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2019-05-03 Firn Characterization of the Accumulation Zone of Kaskawulsh Glacier, Yukon Territory, Canada
Ochwat, Naomi
Ochwat, N. (2019). Firn Characterization of the Accumulation Zone of Kaskawulsh Glacier, Yukon Territory, Canada (Unpublished master's thesis). University of Calgary, Calgary, AB. http://hdl.handle.net/1880/110298 master thesis
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Firn Characterization of the Accumulation Zone of Kaskawulsh Glacier, Yukon Territory,
Canada
by
Naomi Ochwat
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
GRADUATE PROGRAM IN GEOGRAPHY
CALGARY, ALBERTA
MAY, 2019
© Naomi Ochwat 2019 Abstract
While glacier mass balance studies allow investigation of the changing cryosphere, large ice sheets and remote, glaciated regions often lack the potential for detailed field studies. Remote sensing techniques are applied in mass balance studies, however, these methods are challenged because of their assumptions on density. In spring 2018, I extracted two firn cores (21 m and 36 m) from the accumulation zone of Kaskawulsh Glacier, St. Elias Mountains, Yukon and analyzed refrozen ice layers, snow densification processes, and isotopic and ionic signals.
Meltwater percolation and refreezing events are evident in the cores through the quantity of ice layers, the presence of a perennial firn aquifer, and the altered isotope and glaciochemical signatures. These processes resulted in surface lowering of 10 cm/yr and washed out most of the isotope and ion seasonal signal. My study advances understanding in the dynamics of a changing accumulation zone in the St. Elias Icefields.
Keywords: mountain glacier, firn core, densification, stable isotopes, ions, surface lowering
ii
Preface
The intention behind this research is to understand what firn looks like when it is undergoing climate change driven metamorphism. What is inside the cocoon when a caterpillar turns into a butterfly?
All Figures have been created originally, reproduced through the Fair Dealings act, or created using someone else’s data and properly asked.
iii Acknowledgements
Wow. Who to acknowledge? Perhaps this will go in somewhat chronological order?
Shawn Marshall for giving me this opportunity. Brian Moorman for teaching geocryology and for being excited and then supervising me. Kristina…well Kristina Miller will get many thanks.
Alison Crisctiello for saving my fieldwork expedition (and running my ion analysis). Etienne and
Pete for teaching me how to use the drill and working as a great team on the glacier! Stephen
Taylor for running my isotopes! Bob Anderson for all of the mentorship and chats we have had and well..I wouldn’t be pursuing science as a career if it weren’t for him! Shemin Ge for welcoming me back to Colorado and allowing me to go to her group meetings. Karl Kreutz for being keen on discussing his research and sharing his data at the divide site. Luke Copeland for sharing his climate data! Shad O’Neel and Louis Sass for allowing me to analyze their data from
Wolverine Glacier. Thanks for all of the motivational and inspiration talks and letting me practice my presentations: Sarah St. Germain, Mallory Larsson, Phil Orlandani, Kyren Bogolub,
Marin Mikulic, Julia Shates, Tom Ochwat (my dad!), Jasmine Vidrio, Tom Wilson, Johannes
Lohse, Julia Shates, Kyren Bogolub, Melissa Ochwat (my mom!), Dave Roberts, and probably more. Thank you to everyone at the geography department, especially Paulina Medori! Thanks to
Polar Knowledge and NSERC for funding. Thanks to AINA, KLRS, and Icefields Discovery for fieldwork logistics.
iv Dedication
To all of the unanswered questions.
v Table of Contents
Abstract ...... ii
Preface ...... iii
Acknowledgements ...... iv
Dedication ...... v
Table of Contents ...... vi
List of Tables ...... xi
List of Figures and Illustrations ...... xiii
List of Symbols, Abbreviations and Nomenclature ...... xvii
Epigraph ...... xix
Chapter 1: Introduction ...... 1
1.1 Objectives ...... 2
Chapter 2: Literature Review ...... 4
2.1 Mass balance study methods ...... 4
2.1.1 Direct measurements...... 5
2.1.2 Indirect measurements...... 6
2.2 Firn Densification ...... 8
2.2.1 Sorge’s law...... 8
2.2.2 Meltwater retention and ice layers...... 11
2.2.3 Thermodynamics and heat transfer physics...... 13
vi 2.3.4 Necessary climatic conditions...... 15
2.3 Firn Aquifers ...... 16
2.3.1 Process of formation...... 16
2.4 Firn Stratigraphy and Ice Cores ...... 18
2.4.1 Firn stratigraphy: ice cores...... 18
2.4.2 Ice cores in St. Elias Region...... 19
2.6 Isotopes, Ions, and Ice Cores ...... 21
2.6.1 Isotopes...... 21
2.6.2 Ions...... 26
2.7 Summary ...... 28
Chapter 3: Study Area ...... 30
Chapter 4: Methods ...... 35
4.1 Field Methods ...... 35
4.1.1 Drilling...... 35
4.1.2 Field processing...... 37
4.1.3 Snowpit...... 38
4.2 Firn Density Calculation ...... 38
4.3 Density Uncertainty ...... 39
4.4 Background Firn Density ...... 42
4.4.1 Ice content...... 42
vii 4.4.2 Background firn density...... 42
4.5 Ice Layer Stratigraphy ...... 43
4.5.1 Stratigraphy visualization...... 43
4.5.2 Ice layers and density...... 43
4.6 Stable isotopes ...... 43
4.6.1 Laboratory analysis...... 43
4.6.2 Preliminary analysis...... 44
4.7 Ions ...... 45
4.7.1 Laboratory analysis...... 45
Chapter 5: Density Results ...... 46
5.1 Firn Density ...... 46
5.2 Density Uncertainty ...... 51
5.3 Ice Layer Stratigraphy and Content ...... 55
5.3.1 Stratigraphy and content...... 55
5.3.2 Ice layers and density...... 61
5.4 Background Firn Density ...... 62
5.5 Surface Lowering Due to Firn Densification ...... 65
5.6 Summary ...... 65
Chapter 6: Isotope and Ion Results ...... 67
6.1 Stable Isotopes Results ...... 67
viii 6.1.1 δ18O with depth...... 67
6.1.2 Deuterium excess...... 69
6.1.3 Statistics...... 70
6.2 Ion Results ...... 73
6.2.1 Ions with depth...... 73
6.3 Estimated Age of the Firn Cores ...... 78
6.4 Summary ...... 79
Chapter 7: Discussion ...... 81
7.1 Physical Effects of Meltwater in the Firn ...... 82
7.1.1 Ice layers and lenses...... 82
7.1.2 Stratigraphy above firn aquifer...... 83
7.1.3 Kaskawulsh firn aquifer...... 84
7.2 Surface Lowering and Mass Redistribution: Impacts on Geodetic Mass Balance ...... 88
7.2.1 Firn density in context...... 88
7.2.2 Surface lowering...... 91
7.3 Melt-affected Isotopes and Ions ...... 92
7.3.1 Isotopes and ice layer stratigraphy...... 93
7.3.2 Meteoric water lines indicating melt...... 95
7.3.3 Preserved firn...... 98
7.4 Melt-affected Firn vs. Dry Firn ...... 99
ix 7.4.1 The aquifer and the effect of melt on the bottom end of the core...... 102
7.5 Temporal and Spatial Comparison ...... 104
7.5.1 Comparison to Kreutz Camp and Eclipse...... 104
7.5.2 Compare to USGS Wolverine data...... 107
7.6 Discussion Summary ...... 112
Chapter 8: Conclusions ...... 114
References ...... 117
Appendix ...... 130
Ion Statistics ...... 130
x List of Tables
Table 3.1. Positive degree days at Copland weather station from 2013 – 2018 with 2013 being the highest and 2014 the lowest...... 33
Table 4.1. Precision of ion analysis, separated into anions and cations...... 45
Table 5.1. Shapiro-Wilk tests for density and ice layer presence in Core 1 and 2 showing mostly non-normal data...... 61
Table 5.2. Correlation results using Spearman’s Correlation test for Core 1 showing correlations between 0.27 and 0.37 depending on the data used...... 62
Table 6.1. Statistical analysis of distribution of δ18O for Core 1. Where X is the difference between two samples, N is the sample size, Average is the mean value of the parameter, σ is the standard deviation, skewness is the skewness of the distribution, where higher values higher amounts of skew, and kurtosis is the shape of the distribution, where higher values have greater amounts of spikes...... 72
Table 6.2. Statistical analysis of distribution of δ18O for Core 2. Where X is the difference between two samples, N is the sample, Average is the mean value of the parameter, σ is the standard deviation, skewness is the skewness of the distribution, where higher values higher amounts of skew, kurtosis is the shape of the distribution, where higher values have greater amounts of spikes...... 72
Table 6.3. Displays the statistical differences between Core 1 and Core 2 ion concentrations. ... 77
Table 7.1. Density of the upper 10 m of firn for various glaciers around the world (altered from
Huss, 2013)...... 88
Table 7.3. Sections of MWL to investigate post-depositional processes with depth. Sections were divided by the appearance of wash-out or preservation...... 97
xi Table 7.4. Core 1 melt-affected and dry firn isotope and comparison of average, minimum, and maximum ion concentrations...... 101
Table 7.5. Core 2 melt-affected vs. dry firn melt-affected and dry firn isotope and ion comparison of averages, minimums, and maximums...... 102
Table 7.6. Mean concentrations of ions in firn aquifer (32-36 m) compared to 10-20-m section of
Core 1 ...... 103
Table 7.7. Statistical characteristics of oxygen isotopes at Wolverine Glacier, Alaska...... 108
xii List of Figures and Illustrations
Figure 2.1. Examples of densification curves from dry frin in Byrd and Vostok stations,
Antarctica, and the melt-affected Upper Seward Glacier, Alaska (Cuffey and Paterson, 2010). . 10
Figure 2.2. Example of refrozen melt features in a cross section of a snowpit, displaying spatial variation of ice layers and lenses and the formation of ice glands (Sharp, 1951)...... 15
Figure 2.3. Formation of channelized vertical flow; from the start of a melt season to extensive meltwater percolation and refreezing...... 17
Figure 2.4. Map of St. Elias Region and drill sites in the area. a) regional visualization b) local map Source: a) https://www.pc.gc.ca/en/pn-np/yt/kluane/visit/directions/region b) Google Earth, accessed March 16th 2019...... 21
Figure 2.4. (a) A record of Holocene temperature reconstructions from combined Greenland and
Canadian High Arctic cores. B) Insolation over the range of time at 60° N(c) Holocene ice-core
δ18O record from Mt. Logan. (d) Mt. Logan record from PRC (grey) and NWC (black) from
1700-2000 AD. (e) Eclipse Dome cores 1996 in grey and 2001 in black f) Pollen in the ice core from PRC. The arrow is global climate event. (Zdanowicz et al., 2014) ...... 23
Figure 2.5. Time series of accumulation at Eclipse ice core site, annual (a), and cold (black line) and warm (gray line) seasons (b). Time series of average standardized isotopes, annual (c) and cold (black line) and warm (gray line) (d) seasons. Adapted from Kelsey et al., 2012 ...... 24
Figure 3.1. Burwash Landing temperature time series from 1966 to 2018...... 31
Figure 3.2. Copland weather station time series of monthly mean temperatures from 2013 to
2018...... 33
Figure 3.3. Copland weather station time series of hourly data from May 1st 2017 to May 1st
2018 displaying the variations of temperature...... 33
xiii Figure 4.1. a) Eclipse Ice Drill b) Configuration of aerial perspective of drill set up...... 36
Figure 4.2. Examples of firn cores a) clean firn b) melt-affected firn c) firn with a large ice layer.
...... 37
Figure 5.1. Scatter plot of density of both cores displaying a logarithmically-increasing trend. .. 47
Figure 5.2. Density versus depth plot to 16 m of both cores, indicating scatter and variation between the cores...... 48
Figure 5.3. Smoothed 1-m density for Core 1, displaying a logarithmically-increasing trend with scatter still present...... 49
Figure 5.4. Comparison of density curves for two different depths, A) all data for Core 1 and 2
B) Core 1 cut to 21 m for adequate comparison (the equations represent y being depth and x being density)...... 51
Figure 5.5. Uncertainties included with density measurements for Core 1 and Core 2...... 53
Figure 5.6. Distribution of uncertainties in Core 1 displaying a slightly skewed normal distribution...... 54
Figure 5.7. Distribution of uncertainties in Core 2 showing a normal distribution with a right tail.
...... 54
Figure 5.8. Stratigraphy of Core 1 and Core 2. Note that the ice layer thickness is not to scale.
Core 1 displays numerous melt-affected layers with the presence of ice layers and lenses, Core 2 displays many ice layers and lenses but also melt-affected firn, as present at the bottom of Core
2...... 57
Figure 5.9. Ice content with depth of both cores. Core 1 and 2 have ice content of all sizes and a wide range of data. There is a lack of ice content after 30 m in Core 1...... 59
xiv Figure 5.10. Histogram of ice fraction frequency displaying similar distributions in A) Core 1 and B) Core 2...... 60
Figure 5.12. Calculated background firn density, showing a similar trend to the raw density data, with line of best fit included for comparison...... 64
Figure 6.1. δ18O profile for Core 1 and Core 2, displaying partial wash-out of seasonal isotopes.
Possible preserved negative seasonal peaks are indicated with red arrows, while a general negative trend is present at the bottom of the core...... 68
Figure 6.2. δD profile for Core 1 and Core 2, showing a similar pattern to the δO18 profile...... 69
Figure 6.3. d excess with depth of Core 1 and Core 2, with no seasonal peaks...... 70
Figure 6.4. Visualization of the δ18O and d excess data...... 71
Figure 6.5. Cations with depth, indicating seasonal patters in the first ~10 m and subsequent washout thereafter. Cl-/Na+ displays values less than 1.16 indicating washout (Kohshima et al.,
2007), validity of the ratio can change when concentrations are near the detection limit...... 75
Figure 6.6. Anions with depth, indicating seasonal patters in the first ~10 m and subsequent washout thereafter, with the exception of Br-...... 76
Figure 6.7. Summary statistics on ions, with cations displaying greater concentrations than ions.
Na+, Ca2+, Br-, and Mg2+ with the greatest concentrations...... 77
Figure 7.1. Density of core retrieved near Divide in 1960s, there is variation in the data, however most values in the first 15 m are below 0.6 g/cm3. (Grew & Mellor, 1966) ...... 90
Figure 7.2. Samples with ice layers in the δ18O profile. The three negative peaks have ice-rich samples in them but the samples leading up to the minima do not. There is no clear evidence of a relationship between ice layer location and δ18O values...... 95
Figure 7.3. Meteoric water lines of Core 1 and Core 2...... 96
xv Table 7.2. Meteoric Water Lines of Kaskawulsh Glacier, statistically similar slopes are present for Core 1, Core 2, and Kreutz camp core...... 96
Figure 7.4. Refreezing of brine melt-affected firn (inspired by Goto-Azuma et al., 1994)...... 100
Figure 7.5. Statistical comparisons of δ18O in firn cores to display differences in regions...... 105
Figure 7.6. Kreutz Core ice layer stratigraphy showing numerous ice layers and lenses in the first
18 m...... 106
Figure 7.7. Profile of δ18O of Wolverine Glacier from May 2016 showing nearly complete washout...... 110
xvi List of Symbols, Abbreviations and Nomenclature
Symbol Definition
AINA Arctic Institute of North America
KLRS Kluane Lake Research Station a.s.l. Above sea level
IRRP Icefield Ranges Research Project
ρ or ρi Density of ice m Mass, or meter, or constant
L Length
D Diameter
V Volume f Fraction of completeness kg Kilograms d Deuterium excess vp Pore space
ρs Density of snow
ρw Density of water m w.e.. Meters water equivalent
δD Ratio of deuterium and hydrogen isotope
δ18O Ratio of 18O and 16O isotope
‰ Per mille (parts per thousand)
R Ideal gas constant
xvii E Activation energy
T Temperature (°C) t Time b Net mass balance h Depth
PRC Prospector-Russel Col
NWC Northwest Col
PDD Positive degree days
A Area
Aunc Area uncertainty
Dunc Diameter uncertainty
MSA Methanesulfonic acid
xviii Epigraph
“If fire and life have thus been bound together since the instant of life’s first creation, then in a sense so profound as to almost mystical they share in common a birth and a death that mark the beginning and end of all things. When the last animal stops breathing, when the last plant stops processing chlorophyll to turn sunlight into sugar, and when the last earthly fire completes its oxidation of the carbon carcasses these organisms will leave behind, what remains will be…. The
Ice.” – William Cronon
xix
Chapter 1: Introduction
Globally, glaciers are rapidly retreating and contributing to sea level rise at accelerated rates (Meier et al., 2007). Glaciers in the mid-latitudes may be especially susceptible to higher melting rates due to the sensitivity of their location relative to rising temperatures (Meier et al.,
2007; Radić et al., 2014). Mass balance studies are used to measure the year-to-year changes of glacier mass, as a function of climate variability and change. Studies are typically in-situ with field measurements of snow accumulation and ablation (Cuffey & Paterson, 2010). The accumulation zone dynamics and characteristics can lead to a greater understanding of mass balance in the time of global warming.
While glacier mass balance studies are largely established in the scientific community, large glaciers are generally understudied (Dyurgerov & Meier, 1997), due to the logistical challenges required by typical mass balance measurements (i.e., networks of ablation stakes, etc). To understand the change of large glaciers, remote sensing efforts have been utilized to measure the surface elevation change and equate that with changes in volume and mass (e.g.,
Foy, 2009; Surazakov & Aizen, 2006). However, remote-sensing techniques may not account for the increase in surface meltwater retention within the firn layer in the accumulation zone of the glacier, which will alter the surface elevation, changing the volume, but not necessarily the mass of the ice in the accumulation area. They will not be able to account for firn aquifer storage properties relevant to mass balance.
The state of the firn can lend insight into densification and surface lowering processes, and meltwater storage and runoff potential, as well as implications on climate research. Remote- sensing techniques rely on the assumption that mass change is reflected in surface elevation
1 change. Surface elevation (geodetic) methods include ground-based surveying, laser detection and ranging (LiDAR), and satellite altimetry (Surazakov & Aizen, 2006). While geodetic methods work well with small mountain glaciers (Cogley, 2009), they may not be as appropriate on large icefields or polar ice sheets. This is largely because surface elevation change may indicate densification associated with meltwater being retained in the system (e.g., as refrozen subsurface ice layers or liquid aquifers) (Huss, 2013). This may result in a lower elevation but not necessarily mass loss. Liquid water in the form of a firn aquifer can also lead to changes in meltwater storage and run-off processes (Jansson et al., 2003).
My research examines the firn characteristics in the accumulation zone of Kaskawulsh
Glacier, St. Elias Mountains, Yukon, with research based out of the Arctic Institute of North
America’s (AINA) Kluane Lake Research Station (KLRS). I obtained two shallow firn cores (up to 36 m deep) and analyzed the firn stratigraphy and ion and isotope chronology. This study clarifies the physical and chemical processes that distinguish a highly melt-affected glacier. I describe the relationship of surface vs. mass change in the accumulation zone of Kaskawulsh
Glacier and will improve upon the geodetic mass balance measuring techniques for use on large glaciers. I examine the trends of ice layer and firn aquifer development over recent decades, as recorded in the firn core, and the effect of meltwater percolation on the glaciochemical and isotopic signals. Ultimately, this work will lead to better understanding of the impacts of climate change on large glaciers and the glacier mass balance models of this region.
1.1 Objectives
The overarching goal of my thesis is to characterize the firn of the Upper North Arm of
Kaskawulsh Glacier. This can be broken into separate specific objectives:
2 1. Characterize the meltwater percolation and refreezing processes that have been
occurring in recent years and look for changes in surface lowering due to these
processes.
2. Identify any changes in accumulation or firn density as compared to the studies in
Icefield Ranges Research Report (IRRP) from the 1960s and the nearby Eclipse ice
core site.
3. Characterize the isotopic and ionic stratigraphy of firn at this site and identify the
effects of meltwater percolation and refreezing on the record.
4. Compare my drill site on Kaskawulsh glacier with 1) other drill sites in the area 2) the
results in the IRRP reports and 3) a highly melt-affected glacier in Alaska.
3 Chapter 2: Literature Review
In this chapter, I review key concepts that are pertinent to my study. Mass balance studies are used to understand the mass changes that glaciers are undergoing. These studies are undertaken in various methods, from ground measurements to remote-sensing techniques. One of the assumptions that lends to remote-sensing mass balance studies is the density of firn. In order to properly assign mass values to firn, it is necessary to understand densification processes.
When glaciers undergo melt, in certain climate conditions, this can change the densification processes, and in some cases a firn aquifer may develop. Melt influences the firn stratigraphy and can affect ice core studies. Ice core studies are often used for paleoclimate research using isotopes and ions. The effect of melt will alter these signatures and complicate those studies. In this chapter I discuss these foundational concepts in depth.
2.1 Mass balance study methods
Mass balance studies of glaciers quantify how much mass a glacier is losing or gaining.
Mass balance of a glacier is summarized as the sum of the accumulation and ablation of the whole mass of glacier ice annually (Cuffey & Paterson, 2010). Accumulation occurs primarily through snowfall, with additional sources of accumulation from wind deposition and avalanching. Outside of Antarctica, ablation occurs dominantly through snow and ice melt, with additional mass loss from iceberg calving, evaporation, and sublimation. This balance determines whether a glacier loses or gains mass in any given year (Cuffey & Paterson, 2010). The methods to measure mass balance can be separated into indirect and direct methods, as described in the following sections.
4 2.1.1 Direct measurements.
Direct measurements of glacial mass balance can be approached using methods falling into three categories: glaciological, topographical, and hydrological (Rabus & Echelmeyer, 1998).
The glaciological method involves establishing ablation stakes (by placing stakes a few meters into the glacier surface along the glacier centreline with a few transverse transects and measuring repeatedly (Hubbard & Glasser, 2005) and digging seasonal snow pits in the field, in order to measure the change in the mass of ice and snow at the surface of the glacier for the summer and winter season – as to include as much variability as possible (Hubbard & Glasser, 2005). With these measurements mass balance may be approximated using spatial correlation for the area of the entire glacier (Rabus & Echelmeyer, 1998; Cogley et al., 1996). The glaciological method requires extensive stake networks and logistically and time demanding field seasons. In some situations, summer and winter seasons are combined into a single annual visit, making fieldwork less intensive, but it is still high-effort and expensive (Cogley et al., 1996).
The hydrological method estimates mass gained via annual precipitation measurements from weather records and mass loss via the discharge from glacial streams, evaporation, and water storage (Cuffey & Paterson, 2010; Rabus & Echelmeyer, 1998). Challenges arise when complex drainage basins are present and precipitation and discharge data are not easily accessible or readily available.
The topographical method, also referred to as geodetic mass balance determination, involves comparing the surface elevation of the glacier at two different points in time and subsequently calculating the change in volume. This volume is then converted to mass using a bulk density metric for the area (Rabus & Echelmeyer, 1998). In the past, this was done via field surveys and
5 photogrammetry, though recently this method has incorporated more advanced remote-sensing techniques, which will be discussed below. The topographical method can be less financially and time demanding, however, there are challenges when equating surface change to mass gain or loss (Hubbard & Glasser, 2005).
Direct methods can be quite detailed; however they also require extensive fieldwork that is often time consuming and costly and the method is only sufficient for the time period when the measurements took place.
2.1.2 Indirect measurements.
Indirect methods use data from direct methods in order to estimate the mass balance of glaciers; these are primarily melt-models and remote-sensing techniques. Indirect methods of measuring mass balance of glaciers are less expensive due to the lack of fieldwork (except for ground-truthing endeavours). Thus, these methods allow for a greater spatial coverage of changing glaciers. Mass balance melt-models are established utilizing the link between meteorological data and the mass of the glaciers. Weather stations located on or near the glacier record precipitation and temperature, and an energy balance model is applied to this data. The model calculates the amount of precipitation and melt, resulting in a net mass gain or loss (De
Woul & Hock, 2005). Numerous studies all over the globe use melt models to reconstruct glacier change and project change into the future (Oerlemans & Fortuin, 1992; Radić et al., 2014).
There are several remote-sensing techniques to determine mass balance (Bamber & Rivera,
2007), but I will focus here on only geodetic methods. Geodetic methods use the assumption that changes in elevation over time are a proxy for changes in the mass of the glacier. Geodetic methods can be broken into two categories: altimetry or photogrammetry. Altimetry methods use
6 airborne or satellite laser or radar, or field GPS measurements (Bamber & Rivera, 2007). This method is often used in conjunction with photogrammetry, due to the need of more spatial coverage and accuracy (Bamber & Rivera, 2007). Photogrammetric images and interferometric synthetic aperture radar (InSAR) can be used to determine surface elevation changes. Depending on the resolution, these methods can have errors of several meters or more, and are thus often combined with in-situ GPS measurements to increase accuracy (Bamber & Rivera, 2007).
Geodetic approaches all rely on the assumptions that (1) the density of the snow, firn, and ice at the sampling location can be estimated, and has not changed between the two time periods, (2) the bedrock has not changed in elevation and (3) underlying glacial hydrological conditions have not caused surface lowering and rising. Number 2 is a reasonable assumption, as this would only occur due to tectonic activity, erosion, post-glacial isostatic rebound (in some places 10mm/yr) (Sella et al., 2006; Bamber & Rivera, 2007). Over long time periods the change in density of the firn can have little impact on the geodetic methods (Moholdt et al., 2010a). However, over shorter time periods, this may not be true (Moholdt et al., 2010b).
Meltwater percolation and refreezing can significantly change the firn density profile and density of the accumulation zone of a glacier. These modifications of firn density may introduce large uncertainties when using geodetic techniques to determine glacier mass balance; for example
Moholdt et al. (2010a) determined a Svalbard glacier mass balance to be −4.3±1.4 Gt/yr, with the large uncertainty attributed to the assumed density. Understanding the structure and density of firn can help inform the assumed density used in geodetic mass balance techniques. GRACE may be a solution to these density assumptions for it has recently been found to correlate well with mountain glacier mass balance studies (Young et al., 2018).
7
2.2 Firn Densification
2.2.1 Sorge’s law.
Glacier ice is formed in the accumulation zone of a glacier through the compaction and densification of snow. Over time snow, turns to ice under the pressure of the snow above and snow metamorphism processes, as a function of the temperature of the snowpack. Three stages of firn densification occur (Herron & Langway, 1980). The first stage occurs to a density of approximately 550 kg/m3 under the process of snow settling and packing. The second stage occurs from approximately 550-830 kg/m3, beyond which point the air bubbles are closed off
(this is considered ice). This occurs much slower than the first stage. The third stage compresses the air bubbles to the typical density of glacier ice of around 910 kg/m3.
Sorge’s law is a set of equations used for constant snowfall and temperature (ideal conditions), to describe and predict the process of snow densification into ice. Sorge’s Law describes the empirical relationship of density and depth in the accumulation zone of a glacier under conditions such that, at a specific depth the density of the snow does not change over time
(Bader, 1954). For example, if at 20 m depth the firn is 670 kg/m3, then 5 years later at 20 m depth the firn is still at 670 kg/m3 even though it is technically different firn. This only holds true if the accumulation is relatively constant and the temperature is stable, as temperature alters compaction rates (cold snow compacts slower).
In many mass balance studies using geodetic approaches, the density of the firn is calculated (assumed) using Sorge’s law (Moholdt et al., 2010; Arendt et al., 2008). Herron &
8 Langway (1980) further developed a model based on Sorge’s Law, where the change in air space of the firn is linearly related to the weight of the snow above (Herron & Langway, 1980):
!!! = −� �!�ℎ Equation 1 !!
Where �! is the air space in the snow, �! is the density of the snow, h is depth and m is a factor that is determined via local climate conditions. This empirical model describes the rate of densification for the two stages of the snow-ice transition:
!! ! = �(� − � ) Equation 2 !" ! !
!!! !/! � = �!( ) Equation 3 !!
! 3 where � = 575��� − for 550 kg/m < � < 800 kg/m3 Equation 4 ! !" !
� = �!��!/�! Equation 5
! 3 k0 = 11��� − for � > 550 kg/m Equation 6 !" !
Where t is time, and c varies depending on the stage, �! is the density of water, �! is the density of ice, R is the ideal gas constant (8.314 J K-1 mol-1), T is the temperature of the firn (in this case), E is the activation energy, and b is the net balance of the accumulation at the location.
These models allow us to calculate the densification of the snow and thus the age of the firn at a given depth at a constant accumulation rate in dry firn.
9 Firn densification varies due to environmental conditions. Polar glaciers and ice sheets will experience different densification regimes compared to temperate glaciers. Figure 2.1 displays typical density-depth relationships.
Figure 2.1. Examples of densification curves from dry frin in Byrd and Vostok stations, Antarctica, and the melt-affected Upper Seward Glacier, Alaska (Cuffey and Paterson, 2010).
10 2.2.2 Meltwater retention and ice layers.
While there have been some studies modeling meltwater retention in firn and its effects on ice sheet mass balance (Fausto et al., 2009; Ligtenberg et al., 2011; Machguth et al., 2016), little is known about how much meltwater is being retained, what effects it may have on the densification of the firn and the subsequent surface lowering in mountain glaciers (Reeh, 2008).
Sorge’s law and Herron & Langway’s (1980) model are inadequate when accounting for impacts on the density of firn due to melting and refreezing events. When surface snow temperatures are at 0°C paired with net positive energy, melting of surface snow may occur. Depending on how much melt has occurred, this can: 1) round out the grains and increase the rate of the first stage of densification, or 2) with enough melt, water may percolate through the snow and firn layer, filling in the air pockets and refreezing, which causes the transition from snow to ice to be rapid
(Cuffey & Paterson, 2010). This changes the amount of energy available to melt: refreezing and then re-melting meltwater requires energy in addition to that already used to melt it once before.
This may repeat until enough accumulation has occurred to begin the process again and that ice lens is buried (Samimi & Marshall, 2017).
Too much melting and refreezing may create an impermeable surface that encourages more runoff and less retention. This also increases the density in the upper layer of the firn, due to percolating water pooling at the impermeable surface (Braithwaite et al., 1994; Bezeau et al.,
2013). The uncertain and variable density of firn, where melting and refreezing are common, can undermine geodetic mass balance estimates. This has been widely studied in Greenland, where large-scale remote-sensing techniques are used for ice sheet altimetry changes (Reeh et al., 2005;
Reeh 2008; Forster et al., 2014; Bamber & Rivera, 2007; Zwally & Jun, 2002).
11 Reeh et al. (2005) created a simple model to account for ice lenses in the firn by calculating an ice fraction and a firn fraction. Where the ice fraction is calculated from the meltwater and the firn fraction is calculation from the net accumulation minus the melt (Reeh et al., 2005). The model assumes that all meltwater remains in the system and that the only compaction is that of the remaining firn fraction (Reeh et al., 2005). The model that Reeh et al.,
(2005) developed includes a positive degree-day melt model in order to approximate the amount of ice lenses in the system. Their model worked well with their Canadian Arctic core but did not succeed with their Greenland core, because it underestimated the amount of ice lenses (Reeh et al., 2005). In another attempt to account for non-steady state firn densification processes in
Greenland, Reeh (2008) concluded that, under constant climate conditions, changes can still occur due to past variations in the climate.
2.2.2.1 Mass redistribution.
In large areas the density of the firn has spatial variation due to varying depths of winter snow accumulation. Since the density of firn is a function of depth, deeper snow will contribute to a different firn density. Snow is redistributed through gravity and wind forces (Macguth et al.,
2006). Snow avalanches from high mountain peaks and slush flows from the accumulation zone will redistribute snow on the glacier. Wind redistribution is a greater variable that controls the local depth of snow. Wind can redeposit snow as well as carry falling snow to different locations
(Machguth et al. 2006). In Antarctica, constant katabatic winds can redistribute snow into megadunes that have varying firn density and permeability (Albert et al., 2004). On small mountain glaciers and enormous ice sheets snow redistribution can impact the local firn characteristics.
12 Mass redistribution can lead to a surface lowering and perceived mass loss. Internal accumulation is a type of mass redistribution where melt redistributes the annual accumulation’s mass. When surface meltwater percolates down into the previous year’s accumulation, mass balance estimates can become skewed because this type of redistribution is not detectable through typical means (eg. snow pit) because you cannot tell the snow origin of ice layers
(Trabant & Mayo, 1985). For example, on Alaskan glaciers internal accumulation can account for 7-64% of annual accumulation (Trabant & Mayo, 1985). In Sweden, Schneider & Jansson
(2004) found internal accumulation of Storglaciaren accounted for 3-5% of the annual accumulation for any given year. They deduced that though this is a small number, the systematic nature of the uncertainty should be addressed for long-term studies.
2.2.3 Thermodynamics and heat transfer physics.
The frequency and quantity of refrozen melt features require specific thermodynamic and physical conditions to form. Melt features are described as ice layers or lenses, a layer is continuous across a core section, and a lens is laterally discontinuous and does not span the entire cross-section of the core. Ice layer formation depends on the initial snow conditions and meltwater input (Pfeffer & Hunphrey, 1998). When the snow is bombarded with energy from incoming solar radiation it will sublimate or melt, and meltwater may percolate into the snowpack and potentially refreeze. In order for an ice layer to form there needs to be sub- freezing subsurface conditions, this allows for the release of latent heat from the refreezing of water. If there is a strong temperature gradient between the liquid water and the sub-freezing snow next to it then it may refreeze as an ice layer. Besides the thermodynamic limitations of creating ice layers, there are also physical confinements. Ice layers have a tendency to form at coarse-fine grain snow transitions due to the dominance of capillary forces in the finer grains and
13 the inability of gravity on pore water to overcome those forces (Pfeffer & Humphrey, 1998). If meltwater reaches one of these boundaries then it may begin to flow horizontally until it either refreezes, or finds a way to flow vertically again. The vertical flow pathways are referred to as piping and sometimes “glands” (usually when refrozen). Pipes are effective meltwater transport features through the firn. Water can move on top of ice lenses and layers and can spread out from the pipes horizontally or form thicker ice on pre-existing layers and lenses (Figure 2.2) (Sharp,
1951). In some cases, water may saturate the snow and refreeze as a slushy consistency known as infiltration ice (Pfeffer, T., personal communication, December, 2018)
14
Figure 2.2. Example of refrozen melt features in a cross section of a snowpit, displaying spatial variation of ice layers and lenses and the formation of ice glands (Sharp, 1951). 2.3.4 Necessary climatic conditions.
Ice layer and lens formation is dependent on the local climate. The timing and onset of summer warming and the initial conditions of the snow determine the ability and quantity of ice layers and lenses that can form. The snowpack must be cold to refreeze the meltwater percolating through it; this implies the necessity of cold winter conditions and/or an early onset of melt –
15 before the increased summer temperatures warm up the snowpack. Ice layers are indicators an extreme climate with cold winters and summers that arrive quickly (Pfeffer & Humphrey, 1998).
2.3 Firn Aquifers
Meltwater can be a common occurrence that affects the properties of firn. As discussed above, meltwater retention and refreezing can change the density and densification process of firn. When meltwater does not refreeze, it may percolate to greater depths and stay perennially in the liquid state in between the ice grains. These features are referred to as firn aquifers (Fountain
& Walder, 1998). Firn aquifers are indicative of melt processes in the accumulation zone of glaciers, and they have been found on Greenland, Antarctica, and South America (Munneke et al., 2014; Machguth et al., 2016; Miller et al., 2015; Forster et al., 2014; Koenig et al., 2014)
However, if a glacier has recently developed firn aquifers then their hydrologic and mass balance regime will change due to the potential for meltwater storage and lateral flow.
2.3.1 Process of formation.
Firn aquifers require similar thermodynamics as ice layer and lens formation. If the infiltration rate of meltwater is greater than the refreezing rate (or if it cannot refreeze at all) then that water will stay in liquid form. Due to gravity, it will percolate through the firn until it refreezes, capillary forces overcome gravitational forces, or the water reaches an impermeable boundary, such as an extensive ice layer, firn with smaller pores, or the firn-ice transition boundary. If this is below the winter annual cold layer, in a temperate glacier, there will not be a heat sink that will allow refreezing to occur (Pfeffer & Humphrey 1998). If there are numerous ice layers present already, the melt will become concentrated at a point and there will be a greater potential for vertical piping, thus the meltwater escapes the winter cold wave at a quicker rate (Figure 2.3). Like ice layer formation, the thermodynamic barriers are only one component
16 to the formation of firn aquifers. There also needs to be a “goldilocks” amount of annual accumulation. If there is too much accumulation the water is more likely to refreeze due to the snow’s cold content and if there is too little there will not be enough pore space to store the water and it will leave the firn as run-off (Munneke et al., 2014).
Figure 2.3. Formation of channelized vertical flow; from the start of a melt season to extensive meltwater percolation and refreezing.
17 2.4 Firn Stratigraphy and Ice Cores
2.4.1 Firn stratigraphy: ice cores.
While ice core studies have been conducted in order to reconstruct the paleoclimate and understand accumulation rates, little work has been done on snow density variation in firn cores and its relevance to geodetic mass balance studies. The few published firn density studies using cores and snow pits include areas such as in Greenland, Arctic Canada, Karakorum, and
Antarctica (Forster et al., 2014; Bezeau et al., 2013; Wake, 1989; Albert et al., 2004). Analyzing the density in firn cores is an effective way to measure the sensitivity of climate and the subsequent surface lowering of glaciers and ice sheets. In Arctic Canada, several studies have investigated melting in the firn layer, relating the core stratigraphy and ice-layer content to summer temperatures. Core stratigraphy can provide information about the melt history and accumulation rate of glaciers at a precise location (Koerner, 1977; Kinnard et al., 2008).
Braithwaite et al. (1994) found melting and refreezing to be the main control on near- surface firn density in western Greenland. A perceived surface elevation increase in the 1990s detected from satellite altimetry may have been from changes in near-surface firn density – less melt and refreezing in the firn, rather than an increase in accumulation in western Greenland
(Braithwaite et al., 1994). Near-surface firn density and its relationship to elevation change and satellite altimetry have been studied at Devon Ice Cap through ice coring (Bezeau et al., 2013).
They concluded that the near-surface firn density has increased “sharply” in recent years and that the greater amount of ice layer formation due to meltwater percolation and refreezing has inverted the typical depth-density curves found at High Arctic locations (Bezeau et al., 2013).
18 While firn core studies can inform one of the local climate changes and the effect of meltwater percolation and refreezing on surface elevation, there are few snow density studies of large icefields in non-polar regions. The cumulative ice in glaciers is expected to contribute more to sea-level rise in the coming years then anywhere else on the planet (Meier et al., 2007; Radić et al., 2014), and icefields typically have large areas of accumulation area, where firn depth and density are unknown.
2.4.2 Ice cores in St. Elias Region.
Glaciologists working with the Icefield Ranges Research Project (IRRP) extracted ice cores from the St. Elias region in the early 1960s, including ice cores from Kaskawulsh Glacier
(Wood, 1963). These initial coring efforts defined an average accumulation rate (1.6 m w.e./yr)
(Wood, 1963). In the 1961 investigation, four cores were extracted; the first was 15.5 m below the depth of a pit that was 2 m deep, while three other cores (two of which were at the same location) were taken at 15-m depth. Stratigraphy and temperatures were recorded, as well as relative snow/firn grain size in some cores (Wood, 1963). Grew & Mellor (1966) also took a 15 m firn core at the Divide location and recorded density measurements.
Decades later four more drill sites were added to the St. Elias region research; Northwest
Col (NWC) in 1980, Eclipse Icefield in 1996 and 2002, King Col in 2001, and Prospector-Russel
Col (PRC) in 2001-2002, with depths ranging from 100-345 m (Figure 2.4). Extensive analysis on accumulation has been performed on these cores, with average annual rates ranging from 0.42 to 1.38 m w.e./yr, depending on location (Zdanowicz et al., 2014). However, there has been very little research done on the snow/firn density in this area. Mt. Logan is reported to have a firn/ice transition at a depth of 50 m (Kanamori et al., 2004).
19 A
B
20 Figure 2.4. Map of St. Elias Region and drill sites in the area. a) regional visualization b) local map Source: a) https://www.pc.gc.ca/en/pn-np/yt/kluane/visit/directions/region b) Google Earth, accessed March 16th 2019. 2.6 Isotopes, Ions, and Ice Cores
Isotopes and ions are a gateway to understanding ice cores. Isotope analysis involves looking at the ratio of heavier atoms compared to their lighter counterpart. Ion analysis involves looking at the concentration of ions in the ice core. These analyses lead to greater understanding of aerosol distributions, their provenance, melt histories, and possible ways to date the ice core.
2.6.1 Isotopes.
Isotopic fractions for oxygen (18O/16O) and hydrogen (2H/1H) in precipitation and the resulting ratios in ice cores can give information on the climatic history of snow, firn, or ice at a specific location. This is due to the evaporation of lighter isotopes occurring more readily and heavier isotopes precipitating first, and the sensitivity of these fractionation processes to temperature. As a result, the stable isotope ratio δ18O in precipitation is a good proxy for air temperature of the source air mass (outside of the tropics) (Cuffey & Paterson, 2010), where
(!"!/!"!)!"#$%& δ18O = ( – 1) x 1000. In deep ice cores from Antarctica and Greenland, δ18O has (!"!/!"!)!"#$%#&% been used to reconstruct temperature fluctuations over the last million years.
In interior continental climates, there is also a tendency for seasonal cycles in stable isotope ratios of precipitation. On some occasions, intra-seasonal variations are also present.
Depending on the provenance and trajectory of the air mass, in winter the colder air mass will be strongly depleted in the heavier isotope and thus the precipitation will also be depleted, and in summer it will not be depleted as much. Isotope analysis can also help to quantify the seasonal snow accumulation by using δ18O to identify summer vs. winter snow accumulation, because
21 summer has a higher δ18O ratio compared to winter precipitation (Kelsey et al., 2012). While this works relatively well in the polar regions and at high altitudes, where the climate is cold and dry, in many mountain glaciers the isotope stratigraphy is not well preserved due to meltwater percolation diffusing the seasonal and annual isotopes together (Clarke, 1987).
Despite the challenges of ice cores from non-polar regions for isotopic analysis, ice cores from the St. Elias Mountains have been investigated extensively for isotopes and other glaciochemical characteristics. Zdanowicz et al. (2014) examined different characteristics of accumulation and stable isotope prevalence in the various ice cores in this region. They found that in the Holocene the Mt. Logan area experienced atmospheric instability rapidly shifting atmospheric conditions, interpreted as reconfigurations of atmospheric circulation in the North
Pacific over the timescales of centuries to millions of years (Figure 3.4). The Mt. Logan cores also reflect a different air mass association compared with the Eclipse ice cores, despite their close proximity (Zdanowicz et al., 2014). This is likely due to the much higher elevation of the
Mt. Logan core sites, where the heavy isotopes are precipitated out before they can reach such a high elevation (Mt. Logan is 5959 m a.s.l.).
At the Eclipse ice core site, Kelsey et al. (2012) analyzed the core for regional climatic variability and the relationship of the stable isotopes with North Pacific geopotential heights.
They successfully partitioned the stable isotope series into distinct warm and cold seasons and periods of high and low accumulation (Figure 2.4). Yalcin et al. (2006) found that δ18O values typically vary from –24‰ for summer snowfall to winter snowfall values of about –30‰. This region experiences large spatial variability in local climate over short distances, possibly impacting the icefield in a variety of ways. Wood (1963) proposed that the divide between
22 Kaskawulsh and Hubbard Glaciers may be the divide between Pacific and continental climates, which may result in horizontal spatial variability of stable isotopes. There is also an isotopic difference associated with elevation due to the air mass rising and raining out heavier isotopes, leaving higher elevations depleted in δ18O. The spatial variability in the St. Elias Icefields will also be inherently related to elevation.
Figure 2.4. (a) A record of Holocene temperature reconstructions from combined Greenland and Canadian High Arctic cores. B) Insolation over the range of time at 60° N(c) Holocene ice-core δ 18O record from Mt. Logan. (d) Mt. Logan record from PRC (grey) and NWC (black) from 1700-2000 AD. (e) Eclipse Dome cores 1996 in grey and 2001 in black f) Pollen in the ice core from PRC. The arrow is global climate event. (Zdanowicz et al., 2014)
23
Figure 2.5. Time series of accumulation at Eclipse ice core site, annual (a), and cold (black line) and warm (gray line) seasons (b). Time series of average standardized isotopes, annual (c) and cold (black line) and warm (gray line) (d) seasons. Adapted from Kelsey et al., 2012 Due to the lower elevation of my core site, there are likely higher temperatures relative to
Mt. Logan and Eclipse that cause melting to occur in the accumulation zone, blurring the stable isotopic signatures. However, not all is lost –it may be possible to distinguish melt-affected layers from the remaining firn and use isotopic modification as a record of warm periods (Cuffey
& Paterson, 2010).
There have been other studies on isotopic washout outside of the St. Elias region. In a study in Svalbard, Pohjola et al. (2002) determined that the seasonal isotopic cycle is preserved for annual or biannual accumulation, as percolated meltwater did not go deeper than that year’s snow accumulation, even with melting of up to 50%. However, Grumet et al., (1998) found little
24 ability to detect seasonal changes in highly melt-affected sections of the core from Penny Ice
Cap, yet identified trends in the major ions (which will be discussed in the next section). Koerner
(1997) stresses the importance of evaluating melt-affected ice cores with caution due to the wash-out of isotopic signals at the Devon and Meighen Ice Caps.
2.6.1.1 Post-depositional changes and composition of stable isotopes.
Stable isotopes are modified through various processes outside of meltwater percolation and refreezing. Snowdrift is one such process. As snow will drift from high to low places this will cause isotopically light snow to go towards places that would usually have heavier isotopes
(due to the elevation relationship of isotopes) (Arnarson, 1981). Isotopic diffusion also causes post-depositional changes in isotopic composition. Vapour-phase isotopic diffusion occurs more rapidly than solid phase and is most pertinent in seasonal snow (Sinclair & Marshall, 2008). This will attenuate the amplitude of the seasonal cycle and remove events such as large storms
(Shiraiwa et al., 2002; Pohjola et al., 2002).
Firn can become isotopically enriched in post-depositional processes as well. In low humidity conditions, sublimation may cause the surface snow to become enriched (Sinclair and
Marshall, 2008). When melting occurs and meltwater is in contact with the ice, the isotopes will exchange between the liquid and the solid. The heavier isotopes will “prefer” to be in the solid phase, in equilibrium the solid phase will eventually have 30% more O18 and 20% more deuterium (Arnarson, 1981). This will leave the liquid water depleted in heavy isotopes.
However, in the conditions of melt percolating through snow, equilibrium is only partially reached (Arnarson, 1981), this may indicate kinetic fractionation.
2.6.1.2 Meteoric water line as an indication of melt.
25 The meteoric water line describes the relationship between δ18O and δD in precipitation, and can potentially indicate kinetic fractionation. The molecular physics and atmospheric processes of the respective isotopes in water are similar, however H/D undergoes the process 8 times stronger (Sodemann, 2006). The Global Meteoric Water Line (GMWL) describes the average relationship of δ18O and δD:
18 δD = 8δ O + 10‰ Equation 7
The slope is related to the isotopic fractionation factor of both δ18O and δD. The isotopic fractionation factor is identical to the isotope exchange reaction of one atom exchanging with an isotopically different atom (Faure, 1998). A slope that differs from 8 indicates the proportion of the fractionation factor of δ18O and δD is different than the global average (Faure, 1998). This is due to different fractionation processes occurring in non-equilibrium. The intercept is the deuterium excess, indicating that over long time periods there are fractionation differences between δ18O and δD. Variations in the slope and the intercept can occur due to local processes and is described as the Local Meteoric Water Line (LMWL). Meteoric water lines are often used in surface isotope hydrology, however because it describes the relationship between O18 and deuterium it can be used to understand changes in their relationship. Looking at the meteoric water line can indicate if water has been percolating and exchanging isotopically with the firn.
2.6.2 Ions.
Concentrations of ions in ice cores provide proxies for various Earth system processes and relationships that are otherwise difficult to investigate. Many ions in ice cores, such as Na+,
2+ + - 2+ 2- Mg , K , Cl , Ca , and SO4 likely originate as sea salts (through sea spray) and from local terrigenous dust. Some ions are picked up from wind and trace to geologic events (e.g. volcanism). The concentrations of such ions can provide information on the provenance of
26 precipitation and lend insight into large-scale atmospheric-transport dynamics and pollution
(Wake, 1989). For example, there is a relationship between sea salt concentrations and sea ice.
Sea ice extent, formation processes, productivity, and polynyas are related to methanesulfonic acid (MSA) in ice cores (Criscitiello et al., 2016). Secondary aerosols can lead to possible dating constraints on periods of accumulation and snow deposition, while other chemical markers assist with chronology via volcanic horizons (Cuffey and Paterson, 2010).
Distinct atmospheric circulation patterns in my study region are paired with sea salt Cl- and non-sea salt K+, and also indicate distinct peaks and cycles in seasonal ionic concentrations
(Kelsey et al., 2012). At Eclipse Icefield, Na+ and Cl- was found to peak in fall or winter and dust species Ca2+ and Mg2+ peak in the summer or spring (Yalcin et al., 2006; Kelsey et al., 2012).
This seasonal variation in the St. Elias is mainly attributed to sea-salt entrainment from rough seas in the Gulf of Alaska and large-scale atmospheric transport dynamics that vary with season as well as location of entrainment (Yalcin et al., 2006). In rare occurrences, Asian dust has been found in the St. Elias icefields, originating from large dust storms over the Gobi Desert
2- (Zdanowicz et al., 2014). Sulphate (SO4 ) accounts for various different sources and transport trajectories (Pohjola et al., 2002). However, in the St. Elias region, non-sea-salt has been used to date identified volcanic layers in ice cores (Zdanowicz et al., 2014). When sulphate is one standard deviation above the mean, it is associated with a volcanic horizon (Yalcin & Wake,
2001). Sulphate and nitrate are identified to peak with the δ18O peak at Eclipse Icefield (Yalcin et al., 2006). This implies that there is a seasonal variation in sulphate and nitrate deposition in the snow. The seasonal peak in sulphate may be due to dust; however, the nitrate is not as clear due to the variety of mechanisms of nitrate uptake (e.g., biological activity, biomass burning, and release of NOx) (Yalcin et al., 2006). Due to the presence of seasonal ionic peaks and cycles,
27 core chronology may be possible, aiding constraints on seasonal accumulation rates and refrozen ice content.
When melt and refreezing are present, despite dilution and diffusion processes, there can still be a clear atmospheric signal. There will be a greater concentration of strong acids at the
- bottom of melt-affected layers because strong acids (HSO4 and HNO3) are eluted first, and then
+ - + the typical sea salt ions are eluted (Na and Cl ) (Davies et al., 1982). Least affected is NH4
(ammonium) (Pohjola et al., 2002). Pairing ion analysis with stable isotope analysis should allow for detection of “wash-out” because it will be present in both stratigraphies (Wake, 1989). The wash-out may be prevented when liquid water hits a density threshold, such as impermeable surface like an ice layer or a lower density section, and spreads laterally. If meltwater percolation does not go beyond the annual accumulation, then annual ion cycles are still identifiable.
Meltwater that percolates more deeply may complicate the signal and interpretation of the core.
Major ions may still be detected in melt-affected layers due to differential elution, despite the wash-out of the isotope signal (Grumet et al., 1998). In previous studies of the St. Elias regions, major ions were still detected and seasonal cycles identifiable.
2.7 Summary
In the face of rapid anthropogenic warming, there is an urgency to quantify sea level rise, and the changing hydrology of regions, due to the degradation of the cryosphere. While glacier mass balance studies may be effective tools to investigate this change, large ice sheets and remote inaccessible regions often result in a lack of exhaustive field studies. To address this challenge, remote-sensing techniques are being applied to gather data on the Earth’s shrinking glaciers. However, these methods possess their own challenges, including the dynamics of
28 densification and ice lens formation. The icefields in the St. Elias regions are thought to be one of the largest contributors of sea level rise in the Alaska region (Berthier et al., 2010). While numerous mass balance studies have been conducted in this region via remote-sensing (Luthcke et al., 2008, Arendt et al., 2008, Berthier et al., 2010), there is still a need to verify the accumulation zone processes and firn properties. The impact of changing densification rates and the presence of ice lenses may be significant enough to lower the glacier surface where the remote-sensing technologies can detect it. Firn water storage will change the liquid water run-off and storage properties. Future paleoclimate studies may also be impacted by melt-affected firn.
29
Chapter 3: Study Area
In this chapter I discuss the chosen study area and nearby locations that also have had ice cores or firn cores retrieved.
The St. Elias Mountains are located in the southwest corner of Yukon Territory, bordering Alaska (Figure 2.3). These mountains have 90% of their elevation between 300-2200 m above sea level (a.s.l.), with several notable exceptions of mountains higher than 3200 m, including Mount Logan (5959 m a.s.l.) (Foy, 2009). The mountains serve as a boundary between the coastal climate and the continental climate that persists on the eastern side of the range. The
St. Elias range is home to the largest icefield outside of the polar regions with an area of 46,000 km2 (Berthier et al., 2010). Kaskawulsh Glacier is located on the eastern side of the St. Elias
Mountains within the Donjek Range, approximately 95 km from KLRS. Kaskawulsh Glacier consists of three main tributaries, the North, Central, and South Arms, and ice thickness ranges from estimated values of 350 m to 820 m (Flowers et al., 2014). Elevations of Kaskawulsh
Glacier range from 820 m a.s.l. at the terminus to ~ 2660 m a.s.l. at the continental divide.
Kaskawulsh Glacier is approximately 70 km long and meltwater drains out to the Kaskawulsh
River, and previously to the Slims River as well. The Kaskawulsh River carries meltwater to the
Alsek River and then to the Gulf of Alaska (Shugar et al., 2017).
The St. Elias region was chosen due to the need to establish reference sites for regional mass balance information; the World Glacier Monitoring System (WGS) currently lacks benchmark glaciers in St. Elias, Yukon. This project will help characterize accumulation area processes in the St. Elias range. Kaskawulsh Glacier was chosen due to its proximity to KLRS
30 for logistical matters it is most convenient to base research out of the AINA research station at
Kluane Lake. It also has a long history of studies (Shugar et al., 2017; Foy, 2009; Flowers et al.,
2014; Wood, 1963). The drill site was chosen due to the high elevation and flat topography of the area as well as the proximity to previous drill sites in the 60s (Figure 2.3). A weather station has been located on upper Kaskawulsh since 2013
(https://datagarrison.com/users/300034012631040/300234060337540/plots.php), and
Environment Canada has a station located at Burwash Landing (located on Kluane Lake) since the 1970s.
Yukon climate is characterized as a subarctic climate, due to it’s continental locality and southern most latitude of approximately 60°N. Burwash Landing is located at 800 m elevation along the shore of Kluane Lake. The average temperatures vary from -33° to 15°C with an average temperature of -3°C (Figure 3.1). Burwash Landing experiences precipitation of around
1 meter of snow a year. The drill site on Kaskawulsh Glacier is significantly higher than
Burwash Landing (~2600 m), however, Burwash gives some insight into the regional climatology.
Figure 3.1. Burwash Landing temperature time series from 1966 to 2018.
31 At a location in the Icefield Luke Copland has had a weather station present for the last 5 years. Though this is not the exact location of my drill site (Figure 2.3), it is only 58 m lower in elevation than my location (~2600 m a.s.l.). This weather station may give a reasonable approximation for the positive degree-days in which melt may have occurred. Figure 4.2 and 4.3 display the time series of this data. The mean monthly temperature over the last five years has a cyclical trend with highs slightly above 0°C and lows around -16°C. In 2017, the average was much lower, closer to -23°C. Compared to Burwash Landing, this site may experience less extreme cold temperatures because of it’s closer proximity to the continental divide and the ocean. The number of positive degree-days (PDD) at my site is most likely similar to the Divide station due to the comparable elevation and geographic location. Table 3.1 shows the calculated
PDD and the estimated surface melt for each year, using a degree-day melt factor of 3 mm/day°C
(Cuffey & Paterson, 2010). It is important to note that 2013 begins July 2013 so the estimated
106.4 PDD is a minimum, as potential melting that may have occurred in May or June is not accounted for.
32 Figure 3.2. Copland weather station time series of monthly mean temperatures from 2013 to 2018.
Figure 3.3. Copland weather station time series of hourly data from May 1st 2017 to May 1st 2018 displaying the variations of temperature.
Table 3.1. Positive degree days at Copland weather station from 2013 – 2018 with 2013 being the highest and 2014 the lowest.
2013 2014 2015 2016 2017 2018
PDD (°C/yr) 106.4 22.3 90.0 76.6 71.2 84.8 Melt (mm w.e.) 319.0 67.0 270.0 230.0 214.0 254.0
These five years demonstrate that Kaskawulsh Glacier accumulation zone experiences
wide variation in PDD, and the melt season is short, within the 1-month range around July.
In 1961 the Icefield Ranges Research Project (IRRP) was funded by AINA and the
American Geographical Society, with the objective of increasing understanding of glaciers in
this region. Kaskawulsh Glacier was one of the primary glaciers studied in this project (Wood,
33 1963). In 1969 this work was discontinued. My chosen drill site, 60°46'37.20"N,
139°37'48.33"W, is located in close proximity to the IRRP observation and drill sites, between the IRRP Divide and Kaskawulsh Site.
In addition to the IRRP studies, an ice core was drilled at the Eclipse Icefield 20 km to the west in the 1990s (3017 m a.s.l.) (Zdanowicz et al., 2014). Cores were also retrieved from the plateau of Mt. Logan, approximately 50 km away, but at a much higher elevation than the Divide site (4135 and 5340 m a.s.l.) (Zdanowicz et al., 2014). These cores have been extensively analyzed (Zdanowicz et al., 2014; Yalcin & Wake, 2003; Kelsey et al., 2012; and others).
Utilizing these previous studies and their spatial variation in conjunction with my work will aid in understanding the St. Elias region as a whole.
In June 2018, Dr. Karl Kreutz and team retrieved an 18-m firn core, at a drill site 13 km
SW of my site (Figure 2.3) (60.68 N, 139.78 W) (Kreutz, K., personal communication,
November, 2018). Their isotope and stratigraphy data is used for comparisons with my data, due to its close proximity and similar elevation (~2640 m a.s.l.).
34 Chapter 4: Methods
In this chapter I discuss the methods used in retrieving and processing the firn core, and analyzing the subsequent data. First, I relay the field methods, for drilling the firn core, and processing the retrieved core sections. I then discuss density calculations, density uncertainty, calculated background firn, ice layer stratigraphy, stable isotope analysis, and ion analysis.
4.1 Field Methods
4.1.1 Drilling.
Ice cores were collected from the drill site on May 20-23, 2018. The drill that extracted the ice core is the Eclipse Ice Drill, created by Icefield Instruments Inc. of Whitehorse, YT. This drill is intended for intermediate depth (up to 250 m) dry-hole drilling. Once we arrived we dug a
2-m deep trench. The legs of the drill straddled the sides of the trench (Figure 4.1a). This is necessary because the drill barrel moves like a half pendulum and swings from perpendicular to parallel to the snow surface. The drill brings up core sections approximately 1 m in length. We retrieved a 36-m core and a 21.5-m core. Once each section of the core was retrieved we slid the core section out of the barrel and onto an impermeable surface placed behind the windbreak, in order to process the core without contamination from the surface snow (Figure 4.1b).
At 35.6 m we drilled into a saturated layer. When we pulled the drill out it was dripping water; water was draining from the ice core section inside of the drill barrel. This happened three more times. We were able to retrieve two more meters of ice core that had been completely saturated. At 36.6 m depth we stopped drilling because we did not want to lose the drill in the watery ice.
35 a
b
Figure 4.1. a) Eclipse Ice Drill b) Configuration of aerial perspective of drill set up.
36 4.1.2 Field processing.
The core was partially processed in the field. When drilling, each 1-meter core section was removed from the drill and the entire length and the diameter at each end were recorded using a tape measure. When the weather permitted, I took photographs. Starting from the top of the core, ice lenses’ and ice layer’s location and thickness were logged as well as any other noticeable features in the core piece, and any other effects of meltwater refreezing (e.g., dark soot layers). If the layer of ice was continuous across the entire core it was described as an “ice layer” and if the ice was laterally discontinuous and did not span the entire cross-section of the core then it was described as an “ice lens” (Figure 4.2). Other meltwater refreezing effects were distinguished by a change in crystal structure within the firn where irregular shaped bubbles exist, distorting the granular texture of the firn. This is created through refreezing of liquid water
(Figure 4.2b and 4.2c).
Figure 4.2. Examples of firn cores a) clean firn b) melt-affected firn c) firn with a large ice layer.
37 The 1-m core sections were then sealed in the impermeable barrier wrap until further processing later that same day. They were stored in the shadow of the windbreak (Figure 4.1b) in order to prevent partial melting from direct sunlight. Further processing included:
1. Sawing the core into 10-cm pieces and placing into plastic, sealable bags.
2. Weighing each ice-core section.
Bags were included in the mass and the weight of bags was subtracted afterwards. The
Taylor and the OHAUS Scout Pro version SP401 scale, which has an uncertainty of +/- 0.1 g
(https://dmx.ohaus.com/WorkArea/downloadasset.aspx?id=6046), were used to weigh the 10-cm core sections.
4.1.3 Snowpit.
One snow pit was dug to a depth of 1 m. This was dug 30 meters away from the drill location. Every 10 cm a snow-density measurement was made in situ, by extracting a 200-cm3 sample of snow using the SnowMetrics RIP 2 Cutter snow density kit. Within every 10 cm the 5- cm sized sampling box was inserted, ideally in the middle of the layer (between 3-8 cm) but if there was an ice layer I tried to include that. Only one ice layer was not included in the sample (a
1-cm thick layer at 60 cm). Then the samples were bagged and weighed.
4.2 Firn Density Calculation
Using the data collected, I calculated the density. I used the measured value for mass of the 10-cm ice-core section and calculated the volume. Each 10-cm section had its diameter measured at each end and I used the average diameter in my calculations. The equation to determine the density is as follows:
38 2 ρ = m/V, with V = f πL(D/2) Equation 8
Where ρ is the density of the ice, m is mass, D is the average diameter of the ice-core section, L is the length, and f is the subjectively-assessed fraction of completeness of the core section. For instance, if 5% of the core is missing then f would be 0.95. This density is considered to be the raw data. Outliers were removed if they were not physically possible (e.g., values above 917 kg/m3 or below 300 kg/m3 at great depths). To smooth out the density data I averaged the density over 1-m increments in order to understand the general trend.
In order to parameterize the densification I used the built in logarithmic (exponentially decreasing) curve in Microsoft Excel. This line of best fit is a simplified representation of
Sorge’s densification model and is not used to describe the process as a whole. Using this line of best fit for all of the variations and manipulations of data allows us to have a consistent metric to compare and contrast the various versions of the densification curves, such as the different background firn densities and the density of the raw and averaged data.
4.3 Density Uncertainty
In order to calculate the density uncertainty, numerous sources of error have to be taken into account. Random and human uncertainties propagate through this equation and therefore must be calculated carefully.
! ! � = ( !"#)! + ( !"#)! Equation 9 !"# ! !
Where M is mass and V is volume and D is density. The mass uncertainty was assumed to be 0.3 grams. This is a generous uncertainty given the scale’s accuracy, but accounts for potential
39 residual snow or water on the scale during taring. The volume uncertainty is calculated by breaking down the equation for sample volume:
2 V = f πLr Equation 10
Or
V = LA Equation 11
2 A = πr Equation 12 r = D/2 Equation 13
Where A is the area of a circle. There is uncertainty in the measured length of the core section
(L), the radius of the core section and the assessment of the completeness of the core sample (f).
Each of these was calculated independently and then put into the following:
! ! ! ������ = ( !"#)! + ( !"#)! + ( !"#)! Equation 14 !"# ! ! !
Lunc was assumed to be 0.25 cm because the tape measure had ticks at every mm so it could be measured with precision, but core sections were often uneven, with crumbly edges from the core cutters in the drill. I assign the same uncertainty to the measurement of core diameter.
Finding the uncertainty of the diameter and dividing that by two determines the uncertainty of r, following:
! ! �!"# = (0.25) + (0.25) Equation 15
This is equal to 0.18 cm; therefore the uncertainty in r is 0.09 cm. This is squared and used to estimate the uncertainty in the cross-sectional area, Aunc:
40 !! � = πA !"# Equation 16 !"# !!
The uncertainty in f is more difficult to assign. Looking at the quality of the shape of the core and deciding how much of a complete cylinder the core section was determined the values of f.
Three different people performed this evaluation and therefore there is subjectivity in each of the f values. Because of this I decided to assign the following uncertainties for f:
±0.2 for f ≤ 0.8,
±0.1 for f ≥ 0.9
The uncertainty of a higher f value is lower because when a core is of good quality it is obvious; less complete cylinders are more difficult to assess, hence the greater uncertainty for values of f ≤ 0.8. For values greater than 0.9 this creates a problem of having values greater than
1, due to the nature of the uncertainty equation, we cannot assign different uncertainty in different directions (ie. uncertainty of 0.1 above 0.9 and 0.2 below 0.9). To account for this discrepancy an upper bound of 917 kg/m3 is assigned. The value that has the greatest effect on the uncertainty is the f parameter due to the subjectivity of the assessment of f.
f values for Core 2 were not recorded in the field. First, I looked at the f values of core 1; the minimum recorded in core 1 was f = 0.7, with a maximum of 1, with an average of 0.96. In order to have an appropriately conservative estimate I assigned an uncertainty of 0.9 ± 0.1 for all of core 2. The first quartile was 0.95 and 3rd quartile was 0.99; therefore it is reasonable to assign an uncertainty of 0.1 to be consistent with what is applied in core 1.
41 4.4 Background Firn Density
4.4.1 Ice content.
To quantify the changes throughout the firn core stratigraphy I calculated the ice content fraction. Ice content is a percentage of ice in each core section. I calculated ice content instead of melt percent because generally melt percent (as used in Koerner, 1977) assumes the melt has stayed within the annual accumulation and in this case I cannot assume that. Using the measured thicknesses of the ice layer(s), I determined the proportion of ice for each 10-cm section (Fi).
Li + Ll/2 = Totalice Equation 17
Totalice/Length = Fi Equation 18
Where Li is ice layer thickness (cm) and Ll is ice lens thickness, assuming that it occupied 50% of the cross-section.
4.4.2 Background firn density.
To understand the density of the firn without ice layers I removed the ice layers from the samples. I used a 30-cm moving average density data in order to back out the background firn density. I averaged the density in order to smooth out a possible deviation of ±10 cm in assigning the location of the ice features within the stratigraphy. I kept the outliers in the moving average because they could be physically possible within my uncertainty estimates. I assume the ice layers and lenses have a density of 874 kg/m3, based on the average density of firn-core sections that were 100% ice in Greenland (873 kg/m3) and Devon Ice Cap (875/kg/m3) (Machguth et al.,
2016; Bezeau et al., 2013). I then removed the proportion of the ice density from the total density in order to obtain the background firn density. This is described in the equation below, where �!"
42 is the background firn, �! is the measured density, �! is the density of ice, Fi is the fraction of ice, and Ff is the fraction of firn (1- Fi).
�!" = (�!- Fi*�!)/Ff Equation 19
Lastly, a line of best fit was determined for the densification curve.
4.5 Ice Layer Stratigraphy
4.5.1 Stratigraphy visualization.
Stratigraphic information was translated to a visualization of the stratigraphy in a graphics program (Gravit.io and PaintPad Lite). The relative size of the ice layer thickness on the figure was chosen due to the classification scheme that maximizes variance between classes of different ice layer sizes.
4.5.2 Ice layers and density.
In order to visualize the relationship of the ice lenses and layers and the density another stratigraphy log was created combining the ice layer location and the density of the ice core. This was done using the background firn density and drawing the ice layers.
4.6 Stable isotopes
4.6.1 Laboratory analysis.
At Kluane Lake Research Station (KLRS), from May 22-25, 2018 the core samples were melted in a hot room and double bagged, with little to no air in the bags with the samples. Within twenty-four hours, the water samples were poured into scintillation vials with cone shaped caps and kept in a cooler until transport back to Calgary where they were refrigerated before isotope and ion analysis.
43 At the University of Calgary in the Isotope Science Laboratory (ISL) at the Department of Geosciences, the samples were pipetted into 2 ml glass isotope vials with septa caps. Using the Los Gatos Research Liquid Water Isotope Analyzer, Enhanced Performance Model DLT-
100, the samples were run with the standards: Vienna Standard Mean Ocean Water (VSMOW),
Vienna Standard Light Antarctic Precipitation (VSLAP), and Greenland Ice Sheet Precipitation
(GISP).). The accuracy is 0.1‰ for δ18O and 1.0‰ for δD.
4.6.2 Preliminary analysis.
I plotted isotope stratigraphies and calculated deuterium excess (d) from d = δD – 8 *
δ18O for both Core 1 and 2.
Summary statistics of the data were calculated using R. Based on Pohjola et al., (2002), understanding the distribution of the data can lead to inferences on how meltwater percolation and refreezing has affected the data. The statistics used are the following: Average is the average of the raw data, σ is the standard deviation. Skewness is a measure of the symmetry of the statistical distribution (the closer to zero the more Gaussian the distribution is), and Kurtosis describes the tails of the distribution and their relative weight compared to the rest of the data distribution (a higher Kurtosis (>3) implies more outliers or heavier tails). The variable, X, is the sample-to-sample difference and I calculated it by finding the difference between two samples and then determining the average difference for the whole core. X indicates whether transitions from sample to sample are smooth or abrupt.
44 4.7 Ions
4.7.1 Laboratory analysis.
At the University of Alberta, at the Canadian Ice Core Archive, in the Department of
Earth and Atmospheric Sciences, core samples were analyzed for anions and cations using a
- - 2- 3- + Dionex IC5000. The analysis included anions: MSA, Cl , Br , NO3 , PO4 ; and cations: Na ,
2+ + 2+ 2- + Mg , K , Ca , SO4 , and NH4 . The precision of this analysis is in table 4.1.
Table 4.1. Precision of ion analysis, separated into anions and cations.
Anions (ppm)
- - 2- - MSA Cl Br SO4 NO3
0.0004 0.0003 0.0003 0.0002 0.0004
Cations (ppm)
+ + + 2+ 2+ Na NH4 K Mg Ca
0 0.0030 0.0007 0.0003 0.0002
In the chapter above, I discussed the methods used in retrieving and processing the firn core. While in the field I documented the stratigraphy of the ice cores and then weighed the samples for density measurements. I used these density measurements to calculate the uncertainty. Using the ice layer content and the density I calculated the background firn. The isotopes and ions data was examined with alteration and statistical analysis on the distribution of the data was also performed. In the next chapter the results from these analyses will be discussed in detail.
45 Chapter 5: Density Results
In this chapter I delve into the results from my analysis of the densities of Core 1 and
Core 2. I discuss the density-depth profiles and use a line of best fit to make comparisons among the cores. I also discuss the uncertainty associated with the density measurements. I show the ice layer stratigraphy and ice content and interpret the patterns that are present in the profile. Using the calculated density data, I remove the ice content in order to obtain an estimated background firn density. Using this data, I estimate surface lowering that has resulted from densification in the form of ice layer formation.
5.1 Firn Density
The density of firn on Kaskawulsh Glacier increases logarithmically with depth. The raw data of the density versus depth curve shows a logarithmic increase, as to be expected given
Sorge’s Law of densification (Figures 5.1 and 5.2). The density rapidly increases in the first 5 m below the snow surface. Between 5 and 10 m depth the density varies widely, from 300 to 900 kg/m3. Below 10 m, firn density increases logarithmically from about 550 to 750 kg/m3. This corresponds with the second phase of densification and Sorge’s Law. In Core 1, between 20 and
32 m depth there is a wide range of values of density, from about 650 to 908 kg/m3. At 32 m depth the density begins to cluster from 800 to 900 kg/m3, indicating the possible transition to the density of pore close off (glacial ice), however that is density is for dry-firn and this was wet.
Core 1 has an average density of the upper 10 meters of 558 kg/m3 and Core 2 571 kg/m3, giving an average of 564 kg/m3. From 14-36 m, Kaskawulsh firn has an average density of 670 +/- 7.5 kg/m3. As a whole, Core 2 follows a similar pattern with an abrupt initial increase and then scattered values at greater depths. During transport and melting, some of the sample bags
46 containing Core 2 were torn and leaked, meaning not all Core 2 samples could be used to calculate density. Hence, there is a lower density of data points for Core 2 in Figures 5.1 and 5.2.
Figure 5.1. Scatter plot of density of both cores displaying a logarithmically-increasing trend.
47
Figure 5.2. Density versus depth plot to 16 m of both cores, indicating scatter and variation between the cores.
A smoothed version of the density data for Core 1 is displayed in Figure 5.3, based on 1- m averages of a single value. When a line of best fit was applied, it had an R2 of 0.71 (not displayed). In the smoothed data there is still greater scatter at 8-11 m depth, with values ranging from 400-700 kg/m3. At 30 m there is a drop in density and then at 32.5 m density increases
48 again to higher values (~800 kg/m3). This is also when we hit liquid water. Due to the sparse data of Core 2, this density analysis was not performed.
Figure 5.3. Smoothed 1-m density for Core 1, displaying a logarithmically-increasing trend with scatter still present.
49 Figure 5.4 is a combination of the curves of both Core 1 and 2. I removed the bottom 15 meters of data for Core 1 (Figure 5.4B) to compare the density profiles. The line of best fit is almost identical, despite ice layer and lens stratigraphic differences. The values of Core 1 when cut to 21 m are not statistically different than Core 2 (t = 1.36, p-value = 0.17). The slight deviation in the first several meters is likely due to the sparseness of the data of Core 2. When I added the bottom 15 meters of Core 2 the line of best fit differs (Figure 5.4A). This addition makes Core 1 statistically different than Core 2 (t = 4.38, p-value ≪ 0.05). The density changes significantly at depth, pulling the curve to greater densities. This indicates that a 21 m core is not sufficient for understanding how the density changes at the greater depths present in Core 1.
50 A) B)
Figure 5.4. Comparison of density curves for two different depths, A) all data for Core 1 and 2 B) Core 1 cut to 21 m for adequate comparison (the equations represent y being depth and x being density). 5.2 Density Uncertainty
Figures 5.5 display the uncertainty associated with each measurement. The general trends are still apparent even with the large amount of uncertainty. However, the uncertainty can help explain some of the outliers present on the plot. Using the Sharpio-Wilk test, the uncertainties of
Core 1 are normally distributed (Core 1: W = 0.99, p-value = 0.36; Core 2: W = 0.95, p-value ≪
0.05, where W is the test statistic) (Figure 5.6). Core 2 has an outlier (uncertainty of 224 kg/m3) that pulls the distribution away from normality (Figure 5.7). With that one point removed the
51 uncertainty is normally distributed (W = 0.98, p-value = 0.23). A normal distribution of uncertainties is ideal because it implies that the underlying causes are from natural processes and random, rather than systematic, errors. The uncertainties of Core 1 and Core 2 are not consistent.
Core 1 has an average uncertainty of 185 kg/m3 and Core 2 110 kg/m3. While this is quite different, the two cores are similar when uncertainty is expressed as a percentage of the mean density. Core 1 has an uncertainty of 14% whereas Core 2 has an uncertainty of 12%. This is not that great of a difference; therefore the difference in the numbers is likely due to two things 1) the sparseness of data for Core 2 and 2) Core 1 goes to a greater depth, with higher densities yielding a greater numerical uncertainty. Outliers were kept in some analysis, and when removed it was specified.
52
Figure 5.5. Uncertainties included with density measurements for Core 1 and Core 2.
53
Figure 5.6. Distribution of uncertainties in Core 1 displaying a slightly skewed normal distribution.
Figure 5.7. Distribution of uncertainties in Core 2 showing a normal distribution with a right tail.
54 5.3 Ice Layer Stratigraphy and Content
5.3.1 Stratigraphy and content.
The stratigraphy of Kaskawulsh Glacier firn cores indicates significant meltwater percolation and refreezing events. In Figure 5.8, the first meters of the cores displayed several small ice layers. The ice layer thickness increased with depth with several large (> 10 cm) ice layers at 6.6, 14.1, 22.0, and 25.6 m. The largest ice layer found in Core 1 was 22 cm thick, found at 14.1-m depth. At 26.4 m the ice layers and lenses disappeared. Below this the firn is almost entirely meltwater-affected, based on the texture, but is without the quantity of ice lens or layer content that was present in the first 25 m. This is addressed further in the discussion. At 30 m the meltwater effects were absent and there were two small ice layers and an ice lens. At 30.6 m the firn was melt-affected again until 36.6 m.
In Core 2 there were numerous ice layers starting at a depth of 3.8 m and below 4.4 m the firn was meltwater-affected. There was a large ice layer around 6.6 m, which was 30 cm lower than a similar large ice layer in Core 1 (at 6.3 m). There were numerous melt-affected layers between ice lenses much closer to the surface in Core 2. These features may have been missed in the first core. In Core 1 there were several ice layers at ~10 m depth and these layers were not present in Core 2 at all. At 14.4 m another section of the firn had numerous ice layers: this was also seen in Core 1 but is 20-30 cm deeper here. At 14.6 m the largest ice layer was encountered
(12 cm thick); this is 20 cm lower than the depth of the largest ice layer in Core 1.
The spatial inconsistency of ice layers between Core 1 and Core 2 is not surprising because ice layers are often not horizontally consistent, even on length scales less than a meter
(Parry et al., 2007). Spatial continuity of ice layers depends on the thickness of the ice; thicker ice layers would be expected to cover a larger area (Harper and Bradford, 2003), as they tend to
55 indicate a major meltwater event that is more likely to be regional in nature. Between 16 and
21.5 m the firn was melt-affected. At 21.5 m we stopped drilling due to the conclusion of the field campaign.
56
Figure 5.8. Stratigraphy of Core 1 and Core 2. Note that the ice layer thickness is not to scale. Core 1 displays numerous melt-affected layers with the presence of ice layers and lenses, Core 2 displays many ice layers and lenses but also melt-affected firn, as present at the bottom of Core 2.
57 Figure 5.9 display the ice content fraction and depth for Core 1 and 2 as determined from the stratigraphic observations. The total ice content in Core 1 was greatest between 12.2 and 24.4 m depth. The ice content in Core 2 was greater towards the middle of the firn layer then at the surface, or deeper when closer to the glacier ice zone. Both cores had increasing ice content fraction towards around 20 m depth, in Core 1 the ice fraction decreases to zero at 30 m. The ice content in Core 1 and Core 2 tended to be made up of smaller ice layers and lenses instead of large ones (Figure 5.10a and b).
58
Figure 5.9. Ice content with depth of both cores. Core 1 and 2 have ice content of all sizes and a wide range of data. There is a lack of ice content after 30 m in Core 1.
59 A)
B)
Figure 5.10. Histogram of ice fraction frequency displaying similar distributions in A) Core 1 and B) Core 2.
60 5.3.2 Ice layers and density.
Ice layers are more likely to be correlated with higher-density samples. In order to run correlation statistics one must understand the normality of the data, for that will determine which correlation test is best for your data. I ran three different correlations of the density data and ice content. Table 5.1 displays the results of the Shapiro-Wilk normality test.
Table 5.1. Shapiro-Wilk tests for density and ice layer presence in Core 1 and 2 showing mostly non-normal data.
Data Shapiro-Wilk Result
Core 1 Core 2
Ice Fraction W = 0.83, p-value ≪ 0.05 W = 0.34, p-value ≪ 0.05
Not normal Not normal Raw Density Data W = 0.98, p-value ≪ 0.05 W = 0.97, p-value = 0.09
Not normal normal Raw Density Data with W = 0.97, p-value ≪ 0.05 W = 0.97, p-value = 0.04 Outliers Removed Not normal normal Moving Average Density W = 0.96, p-value ≪ 0.05 NA Data Not normal
Spearman’s Correlation is the type of correlation that is best suited for non-normal data.
Though Pearson’s Correlation and Kendall’s Correlation were run, Spearman’s is the most reliable due to the distribution of the data. Unfortunately, Core 2 is not able to have correlation tests because there are not enough samples with ice content (12) which is less than the required for correlations, the tests would be meaningless without more data (Algina and Olejnik, 2003).
61 Table 5.2. Correlation results using Spearman’s Correlation test for Core 1 showing correlations between 0.27 and 0.37 depending on the data used.
Data Spearman’s Correlation Test Core 1 Raw Density and Ice S = 66971, p-value = 0.0004873 Fraction sample estimates: rho 0.368
0.37 correlated
Raw Density Data with S = 65100, p-value = 0.007887 Outliers Removed and Ice sample estimates: Fraction rho 0.291
0.29 correlated
30-cm Moving Average S = 77667, p-value = 0.01286 Density Data vs. Ice sample estimates: Fraction rho 0.267
0.27 correlated
Table 5.2 displays the results of the correlation test with three density data sets vs. ice fraction. The original raw density data is most correlated with the ice fraction data with a correlation of 0.37. When the outliers are removed the correlation decreases to 0.29. I think this is because most of the outliers have both exceptionally high densities and ice layers. When using the 30-cm moving average of the density it is less correlated, with the ice content at 0.27.
5.4 Background Firn Density
The density of background firn was calculated for Core 1 using a 30-cm moving average density and a 30-cm moving average 10-cm ice content (Figure 5.12). A moving average was used in order to remove any discrepancy of a possible vertical error in the placement of ice layers
62 and lenses. Removing the ice lenses and layers creates a curve with less scatter, as evident in the
“tighter” looking relationship. However, there is still variation in the density with depth, which could be due to the meltwater-affected and refrozen firn, as discussed in section 5.3.2. Ice layers play a role but do not fully account for the fluctuations in density with depth. The patterns prevalent in the raw data, the 1-meter averaged data, and the background firn, are all still present, with a large amount of scatter in the 5-10 m depth range and a sudden shift at 32.5 m depth.
63
Figure 5.12. Calculated background firn density, showing a similar trend to the raw density data, with line of best fit included for comparison.
64 5.5 Surface Lowering Due to Firn Densification
The ice layers and ice content are significant due to their potential impact on the surface lowering of the accumulation zone of Kaskawulsh Glacier. The surface lowering was calculated using the background firn density (ρ!"# ). For each individual sample, the thinning (in cm) due to the ice layers and lenses is calculated as follows, for firn and ice fractions Ff and Fi:
!!∗ !"# !"/!! Thinning = (F! + ) ∗ 10 – 10 Equation 20 !!"#
This was summed for the whole core and divided by 100 to convert to meters. The result is 1.3 +/- 0.8 m of surface lowering. With an average density difference of 233 kg/m3 and an average uncertainty of +/- 140 kg/m3 results in a final uncertainty associated with surface lowering of +/- 0.78 kg/m3. This is likely to be a conservative estimate because the change in mass due to the infiltration ice is not included in this estimate. The cores that were affected by this process are likely to be denser and more surface lowering may have occurred. Surface lowering due to ice layer formation in Core 2 was not calculated due to the sparse density data available.
5.6 Summary
In this chapter, I articulated the findings from the density analysis. Kaskawulsh firn had an average density of 670 +/- 7.5 kg/m3 (14-36 m depth) and the average of the first ten meters of firn was 564 kg/m3. There is a drop in density at 30 meters and then increases again at 32.5 meters near the dry firn pore close-off density of 800-900 kg/m3, this may be affected by the presence of liquid water in pores. Despite high uncertainties, the trends are still present in the density data. The ice layer and ice content stratigraphy are heterogeneous between the cores, but numerous refrozen melt features are present in both cores. In Core 1 we drilled into a firn aquifer
65 at 34.5 m depth. Using the ice content and the calculated background firn, I estimate the surface lowering due to the refrozen melt features to be 1.3 m.
66 Chapter 6: Isotope and Ion Results
Chapter 6 discusses the results of the isotope and ion analysis. First, I look at the stable isotopes with depth, then the deuterium excess and statistics on the isotope data. Next, I look at the ions and their patterns with depth. Finally, I estimate the approximate years of data that are present in Core 1.
6.1 Stable Isotopes Results
6.1.1 δ18O with depth.
δ18O with depth is partially intact and partially washed out. In Core 1 (Figure 6.1) the average δ18O for the 36-m core is -24.6 +/- 0.7‰. During winter 2018, the isotopic range is -19.5 to -29‰. The next lowest value, similar to the previous winter’s (2017) isotopic low, is -27.1‰ at a depth of 5.7 m. There is some scatter from 5.7 to 7.1 m depth, and then a period of homogenization to 10.6 m depth, where the range of the values is -23.0 to -25.3‰, with an average of -23.8‰. This is slightly enriched relative to the mean value of the core. The subsequent negative peaks are at 11.6 m (-26.0‰) and 16 m (-27.0‰). At 16.9 m depth, another period of homogenization begins; it ends at 21.6 m with a range of -24.1 to -25.1‰ and an average of -24.7‰. There are three more negative spikes in the core at 22.7 m (-27.0‰), 24.1 m
(-26.6‰), and 26.7 m (-27.1‰). At 26.9 m another period of homogenization starts for the rest of the core. However, at 31.9 m the isotopes begin to decrease. Between 26.9 and 31.9 m the range is -24.8 to -23.2‰ with an average of -23.9‰. The bottom part of the core has a range of -
25.9 to -23.3‰ and an average of -24.8‰. There are no positive summer peaks present in the firn (i.e. values greater than -22‰).
67
Figure 6.1. δ18O profile for Core 1 and Core 2, displaying partial wash-out of seasonal isotopes. Possible preserved negative seasonal peaks are indicated with red arrows, while a general negative trend is present at the bottom of the core. Core 2 also indicates some preservation and washout of δ18O. In Core 2 (Figure 6.1) the average δ18O is -24.8‰ with a standard deviation of 1.4‰. At 8 m there is a negative peak of -
25.8‰. There appears to be homogenization from 8.3 to 10 m depth with a range of -23.8 to -
25.0‰ and an average of -24.4‰. There is possibly another negative peak of -25.9‰ at 10.5 m.
There is some scatter with three points (depths of 12, 12.2, 12.3 m) that deviate from the trend.
At 12.2 m depth the value is -26.4‰. There is a general negative trend from 12.4 to 15 m where there is a positive isotopic peak of -22.2‰. At 16.1 m there is another negative peak of -26.8‰.
A section of possible homogenization starts here (range -26.2 to -24.6‰ with an average of -
25.4‰) to a possible peak at 20.6 m of -26.7‰. No clear positive summer peaks are preserved in
Core 2. Figure 6.2 shows a similar pattern for δD with depth.
68
Figure 6.2. δD profile for Core 1 and Core 2, showing a similar pattern to the δO18 profile.
6.1.2 Deuterium excess.
Figure 6.3 depicts the deuterium excess (d) for Core 1 with depth. In Core 1 the average d is 9.8‰ with a standard deviation of 1.9‰, a maximum of 16.1‰, and a minimum of 3.9‰.
Figure 6 depicts the d for Core 2 with depth. The average d is 10.5‰ with a standard deviation of 2.1‰, a maximum of 15.7‰, and a minimum of 4.0 ‰.
69
Figure 6.3. d excess with depth of Core 1 and Core 2, with no seasonal peaks.
6.1.3 Statistics.
Based off of the work in Pohjola et al. (2002), the shape of the distribution of isotopic data can lead to information on how melt has affected the isotopes in the firn. Tables 6.1 and 6.2 display the statistics. For Cores 1 and 2 the standard deviations of δ18O and δD are not extreme values (Figure 6.4). The skewness for both cores is within +/- 0.5 of zero, indicating minimal deviation from symmetry. The kurtosis is greater than 3 for δ18O and δD in both Cores 1 and 2; this indicates that this is a “spiky” distribution with higher tails, which may be indicative of redistribution isotopes from meltwater percolation (Pohjola et al., 2002). If there were not spiky distributions, the distribution would resemble one indicative of a more cyclic pattern of isotopes corresponding to seasonal oscillations. The X series (the difference between samples, see Section
4.6.2) have low averages, indicating small changes between depths. However, because the data is
70 not normally distributed (according to the Shapiro-Wilk test) and has a large value for kurtosis, this indicates that despite smooth data transitions, there are clearly significant outliers that indicate large variations between two adjacent samples. Large jumps can occur due to various processes such as meltwater percolation, redistribution due to wind, or large precipitation events
(Pohjola et al., 2002). The d data is normal for both cores. Core 1 and Core 2 have similar averages of δ18O, -24.6 and -24.8‰ respectively and are not statistically different, when cut to 19 m (t = -1.19, p-value ≫ 0.05), or when Core 1 is 36 m (t = -1.26, p-value ≫ 0.05). In Core 1 and
Core 2 when cut to 19 m the d excess is not statistically different (t = -0.49, p-value ≫ 0.05), however, when Core 1 is 36 m they are different (t = -3.61, p-value ≪ 0.05).
Figure 6.4. Visualization of the δ18O and d excess data.
71 Table 6.1. Statistical analysis of distribution of δ18O for Core 1. Where X is the difference between two samples, N is the sample size, Average is the mean value of the parameter, σ is the standard deviation, skewness is the skewness of the distribution, where higher values higher amounts of skew, and kurtosis is the shape of the distribution, where higher values have greater amounts of spikes.
Core 1 δ18O X (δ18O) δD X (δD) d X (d) N 354 349 354 349 354 349
Average -24.6 0.5 -187.2 3.3 9.8 0.9
σ 1.1 0.8 8.1 6.1 1.8 0.8
Skewness -0.2 4.2 -0.3 4.3 0.1 1.9
Kurtosis 5.9 23.3 6.7 24.1 3.1 9.4
Shapiro- W = W = W = W = W = W – 0.85 wilk 0.96. 0.51 p 0.94 0.48 0.99; P << 0 normality P <<0 << 0 P << 0 P << 0 P > 0 Not-normal test Not- Not- Not- Not – normal normal normal normal normal
Table 6.2. Statistical analysis of distribution of δ18O for Core 2. Where X is the difference between two samples, N is the sample, Average is the mean value of the parameter, σ is the standard deviation, skewness is the skewness of the distribution, where higher values higher amounts of skew, kurtosis is the shape of the distribution, where higher values have greater amounts of spikes.
Core 2 δ18O X (δ18O) δD X (δD) d X (d) N 204 199 204 199 204 199
Average -24.8 0.7 -187.6 5.2 10.5 1.2
σ 1.4 1.1 10.4 8.2 2.1 1.1
Skewness 0.4 2.7 0.3 2.7 -0.5 2.1
Kurtosis 5.0 10.7 5.3 10.7 3.1 9.3
Shapiro W = W = W = W = W = 0.98 W = wilk 0.96 0.64 0.95 0.62 P = 0.01 0.81 normality P <<0 P << 0 P << 0 P << 0 Normal P<< 0 test Not - Not – Not - Not – Not – normal normal normal normal normal
72
6.2 Ion Results
6.2.1 Ions with depth.
Figure 6.5 display the cations analyzed and Figure 6.6 display the anions. Most of the ion
- 3- concentrations are below detection limits, except for some at certain depths. MSA, Cl , PO4 ,
2- and SO4 all have a spike at around 4.3 m depth. There is another peak of significance at around
- 3- + 2+ + 2+ 9 m depth; this is present in the Br , PO4 , Na , Mg , K , and Ca . For the highly mobile ions,
- 2- MSA, NO3 , and SO4 , there are not any indications of their presence after 9 m depth. However, that is not the case for the other ions. At first glance they, too, seem washed out, however, there are a few instances where there is a blip of concentration. At around 18 m depth there is a
- 3- - 2+ + noticeable peak in Br and PO4 . At 19 m, Cl , Ca and Na appear to have a similar peak. At 20 m K+ has a spike. At 22.5 m it is back to Br-. Below this is a period where the ions are either completely washed out with no detection or without any clear spike until around 29 m depth. At
29 m many of the glaciochemical species increase: Mg2+, K+, Cl-, and Br-. Other ions have a
3- + sudden decrease to 0: PO4 , Na , and MSA. There are three spikes clearly present at 31.4, 33.4,
- - 3- + + 2+ and 35.6 m depth in Cl , Br , PO4 , Na , K , and possibly Ca . These depths do not correspond with anything in the density or ice-layer stratigraphy. However, the spikes at 29.0, 31.4, and 33.4 m match up with the δ18O peaks.
Also included in Figure 6.5 is the Cl-/Na+ ratio (Cl- divided by Na+), displaying values less than 1.16, which is the typical value for the sea-salt ratio without any post-depositional elution of chemical species (Kohshima et al., 2007). Most of my values are below this, indicating washout in most of the core, though some areas are above or around this value. It appears to be
73 above 1.16 from ~22-28 m. This ratio may be affected if the concentrations are near the detection limit of the spectrometer or if the ions have very low values. If Na+ and Cl- experience different elution efficacies the ratio may also not be a valid assessment of wash-out.
Core 2 displays slightly different ion stratigraphy. The peak that is visible in Core 1 at 4.3 m is visible in MSA and potentially Mg2+ and Ca+. Also at 9.0 m depth, there are peaks in MSA,
Br- Ca+, Mg2+ and possible Cl-. MSA has quite a few more spikes with depth at 11.0, 13.0, 14.0, and 17.0 m. However, the next peak that is present in multiple ions occurs at ~19.0 m, in Br-,
+ - + 2+ 3- + 2- Na , possibly Cl , Ca , and Mg . In this core, PO4 , K , and SO4 do not show much variation
- in concentration throughout the entire depth of the core. NO3 displays a somewhat constant concentration from 2-22 m depth. At 19.9 m there is a 2-cm ice layer with nothing 50 cm above and nothing 40 cm below. Figure 6.7 displays summary statistics for the suite of ions for Core 1 and 2 for the first 21 m. However, Br-, Cl-, Na+, Mg2+, and Ca2+, have the greatest concentrations and spreads in both cores. When comparing the cores (Table 6.3) 5 - 21 meters of Core 1 and
Core 2 were used in the statistical analysis. Despite the close proximity of the cores, Br- and
2- SO4 are the only ions that are statistically similar in both cores; all of the other ions have statistically different bulk concentrations.
74
Figure 6.5. Cations with depth, indicating seasonal patters in the first ~10 m and subsequent washout thereafter. Cl-/Na+ displays values less than 1.16 indicating washout (Kohshima et al., 2007), validity of the ratio can change when concentrations are near the detection limit.
75
Figure 6.6. Anions with depth, indicating seasonal patters in the first ~10 m and subsequent washout thereafter, with the exception of Br-.
76
Figure 6.7. Summary statistics on ions, with cations displaying greater concentrations than ions. Na+, Ca2+, Br-, and Mg2+ with the greatest concentrations.
Table 6.3. Displays the statistical differences between Core 1 and Core 2 ion concentrations.
Statistical Comparison of Ions
Ion Core 1 (21 m) and Core 2 Statistically t.test value p - value different? MSA -4.2 ≪ 0.05 Yes Cl- 4.5 ≪ 0.05 Yes Br- 0.9 0.35 No 2- SO4 1.9 0.05 No - NO3 -23.4 ≪ 0.05 Yes 3- PO4 -7.8 ≪ 0.05 Yes Na+ 9.6 ≪0.05 Yes + NH4 NA NA NA K+ 6.0 ≪ 0.05 Yes Mg2+ -4.7 ≪ 0.05 Yes Ca2+ 8.1 ≪ 0.05 Yes
77 6.3 Estimated Age of the Firn Cores
Understanding the net accumulation of Kaskawulsh Glacier will aid in better understanding the mass balance. Therefore, I estimated how many years of data I have. I used an estimated accumulation rate in m w.e. then converted the depth of Core 1 to m w.e. I divided the total m w.e. of Core 1 by the annual accumulation rate gives the number of years of accumulation.
There is a range of values of accumulation reported on upper Kaskawulsh in previous studies. Foy (2009) uses data from unpublished research by Zdanowicz (2012) of winter accumulation totals near the Divide site; the Figure shows net accumulations of 3.2 to 3.6 m from 2005 to 2007. Assuming Foy’s average density of 510 kg/m3, this corresponds to 1.6 to 1.8 m w.e./yr. There are also several earlier estimates from the IRRPs. Marcus and Ragle (1970) found an accumulation of 3.2 m (1.6 m w.e.) at the divide (2620 m a.s.l.) and 3.7 m (1.9 m w.e.) at the upper station (2640 a.s.l.) from 1964-1965. Wagner reports values between 1.3 m to 1.9 m w.e./yr (1963). Holdsworth estimated 1.8 m w.e./yr at the divide site in the 1960s (Holdsworth,
1965). In May 2018, the first ice layer was located at 4.2 m depth, which was also a horizon for several accumulated anion species. This is a likely candidate for the end-of-summer melt crust from 2017. Using an average density of 440 kg/m3 from my data for the upper 4.2 m, the accumulation is 1.8 m w.e. This is consistent with Holdsworth’s estimate at the divide site.
Therefore, I will use an average of the IRRP values for my calculation, which is 1.76 m w.e./yr
(Wagner, 1963; Holdsworth, 1965).
To calculate how much water was in the 36-m core, I converted the measured mass for each 10-cm section into meters of water equivalent and summed that over the entire core. First, I calculated the area of the section (πr2). Then, I calculated the volume if the density was that of
78 water (997 kg/m3) by dividing the measured mass by the density of water. To find the length of water, I divided the volume by the area of the actual core, which results in the length of the core if it had been only water:
! ! !"##(!) πr L (�� ) = ! Equation 21 !.!!" ( ) !!!
Is rearranged as: ����(�) L !"#$% !"#$%&'$()(��) = � 0.997 ∗ πr!(��!) ��!
� (�) 23.23 ��� ����� = 1.76 ���/���� 1.76 ���/����
This is equal to approximately 13.2 years of net accumulation.
To check this it would have been ideal to do the same for Core 2 and compare that to the same depth of Core 1 to find the difference between the two cores and therefore the possible amount of uncertainty. However, due to the sparse mass data this is not possible, and I do not have clear ion or isotopic insights to date using seasonal peaks.
6.4 Summary
In this chapter, I looked at the results of the isotope and ion analysis. The stable isotopes displayed high amounts of melt effect. No summer peaks were preserved and most of the winter peaks were also washed out. Between 21.5-26.9 m there was a partially preserved section with three minimally altered winter peaks. The ions indicate high amounts of meltwater percolation.
The first several meters retained some seasonal variation but after 10 m depth there was little to no evidence of the presence of both cations and ions, with the exception of Br-. Using the previous research’s estimated accumulation rate of Kaskawulsh Glacier, I estimated the number
79 of years that the 36 m firn core represents, as 13 years. The isotope and ion signals (and lack thereof) give an indication of how much melt is occurring and what is characteristic of a continental high mountain glacier experiencing severe melt.
80 Chapter 7: Discussion
The last two chapters presented what we found in Upper Kaskawulsh Glacier firn. The density of the firn of Upper Kaskawulsh Glacier is influenced by the quantity and size of ice layers and lenses. A significant portion of Core 1 has been affected by melt and the crystalline structure of the firn has changed. The isotopes of the firn cores have been partially erased from meltwater percolation and refreezing, and the ions are also almost completely eliminated after 10 m depth. In the next sections I will place these results within the broader context of what processes are required for what we documented. I will also delve into the question of whether or not these processes are contingent upon a warming climate or if Kaskawulsh has possessed them since the 1960s (IRRP).
First, I discuss the physical effects of meltwater percolation and refreezing on the firn.
This includes the spatial and temporal variability of the ice layer and lens stratigraphy, the unique stratigraphy above the firn aquifer, and the firn aquifer itself. Then I discuss the surface lowering and mass redistribution and the potential implications for geodetic mass balance techniques. I jump to the isotope and ion composition in relationship to the melting of the firn in the context of post-depositional changes and I use the meteoric water line to identify those changes. I place these findings in the context of temporal and spatial comparisons with the IRRP,
Dr. Karl Kreutz’s Camp, and Wolverine Glacier. I conclude by suggesting that these processes are indicative of significant changes in the melt regime in the accumulation zone of Kaskawulsh
Glacier.
81 7.1 Physical Effects of Meltwater in the Firn
The presence of high amounts of melt can change the physical portrayal of the firn. High amounts of melt can influence the quantity and location of ice layers and lenses, thus altering the density of the firn. In some cases, melt may create slush and refreeze as a unique type of firn with a noticeable bubbly firn structure (infiltration ice). If enough melt occurs, and the accumulation conditions are right, a firn aquifer can form. These features are indicative of varying degrees of melt and pose complications for understanding the density of the firn, and the potential for storage and run-off of liquid water.
7.1.1 Ice layers and lenses.
Ice layers and lenses are a primary indicator of meltwater percolation and refreezing and can lend insight into the local climate. The presence of ice layers and lenses in Upper
Kaskawulsh indicates that this area is experiencing cold winters and/or early melt seasons. This is common for temperate glaciers or glaciers in the high Arctic that experience occasional melt
(Pfeffer & Humphrey, 1998). In 2012, Bezeau et al. (2013) retrieved 15-m firn cores on Devon
Ice Cap, Nunavut, and studied the stratigraphy and location of the ice layers. They concluded that the frequency and quantity of the ice layers were indicative of a warming climate and not due to changes in accumulation (Bezeau et al., 2013).
The spatial location of ice layers and lenses is highly variable. Even if the local climate has not changed, englacial systems can cause heterogeneous spacing of ice layers and lenses. The differences found in Core 1 and Core 2 are not out of the ordinary for an area that experiences high amounts of melt. It is known that even at length scales of less than 1-m the consistency of ice layers is rarely present (Parry et al., 2007; Harper et al., 2011). In a maritime climate the spatial variability and frequency may be greater due to higher amounts of melt. At Seward
82 Glacier in the 1950s Sharp (1951) found one gland about every five feet in an area of 225 ft2.
The spatial variability can be caused by micro-differences in the snow surface. If snow dunes formed on the icefield there may be a positive feedback mechanism for differential melt, no matter how minor, to start the process of vertical movement of meltwater. Once meltwater is in the subsurface, it is then subject to temperature differences in the snow and differences in the size of the snow grains and the permeability and porosity of the snow. This can lead to the spatial variation as seen at Kaskawulsh Glacier.
The accumulation zone of Kaskawulsh Glacier has had ice layers, lenses, and glands since the 1960s. Wagner (1963) found “slush glands” on Kaskawulsh at the elevation of 2590 m and was not able to find dry firn. Macpherson & Krouse (1969) also found ice layers during their study of the stable isotopes on Kaskawulsh in the 1960s at Divide Station A (Figure 2.3) at an elevation of 2500 m. Both papers refer to Kaskawulsh as temperate. Meltwater percolation and refreezing has been a part of Kaskawulsh’s firn dynamics for the last fifty years.
7.1.2 Stratigraphy above firn aquifer.
The presence of infiltration ice indicates specific climate and thermodynamic conditions.
Infiltration ice is formed when the snow is at the melting point, prohibiting the refreezing of meltwater into an ice layer or lens. This may occur if there is an impermeable surface that prevents the water from draining to deeper, colder, depths of the snow. If the water does not refreeze it can saturate the snow and form slush. When cold temperatures come back in the autumn or winter the slush will freeze, resulting in infiltration ice. The infiltration ice will be buried by the following winter accumulation and undergoes subsequent densification processes until it reaches the density of ice (Pfeffer, T., personal communication, December, 2018).
83 The stratigraphy of the firn core located directly above the water table possesses a different crystalline structure from the rest of the firn, resembling refrozen slush (Figure 5.8).
This nearly 10-m layer consists of firn that has been modified by meltwater, as evidenced by the isotope chronology, but it contains only two 2-cm ice layers and a lens. This section of the firn is likely to be infiltration ice, due to its texture and the lack of ice layers.
This section may have undergone post-refreezing modification in addition to the process described above. If the firn experienced normal densification until it reached the water table and water later infiltrated all of its pore spaces it may change the crystalline structure of the ice.
Though little research has been done on studying the affects of firn aquifers on the crystalline structure of firn.
It is likely that the firn crystalline structure is due to a combination of both refrozen slush and alteration due to the water table of the firn aquifer. The slushy snow is likely to have been almost completely saturated and then refroze when the cold winter came. Then it was buried under more snow and now houses the aquifer – simply by coincidence of the year we drilled not because it is indicative of aquifer-forming processes. This is because this crystalline structure of firn is also present closer to the surface in Core 2. In future years, the stratigraphy above the firn may be regular firn with ice layers present and not refrozen slush. Nevertheless, it is likely that the water table of the aquifer varies from year to year due to the varying quantities of summer melt and thus affects the stratigraphy when it drains.
7.1.3 Kaskawulsh firn aquifer.
Another physical embodiment of high amounts of melt (and Goldilocks accumulation) is the presence of the firn aquifer. Refreezing water releases heat and warms the surrounding snow and firn. Now the snow and firn will be less able to form ice layers and lenses. As discussed in
84 section 2.4.1, there may be a Goldilocks quantity of ice layers that would encourage firn aquifer formation (see Figure 2.2). The climate needs to be suitable for ice layer formation that then promotes vertical piping into warm firn and into the aquifer. This may allow the firn aquifer to be consistently recharged. Because the formation of ice layers warms the firn and encourages preferential vertical pathways, ice layers may be necessary for deep percolation to occur. Firn aquifers are not a new concept; their formation is relatively well understood and they are an important aspect of glacier hydrology and water storage (Fountain and Walder, 1998).
In the most recent research on firn aquifers in Greenland and Antarctica, researchers have used remote sensing techniques to detect aquifers. In Greenland, airborne radar data indicates that one of the firn aquifers there was already present in 1993 (and possibly earlier), although it was not identified until recently (Miége, et al., 2016). Another way to find out how long the firn aquifer has been in existence is to drill beyond the aquifer, deeper into the ice, past where the aquifer is located. Below the aquifer there is layered crystal clear ice. There is a relationship between crystal clear ice layers and the duration of the firn aquifer’s existence (Milége, C., personal communication, February, 2019). In Antarctica, a research team used satellite passive and active microwave detection systems in order to identify areas of potential firn aquifers in
Antarctica. However, the satellite data are limited due to their spatial extent, resolution, and the duration of when they have been in orbit. Most of them also do not penetrate to such great depths as 35 m (Miller, J., personal communication, February, 2019).
The firn aquifer stores mass and changes the hydrologic regime of the glacier. Research on the firn aquifers in Greenland show their ability to store water, and possibly delay or prevent the surface meltwater from accessing the internal hydrologic transport system (Munneke et al.,
2014). Koenig et al. (2014) hypothesize that an aquifer may retain water until a critical threshold
85 is reached, when the pore space is completely filled and a catastrophic outburst may occur, similar to jokülhaulps. This type of mass redistribution may cause lowering of the glacier due to the mass of snow filling the pore space of the aquifer instead (ultimately densification because water is more dense than ice!).
The timing of formation of the firn aquifer is unknown, yet this piece of the puzzle may determine if the accumulation zone of Kaskawulsh Glacier is significantly changing. Temperate glaciers are known to contain water-saturated zones and firn aquifers (Fountain and Walder,
1998; Jansson et al., 2003; Arcone, 2002). If Kaskawulsh has always had a firn aquifer at this elevation then this discovery does not indicate change. However, if Kaskawulsh has developed a firn aquifer at this elevation in the recent decades, then this might indicate a shift to temperate conditions or increased melt rates on the upper glacier.
The firn aquifer implies that the firn of Kaskawulsh Glacier is warm enough to have more melt than refreezing. We do not know how thick the firn aquifer was in May 2018. Clarke (1967) found that the firn-ice interface was at 40 m depth at the Divide site in the IRRP; if this has not significantly changed at this location then there might be 6 more meters of firn aquifer below where we stopped drilling, assuming the aquifer goes until the depth to impermeable ice. It is possible that the aquifer thickness varies every season due to the varying amounts of melt every year, but it is uncertain whether summer meltwater always penetrates to this depth. Recharge may only occur in summers with extensive melting.
The presence of a perennial firn aquifer has widespread implications for the perceived and actual mass balance of Kaskawulsh Glacier, as well as effects on future climate reconstructions. The mass balance is impacted by the flow of the firn aquifer and the meltwater
86 storage. Future studies of climate reconstruction may be highly difficult due to the effect of melt on the isotopes and the flow of the aquifer removing the ions.
The water in the firn aquifer on Kaskawulsh Glacier is likely to be flowing, moving mass from one place to another (Milége et al., 2016). This is also evident in the ion profile and the absence of mobile ions in the saturated firn. Because they are not more concentrated at the impermeable surface, they are likely being transported out of the system. While there may be temporal variations in the firn aquifer thickness, we drilled in May so it must have persisted through the winter of 2017-2018. Liquid water at depths greater than about 10 m will be below the winter temperature wave (Cuffey & Paterson, 2010). The aquifer argues for a thick layer of saturated, temperate firn near the firn-ice transition. There was not mention of the firn aquifer in any of the IRRP reports, however they did not dig to greater depths of where the firn aquifer could have been.
A firn aquifer also appears to be at the Kreutz Divide Camp, 13 km to the southwest.
Kreutz and colleagues found evidence of the aquifer at around 30 meters depth using a GPR
(Kreutz, K., personal communication, November, 2018). Because the conditions necessary to create a firn aquifer are present at my location and at Kreutz’s location, there may be more firn aquifers in this area (or one large one). This exacerbates the discrepancies in mass balance and water storage discussed above.
Eclipse Drill Site (Figure 2.3) has been a prime location for ice core drilling and climate reconstructions (Zdanowicz et al., 2014). The Kreutz Divide Site has also been a candidate for drilling with the intention of climate reconstructions. The presence of a firn aquifer may make drilling complicated and logistics more difficult, and it may also indicate that the isotopic and ionic record will be washed out.
87 7.2 Surface Lowering and Mass Redistribution: Impacts on Geodetic Mass Balance
Geodetic mass balance techniques require an assumed density of glaciers in order to
determine the mass loss from calculated volume losses. Surface lowering is one variable in the
equation of volume loss. In order to understand how meltwater effects have shifted the density
and subsequent surface lowering, I explored the firn density and surface lowering in the context
of other glaciers and previous studies on Kaskawulsh.
7.2.1 Firn density in context.
For the conversion of surface lowering and subsequent volume change for geodetic mass
balance techniques, the density of the firn must be assumed. Table 7.1 (adapted from Huss,
2013), displays the average density of the upper 10 m of firn for many glaciers around the world
that have been qualified as temperate or polythermal. Both temperate and polythermal glaciers
have a range of density for the first ten meters because they do not have consistent densification
processes across geographical areas. Due to this wide range of values, it is necessary to fully
investigate the density of the firn before making assumptions of the density for geodetic mass
balance techniques.
Table 7.1. Density of the upper 10 m of firn for various glaciers around the world (altered from Huss, 2013).
Reference Location Number of Density (upper 10 Accumulation rate Type cores m) (kg/m3) (m w.e./yr) Ambach & Eisner European Alps 1 700 1.2 temperate (1966) Oerter et al. (1982) European Alps 3 600 ~1 temperate Sharp (1951) Western Canada 1 650 ~1.5 temperate Zdanowicz et al., Arctic Canada 2 560 0.3-0.6 polythermal (2012) Kreutz et al., Tien Shan 1 650 1.3 polythermal (2001) He et al., (2002) Himalaya 1 640 0.9 polythermal Matsuoka & Patagonia 1 620 2.2 temperate Naruse (1999) Shiraiwa et al., Patagonia 4 550 5-15 temperate (2002) Pälli et al., (2002) Svalbard 1 510 0.4 polythermal
88 Nuth et al., (2010) Svalbard 3 510 ~0.5 polythermal Sass & O’Neel Alaska 1 669 1.5 - 2 temperate (personal communication, February, 2019) ME! (and everyone Western Canada 2 564 1.8 ??? else) (Kaskawulsh)
Compared to the glaciers in Table 7.1, Kaskawulsh’s density does not appear
significantly different because it falls within the range already reported. The average density of
Kaskawulsh firn is 670 +/- 7.5 kg/m3 (14-36 m) and for the first ten meters of firn it is 564
kg/m3. However, when comparing Kaskawulsh’s density to previous studies of the same area, it
is evident that Kaskawulsh has become denser. In 2007, Foy (2011) measured the average
density of the first 8 meters as 510 kg/m3 with a standard deviation of 110 kg/m3. This does
include some snow. Grew & Mellor (1966) discuss a 15-m core that was retrieved at IRRP
Divide Camp A (Figure 1.3). They reported densities that are quite different than the first 15 m
of Cores 1 and 2 (Figure 7.1). The first 15 m is much less dense then the data I collected. For
example, the firn densities do not reach 600 kg/m3 until depths of more than 11 m, while I
measured values of 600 kg/m3 (and greater!) within the first five meters (Figure 6.2). This also
implies a lack of numerous ice layers. It is reasonable to infer that Kaskawulsh firn has become
denser since the 1960s.
89
Figure 7.1. Density of core retrieved near Divide in 1960s, there is variation in the data, however most values in the first 15 m are below 0.6 g/cm3. (Grew & Mellor, 1966) It is important to acknowledge that calculations using the density data have a high amount of uncertainty. The primary source of uncertainty in the density values is f, due to the subjectivity and difficulty in estimating the amount of missing firn. In future, it would be best to discuss with the team beforehand what different levels of f look like in the field; that way, every person that is processing the core will have the same idea as to how much a core deviates from
90 the perfect cylinder. There may be geophysical methods to estimate the density of the samples. It may also be advisable to bring the cores back to a cold lab and have them processed in a facility that would remove the subjective aspect of evaluating the core quality. In cold labs it is possible to sub-sample the sections and remove the outer layers of the sample with a machine in order to have a known volume. On the other hand this comes with it’s own challenges as it is difficult to keep samples frozen during storage and transport for spring or summer ice-coring operations at sub-polar latitudes.
7.2.2 Surface lowering.
One of the effects of meltwater percolation and refreezing is surface lowering due to densification. Numerous studies have used geodetic mass balance techniques. Uncertainties within these studies take into account possible mass redistribution into previous annual layers and surface lowering. Over long periods this may be insignificant due to the natural variability in the system and the uncertainties associated with the estimates. However, it could be skewing data on short periods if there are significant years of surface lowering (Huss, 2013; Schneider &
Jansson, 2004). For Greenland, Bamber & Riveria (2007) say that if the surface lowering due to densification is on the order of centimeters per year it will not significantly affect geodetic mass balance techniques, but if it is on the order of meters per year then it needs to be taken into consideration. Huss (2013) concludes that the uncertainties in densities when converting to volume change can account for 2-15% of mass change. Moholdt et al., (2010a and 2010b) studied Svalbard glaciers and found the surface lowering uncertainty was a third of the total estimated value. They acknowledged that Svalbard experiences high seasonal melt (up to 80%
Moholdt et al. 2010a and b) and that one cannot assume Sorge’s Law, so firn density estimates are highly uncertain.
91 Kaskawulsh has had its own research on its thinning and thickening. Foy (2009) found that from 1977-2007 the accumulation zone thinned by an average of 0.04-0.11 m/yr: a total thinning of 1-3 m over this period. In my study the accumulation zone of Kaskawulsh is calculated to have lowered by 1.3 m over 13 years due to refreezing (0.10 m/yr, approximately
2% of annual accumulation). This is within Foy’s estimate; suggesting that some or all of the lowering she calculated could be due to mass redistribution and not mass loss. If Kaskawulsh has always been densifying at this rate, the signal from Foy’s report may be part of the natural accumulation and melt variation Kaskawulsh experiences. However, she also found that
Kaskawulsh has occasionally thickened over shorter intervals of time, indicating the complexities of assuming a constant rate of surface lowering or equating lowering to mass loss in accumulation zones.
To develop better density and densification models for geodetic mass balance techniques, it is necessary to obtain in-situ measurements. Drilling to the firn-ice interface and digging extensive snowpits may be one way to obtain accurate estimates of how the firn densifies in this area. This will inform the assumptions made in remote-sensing mass balance. This is especially important with the new altimetry satellites used for the cryosphere, such as the recently launched
ICESat2 that can be used for future mass balance studies.
7.3 Melt-affected Isotopes and Ions
The presence of high amounts of melt can alter the stable isotope seasonal signal and the concentration of ions. Meltwater and refreezing events may be preserved in the isotope record.
Severe melting may alter the meteoric water line and change the determined slope. It may be possible to distinguish dry firn from melt-affected firn by looking at the isotopes and ions of those sections. The chemical characteristics of the firn aquifer may be useful for other melt-
92 affected study areas and future research. Characterizing the isotopes and ions will help elucidate the extent of melt in this area and future implications for paleoclimate research.
7.3.1 Isotopes and ice layer stratigraphy.
The relationship between the ice layer stratigraphy and isotopes indicates meltwater refreezing and percolation events. Core 1 has an average δ18O value of -24.6 +/- 0.7‰. and experiences 3 periods of homogenization. There is a preserved section where the signals are not entirely washed out from 21.5-26.9 m, with values from -27.1 to -23.8‰. At the bottom of the core starting at 32.4 m depth, the isotopic ratios linearly decrease.
There may be a relationship to δ18O values and ice layer stratigraphy. Figure 7.2 displays
Core 1’s isotope values, with samples that have ice content marked in red. I ran statistics on Core
1 to see if there is any relationship between the isotopic values and ice layer content. The p- values for three correlation tests (Kendall, Pearson, and Spearman) were 0.02, 0.03, and 0.04.
Those values are close to the 95% confidence interval, but the correlation coefficients were low:
0.24, 0.16 and 0.22, respectively. The average δ18O in the samples with ice content is -24.8‰, with a standard deviation of 0.9‰. This is minimally more depleted than the average for the whole core (-24.6 +/- 0.7‰); therefore, there may be a weak relationship present with the ice- layer isotopic values having more negative δ18O, but the difference is not statistically significant
(t-test = 1.3, p ≫ 0.05).
From Figure 7.2 and the statistics, it is clear that the possible relationship of isotopic fractionation between water that has percolated and refrozen may be present in a few data points, but as a whole not all samples with ice content show a relationship with more depleted values of
δ18O. There are two data points where different processes may be at work in the ice layers. At
22.7 m below the surface the value is -27.0‰. This sample has a 7.5-cm ice layer in it. This is
93 after a period of isotopic homogenization (17.1 to 22.1 m), and may be refrozen winter melt. At
24.1 m the value is -26.6‰, and there is a 9.5-cm thick ice layer located here. There are several large ice layers, which do not have significantly depleted isotope values. For example, at 24.9 m depth there is a 10-cm thick ice layer with an isotopic value of -24.1‰. Starting at 26 m the stratigraphy displays highly melt-affected firn; this is also where the isotopes have been washed out. This section of the firn may have been created by infiltration ice, or the water table of the firn aquifer is moving up and down, altering and homogenizing the isotopes. Therefore, there is no clear consistent relationship with isotope values and location of ice layers.
94
Figure 7.2. Samples with ice layers in the δ18O profile. The three negative peaks have ice-rich samples in them but the samples leading up to the minima do not. There is no clear evidence of a relationship between ice layer location and δ18O values. 7.3.2 Meteoric water lines indicating melt.
Post-depositional changes in stable isotopes may be associated with periods of melt. In order to understand post-depositional changes in the relationship of δ18O and δD due to melt, we can compare the Local Meteoric Water Line (LWML) in different sections of the firn core. The local meteoric water line for this location was determined using the isotopes from the first 4
95 meters of snow. This line reads δD = 7.7δ18O + 0.8, R2 = 0.99. The Kreutz team’s snowpit (the first 4 meters) has a LMWL of δD = 8.0δ18O + 4.9, R2 = 0.99 (Table 7.2). I ran a two-sample t- test to see if the δ18O and δD values of Core 1 and Kreutz’s core were significantly different.
Neither the mean δ18O or δD values were not significantly different (t = -0.79, p-value ≫ 0.05, and t = -1.5, p-value ≫ 0.05).
Figure 7.3. Meteoric water lines of Core 1 and Core 2.
Table 7.2. Meteoric Water Lines of Kaskawulsh Glacier, statistically similar slopes are present for Core 1, Core 2, and Kreutz camp core. Meteoric Water Lines
Core and Depth Slope Intercept R2
Core 1 (21 m) 7.2 - 8.8 0.98
Core 2 (21 m) 7.3 - 6.7 0.96
Kreutz Camp Core (19 m) 7.8 - 1.2 0.97
Kaskawulsh snow pit (4m) 7.7 + 0.8 0.99
Kreutz Camp snow pit (4m) 8.0 + 4.9 0.99
96
Figure 7.3 displays the meteoric water line for Core 1 and 2. The equation for the
meteoric water line for Core 1 is δD = 7.15δ18O – 11.14 with an R2 of 0.96, Core 2 is δD =
7.30δ18O – 6.66 with an R2 of 0.96. When I use the first 21 m, the slopes are nearly the same
(Table 7.2).
Table 7.3. Sections of MWL to investigate post-depositional processes with depth. Sections were divided by the appearance of wash-out or preservation. Core 1 MWL sections
Depth (m) Samples Slope Intercept R2 Average d-excess (over
specified depth)
0 – 4.3 32 7.7 + 0.6 0.99 8.70
4.3 – 9.8 54 6.8 - 19.9 0.98 9.11
9.8 – 16.9 68 6.9 - 17.3 0.98 10.14
16.9 – 21.5 117 3.9 - 89.3 0.41 9.82
21.5 – 26.9 53 7.1 - 10.9 0.89 10.51
26.9 – 33.4 64 6.3 - 31.8 0.83 9.70
33.4 – 36.8 31 3.6 - 98.2 0.82 11.18
To compare post-depositional relationships between the δ18O and δD, I separated the core
into sections and looked at the slopes and intercepts of the MWL. Table 7.3 displays these
numbers. There is only one section of the core where the slope of the MWL is close to the upper
4 m of the core; this occurs at depth 21.5 - 26.9 m. This may suggest that some but little
meltwater percolation and refreezing has impacted the isotopic record at these depths. There are
97 two sections where the isotopes appear to have undergone significant alteration compared to the seasonal snowfall. This is identified through extremely low LMWL slopes in sections from 16.9
– 21.5 m and 33.4 – 36.8 m depth. The other three sections with slopes around 6 indicate that some alteration has occurred but not a significant amount. The LMWL in the section from 16.9 –
21.5 m has the lowest R2 value, 0.41, indicating lots of scatter of the data points. These variations indicate that the site has experienced varying amounts of meltwater percolation and refreezing over the years, changing the isotopes in different ways. This implies that the firn is melting and refreezing, but not in a predictable manner. This analysis was not performed on Core 2 because there were not enough distinct sections to compare.
7.3.3 Preserved firn.
Despite Core 1 experiencing significant alteration of isotopes due to meltwater, one section of the core has preserved peaks. From depths 21.5-26.9 m there are three peaks. Due to the LMWL of this section being closest to the unaltered first 4 meters of snow compared to the other sections (7.15 compared to 7.67), I would suggest that this section has experienced melt but remained mostly intact even with meltwater percolation and refreezing. In areas of up to 55% melt, the seasonal signal can still be preserved (Pohjola et al., 2002). This means that over that depth there are two full years of accumulation, spanning 5.4 m. The average density in that section is 737 kg/m3. This equates to an annual accumulation rate of 2.0 m w.e./yr for this part of the core. That is higher than net accumulation estimates from Eclipse (Zdanowicz et al., 2014) and the upper Kaskawulsh Glacier (Foy, 2009), which report between 1.6 and 1.8 m w.e./yr.
Natural variability of accumulation may account for this difference, or a cold summer with less melt. However, I think it is more likely that meltwater percolation and refreezing may be responsible for this higher density and accumulation; meltwater from subsequent years may have
98 percolated into this zone and refrozen, adding to the local accumulation. The presence of ice layers in this section of the core is consistent with this, though they could be from the same year’s accumulation. However, it is obvious that melt percolates into previous years due to the presence of the firn aquifer.
7.4 Melt-affected Firn vs. Dry Firn
If there are significant isotopic and ionic differences between melt-affected firn and dry firn, then ice-core based paleoclimate studies may be impacted by periods of high melt in this region. To characterize the melt effects, I compared the isotopic and ionic differences between melt-affected facies of firn and dry firn through summary statistics. If the sample had been visibly melt-affected or contained an ice content greater than 0.6 it was placed in the melt- affected firn category. The dry snow was placed in the dry firn category. Table 7.4 and 7.5 display the results for Core 1 and 2. The range of values of δ18O is the main indicator of dry vs. melt-affected snow and firn. The averages are close, but lower ranges in the melt-affected firn
(Core 1: 4‰, Core 2: 8‰) indicate homogenization of the isotopes. For some ions the dry firn has higher ranges of concentrations (e.g., for Cl-, Na+, K+), which is consistent with preservation.
- 2+ In other cases the melt-affected ion concentration range is higher (e.g., Br , Ca core2). This may be due to the different elution efficacies and the presence of certain ions in ice layers due to the refreezing of brine. Ion peaks may occur from meltwater that removes ions from the firn. This can form brine if it hits an impermeable layer (such as a thick ice layer), and if it slowly refreezes with all of the ions (Goto-Azuma et al., 1994) (Figure 7.4). This requires cold temperatures, but it is possible in early summer. If it does not refreeze it will continue melting around it. The ion peaks at lower depths (29-36 m) (Figure 6.4 and 6.5) are not likely to be seasonal peaks because of the distance between the peaks. This is also where the infiltration ice is located; perhaps it is
99 related to depths of the seasons that had infiltration ice, if the melt that forms the infiltration ice ended up percolating below the winter seasons snow, into the past season’s firn, then it may mimic a seasonal signal. The peaks may be related to the movement of the water table.
Figure 7.4. Refreezing of brine melt-affected firn (inspired by Goto-Azuma et al., 1994).
100 Table 7.4. Core 1 melt-affected and dry firn isotope and comparison of average, minimum, and maximum ion concentrations.
Core 1
Species Average Minimum Maximum N=261 (dry) Dry Melt- Dry Melt- Dry Melt- N=94 (melt) affected affected affected δ18O -24.7 -24.6 -29.5 -27.1 -19.5 -23.3 d excess 9.5 10.7 3.9 7.1 14.4 16.1 MSA 0.37 0.04 0.0 0.03 1.09 0.11 Cl- 0.29 0.28 0.0 0.02 4.11 0.86 Br- 0.10 0.34 0.0 0.0 1.18 1.31
SO4 0.09 0.05 0.0 0.0 4.65 0.06
NO3 0.06 0.09 0.0 0.0 0.30 0.14 3- PO4 0.04 0.07 0.0 0.0 0.11 0.11 Na+ 0.25 0.13 0.0 0.0 2.46 0.67 + NH4 0.02 0.0 0.0 0.0 0.76 0.0 K+ 0.11 0.01 0.0 0.0 1.83 0.19 Mg2+ 0.07 0.09 0.0 0.0 1.84 0.19 Ca2+ 0.32 0.22 0.0 0.07 3.92 0.50
101 Table 7.5. Core 2 melt-affected vs. dry firn melt-affected and dry firn isotope and ion comparison of averages, minimums, and maximums.
Core 2
Species Average Minimum Maximum N=126 (dry) Dry Melt- Dry Melt- Dry Melt- N=78 (melt) affected affected affected δ18O -24.6 -25.1 -29.5 -28.1 -19.5 -20.6 d excess 10.4 10.5 3.9 5.9 15.7 14.1 MSA 0.04 0.06 0.0 0.03 0.28 0.31 Cl- 0.18 0.11 0.0 0.04 0.77 0.33 Br- 0.08 0.06 0.0 0.03 0.47 0.49
SO4 0.05 0.05 0.04 0.05 0.05 0.06
NO3 0.15 0.15 0.0 0.0 0.30 0.17 3- PO4 0.06 0.06 0.0 0.0 0.28 0.23 Na+ 0.03 0.09 0.0 0.0 0.25 0.26 + NH4 NA NA NA NA NA NA K+ 0.0 0.0 0.0 0.0 0.04 0.0 Mg2+ 0.17 0.11 0.0 0.06 0.32 0.32 Ca2+ 0.06 0.05 0.0 0.0 0.39 1.22
7.4.1 The aquifer and the effect of melt on the bottom end of the core.
Meltwater percolation may be responsible for causing the lower depths of Kaskawulsh firn to be depleted in heavy isotopes. The deepest part of the firn core (depths of 32-36 m) has a decreasing δ18O trend, from -23.3‰ to -25.9‰, with an average of -24.8‰. This is where the firn aquifer was located. As meltwater travels through the firn column any refreezing would involve fractionation, with the heavier isotopes being the first to freeze. The residual water would then become more depleted; this might explain why the deeper parts of the aquifer section are more isotopically depleted. The disappearance of the ions in the firn suggests that they are being transported out of the system. Table 7.6 displays the mean concentrations for the 10-20-m section of Core 1 and the aquifer 32-36-m section. The concentrations are higher in the aquifer
102 for all ions except Na+ and K+. Aristarain & Delmas (1993) suggest that where there is a water table, soluble impurities may be washed out and the movement of a water table may replace the seasonal variations of ions with other changes. They also may be even more concentrated deeper into the aquifer where we did not drill. The presence of Br-, Cl-, and Na+ (the salts) can be explained because they are likely ice nuclei at the center of the ice crystal and will therefore be
3- minimally eluted (Eichler et al., 2001). PO4 is also still present, and is not described in glaciochemical studies very often. Therefore it is difficult to speculate why it is still present in the aquifer when almost all of the other ions are washed out. This may be a place for further research.
Table 7.6. Mean concentrations of ions in firn aquifer (32-36 m) compared to 10-20-m section of Core 1
Ion Mean (ppm) Mean (ppm) Statistically Different? Concentration 10- Concentration 20 m 32-36 m
MSA 0.03 0.04 Yes (t = -6.7, p-value ≪ 0.05) Cl- 0.18 0.31 Yes (t = -4.2, p-value ≪ 0.05) Br- 0.06 0.40 Yes (t = -8.7, p-value ≪ 0.05) 2- SO4 0.05 0.06 Yes (t = -14.0, p-value ≪ 0.05)
- NO3 0.08 0.13 Yes (t = -14.9, p-value ≪ 0.05) 3- PO4 0.05 0.07 Yes (t = -13.5, p-value ≪ 0.05) Na+ 0.20 0.03 Yes (t = 9.6, p-value ≪ 0.05)
+ NH4 NA NA NA K+ 0.02 0.01 No (t = 0.8, p-value ≫ 0.05) Mg2+ 0.04 0.12 Yes (t = -24.6, p-value ≪ 0.05) Ca2+ 0.14 0.20 Yes (t = -3.8, p-value ≪ 0.05)
103 7.5 Temporal and Spatial Comparison
To put the firn of Kaskawulsh Glacier into perspective, comparing it to what has been in the past and to other melt-affected glaciers, is useful. This will allow us to understand where
Kaskawulsh has been, where it is now, and maybe understand where it could go in the future, in the context of firn dynamics. Before comparing to other locations, first I will summarize the similarities and differences between Core 1 and Core 2. Within the first 21 m Core 1 and 2 have similar densities (t = 1.36, p-value = 0.17), density-depth relationships (Figure 5.1), ice content
(Figure 5.9), δ18O values (t = -1.19, p-value ≫ 0.05), d excess (t = -0.49, p-value ≫ 0.05), and
- 2- the Br and SO4 concentrations (Table 6.3). The cores are different in the details of the ice layer
- 2- stratigraphy, and the bulk cation and anion concentrations (besides Br and SO4 ). They also differ in density and d excess when the 15 meters of Core 1 are accounted for (t = 4.38, p-value
≪ 0.05, and t = -3.61, p-value ≪ 0.05). In order to understand whether this firn is regionally representative, I discuss regional and temporal comparisons in the next sections.
7.5.1 Comparison to Kreutz Camp and Eclipse.
To find out if the firn at my drill site is representative of the broader accumulation area of
Kaskawulsh Glacier, I compared my isotope values to Kreutz’s 18-m core. I also compare my data with the Wolverine Glacier core for a regional perspective (see section 7.6.2). The statistical analysis is located in Figure 7.5. As discussed in section 4.6.2 the kurtosis and skewness of data can tell you about the data, where skewness tells about the symmetry of the data and kurtosis tells of the relative weight of the outliers. These characteristics are markers of post-depositional modifications, including meltwater effects. One would expect a seasonal signal to be symmetrical because seasons do not change abruptly, instantly altering the isotopes; it is usually a gradual shift. A high kurtosis (3 is for a normal distribution) indicates heavy outliers (or
104 “tails”), this can be revealing of partial melt because if it was entirely washed out there would not be outliers, and if there was no melt then the kurtosis would show no outliers as well. Core 1 and Kreutz’s core show the most similarities in the statistical characteristics of the isotopes.
However, Core 2 deviates from this group, most likely due to the greater presence of infiltration ice, which may affect the isotopes differently. Wolverine data will be discussed in the next section.
Figure 7.5. Statistical comparisons of δ 18O in firn cores to display differences in regions. Melting and refreezing are not limited to my drill site; these processes are also evident at
Kreutz camp. Figure 7.5 shows the stratigraphy found at Kreutz camp. Due to the spatial variability of ice layer and lens stratigraphy, it would be unlikely to find continuous ice layers all the way to Kreutz site. With that said, there is a significant number of ice layers around 14 m depth, which are also found at my location. There is no record of infiltration ice, which may or
105 may not be present at this location and density measurements were not taken. Kreutz core has a cumulative ice content of 0.78 m w.e. (assuming density of 900 kg/m3). Using the upper 19 m of
Core 1 and Core 2 the ice content is similar, Core 1 has 1.3 m w.e. and Core 2 has 0.74 m w.e..
Kreutz core ice content is more similar to Core 2 in the amount of ice content present. This reinforces the concept of the spatial variability of ice layers, however it is evident that the accumulation zone likely experiences similar melt at similar elevations.
Figure 7.6. Kreutz Core ice layer stratigraphy showing numerous ice layers and lenses in the first 18 m.
106 Highly melt-affected firn has also been found at other locations on the St. Elias icefield.
Eclipse drill site is located at ~3000 m a.s.l. and is almost directly north of my drill site. It is also experiencing changing melt regimes. Yalcin & Wake (2001) found that in 1996 Eclipse experienced 5% melt, but that the meltwater percolation and refreezing did not alter the isotope or ion record. This has since changed; in 2016 a new core at Eclipse revealed that the area has experienced increased melt since the 1990s (Kreutz, K., personal communication, January,
2019).
7.5.2 Compare to USGS Wolverine data.
Maritime temperate glaciers commonly experience severe amounts of melt. Wolverine
Glacier is a USGS benchmark glacier and has been studied for many decades. Wolverine Glacier has been used in comparison to other temperate glaciers, such as studies on internal accumulation and mass balance, due to its extensive research history (e.g., Trabant & Mayo,
1985; Arendt et al., 2002; Foy, 2011). In the last few years, firn cores have been retrieved from
Wolverine Glacier at 1400 m a.s.l. I decided to compare Kaskawulsh to Wolverine for two reasons; it has been compared in the past net accumulation balance trends (Foy, 2011), and because Wolverine experiences a temperate, marine climate, its accumulation zone experiences a high amount of melt. I compare to Wolverine in order to place Kaskawulsh on the spectrum of extremely to partially melt-affected glaciers. Wolverine experiences extreme amount of melt with significant ice layers, with almost no snow unaffected by melt every year. By understanding what extreme amounts of melt look like, we can better understand the degree of melt that
Kaskawulsh is experiencing and what may lay in store for Kaskawulsh in the future.
107 Table 7.7. Statistical characteristics of oxygen isotopes at Wolverine Glacier, Alaska.
Wolverine Glacier Firn Core
δ18O X (δ18O) δD X (δD) d X (d) N 113 112 113 112 113 112
Average (‰) -15.2 1.4 -112.5 10.1 9.0 2.2
σ (‰) 2.0 2.0 14.3 15.1 4.9 4.9
Skewness -0.3 2.3 -0.3 3.0 -2.8 3.6
Kurtosis 4.5 9.5 5.2 13.7 17.8 15.9
Shapiro wilk W = 0.95 W = 0.70 W = 0.94 W = 0.64 W = 0.74 W = 0.49 normality test P <<0 P << 0 P << 0 P << 0 P << 0 P<< 0 Not- Not – Not- Not – Not - Not – normal normal normal normal normal Normal
As discussed in Sections 6.1.3 and 7.6.1, the distribution can give insight into the extent
of meltwater percolation and refreezing. The statistical tests in Table 7.7 show the distribution of
the isotope data collected on Wolverine Glacier in 2016 (also Figure 7.4). If meltwater has
homogenized some of the isotope data, then it will change the distribution. Here, the data is not
skewed in either direction. However, the kurtosis is very high (>3) for all parameters; this
indicates heavily tailed distributions. This implies that there is lots of melting that is washing out
the seasonal isotopic signature as discussed in Carter (2018). This is evident in the average and
standard deviation of δ18O; there is not a large standard deviation (2.0‰) implying that the
seasonal cycle is obliterated. The average δ18O is much higher than Kaskawulsh, -15.19‰, but
that is possibly due to the proximity to the ocean (precipitation becomes more depleted as air
masses travel inland), altitude, and condensation temperature. One of the big differences between
Kaskawulsh and Wolverine statistical analysis is the d-excess, Wolverine experiences a much
greater amount of variation. This might be due to various sources of precipitation (Carter, 2018).
108 However, d on Kaskawulsh does not necessarily indicate moisture source; Kelsey et al. (2002) state that at Eclipse it is not a good proxy for that (only at higher elevations such as on Mt.
Logan). The variation of d on Kaskawulsh is likely due to post-depositional processes. The d can increase or decrease in the refreezing processes depending on the starting conditions and the isotopic composition differences between the two phases (Shiqiao, et al., 2008).
The LMWL shifted over time at Wolverine when multiple firn cores were taken in the
2016 season. From May 2016 is δD = 6.9δ18O - 8.23 (Carter, 2018). In September it was δD =
7.2δ18O - 2.44. Carter (2018) suggests this change is due to ice layer formation. If correct changes in the different sections of Kaskawulsh may also be due to meltwater percolation and refreezing processes, and this could also explain the changes in the slope reported in Pohjola et al. (2002). Despite geographical and climatic differences Kaskawulsh and Wolverine share similar slopes of LMWL (Kaskawulsh Core 1 has a slope of 7.15).
109
Figure 7.7. Profile of δ18O of Wolverine Glacier from May 2016 showing nearly complete washout. Figure 7.7 shows the isotope profile of the Wolverine firn core from May 2016. This profile does not have distinguishable seasonal cycles, and is mostly washed out started at 15 m depth. At this drill site on Wolverine Glacier, the annual accumulation is between 3-4 m w.e./yr but net accumulation is 1–1.5 m w.e./yr (Sass, L., personal communication, January, 2018), which is similar to my drill site at Kaskawulsh (1.6-1.8 m w.e./yr). However, the climate is different. Wolverine Glacier experiences a maritime climate with mean annual temperatures of about -1°C, with daily ranges from -25 to 15°C (USGS, 2009.) The isotopic stratigraphy is
110 shares some similarities to what we found at Kaskawulsh. The washed-out sections appear as a mostly homogenous vertical straight line with little variation, as did the washed-out sections of
Kaskawulsh (Figure 6.1). The winter accumulation is still present at this time at Wolverine and has some variability, as does Kaskawulsh’s winter accumulation. However, Wolverine’s isotopes are entirely washed out after the summer season, and Kaskawulsh’s firn continues to have partial preservation some years.
Kaskawulsh is a continental glacier and part of a large icefield; Wolverine is a maritime temperate mountain glacier. While the mean isotopic values differ between the sites, their isotopic profiles are similar. From a 17-year weather record, Wolverine has an estimated annual
PDD of 650°C/yr which is more than 6 times greater than the estimate at Kaskawulsh (Sass, L., personal communication, March, 2019). This is especially high even given the climatic and elevation differences of Wolverine and Kaskawulsh. Wolverine is experiencing significant surface lowering, up to 7 m in one season and redistributes nearly all of its mass by meltwater percolation and refreezing (O’Neel, S., personal communication, February, 2019). Kaskawulsh is different than Wolverine in this respect because Kaskawulsh does not yet redistribute all of its mass. Kaskawulsh and Wolverine share similarities and their representation of wash-out in their isotope profiles. However, they are different because Wolverine experiences melt to the extent that the surface lowers by more than half of the annual accumulation. Wolverine represents one end of the spectrum, which can help better understand where Kaskawulsh lies on that spectrum.
Kaskawulsh is melting, and the accumulation zone is changing, but it is not as altered as
Wolverine.
111 7.6 Discussion Summary
To characterize the firn of Kaskawulsh Glacier I investigated meltwater effects on the physical and isotope and ion attributes of the firn. The physical effects of melt are distinguished by the presence of ice layers and lenses and in this case a firn aquifer. The effects of meltwater percolation and refreezing have made the firn denser than previous studies, as documented in the
IRRP in the 1960s. The firn aquifer complicates understanding of mass balance measurements and meltwater runoff processes. The various firn densities of glaciers around the world indicate that not one assumed density is adequate for geodetic mass balance techniques. In the case of
Kaskawulsh, firn densification due to meltwater refreezing results in ~0.1 m/yr of surface lowering of the accumulation zone. This could explain some of the calculated surface lowering estimated by Foy (2009), because of increased surface drawdown from the meltwater percolation and refreezing process. Melt-affected isotopes are distinguished by homogenization of the seasonal signal. By comparing sections of the core with the meteoric water line as a metric, there is one section that has been partially preserved and not entirely washed out, between 21.5 m and
26.7. Preserved firn can also be identified through higher ion concentrations in the less mobile ions. Lastly, the presence of the firn aquifer has potentially altered the isotopes; it is possible that more depleted isotopes have accumulated in the liquid water due to fractionation processes during partial refreezing as meltwater percolates. This is identified through a decreasing trend in the section with liquid water. The melt regime identified at my drill site is also evident at the
Kreutz site, which may indicate that this high amount of melt is representative of the accumulation zone of Kaskawulsh Glacier (given we are at the divide after all!). To understand the extent of melt possible, Wolverine Glacier is used to identify one side of the spectrum.
112 Wolverine experiences severe melt where all of the firn is affected by percolation and refreezing.
Kaskawulsh is not to this extreme yet.
113
Chapter 8: Conclusions
The objective of my research was to characterize the firn of Kaskawulsh Glacier. I have documented the physical and chemical properties that distinguish partially melt-affected firn. My key research findings are as follows:
1. Meltwater percolation and refreezing are identified through the presence of numerous
ice layers, lenses, infiltration ice, and a perennial firn aquifer. Ice layers have resulted
in a surface lowering of a minimum of 0.1 +/- 0.06 m/yr from mass redistribution.
2. The firn has become denser since the 1960s and early 2000s. The estimated annual
accumulation rate calculated from the preserved two years of firn is slightly higher
than previous reports, at 2.0 m w.e., however this is most likely affected by natural
variation and meltwater migration in from other layers and not net annual snowfall.
3. The isotopic and ionic signatures are mostly washed out due to meltwater percolation,
except for one section where the signal was partially preserved. As stressed by
Koerner (1997), because of the washed-out nature of my cores, I have interpreted the
isotopic and ionic variations with caution.
4. Due to the similarities at Kreutz Camp, the melt regime at my site may be
representative of the accumulation zone as a whole. However, the melt is not as severe
as what is experienced in maritime temperate glaciers, such as Wolverine Glacier (at
1400 m a.s.l.)
These conclusions have implications for the greater scientific community and humans as a whole. Kaskawulsh Glacier is changing. The processes present in this study were not present in
114 the previous studies on Kaskawulsh or colder glaciers. This study informs us that the assumptions we make for mass balance studies and the intentions of paleoclimate research need to be evaluated and possibly revised. Surface lowering may complicate mass balance studies on
Kaskawulsh Glacier, and other glaciers that have similar melt regimes and are being studied by remote sensing. Our mass balance techniques and assumptions of density need to be evaluated to account for other sources of densification and internal accumulation and mass redistribution.
This will lead to better quantification of sea level rise, especially when taking these ideas into account when considering ice sheets. Accumulation rates on the upper Kaskawulsh Glacier are still confounded due to the presence of internal accumulation. The net accumulation rate is still not entirely known, and needs to be quantified as part of additional mass balance studies at this site. This assessment has lead to greater insight into meltwater storage and retention in the firn layer; however, the dynamics, spatial extent, and timing of the firn aquifer need to be established. The meltwater storage and run-off needs greater evaluation. We have now established Kaskawulsh Glacier as a prime example of a location that experiences severe melt that affects the densification, meltwater storage, and isotopic and ionic stratigraphy of firn.
Future research includes more in-situ density measurements of firn, developing remote- sensing techniques for detecting and understanding firn aquifer dynamics, and more research on the chemistry of melt-affected firn. Firn density is an important aspect of calculating the mass balance of large glaciers from remote-sensing techniques, to better estimate surface lowering and thus volume loss more in-situ density measurements and research needs to be conducted, and development of density detecting remote-sensing techniques may also be a viable option. Firn aquifers are still not fully understood and they are appearing in more places around the cryosphere, it is pertinent to understand their flow dynamics, meltwater storage, lifetime, and
115 how they recharge from melt and discharge mass from the accumulation zone. Melt-affected firn has a unique chemistry and understanding how much melt is required to create such wash-out affects and partial preservations will help aid in understanding how much melt is occurring on large glaciers, like Kaskawulsh.
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129 Appendix
Ion Statistics
Core 1 Ions (all units in ppm)
Ion Average σ Minimum Maximum
MSA 0.03403 0.0183 0.00 0.2334 Cl- 0.2531 0.2021 0.006 1.3240 Br- 0.1702 0.2106 0.00 1.312 2- SO4 0.0602 0.0347 0.0458 0.555 - NO3 0.09997 0.0322 0.00 0.3049 3- PO4 0.04768 0.2972 0.00 0.1126 Na+ 0.2539 0.2893 0.00 2.458 + NH4 0.1311 0.1735 0.0003 0.7566 K+ 0.0848 0.2468 0.00 1.829 Mg2+ 0.7552 0.1250 0.00 1.838 Ca2+ 0.2931 0.3488 0.00 3.922
Core 2 Ions (all units in ppm)
Ion Average σ Minimum Maximum
MSA 0.04768 0.0455 0.00 0.3117 Cl- 0.1562 0.1108 0.00 0.7736 Br- 0.0726 0.0620 0.00 0.4944 SO4 0.05335 0.0081 0.00 0.0580 NO3 0.1541 0.0414 0.00 0.3049 3- PO4 0.06315 0.0336 0.00 0.2837 Na+ 0.05477 0.0718 0.00 0.2618 + NH4 0.0 0.00 0.00 0.00 K+ 0.000475 0.0045 0.00 0.0449 Mg+2 0.1467 0.0660 0.061 0.3190 Ca+2 0.0617 0.1114 0.00 1.2200
130