelling. A multi-year timeseries would also be of use in determining whether the location of the highest frontal ablation ratewas generally stable or mobile.

However, the explanation for why the greatest frontal ablation was observed at flowline 6 (see figure12 for location) may also lie in considering how the bathymetry along a flowline changes, such as isshown in figure 17. In this graph it is clear that the bathymetry changes considerably, even over theshort distance of around 650m that the glacier retreated at this flowline. It is apparent that the fjordalternates between having a forward slope and a retrograde slope, with the glacier ending the studyperiod on a retrograde slope. This retrograde slope could help to explain the high frontal ablationrate at this flowline.

As identified in the results section, terminal velocities averaged over the study period were shown tobe highest near the centre of the glacier. This is likely due to the fact that frictional hindrance fromthe fjord walls is reduced with distance from the edge of the glacier. If considering a fluid dynamicsperspective, this pattern is also what is expected from classical plough-flow profiles (Nakayama andBoucher, 1999). The confluence of Kronebreen and Kongsvegen (at the south end of Kronebreen) isalso likely to play a role, with the stagnant Konsgvegen acting to hinder the movement of Kronebreen.Interactions between the two glaciers are likely to play a role in the spatial retreat and velocitystructures of the southern part of Kronebreen, although this has not been quantified.

The temporal variation in velocity is shown in figure 20 and is complicated to interpret. Previousstudies of glacier velocity provide evidence for an early summer speed-up, with lower temperaturesduring late summer and winter. This is related to the basal hydrological system, with the evolutionfrom a distributed system, which operates at high pressure, to a channelised system, which operatesat low pressure (Schoof, 2010; Hoffman and Price, 2014). However, the data from this study showsvelocities which, on the whole, increase from a low in late summer to a high in early/mid summerand thus do not follow the aforementioned expected pattern.

A paper by Moon et al. (2014) uses data from around Greenland to identify different types ofbehaviour when it comes to velocity. They identify ’Type 3’ behaviour as when glaciers exhibita mid summer deceleration leading to a pronounced late summer minimum. After this, glacialvelocities begin to increase again during the winter months. This description fits the time series fromKronebreen shown in figure 20 well. It is thought that this behaviour is related to high melt and longmelt seasons, leading to high levels of water reaching the base of a glacier. It is unclear if Kronebreenin the period from 2016 to 2017 can be classified as having a long melt season with high melt rates,but an multi-year time series of melt rates and melt season length would help to address this. Type3 behaviour is also thought to be associated with glaciers which have a high inter-annual velocityrange, and the behaviour that a glacier exhibits may depend on the development of the sub-glacialhydrological system during a certain year (Moon et al., 2014). Consideration of surface mass balancedata found in a study by Vallot et al. (2017) suggests a degree of inter-annual variation in both thelength of the melt season, and the area over which melt occurs on Kronebreen. Without running asurface mass balance model, it is not possible to compare the figures from the (Vallot et al., 2017)paper to the data from this year, but it does suggest that the mass balance of Kronebreen during theperiod studied by (Luckman et al., 2015) may well be different to that during the period of this study.This observed inter-annual variations adds weight to the idea that Kronebreen can be classified as a’Type 3 ’ glacier.

The velocity time series in figure 20 can be compared to the time series shown in the Luckmanet al. (2015) paper, which used similar methods. In the paper by Luckman et al. (2015), Kronebreenexhibits different behaviour; an early summer speed up is apparent, with velocities remaining fairlyhigh during summer before being depressed during the winter months. Thus, the difference betweenthe two time series needs to be explained. One explanation is that the glacier exhibits a differenttype of behaviour during the different years, as a result of inter-annual variability in either internaldynamics or external forcings, suggesting that an inter-annual record of velocities may shed light

41

on changing basal conditions of Kronebreen (Moon et al., 2014). Another possibility is the use ofdifferent data sources and methodologies may have led to a divergence in the measured velocitiesbetween the two papers. In particular, it is possible that the methods used in this study miss someof the higher velocities that are known to occur on Kronebreen.

The period of re-advance of the glacier front, shown in figure 22 seems to correspond to the periodof lowest fjord temperatures and greatest sea ice cover, suggesting that the presence of fjord tem-peratures and sea ice at the glacier front plays a significant role in the terminus advance and retreatpatterns. It is unclear from the raw data alone whether it is fjord temperatures or sea ice whichis of greatest importance, but the two are interconnected. The relationship between sea ice andglacial retreat has been researched before, for example by Carr et al. (2013) for glaciers in NorthwestGreenland. Here, it was found that sea ice and air temperatures are the primary external control onglacier retreat at seasonal timescales, corroborating the relationship seen in figure 22. However, it ispertinent to consider that these results were found to be site specific with fjord geometry being onekey determinant identified (Carr et al., 2013).

The retreat of the glacier was, when averaged over flowlines 1 to 14, 396.8m, with the greatest retreatbeing 650m at flowline number 6. If this is a representative figure for how Kronebreen is respondingto current climatic changes, it is clear that it is retreating at a considerable rate. However, it mustbe noted that as the retreat of tidewater glaciers is linked to bathymetry and that it can be haltedif a pinning point is reached. Thus, without consideration of both inter-annual variability and thesubglacial topography over which Kronebreen is retreating, the data here cannot be used to informpredictions about the future of Kronebreen.

4.3 Regression analysis

Some of the R2 values can be compared to those stated in the Luckman et al. (2015) paper. InLuckman et al. (2015), the R2 value for ocean temperature between 20 and 60 m to the frontalablation rate was 0.84 (compared to 0.47 in this study). When looking instead at air temperature,the R2 value was 0.43 (compared to 0.37 in this study). It is immediately clear that these values aregreater than the values obtained in this study, particularly when considering ocean temperatures atdepth (see table 8 for the complete list of values). One explanation for this is that, in the Luckmanet al. (2015) paper, the value for ocean temperature spanned 20 to 60 m and so may have captureddifferent water masses which more fully explain the variation in the frontal ablation rate. However, itis also possible that the ocean temperatures recorded for the previous paper were not representative ofthe ocean temperatures that reached the glacier front, as the mooring was located much further away.Although the R2 value is higher, it may not be a good representation of the relationship between thefrontal ablation rate of Kronebreen and fjord temperatures at depth. One final possibility is that thedifferences are a function of inter-annual variability, whereby different processes exerted influence todifferent extents in different years. What does appear clear, however, is that fjord temperatures doexert influence on the frontal ablation rate of Kronebreen, and that this influence is greater thanthat of other factors such as atmospheric temperature.

It was mentioned in the previous section that sea ice presence appeared to be of importance forthe retreat and advance patterns of Kronebreen, and that studies in Greenland had found a similarrelationship. It was thus unfortunate that sea ice was not included in the multivariate regressionanalysis due to a low p value. However, the very low, but not rejected due to p value, R2 values in theunivariate analysis suggest that sea ice is limited importance to frontal ablation at Kronebreen.

4.4 Sound velocity profiles

Figure 23 clearly shows that SVP 245 stands out from the other profiles in terms of temperature,with generally warmer conditions prevailing. This is likely due to the fact that it was taken muchfurther from the frontal region of Kronebreen than the other SVPs. It is the top section of theSVP that shows the most pronounced difference, suggesting that it is the surface waters which aremodified between the terminal area of Kronebreen and the location of SVP 245. The circulation ofKongsfjorden is characterised by a surface outflow of water, with temperatures near the terminus of

42

Kronebreen generally being slightly cooler as a result of glacial run-off (Sundfjord et al., 2017). Thus,the warmer temperatures seen at SVP 245 are likely to relate to the modification of this cold glacialrun-off as it moves away from the glacier and mixes in with the warmer surrounding water.

Focusing solely on the salinity data (figure 24), the oscillations with depth show a range of around6psu. In contrast, a year long time series of salinity kindly provided by AWIPEV shows a range insalinity values of only 1.3psu. This time series is located near the town of Ny Ålesund and is from amooring at 11m of depth. The difference is magnitude between these two sources suggests that thevariation in salinity observed by the SVPs is of considerable significance.

A key question of interest is whether the ocean temperatures collected for use in this study capture thewarmest ocean temperatures that impact upon Kronebreen. An examination of figure 23 suggeststhe warmest water in Kongsfjorden is found at a depth of approximately 20m, raising a questionmark over whether the LoTUS buoy was placed in the best location to record the intrusion of warmAtlantic water. Of course, it is likely that the water properties and stratification of water massesvaries on both seasonal and longer term time scales (Cottier et al., 2005), meaning it is hard to saywhether the LoTUS buoy is likely to have measured the warmest ocean temperatures when onlyconsidering SVP data taken from one day. However, when the SVP data is regarded in conjunctionwith the time series from the LoTUS buoys which were placed in a vertical array (11 and 12), it ispossible to tentatively say that the 67m depth of LoTUS 8 was too deep to capture the warmestocean temperatures. Once more, it must be noted that data from one fjord cannot be extrapolated toanother. Thus, the main conclusion that can be drawn is that a vertical array of sensing equipmentis by far the ideal situation and should be implemented where possible.

The sea surface temperature in Kongsfjorden on the 24th August 2016 was 1.9 ◦C, meaning it isimpossible to distinguish a separate water mass for the surface of the fjord, but it is too cool to beAtlantic water.

Looking more closely at the water mass classification transects in conjunction with those from(Promińska et al., 2017), it is clear that there was a particularly high proportion of Atlantic wa-ter near the terminus of Kronebreen in 2016. Although Atlantic water is found in 2013, 2014, and2015 by Promińska et al. (2017), it only extrudes far enough up the fjord to make contact withKronebreen in 2014. In other years, such as 2015, it is found in smaller quantities and at a greaterdepth and thus does not appear to come into contact with the glacier. However, it is important toremember that all of the data has been collected during summer and, in the case of the 2016 data,only on one day. Thus, whilst it allows for discussions surrounding the inter annual variability of thestate of Kongsfjorden, it is only a short snapshot in time.

4.5 Physically based melt rate

The use of only two fjord water temperatures, located at 0m and 67m depth, means that the calcula-tion potentially misses out much of the vertical temperature variation in the fjord. This is especiallytrue in the summer months where the fjord is stratified and the physical properties of the fjord mayvary considerably with depth (Noufal et al., 2017). Additionally, temperatures may vary spatiallyacross the front of the glacier, but only one data point at depth, and one data point at the surfacewere available. Thus, whilst the calculated melt rate is of interest, greater spatio-temporal samplingwould be required to yield more meaningful figures.

The two rates (melt and frontal ablation) are of course connected and impact upon each other, whichis exemplified by the fact that submarine melt leads to undercutting and thus to calving. Numericalmodelling would be required to investigate this relationship at Kronebreen more fully. Nevertheless,it does appear clear that submarine melting is of importance at Kronebreen.

The comparison between the melt rates calculated using SVP and LoTUS/ESSO-NCAOR data showsthe figure calculated from the SVP data to be considerably higher. This can be explained througha consideration of the variation in temperature with depth recorded by the SVPs and shown infigure 23. Here, the warmest temperatures are found at depths of around 20m corresponding to thedepths at which the highest melt rates were calculated. However, the temperature data from theLoTUS buoy did not record these temperatures as it was moored at a greater depth of 67m. If an

43

assumption is made that this vertical profile is stable during the whole year, then the melt rate timeseries calculated is likely to be an underestimate. Despite this, it must be noted that it is not possibleto extrapolate the vertical temperature profiles from one day to an entire year and the possibilityremains that, later on in the study period, the LoTUS buoy was moored at the optimal depth torecord the greatest ocean temperatures. It is also worth considering that although it is unlikelythat the water properties near the terminus of Kronebreen changed by much during the course ofone day, some of the difference between the two figures may be explained by the inflow, outflow, ormodification of the water present in Kongsfjorden.

4.6 Anti-fouling experiment

As not much divergence was seen between the different data sources from the experiment, apart fromthose assumed to be due to sensor faults, it is difficult to determine which of the experimental setups worked best as an anti-fouling mechanism. This may be partly due to the fact that the watertemperatures during the study were cold and, at least during a short period, ice cover limited theamount of light penetrating the water surface. Combining these conditions with the relatively shorttime periods during which the sensors were underwater may mean that levels of bio-fouling were lowdue to the poor growing conditions.

It is a possibility that the rubber housing may reduce the likelihood of bio-fouling, as the salinitymeasurements from the functioning Star-Oddi sensors and the MicroCat started to diverge after therubber housing was removed. However, this was not seen in the temperature or depth data, as wouldbe expected if the sensors had been clogged by fouling.

A key hindrance to this experiment was the fact that one of the sensors did not seem to be functionalwith regards to salinity and depth measurements, meaning that these results were not used duringanalysis. However, it does cast doubt over the functionality of the Star-Oddi CTD sensors and raisethe question of whether the lack of data from the sensor placed in Kongsfjorden was due to a technicalmalfunction rather than due to biofouling.

5 Conclusions and Further Research

The frontal ablation rate of the marine terminating glacier Kronebreen is the result of the interplayof a number of different variables, making it difficult to determine a simple relationships betweenfrontal ablation and water temperature. However, fjord temperatures at depth is the variable whichexplains the greatest amount of the variation in frontal ablation rate over the period August 2016until September 2017. Sea surface temperatures are also of significance. This overarching conclusionstherefore builds on previous studies such as that by Luckman et al. (2015) which identified oceantemperature as a key determinant of the frontal ablation rate of Kronebreen. However, despitethis agreement, the absolute values of the R2 values obtained in this thesis are lower than thoseobtained in the aforementioned Luckman et al. (2015) study. This suggests that it is importantto use observational data on fjord temperatures from as close to the calving front of a glacier aspossible, as the water properties are likely to vary with distance from the calving front. Inter-annualvariability is also likely to play a role in explaining the differences in results between this thesis andearlier studies.

On the topic of inter-annual variability, the data from the SVPs showed that there was a considerableamount of warm Atlantic water in Kongsfjorden at the beginning of the research period (August 2016)which, with reference to the paper by (Promińska et al., 2017), is not the norm. Extending the timeseries of water property observations in Kongsfjorden would allow for any directional trends to bedisentangled from the natural variability.

Processes occurring at the calving front of a glacier are complex, with some occurring under the waterline and so being hard to observe. The results from the calculation of a physically based melt rateshowed the submarine melt is of significance. Improving this calculation by having a proximal dataset of salinity, temperature, and current velocities from a number of locations along the glacier front

44

would help to shed more light on frontal ablation and the precise relationship between submarinemelt and calving.

The results and discussions from this thesis highlight the importance of ocean temperatures for themass balance of tidewater glaciers, and so provides an impetus for future research. Subsequent in-vestigations should aim to use the observational data collected to inform numerical models whichcan help to explain the underlying physical components of frontal ablation at Kronebreen. Observa-tions and modelling results from this fjord setting can be compared to those from other domains toinvestigate how the glacier terminal environment varies spatially.

6 Acknowledgements

I am very grateful to Nina Kirchner for all her help during the whole process of the thesis work.Thanks is given to Jakob Kuttenkeuler and Jari Krutzfeldt for their work with the development ofthe LoTUS buoys, to Simon Holmgren and Lasse Byren for their help with fieldwork in Svalbard,and to Niclas Englesson for statistical guidance. I am also appreciative for the bathymetry dataprovided by Riko Noormets and Nina Kirchner, the salinity data from ESSO-NCAOR, and the dataon the sub glacial topography of Kronebreen from Jack Kohler. The staff at the Askö laboratory arethanked for their help with fieldwork related to the anti-fouling experiment. Finally, I am grateful tothe Bolin Centre for Climate Research, and the Dept. of Physical Geography, Stockholm Universityfor supporting the fieldwork in Svalbard.

45

ReferencesAliani, Stefano, Roberta Sciascia, Ilaria Conese, Alessandra D’Angelo, Fabrizio Del Bianco, Federico

Giglio, Leonardo Langone, and Stefano Miserocchi. “Characterization of seawater properties andocean heat content in Kongsfjorden, Svalbard Archipelago.” Rendiconti Lincei 27 (9 2016): 155–162.

Beckmann, A and H Goosse. “A parameterization of ice shelf–ocean interaction for climate models.”Ocean Modelling 5 (1 2003): 157–170.

Błaszczyk, M, JA Jania, and JO Hagen Res. “Tidewater glaciers of Svalbard: Recent changes andestimates of calving fluxes.” Polish Polar Research 30 (2009): 85–142.

Carr, J. Rachel, Andreas Vieli, and Chris Stokes. “Influence of sea ice decline, atmospheric warming,and glacier width on marine-terminating outlet glacier behavior in northwest Greenland at seasonalto interannual timescales.” Journal of Geophysical Research: Earth Surface (2013).

Carr, J.R., A. Vieli, C.R. Stokes, S.S.R. Jamieson, S.J. Palmer, P. Christoffersen, J.A. Dowdeswell,F.M. Nick, D.D. Blankenship, and D.A. Young. “Basal topographic controls on rapid retreat ofHumboldt Glacier, northern Greenland.” Journal of Glaciology 61 (7 2015): 137–150.

Chauché, N., A. Hubbard, J.-C. Gascard, J. E. Box, R. Bates, M. Koppes, A. Sole, P. Christof-fersen, and H. Patton. “Ice–ocean interaction and calving front morphology at two west Greenlandtidewater outlet glaciers.” The Cryosphere 8 (8 2014): 1457–1468.

Christoffersen, P., R. I. Mugford, K. J. Heywood, I. Joughin, J. A. Dowdeswell, J. P. M. Syvitski,A. Luckman, and T. J. Benham. “Warming of waters in an East Greenland fjord prior to glacierretreat: mechanisms and connection to large-scale atmospheric conditions.” The Cryosphere 5 (92011): 701–714.

Cokelet, Edward D., Nicole Tervalon, and James G. Bellingham. “Hydrography of the West Spits-bergen Current, Svalbard Branch: Autumn 2001.” Journal of Geophysical Research 113 (1 2008):C01006.

Cottier, Finlo, Vigdis Tverberg, Mark Inall, Harald Svendsen, Frank Nilsen, and Colin Griffiths. “Wa-ter mass modification in an Arctic fjord through cross-shelf exchange: The seasonal hydrographyof Kongsfjorden, Svalbard.” J. Geophys. Res 110 (2005).

Favier, L., G. Durand, S. L. Cornford, G. H. Gudmundsson, O. Gagliardini, F. Gillet-Chaulet,T. Zwinger, A. J. Payne, and A. M. Le Brocq. “Retreat of Pine Island Glacier controlled bymarine ice-sheet instability.” Nature Climate Change 4 (1 2014): 117–121.

Hoffman, Matthew and Stephen Price. “Feedbacks between coupled subglacial hydrology and glacierdynamics.” Journal of Geophysical Research: Earth Surface 119 (3 2014): 414–436.

Holland, David M., Robert H. Thomas, Brad de Young, Mads H. Ribergaard, and Bjarne Lyberth.“Acceleration of Jakobshavn Isbræ triggered by warm subsurface ocean waters.” Nature Geoscience1 (10 2008): 659–664.

Ingólfsson, Ólafur. “Outline of the geography and geology of Svalbard.” UNIS course packet (2004).

Kääb, Andreas, Bernard Lefauconnier, and Kjetil Melvold. “Flow field of Kronebreen, Svalbard,using repeated Landsat 7 and ASTER data.” Annals of Glaciology. 2005, 7–13.

Khazendar, Ala, Eric Rignot, Dustin M. Schroeder, Helene Seroussi, Michael P. Schodlok, BerndScheuchl, Jeremie Mouginot, Tyler C. Sutterley, and Isabella Velicogna. “Rapid submarine icemelting in the grounding zones of ice shelves in West Antarctica.” Nature Communications 7 (102016): 13243.

Luckman, Adrian, Douglas I Benn, Finlo Cottier, Suzanne Bevan, Frank Nilsen, and Mark In-all. “Calving rates at tidewater glaciers vary strongly with ocean temperature.” NatureCommunications 6 (2015).

46

Moon, Twila, Ian Joughin, Ben Smith, Michiel R. Van Den Broeke, Willem Jan Van De Berg, BriceNoël, and Mika Usher. “Distinct patterns of seasonal Greenland glacier velocity.” GeophysicalResearch Letters (2014).

Nakayama, Y and F Boucher, R. Introduction to Fluid Mechanics. Ed. Matthew Flynn. ButterworthHeinemann, 1999.

Nilsen, F., F. Cottier, R. Skogseth, and S. Mattsson. “Fjord–shelf exchanges controlled by ice andbrine production: The interannual variation of Atlantic Water in Isfjorden, Svalbard.” ContinentalShelf Research 28 (8 2008): 1838–1853.

Nilsen, Frank, Ragnheid Skogseth, Juni Vaardal-Lunde, Mark Inall, Frank Nilsen, Ragnheid Skogseth,Juni Vaardal-Lunde, and Mark Inall. “A Simple Shelf Circulation Model: Intrusion of AtlanticWater on the West Spitsbergen Shelf.” Journal of Physical Oceanography 46 (4 2016): 1209–1230.

Noufal, K.K., S. Najeem, G. Latha, and R. Venkatesan. “Seasonal and long term evolution ofoceanographic conditions based on year-around observation in Kongsfjorden, Arctic Ocean.” PolarScience 11 (3 2017): 1–10.

Paolo, Fernando S., Helen A. Fricker, and Laurie Padman. “Volume loss from Antarctic ice shelvesis accelerating.” Science 348 (2015).

Payne, Antony J., Paul R. Holland, Andrew P. Shepherd, Ian C. Rutt, Adrian Jenkins, and IanJoughin. “Numerical modeling of ocean-ice interactions under Pine Island Bay’s ice shelf.” Journalof Geophysical Research 112 (10 2007): C10019.

Price, Stephen F, Antony J Payne, Ian M Howat, and Benjamin E Smith. “Committed sea-level risefor the next century from Greenland ice sheet dynamics during the past decade..” Proceedings ofthe National Academy of Sciences of the United States of America 108 (5 2011): 8978–83.

Promińska, Agnieszka, Małgorzata Cisek, and Waldemar Walczowski. “Kongsfjorden and Hornsundhydrography – comparative study based on a multiyear survey in fjords of west Spitsbergen.”Oceanologia 59 (10 2017): 397–412.

Saloranta, Tuomo M. and Harald Svendsen. “Across the Arctic front west of Spitsbergen: high-resolution CTD sections from 1998-2000.” Polar Research 20 (12 2001): 177–184.

Schoof, Christian. “Ice-sheet acceleration driven by melt supply variability.” Nature 468 (12 2010):803–806.

Seale, Anthony, Poul Christoffersen, Ruth I. Mugford, and Martin O’Leary. “Ocean forcing of theGreenland Ice Sheet: Calving fronts and patterns of retreat identified by automatic satellite mon-itoring of eastern outlet glaciers.” Journal of Geophysical Research 116 (8 2011): F03013.

STEP. 2016 “Sentinel-1 Toolbox Tutorials | STEP.”.

Straneo, Fiamma and Claudia Cenedese. “The Dynamics of Greenland’s Glacial Fjords and TheirRole in Climate.” Annual Review of Marine Science 7 (1 2015): 89–112.

Straneo, Fiammetta, Gordon S. Hamilton, David A. Sutherland, Leigh A. Stearns, Fraser Davidson,Mike O. Hammill, Garry B. Stenson, and Aqqalu Rosing-Asvid. “Rapid circulation of warmsubtropical waters in a major glacial fjord in East Greenland.” Nature Geoscience 3 (3 2010):182–186.

Sundfjord, A., J. Albretsen, Y. Kasajima, R. Skogseth, J. Kohler, C. Nuth, J. Skarðhamar, F. Cottier,F. Nilsen, L. Asplin, S. Gerland, and T. Torsvik. “Effects of glacier runoff and wind on surfacelayer dynamics and Atlantic Water exchange in Kongsfjorden, Svalbard; a model study.” Estuarine,Coastal and Shelf Science 187 (3 2017): 260–272.

Svendsen, Harald, Agnieszka Beszczynska-Møller, Jon Ove Hagen, Bernard Lefauconnier, VigdisTverberg, Sebastian Gerland, Jon Børre Ørbøk, Kai Bischof, Carlo Papucci, Marek Zajaczkowski,Roberto Azzolini, Oddbjørn Bruland, Christian Wiencke, Jan-Gunnar Winther, and WinfriedDallmann. “The physical environment of Kongsfjorden?Krossfjorden, an Arctic fjord system inSvalbard.” Polar Research 21 (6 2002): 133–166.

47

Teigen, S. H., F. Nilsen, and B. Gjevik. “Barotropic instability in the West Spitsbergen Current.”Journal of Geophysical Research 115 (7 2010): C07016.

Todd, J. and P. Christoffersen. “Are seasonal calving dynamics forced by buttressing from ice mélangeor undercutting by melting? Outcomes from full-Stokes simulations of Store Glacier, West Green-land.” The Cryosphere 8 (12 2014): 2353–2365.

Truffer, Martin and Roman J Motyka. “Where glaciers meet water: Subaqueous melt and its relevanceto glaciers in various settings; Where glaciers meet water: Subaqueous melt and its relevance toglaciers in various settings.” Review of Geophysics (2016).

Trusel, Luke D., R. D. Powell, R. M. Cumpston, and J. Brigham-Grette. “Modern glacimarineprocesses and potential future behaviour of Kronebreen and Kongsvegen polythermal tidewaterglaciers, Kongsfjorden, Svalbard.” Geological Society, London, Special Publications 344 (1 2010):89–102.

Vallot, Dorothée, Rickard Pettersson, Adrian Luckman, Douglas I Benn, Thomas Zwinger, WardJ J Van Pelt, Jack Kohler, Martina Schäfer, Björn Claremar, and Nicholas R J Hulton. “Basaldynamics of Kronebreen, a fast-flowing tidewater glacier in Svalbard: non-local spatio-temporalresponse to water input.” Journal of Glaciology 63 (2017): 1012–1024.

Weertman, J. “Stability of the Junction of an Ice Sheet and an Ice Shelf.” Journal of Glaciology 13(1 1974): 3–11.

48

7 Appendix: R code extract

49

50