Thesis Submitted to The

A CORRELATION OF WESTERN ARCTIC OCEAN SEDIMENTATION DURING THE LATE HOLOCENE WITH AN ATMOSPHERIC TEMPERATURE PROXY RECORD FROM A GLACIAL LAKE IN THE BROOKS RANGE, ALASKA

A thesis submitted to the

Kent State University Graduate College

in partial fulfillment of the requirements

for the degree of Master of Science

Jeffrey M. Harrison

May 2013

Thesis written by

Jeffrey M. Harrison

B.A. Ohio Wesleyan University, 2008

M.S. Kent State University, 2013

Approved by

______, Advisor Dr. Joseph D. Ortiz

______, Chair, Department of Geology Dr. Daniel K. Holm

______, Associate Dean, College of Arts & Sciences Dr. Raymond A. Craig

TABLE OF CONTENTS

LIST OF FIGURES ………………………...………….…………………………… v

LIST OF TABLES ……………………………………...….…………………..…… vii

ACKNOWLEDGMENTS ……………………………………………..…………... viii

CHAPTER

1. INTRODUCTION …………………………………………………… 1

2. MARINE SEDIMENT CORE

2.1 Background …..………………………..…………...…………… 7 2.2 Significance of the Core Location ….....…….……...... …...…… 9 2.3 Methods …….…………………...……..…………...... …...…… 12 2.3.1 Chronology .……………….………...…...…… 13 2.3.2 Malvern Analysis .………………..…...... ….… 15 2.3.3 VPCA Statistical Analysis .……………..….… 22 2.4 Results ………………...... ….....……..…………..…….……… 24 2.5 Discussion ………….…………...……..……………..………… 31 2.5.1 Sedimentation Processes ……...... …………. 34 2.5.1.1 Sea-Ice …………………...... ……. 35 2.5.1.2 Sea-Ice Sedimentation …….....……. 38 2.5.1.3 Sea-Ice Circulation ……...... ……. 42 2.5.1.4 Intermittent Suspension ……...……. 45 2.6 Conclusion …….……...... ….....……..…………..…….……… 48

iii TABLE OF CONTENTS (Cont.)

CHAPTER

3. CORRELATION OF TIME-VARYING MULTIVARIATE DATASETS

3.1 Introduction ……………..…….…….....………………...…..… 51 3.2 Temperature Proxy …………….....…..………………….…..… 52 3.3 Correlation of Marine Sedimentation ……………..……...... … 59 3.4 Periodicities ……………..……………..………………….....… 66 3.5 Discussion ……………………………..………………..…...… 72 3.6 Conclusion ……………..……………...………………….....… 80

4. SUMMARY ………..…………………...…………………...….…… 82

REFERENCES ……………………………..………………………………..…...… 85

APPENDIX A: Sample Component Scores ……….…..…..……….……..….…… 98

APPENDIX B: Comparison of VPCA Datasets ……….…….…….……….…… 103

iv LIST OF FIGURES

Figure 1. Map of the Arctic Region and Surface Circulation …………………...…...… 3

Figure 2. Detailed Map of Study Area and Core Locations ………….….……....…...…4

Figure 3. Map of Arctic Sea-Ice Extent ………………………………….……...…...… 6

Figure 4. Age Model for Core JPC16 ………………………...………….…….…...… 14

Figure 5. Malvern Mastersizer 2000 ……………….…………………….…….…...… 17

Figure 6. Downcore Variation of JPC16 Mean Grain-size ...…………….…….…...… 19

Figure 7. Contour Plot of JPC16 Grain-size Distributions ………………...... … 20

Figure 8. Standardized JPC16 Grain-size Data (Z-scores) …..……………………..… 21

Figure 9. Malvern VPCA Component Scores …………………………..…...... … 26

Figure 10. Component Scores Plotted against Grain-size Class …...………….…....… 28

Figure 11. Downcore JPC16 Components through Time ………………..……...... … 30

Figure 12. Mean Filed of Arctic Ocean Ice Drift ………………..……….….....…...… 33

Figure 13. Blue Lake Temperature Reconstruction ….….…….…………...….…....… 56

Figure 14. Magnetic Susceptibility from Burial Lake ……...…..……….……..…...… 57

Figure 15. Composite Blue Lake Varve Thickness Measurements ….…….….…....… 58

Figure 16. Interpolated Age Measurements for PC-1 …..……………….….….…...… 60

Figure 17. Comparison of JPC16 Components with Blue Lake Varves ...….….….. 61-63

Figure 18. Detrended PC-1 Data ……………………….……….……….……..…...… 68

v LIST OF FIGURES (Cont.)

Figure 19. Wavelet Analyses ………………………..…………..……….…….….. 70-72

Figure 20. Comparison of JPC16 Grain-size Data and Varves Thicknesses .……....… 73

Figure B.1. Comparison of the Mean Grain-size Composition for JPC16 …….…...…105

Figure B.2. Comparison of the PCA Components for the JPC16 Datasets ……...... …108

Figure B.3. Comparison of the Principal Components from the JPC16 Datasets .……110

vi LIST OF TABLES

Table 1. VPCA Component Correlation Matrix …...…………...……………….…… 31

Table 2. Correlations between JPC16 components and Blue Lake varves …..….…… 65

Table B.1. Total Variance Explained by the Darby PCA ……………….....…………111

Table B.2. Total Variance Explained by the Thesis PCA …………....………….…...112

vii ACKNOWLEDGMENTS

This study is based on a marine core collected by the U.S. Coast Guard Cutter

Healy during a sedimentological and oceanographic study in the summer of 2002. A special thanks to Dr. Lloyd Keigwin and crew for obtaining the marine sediment core

(JPC16) used here during HLY0204 cruise aboard the USCGC Healy. Funding for the research within this thesis was provided by a NSF grant (ARC-0612384 to Dr. Ortiz). I wish to thank Dr. Dennis Darby, Old Dominion University, for providing an age-depth model for JPC16 and for giving access to samples of the JPC16 sediment core for further analysis at KSU. Additionally, his comments and guidance were very valuable in strengthening the quality of work presented here. I am very thankful to Dr. Mark Abbott,

University of Pittsburgh, for providing permission and access to the atmospheric temperature proxy data from Blue Lake and Burial Lake that enable for direct comparison to the marine sedimentation data.

I am very grateful to my Advisor, Joseph Ortiz, for his dedication, guidance, and support through the entire research process, especially in helping to comprehend the vast paleoclimate and oceanographic processes of the Arctic Region, plus the various analytical methods that were new to me prior to this research. I cannot thank him enough for showing me the Malvern sample analysis and for his guidance with the PCA statistics and other data analysis procedures. I am thankful to have the privilege to work with

viii someone so knowledgeable.

Many thanks are due to my thesis committee members: Dr. David Hacker and Dr.

Elizabeth Griffith. You both provided me with valuable experiences during my time at

Kent State. Dr. Hacker, I cannot thank you enough for the chance to see and learn about

“World-Class Geology” and for selecting me as a T.A. for Field Camp; you opened my eyes to what geology is really about.

I appreciate the support and help that I received from everyone in the department throughout this process. Merida Keats deserves some special gratitude for her help and patience to resolve minor issues that arose with the Malvern and other laboratory items.

Her willingness to help make sure everything was operating properly and to preform quick fixes was a huge blessing. Furthermore, I want to thank the Department of Geology at Kent State University for support during my Master’s program. I am thankful to have had this opportunity to pursue a graduate degree and conduct research on the arctic.

Finally, I don’t think I could have accomplished what I have without a great network of support from family and friends. I want to thank my family for their constant encouragement in all of my endeavors. Their help and encouragement has been essential in my pursuit of life goals. I am extremely grateful to my best friend and wonderful wife,

Rachel, who has been by my side every step of the way. I am thankful that she was able to read through these pages before the final version and for making valuable suggestions.

If she was able to understand everything, I don’t know, but without her I would not have been able to achieve what I have.

CHAPTER 1

INTRODUCTION

Recent climate change has become one of the most popular topics of discussion over the past few years. The Arctic has experienced dramatic environmental changes over the last 30 years (Serreze et al. 2000), marked by a significant decline in the extent and thickness of sea-ice cover both during the summer and winter (Serreze et al. 2003). In the western Arctic Ocean, there has been an overall shift from a predominantly perennial ice pack toward a seasonal sea-ice cover (Rigor and Wallace, 2004; Nghiem et al., 2007).

The Arctic Ocean has been viewed by many as an important region because of its high sensitivity to changes in the global climate, as effects tend to be amplified in the Polar

Regions. Observations of climate patterns in the Arctic are vital to give scientists a better understanding of current and future variations in our climate system. There is much debate on the relative influence of natural versus anthropogenic forcing on these changes observed within the Arctic sea-ice.

There are major changes occurring in the Arctic that researchers have long considered to be indicators of climate change, including later onset of ice formation and earlier break-up of Arctic ice, the overall decline in ice extent, accelerated rates of sea-ice drift and freshwater export, changes in the magnitude and frequency of storm tracks, and

1 2

warming of surface waters and the atmosphere (Darby et al., 2006; Serreze et al., 2000).

Detailed records of natural climate variability from the western Arctic allow for better interpretation of climatic patterns and inference of possible forcing mechanisms. To track the climate history of the southern Arctic region and to better predict its future, there needs to be a better understanding of the linkage between variations in atmospheric and marine cycles, as these are important modulators of the global climate cycle.

High-resolution studies of the Arctic climatic system will better our understanding of its longer-term variability and further constrain climate models through the use of past analogs. This study is of particular interest because it provides information regarding the connections between the atmosphere and ocean along the northern margin of Alaska (Fig.

1). The anticipated outcome of this project will contribute to a better comprehension of the past and future variability of ocean sedimentation, and direct or indirect influences from atmospheric temperature, which may help to clarify the climatic linkages of the region. By understanding how ocean sedimentation and terrestrial atmospheric temperatures are related in a quantitative way, we can begin to learn something about the processes that relate them over longer time periods.

Fig. 1. The International Bathymetric Chart of the Arctic Ocean (IBCAO, Jakobsson et al., 2012) showing the study region in the eastern Chukchi Sea and surrounding regions; the primary ice drift and surface circulation patterns (white arrows) are dominated by the Transpolar Drift (TPD) from the Laptev Sea to Fram Strait and the Beaufort Gyre (BG). The BG is the primary transporter of IRD to the core location for JPC16. During +AO phases, the TPD is shifted toward North America (black arrow) and the BG weakens. (Darby and Bischof, 2004; Darby et al., 2012). Also shown are primary features of the Arctic seafloor: Lomonosov Ridge (LR), Mendeleev Ridge (MR), Alpha Ridge (AR), and Chukchi Borderland (CB).

The primary objective of this research is to investigate the variability of depositional mechanisms, including ice rafting, in the western Arctic Ocean during the late Holocene and to relate those mechanisms to atmospheric climate over the northern

Brooks Range, AK. The main goal is to determine potential atmosphere-ocean connections within the region (Fig. 2). We are primarily interested in how large-scale oceanographic variability in sedimentation patterns are linked to an atmospheric climate proxy record from an inland glacial-fed lake (Blue Lake).

Fig. 2. Location of core HLY0204-JPC16 on the Alaskan continental shelf adjacent to Barrow Canyon. Additional cores for the temperature proxy records used in the study are shown in the Brooks Range. Also illustrated are rivers from the Brooks Range that transport material to the coast. Base maps used are from Jakobsson et al. (2012). 2a. Detailed map of the Alaskan shelf slope. White arrows indicate predominant current systems in the western Arctic Ocean: the Alaskan Coastal Current (ACC), the Beaufort Gyre surface drift (BG), and the Beaufort Undercurrent (BU). Eddies form off of the major currents along the continental slope and move down into the Canada Basin. Current patterns modified from Darby et al. (2009).

This research builds upon and revisits the results found by Darby et al. (2009) via analysis of a marine core that contains high sedimentation rates from the eastern Chukchi

Sea shelf. The core location is adjacent to Barrow Canyon off of the north coast of

Alaska (Fig. 2a). This research provides a more complete time series of grain-size variability of marine sediment spanning approximately the last 2,000 years. The continental shelf north of Alaska and Canada receives a considerable load of fine-grained sediment from the Mackenzie River and numerous smaller rivers that flow north from the

Brooks Range (Fig. 2). Along the Chukchi-Alaskan slope, eddies spin off the eastward flowing ACC and move offshore into the western basin (Fig. 1). These eddies along with

density flows from sea-ice formation can move sediment offshore towards the continental slope and the location of JPC16 (Darby et al., 2009). Additionally, occasional storm waves and associated currents during ice-free intervals can transport sediment offshore toward the slope. Variations in downcore sediment sizes are reliable indicators of shifts in the transport of sediment and depositional mechanisms at the core location during the

Holocene (Darby et al., 2009).

The Chukchi Sea lies north of the Bering Strait and extends to the seasonally fluctuating limit of Arctic pack ice at 71° to 75°N Latitude. Sea-ice in shallow shelf areas of the Chukchi-Beaufort Seas retreats during the summer because these areas are subject to influxes of warmer ocean waters and river runoff. In the Chukchi-Beaufort Seas, the minimum edge of the sea-ice (Fig. 3) is usually situated near the shelf break during the transition from summer to fall (Nghiem et al., 2012), while thicker, perennial ice may remain over the deeper basin. The western Arctic Ocean is mostly covered by perennial sea-ice (Fig. 3), though it has experienced the most dramatic changes during the 20th century as a result of global changes, resulting in major reductions in sea-ice cover

(Stroeve et al., 2008).

Fig. 3. Map of the Arctic showing the extent of median sea-ice for the period of 1979-2000 – September (Orange) and March (Blue); white field indicates the sea-ice extent in September 2010 and the light blue is the March 2010 extent (images obtained from the National Snow and Ice Data Center (NSIDC), Boulder, Colorado). This illustrates that the northern Alaskan shelf is seasonally covered by sea-ice. Also shown are the generalized ocean currents and features of the Arctic Ocean: Beaufort Gyre (BG), Transpolar Drift (TPD), Chukchi Sea (CS), Laptev Sea (LS), Bering Strait (BS), and Fram Strait (FS).

The sedimentation record obtained from the marine core is further compared to an atmospheric temperature proxy record from Blue Lake as recorded in annual varve thickness measurements. A direct correlation is presented between varve deposits during the recent Holocene (2000 yr BP to the present) and the patterns of marine sediment deposition. This correlation is based on high-resolution varve sequences (annual resolution) and a Varimax-rotated, Principal Component Analysis (VPCA) of marine sediment (decadal to multi-decadal resolution) with robust age constraints. The direct varve-marine sedimentation record comparison will allow us to determine the connections between climatic events detected from variations in varve thickness in northern Alaska to changes in the marine depositional mechanisms and/or oceanic circulation (transport), including sea-ice variations over the continental shelf.

CHAPTER 2

MARINE SEDIMENT CORE

2.1 Background

Multiple sediment cores retrieved during the two Healy-Oden Trans-Arctic

Expedition (HOTRAX) cruises (http://sci.odu.edu/oceanography/research/hotrax) have provided researchers with a better understanding of the Quaternary stratigraphy and paleoceanography in the western Arctic Ocean (Darby et al., 2005). The approach of this research includes a record of marine depositional characteristics in marine sediment using downcore grain-size analysis from a sediment core recovered in 2002 onboard the U.S.

Coast Guard Cutter (USCGC) Healy (Keigwin et al., 2006). This core is located on the eastern flank of the Barrow Canyon in 1250 meters water depth (mwd) (Fig. 2).

The Arctic Ocean is commonly divided into the Canada (or the Amerasian) and

Eurasian basins, referred to as the western and eastern Arctic, respectively (Polyak et al.,

2009; Stein, 2008; Tomczak and Godfrey, 2003); these two deep-ocean basins are separated by the shallow Lomonosov Ridge (Fig. 1). There are two predominant wind

7 8

driven current systems acting on the circulation patterns of surface water and sea-ice in the Arctic Ocean (Fig. 1): the anticyclonic Beaufort Gyre over the Amerasian Basin and various branches of the southward trending Transpolar Drift (TPD) across the Eurasian

Basin (Tomczak and Godfrey, 2003). The Beaufort Gyre is largely constrained to the

Chukchi-Beaufort Seas while the TPD flows from the Siberian shelf along the

Lomonosov Ridge toward Fram Strait (Sellén et al., 2010). The western portion of the

Arctic Ocean is therefore hydrographically isolated due to the presence of the Beaufort

Gyre (Serreze et al., 1993).

The northern Alaskan shelf is characterized by complex ocean currents that operate at different water depths, and show strong seasonal or interannual variability

(Darby et al., 2009). Surface water circulation in the Chukchi-Beaufort Sea is strongly influenced by the counterclockwise Beaufort Gyre that drives offshore currents westward along the shelf break. Additionally, the Beaufort Undercurrent is present below 50 mwd and transports Pacific waters eastward along the continental slope (Pickart, 2004) (Fig.

2a). The Beaufort Gyre has remained stable throughout the Holocene (Bischof and

Darby, 1997), and acts to recirculate sea-ice through the western Arctic or intermittently release it to the central Arctic and the TPD. These current systems strongly influence the distribution of sediments from the continental shelves throughout the Arctic Ocean.

2.2 Significance of the Core Location

Piston coring aboard the USCGC Healy was implemented to target thick

Holocene sediment accumulations in order to investigate the past extent of sea-ice cover and history of ocean surface circulation within the western Arctic Ocean throughout the

Holocene (Darby et al., 2005; Keigwin et al., 2006). The jumbo piston core (JPC16), and its companion multicore, used in this study were collected from the eastern Chukchi Sea

(72.1555° N, 153.50817° W, ~1250 mwd) north of Barrow, Alaska (Fig. 2).

The core site is on the edge of the continental shelf where numerous canyons dissect the continental slope and flow into the Canadian Basin of the western Arctic

Ocean. This location has experienced continuous sedimentation that records variations in multiple Arctic processes including sea-ice drift, transport within the Beaufort Gyre, and the inflow of Pacific water into the western basin (Darby et al., 2005; Shroyer, 2012); a substantial portion of water from the Pacific flows through Barrow Canyon as it enters the Arctic Ocean (Shroyer, 2012). Strong down-canyon density flows also occur along

Barrow Canyon from brine rejection and other turbidity currents, which bring near surface water to the bottom and down the canyon. Brine waters are produced on the continental shelf during the winter, sea-ice formation season and drain off of the shelf through various canyons (Keigwin et al., 2006). These may produce eddies that resuspend sediments as water moves down the Barrow Canyon and can laterally deposit fine-grained sediment up on the flank of the canyon to the core site (Darby et al., 2009).

Sediment cores collected along the Chukchi shelf and slope record various patterns of sedimentation and highlight the role of paleoclimatic variability in the region off the northern coast of Alaska. The initial results of sedimentation from JPC16 reported by Darby et al. (2009) were compared to additional cores from HLY05-01 (JPC5 and

JPC8). The sedimentation history and grain size distributions are constrained by radiocarbon chronology for JPC16 (Darby et al., 2009). JPC16 contains a continuous record of offshore sedimentation spanning the last 8000 years that is constrained by paleomagnetic data and Accelerator Mass Spectrometer (AMS) radiocarbon dates.

Similar to other cores from the region, the sediment record in JPC16 is uniform throughout the measured interval with relatively constant, high sedimentation rates of 234 cm/kyr, allowing analysis with a decadal to multi-decadal scale resolution (Darby et al.,

2009; 2012).

Sediment accumulations in this region are likely the result of a combination of the direct settling of sediment from sea-ice melting and riverine sources (Yukon and smaller

Alaskan rivers) that are redistributed by currents (Darby et al., 2009) and lesser contributions from the resuspension and redistribution of sediments by bottom currents.

This region of the western Arctic also experiences changes in surface ocean circulation related to the two separate phases of the Arctic Oscillation (AO). The study of well-dated, high latitude sedimentary sequences produces a reliable, continuous, and high-resolution record of depositional variations through the Holocene. Physical properties of marine sediment provide an effective means of extracting paleoceanographic information about the Arctic Ocean. Changes in the main depositional mechanism, such as ice rafting,

bottom current intensity, and density flows (Darby et al., 2009), lead to a difference in the primary grain-size class being transported to the core location.

This research examines the last 2,000 years and provides a high-resolution record of sedimentation history from core HLY02-04-JPC16 (0.39-4.53 meters) and its associated multicore HLY02-05-MC-14D (0.47 meters), herein, referred to collectively as JPC16 (Fig. 2). The multicorer (MC-14D) was used to collect an undisturbed sample of the sediment-water interface. These cores were raised from the Alaskan-Chukchi margin (~1250 mwd) as part of the 2002 HOTRAX cruise (Keigwin et al., 2006).

This area of the continental shelf received higher rates of sedimentation during the

Holocene as compared to locations further offshore in the central Arctic basin (Darby et al., 2006). Sedimentation rates for the shelf sediments are estimated between 40 and 1200 cm/kyr (average: 150-313 cm/kyr) throughout the late Holocene (Darby et al., 2009).

Because of the high sedimentation rates observed in the marine cores, we are able to observe decadal to multi-decadal scale variations in Arctic grain-size deposition. The research herein, is based upon previous results reported by Darby et al. (2009) and focuses on constructing a much higher-resolution of grain-size variability in the selected marine core. Studying this sediment core provides us with a detailed record of past shifts in regional climate and ocean circulation evidenced through variations in sediment grain size.

2.3 Methods

Bottom sampling during cruise HLY02-04 was conducted using a 10 cm-diameter jumbo piston corer, along with a multicorer with a rosette of 8 core tubes, to collect an undisturbed sample at the sediment-water interface (Keigwin et al., 2006). Darby et al.

(2009) conducted the initial analysis of the sediment composition from JPC16. Our method for the determination of source sedimentation mechanisms of ice rafted debris

(IRD) and other depositional processes are similar to those described by Darby et al.

(2009). Subsamples of JPC16 were made available through Dr. Dennis Darby at Old

Dominion University in order to produce the high-resolution sedimentological record.

Except for a few missing intervals, the upper portion of JPC16 was sampled for grain-size analysis every 8 cm until a depth of 2 meters, after which, it was sampled at a 4 cm interval; subsamples were taken at 2-cm-thick intervals. This corresponds to a sampling interval of ~ 35 and ~ 18 cal yr, respectively. Samples from the multicore,

MC14D, were collected continuously every 1 cm (~ 5 yr interval). The sedimentological analysis was conducted on the < 45 µm size fractions to eliminate the highly variable coarse fractions that are related to ice-rafted debris (IRD). All samples were wet-sieved after sonification at 45 µm during previous analysis. The overall core has a uniform sedimentation rate of 234 cm/kyr (Darby et al., 2009; 2012) and consists primarily of fine-grained sediments of silts and clays.

2.3.1 Chronology

Sedimentation rates determined for JPC16 are comparable to the rates reported for other Chukchi shelf and slope Holocene deposits (Darby et al., 2009; Keigwin et al.,

2006). Samples from JPC16 have a well-constrained age-depth relationship. To determine the depositional age for each sample interval, an age-depth model for JPC16 was constructed using six Accelerator Mass Spectrometer (AMS) radiocarbon dates on benthic foraminifers or mollusk shells (Darby et al., 2009). Based on AMS-14C dating, a very reliable chronology is available for the sediment interval contained in JPC16, representing a total span of 8 kyr BP. Radiocarbon dates throughout JPC16 were also supplemented with paleomagnetic intensity measurements. A detailed discussion of the samples, selection of a reservoir correction function, conversion to calendar ages, and the determination of a depth correction factor for JPC16 is presented in Darby et al. (2009).

To further constrain the age model for JPC16, Darby et al. (2012) refined the earlier version of the model produced by Darby et al., (2009). A brief description of the more robust age model is provided here.

A composite age model for JPC16 was developed through the correlation of the previously obtained radiocarbon dates and several other physical properties of the sediment (paleomagnetic intensity, inclination, declination and diffuse spectral reflectance of clay minerals determined by visible derivative spectroscopy) between

JPC16 and nearby cores as a function of depth (Darby et al., 2012). Nine AMS dates from a nearby, upslope core (GGC19) were highly correlated to JPC16 using several

sedimentological features. 11 additional AMS dates were transferred to JPC16 from two other nearby cores through the correlation of paleomagnetic features. The dates obtained from JPC16 and those from nearby cores are in significant agreement. Therefore, a composite age model was constructed for JPC16 based on a total of 26 AMS radiocarbon dates. The resulting age model was tested for robustness by comparing the prominent magnetic features in JPC16 to other global paleomagnetic records.

Fig. 4. Sedimentation rates and age model calculated for JPC16 (modified from Darby et al., 2012, Supp. Info.). Equation for the Age-Depth relationship was used to determine the calendar ages for each sample interval.

All radiocarbon dates were converted to calibrated ages before present (BP) using

CALIB6.0 (Darby et al., 2009; 2012). An estimated offset of MC14D and JPC16 was calculated to be 25 cm by Darby et al. (2009) based on downcore correlations of AMS

dates, sediment mineralogy, VIS components, and several other factors. The upper portion of the JPC record was missing due to core by-pass. Therefore, the depth scale of

JPC16 was adjusted downwards by 25 cm and all sample depths referenced herein for

JPC16 refer to the corrected depths. A linear regression was fit to the calendar age vs. depth by Darby et al. (2012) to determine the age-depth relationship for the sediment core

(Fig. 4). Based on the dates obtained, the following age model was constructed and used as the basis for age calculations in the present study:

Average age = 4.39 * depth

The best-fit curve through the data produced a slope of 4.39 yr/cm. The linear depth-age trend was applied to JPC16 from 39-453 cm (corrected depth) and to MC14D, which contains the interval of Holocene silty-clay sediments used in this study. The projected age at 453 cm is approximately 1990.84 cal yr BP.

2.3.2 Malvern Analysis

Most sedimentological studies in the Arctic have focused on the coarse IRD fraction (> 45 µm). The finer fractions can provide valuable insight into the deposition of the most abundant fraction – clay to silt-sized particles. Samples from JPC16 consisted primarily of homogeneous dark grey “mud” with less than 1–3% sand (> 63 µm) (Darby et al., 2009), but texturally there are various fine fractions (< 45 µm) with a range of fine particle sizes. These variations are locally driven by the release of sediment from sea-ice

drift through melting, or by re-suspension, transport, and deposition via other marine mechanisms including bottom currents. To determine the mechanisms of marine sedimentation at a high-resolution, additional sediment analysis of JPC16 (< 45 µm) was conducted at Kent State University in Dr. Ortiz’s sedimentology lab using a Malvern

Mastersizer 2000 Hydro MU laser particle-size analyzer (Fig. 5). The Malvern

Mastersizer (“Malvern”) is an optical method of gathering detailed sediment size analyses of very fine particles (range claimed by the manufacturer: 0.02 - 2000 µm) through a rapid, non-invasive, high-resolution process (Sperazza et al., 2004). Laser diffractometry is the most time-efficient and robust way to obtain a grain-size distribution of a sediment core.

The technique of laser diffraction is based on the principle that particles passing through a laser beam will scatter light at an angle that is directly related to their size: large particles scatter at low angles, whereas small particles scatter at high angles (Jones,

1999; Malvern, 1997). Each sample measurement uses multiple detectors to determine percentages of light scattering based on the particle sizes of the sample. Computer software with the Mastersizer 2000 analyzes the scattering patterns to calculate a particle size distribution for the sample. Information obtained by the Malvern instrument provides a high-resolution characterization of downcore grain-size variations throughout the sampled core interval. Analysis requires little sample preparation and minimal sample input to obtain robust results. The amount of material needed for laser analysis depends on the grain size characteristics, typically 0.1-0.2 grams for fine-grained samples (Konert and Vandenberghe, 1997).

Fig. 5. Malvern Mastersizer 2000 utilized to acquire downcore grain-size data from JPC16.

The previous work by Darby et al. (2009) demonstrated the utility of Malvern grain-size analysis as a means of extracting information regarding variations in sediment distributions both spatially and temporally. All samples taken from JPC16 were analyzed by laser diffraction using the Malvern Mastersizer with a standard operating procedure with a 20-second measurement period. Similar to the previous analysis, the Malvern was set to measure irregularly shaped particles of opaque quartz rather than the standard idealized spheres. In general, laser diffraction is comparable to results from sieving methods but will report a wider range of particle size due to the irregular particle scattering of light (Konert and Vandenberghe, 1997). For this reason, the Malvern

Mastersizer system provides users with the ability to select an analysis mode that compensates for irregular-particles, ensuring more accurate and replicable results (Rawle,

2003).

Each sample interval was sonified in 1L of distilled water (~ 60 seconds) to

ensure complete separation of individual grains. Sample was added to the Malvern so that the obscuration, a measure of the concentration of suspended sediment, fell between 10 -

20% (average of 15%), the range at which produces the most reproducible results

(Sperazza et al., 2004). The pump speed for the Malvern was set to ~ 2400 rpm for the duration of analysis. Between each sample run, the Malvern was flushed with 3-liters of clean, distilled water. The Malvern was set to analyze each interval through two measurement cycles, and an average was taken. Further analysis conduced herein uses the averaged results for each sample interval.

The Malvern models particle size using the Mie scattering theory, the interaction of light with matter, to obtain the volume scattering function of the sample medium as a function of particle size, allowing for highly accurate results over a large size range

(typically 0.02 – 2000 µm) (Malvern, 1997; Rawle, 2003). The output from the Malvern is obtained as volume percentages, and data were collected into various grain-size bins pre-determined by the standard operating procedure of the instrument.

Discrete sub-samples from cores JPC16 and MC-14D used in this analysis were obtained from the Darby Lab (Old Dominion University) and therefore did not require any collection of field data. A total of 47 samples were analyzed from MC-14D and 75 samples from JPC16, which approximately represent the last 2,000 years of the

Holocene. The Malvern equipment was used to obtain a high-resolution time-series data set of downcore grain-size variability. The portion of JPC16 analyzed here was sampled every 2 cm for the first 20 cm, and every 10 cm for the remaining section of JPC16 while

MC-14D was sampled every centimeter. This corresponds to an average sampling interval of ~35 years for JPC16 as compared to a ~ 50 year interval previously reported by Darby et al. (2009). The sediment texture at the location of JPC16 appears to have changed very little during the late Holocene with only a slight increase towards the recent

(Fig. 6). Collectively, the fine-fraction (< 45 µm) analyzed from core JPC16 has a mean grain-size between 9 and 11 µm (fine-silt) with fairly uniform down-core distributions through most of the late Holocene (Fig. 6 and 7).

Fig. 6. Downcore variation in mean grain-size (< 45 µm) for core JPC16 during the last 2,000 years.

Because real particles have irregular shapes and thus may fit through the sieve when their short axis is oriented parallel to the square opening, but be retained if the long axis is perpendicular to the openings, the laser diffraction analysis of non-spherical particles can lead to small percentages of grain distributions larger than the < 45 µm mechanical sieving cutoff due to the wider range of light scattering (Malvern, 1997;

Rawle, 2003).

Fig. 7. Contour plot of the grain-size distribution data for core JPC-16 versus age showing the concentration of data within each grain-size class. The mean grain-size for the measured portion of the core is centered on 10 µm.

Fig. 8. The standardized concentration of JPC16 grain-size data versus age showing the standard deviations around the mean. The variables were standardized using z-scores.

The grain-size data were displayed in a contour plot where the volume percentages of the different Malvern bins are plotted against the grain-size classes and age (Fig. 7). This detailed grain-size distribution spectrum for core JPC16 reveals information on the mean size for the fine fraction analyzed here (< 45 µm) and how the grain-sizes vary through time. The Z-score for an individual sample represents how many standard deviations from the mean the sample is. Standard deviations of the data quantify the variability of the different size classes around the mean values through time (Fig. 8).

This shows a combination of different populations of particles. Using this method, we can begin to determine groups of grain-sizes having the most important variation through time. Few methods can completely discriminate the different populations. This is where the VPCA was used in order to identify the grain-size intervals that contribute the highest variability in the data matrix.

2.3.3 VPCA Statistical Analysis

The size distributions obtained from the Malvern Mastersizer were further analyzed by a Varimax-rotated, Principal Component Analysis (VPCA) to determine various mechanisms involved in depositing JPC16 sediments. This approach allows researchers to pull out independent grain-size components, and reveal simpler patterns within the grain-size matrix. These Principal Components (PCs) can then be further related to potential Arctic transport or depositional processes.

Multivariate statistics provide powerful methods that allow a number of properties to be analyzed concurrently to evaluate changes in a dataset over time (Davis, 1986).

With the use of multivariate statistics, we can quantitatively explain the relationship of multiple sedimentological parameters as a function of environment or process. Once the grain-size distributions were obtained from the Malvern analysis, they were converted to a standard ASCII text file (.txt file format) and exported into Microsoft Excel® so that the data could be further analyzed. The grain size data obtained directly from the Malvern instrumentation, while useful in its own right, lacks the ability to provide insight into the source of variability within the data as a result of the mixing of sediment delivered by various transport mechanisms. To quantitatively determine what processes influence the grain-size distributions in core JPC16 and specific marine mechanisms, a Varimax- rotated, Principal Component Analysis (VPCA) was applied to the Malvern grain-size matrix using the SPSS® (14.0) statistical software package.

VPCA is a data reduction method that enables researchers to extract meaningful

results from a complex dataset by pulling out Principal Components (PCs) responsible for variation within the dataset (Ortiz, 2011). In particular, this method seeks to determine if the observed variation can be explained largely in terms of a much smaller number of variables referred to as components. This is done by grouping variables that are independent of one another into separate categories (components) according to their relative similarities and overall importance within the dataset (Davis, 1986). The analysis presented here is consistent with that conducted previously by Darby et al. (2009), but the output terminology was changed from “factors”, the term used by the SPSS software, to

“components”, which is more acceptable (Ortiz, 2011).

The grain-size data matrix was imported into SPSS® 14.0 for Windows.

Lithological variations in marine sedimentation representing different depositional mechanisms were determined by the sample-based, Varimax-rotated, Principal

Component Analysis (VPCA) using the correlation matrix of grain size spectra for the

JPC16 grain-size classes ranging from 0.30 to 60.0 µm as the input. This is similar to the methods employed previously by Darby et al. (2009). This analytical method is used to explain the structure of variance between variables within the grain-size dataset.

Varimax-rotation is a rigid rotation of the principal axes that does not distort the structure of the data, allowing the results to be easily interpreted (Ortiz, 2011). The sediment analysis conducted on JPC16 is based on a total of 122 samples, as compared to the 26 samples previously analyzed and reports by Darby et al. (2009). The VPCA was employed here to simplify the complexity of the grain-size distributions at the core location and determine specific Arctic Ocean sedimentation mechanisms responsible for

sediment transport of particular grain-size classes at each sample depth.

Eigenvalues produced from the analysis account for a percentage of variance in the dataset that is explained by each component (Davis, 1986). They demonstrate how much information each PC contains within the overall matrix. Component scores from the VPCA provide downcore patterns of grain-size variations that demonstrate the variability of each PC through time. Applying this method to the grain-size data, we are able to generate curves that are representative of the main grain-size classes, within the

< 45 µm range, responsible for a majority of the observable variation in the marine sediment core. By plotting the downcore components from JPC16, we can begin to determine how each fluctuates through time and which are responsible for the most variation during different climate cycles.

2.4 Results

JPC16 was selected for detailed analysis due to its high resolution based on sedimentation rates, good stratigraphic recovery, and the location being suitable to monitor sea-ice cover and ocean currents on the edge of the Alaskan-Chukchi margin.

The VPCA analysis provides statistics that are used to assess the quality of the PCs extracted, identify the mechanisms represented by these, and explore the temporal variation of each. The coarse fraction (> 45 µm) previously analyzed from JPC16 differs independently from the mean size of the finer material (Darby et al., 2009), indicating that there were multiple processes that delivered sediment to the core location. The

sediment grain-size distributions for JPC16 can therefore be related to transport processes such as ice rafting, suspension freezing, density flows, bottom currents, and other marine mechanisms.

The Varimax-rotated PCA, a statistical sorting and data reduction method, was conducted on the Malvern-generated grain-size data to determine the major components responsible for the variations in downcore composition. The VPCA revealed four leading PCs from the Malvern sediment analysis, and the component loadings (Appendix

A) for each were evaluated for their grain-size trends (Fig. 11). The PCs obtained here were compared to the component loading data extracted from JPC16 through previous research conducted by Darby et al. (2009) with similar results (Appendix B). The sediment analyzed from JPC16 was previously compared to sediment samples collected from modern sea-ice from north Alaska and the central Arctic to determine the role of sea-ice as a sediment transport mechanism (Darby et al., 2009). Darby et al., (2009) compared the VPCA data from the Alaskan-margin core to the analysis from the central

Arctic, which receives most of its sediment input from sea-ice, and determined that the two leading components (anchor ice and nepheloid flows) are similar between the two locations.

Fig. 9. Component scores from the VPCA analysis of the size distributions on the fine fraction (< 45 µm) from core JPC16. PC-1 (anchor ice) and PC-2 (nepheloid transport) are similar to the leading components produced by Darby et al. (2009) (Appendix B), while PC-3, containing a peak in the finest sediment, is absent from the JPC16 analysis in Darby et al. (2009) and is interpreted as suspension freezing. PC-4 contains the coarsest sediment and is similar, though lower in intensity, to the third component produced by Darby et al. (2009), interpreted as intermittent suspended load.

The leading PCs from the JPC16 analysis account for 99.3% of the cumulative variance within the dataset (Table B.1 in Appendix B). The leading component (PC-1) accounted for 79.6% of the variance while the second (PC-2), third (PC-3), and fourth

(PC-4) components accounted for 13.9%, 4.8%, and 0.9% of the variance respectively.

The communality in the data as a function of grain-size class was very close to 1, indicating that the model sufficiently explains the variation for each bin (or individual grain-size class) (Fig. 9).

The average size distribution of the first VPCA component loading has a broad positive peak at 0.38–3.7 µm (~2 µm) and an anti-correlated negative peak at 13.4 µm, after which it shows decreasing negative loadings with increasing grain size (Fig. 9).

Anchor ice sediment likely contains a more variable size fraction reflecting the shelf sediment from which it formed, and anchor ice also has the capacity to transport both fine and coarser grained particles. Due to the more variable nature of the first component in the PCA, this component was inferred as sediment related to anchor ice transport and deposition onto the Alaskan shelf. PC-3 represents extremely fine particles from this analysis, which peak at < 0.5 µm (0.3 µm) followed by more neutral loadings and a pronounced low in the 3.5–11 µm range that is possibly related to the removal of this range, attributed to post-depositional reworking by bottom currents. Additionally, there are negative loadings above the 13 µm range indicating the lack of transport capabilities of suspension freezing for these coarser grain-size fractions. This very fine grain-size component, within the sediment fraction analyzed here, represents that which would be suspended by currents and small waves in areas where sea-ice entrainment by suspension freezing occurs during the winter freeze-up.

The second mode has a positive peak around 5-8 µm and strong anti-correlated loadings around 47 µm. This likely represents the sortable silt fraction with a mean grain size about 5 µm. This is commonly transported in suspension by weak currents along the bottom nepheloid layer and down Barrow Canyon. The finer particles are more difficult to entrain due to the cohesive nature of clay particles and the coarser sizes would not be

entrained due to their greater mass. PC-4 represents coarse particles, and has a bimodal distribution with peaks at 7.5 µm and > 53 µm. This component is strongly correlated to the coarser size sediment and likely represents the reworking and transport by local bottom currents along the slope (Beaufort Undercurrent).

Fig. 10. Extracted VPCA components plotted against their respective grain-size class: a) PC-1 Fine vs. 0.75 µm. b) PC-2 Intermediate vs. 8.45 µm. c) PC-3 Very Fine vs. 0.35 µm. d) PC-4 Coarse vs. 59.8 µm.

Fig. 10 illustrates the different PCs plotted against the respective grain-size class that each closely corresponds to. This indicates a good relation between the grain-size matched to each of the statistical components. The processes that generated these

different signatures can be determined based on the primary grain-size that is likely to be transported and the relationships between the different components. The leading two modes in the JPC16 analysis are very similar in rank and shape to the leading modes established by Darby et al., (2009) for the Chukchi-Alaskan margin sediments. The third and fourth modes in each of the two VPCA models, however, are very distinct. A full comparison of the different models is presented in Appendix B. Initial VPCA models from the JPC16 data that extracted three modes, similar to that conducted by Darby et al.

(2009), failed to find the coarse mode that was found in the Chukchi-Alaskan margin data set. This indicates some fundamental difference in the grain size spectra found at the higher-resolution analysis; therefore, the analysis used here extracted four component modes in order to obtain the coarse grained mode (> 53 µm) that was presented in the previous work by Darby et al., (2009).

It should be noted that the fourth component extracted as part of this analysis is much less important than the other three components because the eigenvalue had to be extended to 0.4 in order to gain the additional component (PC-4) in the PCA (Appendix

B). Typically, an eigenvalue greater than one indicates that the PC accounts for more variance than is explained by any one of the original variables in the grain-size matrix.

Values greater than one are commonly used as a cutoff point for which principal components are retained.

Fig. 11. Comparison of downcore VPCA component loadings from JPC16 throughout the late Holocene.

Plotting the downcore component scores versus age, we are able to visualize how each fluctuates through time and can begin to connect the depositional and transport processes to regional climate patterns (Fig. 11). Because there is more than one significant PC within the dataset, it becomes important to determine whether these components are correlated with each other. Table 1 presents a correlation matrix indicating the degree of correlation between each component over the 2,000 years examined here. It is evident that each component, except PC-1 and PC-3, are independent of one another during the study interval as there are very weak and non-significant correlations between each variable. The correlation between the sea-ice components (PC-

1 and PC-3) was determined to be 0.62, which is statistically significant (p < 0.01). This indicates that there is at least an intermittent correlation and some relation between the

processes that deposited these sediments during the last 2,000 years. Over longer periods, this correlation becomes weaker; therefore, the time interval studied here shows that PC-

1 and PC-3 appear to be interdependent on similar depositional mechanisms, while the other PCs examined here follow independent processes.

Table 1. Matrix indicating the degree of correlation between the significant components from the JPC16 grain-size VPCA during the last 2,000 years.

Component 1 Component 2 Component 3 Component 4 Component 1 1.00 Component 2 -0.17 1.00 Component 3 0.62 -0.22 1.00 Component 4 0.13 -0.28 0.03 1.00

2.5 Discussion

Several mechanisms operate in the western Arctic Ocean to transport sediment.

Mechanisms of sediment transport active in the polar region include the movement of sea-ice, nepheloid flows above the ocean floor that suspend sediment, and ocean currents.

Waters that drain off of the Alaskan shelf or flow in from the Pacific move through canyons along the shelf and form eddies as water is transported into the Canada Basin

(Fig. 2a). The location of JPC16 on the eastern side of Barrow Canyon suggests water funneled down the canyon likely deposits sediment along its flank by forming eddies that raise sediment from within the canyon to its flanks (Darby et al., 2009). Upwelling currents along the shelf slope may mix with the down-canyon density flows and also create lateral eddies as flow velocities decrease.

Additionally, the core location is influenced by suspended and bed load transport by currents, including the eastward flow of Pacific waters and the Beaufort Undercurrent and the westward movement of the Beaufort Gyre (Fig. 2a) (Darby et al., 2009). An important mechanism operating at the location of JPC16 that imparts a fine-grained signature on the deposits is likely associated with sea-ice, including suspension freezing and anchor ice formation along the shelf (Reimnitz et al., 1992, 1993). Additionally,

Darby et al. (2009) indicated that the sand fraction from cores along the continental margin is primarily deposited through release from transported sea-ice.

The mean size of the sediment from JPC16 increases from less than 10 µm through most of the core up to more than 12 µm, with a coarse interval from 100-1000 yr

BP (Fig. 6). The analysis provided herein shows a slightly coarser sedimentary composition than that produced through previous research (Darby et al., 2009), likely due to greater sonification of sample for the earlier study (Appendix B). But this slight, essentially constant instrument shift does not influence the VPCA results, which are dependent upon the correlation structure of the dataset. The location of JPC16 was selected to minimize the influence of turbidity currents. The sedimentary structures within the core indicate that turbidite deposits are not a significant source of sediment accumulation at the core site, although this location likely receives some fraction of its fine grained load from eddies spun off of turbidity flows that move down the central axis of Barrow Canyon (Darby et al., 2009).

The average grain-size over the last 2,000 years for JPC16 is approximately 10

µm. This likely reflects deposition from ocean currents that were continuous through the

Holocene, in addition to the annual melting and release of IRD and/or influxes of ice entrained sediment from sources where it preferentially exports coarser sized sediments into the Beaufort Gyre especially during positive AO anomalies (1500 yr BP) (Darby et al., 2009; 2012; Reimnitz et al., 1998). In the Arctic, sea-ice commonly remains in the

Arctic Basin, taking several years to make a circuit within the closed Beaufort Gyre surface current system (5-10 years, Fig. 12) (Stein, 2008). Darby et al. (2011) indicated that sea-ice originating from northern Canada would most likely become entrained in the

Beaufort Gyre for 3 to as many as 20 years before being exported to the TPD and lost from the Arctic through Fram Strait.

Fig. 12. Mean field of ice drift in the Arctic Ocean indicating the number of years required for an ice parcel to be exported through Fram Strait (Figure modified from Rigor et al., 2002). Velocities are indicated by arrow length. Data derived from buoy drift from 1979 to 1994. (BG, Beaufort Gyre; TPD, Transpolar Drift).

The Arctic sea-ice extent and other characteristics are determined by the effects of the atmosphere and ocean acting on ice at various time scales (Rigor and Wallace, 2004).

During the last couple of decades in the Arctic, there has been a shift from sea-ice consisting mainly of multi-year ice to first year ice, resulting in a less robust ice cover that is more sensitive to negative forcing mechanisms and melting (Perovich, 2011). An increase in ice export through Fram Strait leads to more open water in the central Arctic, more solar heating of the ocean mixed layer, and stronger summer melting (Perovich et al., 2008). These changes in the Arctic Ocean will affect the onset of ice formation during the winter months (Meier et al., 2005). Increases in surface water temperatures may also provide more energy and moisture to the atmosphere to support the development of cyclone activity across the Arctic (Zhang et al. 2004). Recent changes in ice regimes within the Chukchi and Beaufort Seas (thinning, increased summer break-up, reduction in multi-year ice, altered drift paths, and mid-winter landfast break-out events) have likely resulted in an increase delivery of sediment-entrained sea-ice to the Alaskan shelf

(Eicken et al, 2005; Stein, 2008).

2.5.1 Sedimentation Processes

When interpreting sedimentation processes at the location of JPC16, one has to keep in mind that the core site is situated at the edge of the continental shelf in an area that experiences seasonal sea-ice conditions. The continental shelf near Alaska is completely ice covered for approximately 8 - 9 months out of the year (Reimnitz et al.,

1998). Sediment incorporated into sea-ice must be entrained through either suspension freezing or anchor ice formation. Suspension freezing tends to entrain very fine particle sizes lacking coarser silt and sand, while sediment sizes associated with anchor ice are more variable (Darby et al., 2009). Anchor ice entrainment reflects the shelf sediments where formation occurs and may end up being transported to the core location from northern Canadian and Russian shelf source areas.

2.5.1.1 Sea-Ice

Sea-ice entrains sediment from the inner shelf areas (Reimnitz et al., 1998), and has a strong capacity to transport sediment throughout the entire Arctic Ocean (Eicken et al., 1997). Sea-ice entrainment by suspension freezing produces similar sediment textures regardless of the source area (Reimnitz et al., 1998). Anchor ice is not selective and tends to entrain whatever sediment clasts are present on the floor of the continental shelf in water depths < 50 m. It is likely that most sea-ice entrained sediment founded in the

Chukchi Sea is formed and exported from Canadian shelf regions, with additional influxes of sea-ice transported into Alaskan waters from Siberian sources under strongly positive AO phases (Eicken et al., 2005; Eicken et al., 1997; Darby et al., 2011).

The AO, which ranges between two distinct modes, is related to the distribution of atmospheric pressure systems (wind patterns) over the Arctic region and the middle latitudes of the Northern Hemisphere. Changes in the winds primarily affect the drift patterns of sea-ice. When the AO is in a negative phase, the winds and ice tend to flow in

a clockwise direction, generally keeping more of the older, thicker ice in the middle of the Arctic (Darby and Bischof, 2004; Darby et al., 2012). During a positive phase, multi- year ice tends to get pushed out of the Arctic through Fram Strait or transport towards the

North American coast where melt conditions are likely to be encountered (Darby et al.,

2012). Additionally, the sea ice tends to be younger and thinner and more prone to melt after a winter with a strong positive Arctic Oscillation.

JPC16 most likely contains sediments primarily derived from nearby shelf regions of northern Canada (Darby, 2003; Darby and Bischof, 2004), and the main source of sea- ice entrained sediment into the Beaufort Gyre is the relatively narrow and shallow (< 30 m) Beaufort Sea shelf, east of Barrow Alaska (Reimnitz et al. 1993). Under prolonged positive AO conditions, sea-ice drifts into the Beaufort Gyre from Russian sources

(Darby et al., 2012) causing an influx of ice-entrained sediments. This might explain the increased positive loadings of PC-1 and PC-3 observed in the JPC16 record prior to 1250 yr BP (Fig. 11).

Eicken et al. (2005) found that sea-ice sampled in the vicinity of Barrow Canyon likely formed on the shallow shelf along the Beaufort Sea and followed an East-to-West trajectory along the Alaskan coast from north of the Canadian Archipelago, due to the anticyclonic, westward drift of the Beaufort Gyre. Entrainment of sediment was associated with the formation of frazil ice accumulating underneath the existing ice sheet.

Darby (2003) and Darby et al. (2011) found that the source of entrained sediment in sea- ice collected off Alaska changed interannually and could come from the Bering Strait

area, the Banks Island area, other Canadian shelf regions such as off Ellef Ringnes Island, or from Russian shelves. The area east of Barrow, in the vicinity of the Amundsen Gulf and Banks Island, is a significant area of suspension entrainment and sea-ice formation at times due to the frequent polynyas that occur in the region throughout the winter (Darby,

2003). This is even more likely when offshore winds prevail (Reimnitz et al., 1993) because the winds produce more open water by pushing ice away from the land.

Although suspension freezing is likely to be more widespread around the Arctic, sediment entrainment via anchor ice accounts for much higher sediment concentrations in samples taken directly from sea-ice (Darby et al., 2011). This is consistent with our observation of greater variance in component 1 than in component 3. Because the North

American shelves are narrow and there is less area for suspension freezing or frazil ice formation, there is a greater amount of anchor ice-sediment transported and deposited in the western Arctic than IRD related to suspension freezing. Therefore, it is expected that the VPCA component related to anchor ice sedimentation would explain more of the variance observed in the core provided that the sediment is entrained from North

American shelves.

The entrainment of sediment in the Laptev Sea likely occurs yearly due to the availability of large open water areas during freezing storms at the close of the summer season. Previous work has also indicated that a majority of the Arctic sea-ice entrained sediment is derived from Siberian shelf sources of the Laptev Sea along with additional

Canadian sources (Eicken et al., 1997; Eicken et al., 2005; Stein, 2008). The broad, shallow Siberian shelves over vast stretches of seasonally ice-free marginal seas provide

ideal conditions for the entrainment and export of sediments during the ice formation season (Eicken et al., 1997; Reimnitz et al., 1994; Stein, 2008). The Laptev Sea is generally regarded as an important area of primary ice production and sediment entrainment by sea ice due to the wide, shallow shelf area that is highly favorable for suspension entrainment (Darby, 2003; Stein, 2008). In the Laptev Sea, rapidly forming ice on the Siberian shelf is continuously advected offshore by prevailing westerly winds, producing more open water conditions to form new sea-ice (Reimnitz et al, 1994). This makes the Laptev Sea a major ice-sediment exporter to the TPD with lesser contributions to the western Arctic Ocean and incorporation into the Beaufort Gyre. The main mechanism of ice loss from the central Arctic, aside from melting, is related to the southward drift through Fram Strait via the TPD. Atmospheric and ocean currents cause some of the Arctic sea-ice to naturally flow out of the Arctic Ocean through Fram Strait, while much of it also melts/thins in place during the summer months.

2.5.1.2 Sea-Ice Sedimentation

Proxies for determining the occurrences of sea-ice are derived from coarse IRD the fine-grained sediment that melts out and settles from sea-ice as it drifts under the influence of wind and ocean surface currents from entrainment regions (Polyak et al.,

2010). During warmer periods, the breakup of the Arctic Ocean sea-ice allows sediment- laden ice to circulate easily by currents in the central Arctic Ocean and progressively release their sediment load as ice circulates to the west and north within the Beaufort

Gyre (Fig. 3). It is likely that the major ocean currents of the Arctic Ocean, dominated by

the clockwise Beaufort Gyre and the east flowing Transpolar Drift, have controlled sea- ice drift patterns through the Holocene (Bischof and Darby, 1997). Darby et al. (2012) has demonstrated that drift patterns during this period are closely associated with shifts in atmospheric pressures of the Arctic Oscillation.

Sediment entrainment into sea-ice occurs mostly during periods of winter freeze- up on the shallow shelves that border the Arctic Ocean. Anchor ice may be formed in the shallow shelf regions during winter storms, when the uppermost layers of the water column are churned up by strong winds or waves and become super-cooled (Reimnitz et al., 1994). Anchor ice can form throughout the winter even without open water. Sediment grains from anchor ice are usually defined as the coarse sediment fraction (> 63 µm), though it is poorly sorted and can entrain any available sediment as it forms in areas of shallow shelves (Polyak et al., 2010; Darby et al., 2009, 2011). Therefore, PC-1 reflects shelf sediments that are transported and redeposited by ice rafting related to anchor ice formation, transport, and melt, and provides a high-resolution record of anchor ice sedimentation through the JPC16 record.

Suspension freezing is the other principal mechanism of sediment entrainment into the Beaufort Gyre pack ice (Reimnitz et al., 1992), and is largely restricted to very fine silt and clay-size sediments available in suspension within the water column (Darby,

2003). The very fine-grained mode of the VPCA (PC-3) is likely related to frazil-ice sediment entrainment or suspension freezing within the water column (Darby et al.,

2009). Suspension freezing generally involves sediment that is less than < 0.5 µm and

occurs in areas of the continental shelf (10-30 mwd) during fall and winter freeze-up

(Reimnitz et al., 1992). Suspension freezing occurs during rapid ice formation in open, shallow water during freezing storms and strong winds, as the seawater becomes super- cooled and turbulent (Reimnitz et al., 1993). Suspension freezing occurs primarily in areas where winter polynyas or flaw leads are common. Frazil ice attaches to or incorporates sediment within the water column and lifts entrained sediments to the sea surface where it freezes into sea-ice and can be transported by ocean currents. Reimnitz et al. (1993) indicates that some of the first year sea-ice formed on the shelf melts and releases the entrained sediment load back to the source area.

The similarity of the PC-1 and PC-3, both thought to be associated with freezing conditions, sea-ice formation and transport, could also indicate that at least some portion of the fine-grained shelf sediments are originally deposited from the release of sea ice.

The higher loadings of sea-ice sedimentation in the early portion of the record might reflect an increase in melt conditions, and/or a more positive AO that facilitated the influx of entrained sediment into the Chukchi-Beaufort Seas from Siberian sources which would lead to the increased component scores observed in the record. Additionally, the higher loadings of suspension freezing during the period prior to 1500 yr BP would be indicative of more open water conditions and continued frazil ice formation. Both of the fine-grained sea-ice components defined by the VPCA show a decline after 1500 yr BP.

This likely corresponds to a decline in sea-ice sedimentation related to cooler temperatures and ice growth, or less influx of sea-ice into the Beaufort Gyre from

Siberian sources during more negative AO excursions.

Because the Chukchi-Beaufort Sea is seasonally covered with sea-ice (Fig. 3) and the export of ice from the Beaufort Sea is intermittent (Reimnitz et al, 1994), the location of JPC16 records sedimentation from IRD throughout the entire Holocene. Intervals of greater component scores of sea-ice deposition likely coincides with periods of more extensive sea-ice melt or an increased influx of ice into the Beaufort Gyre; whereas, intervals of reduced sea-ice loadings are related to intervals of greater multiyear sea-ice accumulation and therefore lower amounts of released sediment, or a lower degree of ice entrainment and export of sea-ice from Siberian sources.

The nepheloid sedimentation seems to be somewhat inversely related to the sea- ice components discussed previously (Fig. 11). The mode related to the winnowed silt, which has been linked to nepheloid transport or density flows (Darby et al., 2009), shows higher loading after 1,500 yr BP when both sea-ice entrainment components (anchor and suspension freezing) decline. This is likely due to the formation of a persistent sea-ice cover, increasing the likelihood of downslope density flows and lower concentrations of sediment settling out of sea-ice. It has been suggested that Barrow Canyon drains dense winter waters from the Chukchi Shelf, and that this is dependent on freezing within coastal polynyas that form the very saline waters (Woodgate et al., 2005). It is likely that this winter freezing has varied throughout the Holocene. As long as there has been sea- ice present in the Arctic, there have been mechanisms to export sediment from the shelf regions and transport fine-grained sediments to more offshore continental slope regions.

Sea ice cover, surface water salinity, and dinocysts assemblages were studied in

this region using a nearby core (JPC5) (McKay et al., 2008). During the last ~9000 years, peak summer sea-surface temperatures (SST) and summer sea-surface salinities (SSS) occurred around 2000-1500 yr BP (McKay et al., 2008). McKay et al. (2008) suggests that this period is marked by a minimum in sea-ice extent, likely associated with stronger vertical mixing in the upper water column. This corresponds to maximum values in PC-1 and PC-3 and a minimum in PC-2 (Fig. 11) at the location of JPC16, likely related to increased ice melt and settling of sediments from sea-ice. Additionally, influences from

Pacific water advection into the Arctic occurred from 4600 to ~1300 BP and are associated with shifts in phases of the AO (Bringué and Rocjon, 2012). Ortiz et al. (2009) found a greater influx of chlorite into the Arctic around 2000 yr BP related to the transport of warm water from the North Pacific.

2.5.1.3 Sea-ice Circulation

The annual pressure field of the western Arctic Ocean is characterized by an anticyclonic gyre centered near 75-80°N, 150-170°W, producing the Beaufort Gyre current (Walsh et al. 1996). This current, along with changes in atmospheric pressure fields, greatly influences the motion and distribution of sea-ice in the Arctic Basin (Stein,

2008). The Beaufort Gyre varies in size and intensity depending on changes in atmospheric circulation related to shifts in the modes of the Arctic Oscillation (AO)

(Deser et al., 2000; Maslanik et al., 1996; Mysak, 2001; Rigor et al., 2002; Stein, 2008).

The AO is considered by some to be analogous to the North Atlantic Oscillation (NAO).

When the AO shifts from one mode to another, it represents a fundamental change in the circulation of the atmosphere and the motion of ocean currents in the western Arctic.

Variations in the AO generally affect the transport of sea-ice along with the rates of ice and water export out of the Arctic.

The AO affects how sea-ice moves in the Arctic, which can affect the extent to which ice melts in the summer months. When the AO is in a predominant positive phase, ice is transported from Siberia toward North America, and also south out of the Arctic through Fram Strait. This pattern favors a thinner, younger ice cover the following summer that is more prone to melting. In general, the negative phase of the AO tends to retain ice within the Arctic Ocean, leading to a stronger, more resilient summer sea-ice cover that would be more resistant to melt conditions. The –AO also promote the formation of a more extensive multi-year ice pack within the central Arctic.

Shifts in the phases of the AO, as indicated by long-term oscillations in sediment source areas, modulate variations in sea-ice and freshwater export through Fram Strait via the TPD (Darby and Bischof, 2004; Darby et al., 2012). Nghiem et al. (2007) analyzed sea-ice drift patterns based on buoy observations and linked the observed increase in drift rates to changes in wind stresses related to the AO. During a +AO, the Beaufort Gyre contracts over the Canada Basin (Fig. 1) and the TDP strengthens, shifting toward the

Alaskan coast. Model results presented by Zhang et al. (2003) indicate that a shift from a negative to a positive AO phase leads to a decrease in sea-ice extent in the Arctic Ocean with an increase in the export of sea-ice and freshwater through Fram Strait via the TPD.

This decline in sea-ice, which began in the late 1970s, coincided with a major shift in the

Arctic atmospheric and oceanic circulation (Walsh et al. 1996), related to the weakening of the low-pressure field and the anticyclone over the western Arctic (Walsh et al. 1996).

Decreased concentrations of Arctic ice cover that occurred during + AO phases are associated with the contraction of the Beaufort Gyre as the TPD shifts away from Siberia towards North America (Darby et al., 2012; Stein, 2008). This results in the rapid export of older, thicker ice from the Beaufort Gyre through Fram Strait (Meier et al. 2005).

Sediments originating from the Siberian shelf (Kara and Laptev Seas) can be transported via sea-ice into the western Arctic and deposited on the Alaskan shelf in the

Chukchi-Beaufort Seas during positive phases of the AO (Darby et al., 2012). Results also show positive anomalies of sea-ice storage in the western Arctic Ocean confined within the constricted Beaufort Gyre. The changes in sea-ice cover between the eastern and western Arctic are not spatially proportional; there appears to be reduced ice cover in the east and additional accumulations of ice in the western Arctic during the positive phase (Rigor et al., 2002; Zhang et al., 2003). This would contribute more ice to the continental shelf regions where melting is more likely to occur. Some of the more recent declines in summer sea-ice extent in the Arctic have been related to increased export of ice from the Arctic (Hakkinen et al., 2008) associated with an enhanced TPD (Nghiem et al., 2007).

Changes in the phase of the AO also controls how sea-ice is transported through the Arctic Ocean, which has developed great interest among climate scientists. The

oscillating influx of anchor ice sediment into the Beaufort Gyre can be explained by an expanded Transpolar Drift (TPD) that shifts towards North America and delivers dirty ice from Siberia to the Beaufort Gyre, which in turn transports ice to the site of JPC16.

Variations in the AO are associated with changes in pack ice drift and/or sources of sea ice entrained sediments (Darby and Bischof, 2004; Darby et al., 2012; Stein, 2008) that are also linked to changes in the Arctic climate. The positive phase of the AO is also associated with more frequent cyclonic atmospheric circulation patterns (Maslanik et al.,

1996), and sea-ice is transported from the eastern Arctic Ocean into the waters off of

Alaska under positive AO conditions (Darby et al., 2011; 2012).

2.5.1.4 Intermittent Suspension

Another transport mechanism of fine-grained sediment in the western Arctic is the flow of turbid water associated with resuspension by bottom currents or storm-driven events. The Chukchi Sea is influenced by fall and winter atmospheric storms that track northward through Bering Strait (Woodgate et al., 2005). Cyclonic activity influences the flow of Pacific waters into the ACC and down Barrow Canyon, which increase during higher AO indexes (Panteleev et al., 2010). The flow through Barrow Canyon also correlates well with local atmospheric wind patterns that show little seasonal variations

(Woodgate et al., 2005).

Currents along the Alaskan margin have played an important role in marine sedimentation on the Arctic shelves (Darby et al., 2009). PC-4 from the analysis is

associated with the coarsest sediment class (> 50 µm) and is similar to that found in the third mode during previous analysis (Darby et al., 2009). Alternatively, storms and/or intermittent bed-loads transport a full range of sediments with variable sizes, from relatively fine grained to coarse sands. Darby et al. (2009) reported that this component is limited to processes that occur along the continental margin not associated with sea-ice movement, and is enhanced in these areas through the winnowing of sediment deposits

(Darby et al., 2009). The fact that PC-4 is largely independent of PC-2, which likely represents nepheloid sediment transport through Barrow Canyon, suggests that the forth component of the JPC16 analysis is associated with a different sediment transport current. PC-4 could be related to sediment transport via bottom currents along the continental slope (ACC, BU, etc.) that do not show major fluctuations during the

Holocene or other intermittent suspension mechanisms that are possibly connected to

Arctic cyclones.

The ACC (Alaskan Coastal Current) flows along the western coast of Alaska with mean bottom velocities of about 5-15 cm/s toward the head of Barrow Canyon

(Woodgate et al., 2005, Weingartner et al., 2005). Within the canyon, flows generally have a mean velocity of 10-20 cm/s (Woodgate et al., 2005). Eddies that spin off the eastward flowing ACC and move offshore into the basin, intermittently transport coarser sediment down slope. The eastward flowing BU (Beaufort Undercurrent) appears to be the dominant current below the 50 m shelf contour and extending to the base of the continental slope (Darby et al., 2009). This flow is ~10 cm/s becoming apparently stronger with depth, and maybe associated with basin-wide circulation patterns and

therefore not locally driven (Aagaard, 1984). The BU is capable of transporting particles of 6.4 and 53 µm along the Alaskan slope (Darby et al., 2009).

Cyclones are key weather elements that have a major impact on climate trends, and also cause rapid changes in wind, temperature, precipitation, and sea-ice extent.

Variations in the patterns of stormy weather can alter the dynamics of sedimentation along the continental shelf, especially during periods of high-energy cyclonic activity.

Researchers have made connections between increasing trends in Arctic storminess and the rate of sea-ice drift, as surface winds are known to be the "driving force" behind sea- ice movement (Bader et al., 2011; Serreze et al., 1995; Zhang et al. 2004).

The AO controls the strength and direction of westerly winds, as well as storm tracks across the central Arctic. Zhang et al. (2004) determined that the frequency of

Arctic cyclones is connected to more positive phases of the AO, and that the frequency and intensity of storms entering the Arctic has increased since 1960 (predominant +AO excursions). During the +AO, the north-south pressure difference is enhanced and the west-to-east winds become stronger, effectively creating a wall that keeps cold air in the

Arctic. The movement of recent storms into the western Arctic favors stronger and more frequent warm southerly winds that enhance ice melt and reduces the surface albedo feedback of the Arctic (Bader et al., 2011; Serreze et al., 1995). Model projections have indicated a greater number of summer storms with stronger intensities in the Arctic

Ocean related to global warming and reductions in sea-ice extent (Orsolini et al, 2009).

Storm tracks in the North Atlantic and North Pacific, by which most cyclones

travel, have shifted poleward into the Arctic, increasing the frequency and magnitude of storms that enter from midlatitudes, especially during the summer season (Zhang et al.,

2004). With more uniform temperatures over the northern parts of the Atlantic and

Pacific oceans, more cyclones trend towards higher latitudes (Stone, 1997). Hakkinen et al. (2008) suggests that there has been an increase in storm activity in the Arctic over the last ~ 50 years, during which the atmospheric air temperatures in the region have risen considerably. Stone (1997) found that warmer conditions occur in Barrow, Alaska when cyclonic activity increases in the North Pacific, thus moving warmer, moist area into the

Arctic. Koenig et al. (1993) also suggested that warming trends in the Arctic are associated with increased cyclone frequency at higher latitudes.

Cyclones in the central Arctic are of particular interest because they seem to reflect a persistent low in sea level pressures during the summer. This may be associated with temporary reversals in the clockwise ice motion in the Beaufort Sea (Serreze et al.,

1993) with effects on sea-ice concentrations in the Arctic and export through Fram Strait.

2.6 Conclusion

Multivariate decomposition of the downcore grain-size variations obtained using the lab-based Malvern Mastersizer indicated the occurrence of various depositional mechanisms that have affected the physical characteristics of the fine sediment fraction

(< 45 µm) at the location of core JPC16. These mechanisms have been related to processes associated with sea-ice transport and ocean currents during the Holocene. This

study demonstrated that grain-size data from a marine core could be decomposed using

VPCA methods to extract specific features that characterize various sedimentation processes. The component scores obtained from the VPCA cannot be directly related to proportional volumes of sediment deposition; the presence of the two sea-ice components at the location of JPC16 indicate that sediment released during sea-ice melt imparted a unique textural signature on the deposited sediments, not that it dominates deposition.

Continuous and constant sedimentation along the continental margin during the Holocene was likely dominated by ocean currents (Beaufort Undercurrent) and eddies that developed along the crosscutting canyons in the continental shelf (Darby et al., 2009).

Reworking of bottom sediments by ocean currents and movement down Barrow

Canyon (eddies) are the dominant transport and depositional processes at the core location during the Holocene (Darby et al., 2009). The various modes of the Arctic

Oscillation (AO) that affects climate in the U.S. also affect the movement of sea-ice in the Arctic Ocean. The sediment analyzed from JPC16 appears to have a sedimentary signature that is related to ice entrainment and sea-ice transport that has been associated with variations in the AO. PC-1 and PC-3, the fine grain-size fraction, extracted by the

VPCA explain approximately 86% of the total variance found within the core.

Darby et al. (2009) previously indicated that the sedimentation in the slope region of JPC16 is associated with localized processes during the Holocene as sediments are deposited out of suspension. It is likely that eddies developing from down-canyon flows and other ocean currents such as the Beaufort Undercurrent have remained stable over

decadal timescales. PC-2, the intermediate grain-size fraction, explains 12.7% of the observed variance within JPC16. This component is indicative of the winnowing of deposited sediments and the removal of the finest portions as density flows move down slope and through canyons along the continental shelf. PC-4 is associated with the coarsest sediment fraction, and thus is much harder to interpret, as multiple Arctic processes including density flows, bottom currents, storms, and sea-ice influences the transport and deposition of this grain-size class.

The consistency and reproducibility of the extracted sedimentation components from the Malvern and VPCA analysis, in the comparison of the JPC16 datasets and results (Appendix B), indicate that the multivariate approach is a method that may be applied efficiently to sediment cores throughout the entire Arctic. The analysis of this dataset shows that VPCA is useful for the interpretation of sediment matrices, such as those influenced by multiple marine sedimentation mechanisms, which are very challenging to interpret using traditional analyses. Variations in the distribution of grain- sizes throughout the analyzed marine sediment core can be used to infer and relate past climate conditions. A strong understanding of past climates will be important in assessing the potential impacts of current and future anthropogenic changes on the earth’s atmosphere and ocean patterns.

CHAPTER 3

CORRELATION OF TIME-VARYING MULTIVARIATE DATASETS

3.1 Introduction

Sea-ice drift in the Arctic Ocean is modulated mainly though atmospheric mechanisms and varies primarily due to changes in sea level pressures and resulting wind patterns. Reconstructing climate cycles of the recent past is essential in constraining the magnitudes and rates of natural climate variability in order to predict future changes or to determine potential human influences on the climate. This chapter focuses on the correlation of two time-varying datasets to examine the connections among the different variables and determine ocean-atmospheric relationships. We used the constructed composite record of varved sediments from a near-by glacial lake to determine if a relationship is present between atmospheric temperature and the variability observed in the marine sedimentation record (JPC16).

The sedimentation proxy records recovered from the eastern Chukchi Sea were compared with an annually resolved 2,000-year atmospheric terrestrial climate record from Blue Lake located in the Brooks Range of northern Alaska, approximately 465 km southeast of JPC16 (Fig. 2). The records of marine sedimentation patterns and atmospheric temperature were statistically correlated to investigate the spatial and

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temporal variability of climate change, as well as to compare the paleoclimate varve record with general marine sedimentation patterns inferred from JPC16. Determining how terrestrial climate relates to each down-core component of deposition from JPC16 can help to place constraints on the major influences that affect sedimentation in the western Arctic Ocean.

3.2 Temperature Proxy

Instrumental data used for climate simulations span the last 40 years at most, and therefore, are insufficient to understand climate cycles over longer time scales (decadal to millennial). The presence of thinly laminated lake sediments, referred to as varve sequences, provide a proxy that indicate variations in summer melt conditions on an annual scale. Because of the pronounced seasonality in the type of material deposited, varve sequences provide direct calendar dating with extremely high resolution (Bradley,

1999). The layered deposits are produced by seasonal climatic fluctuations in a lake basin and are preserved due to sediment anoxia and the lack of bioturbation on the lake bottom

(Bradley, 1999; Kalff, 2002). By measuring the thickness of these laminations, researchers are able to record annual changes in the rate of deposition throughout a lake’s geologic history. Therefore, variations in the varve thicknesses or fining/coarsening of sequences in a lake can be used to reconstruct shifts in water levels or atmospheric paleoclimates.

Bird et al. (2009) studied the last 2,000 years of the late Holocene, and developed

a quantitative relationship between varve sedimentation and atmospheric temperatures in the Blue Lake basin (1275 m a.s.l) (Fig. 2). Blue Lake is a small, shallow glacial fed lake near the crest of the Brooks Range (68.0869° N, 150.4650° W) in north-central Alaska and experiences long, cold winters with short, cool summers (Bird et al., 2009). The

Brooks Range is the northernmost section of the Rocky Mountains and is found in the northwestern part of Alaska within the Arctic Circle, north of the 50-degree latitude tree line. Therefore, the use of varve sequences is important to determine paleoclimate proxies in this barren region. Comparison of the independently dated varve record with the sediment PCs from JPC16 will enable evaluation of the quality of the age model in

JPC16 and determine which component of the sediment grain-size record, if any, relate to the atmospheric processes recorded by the glacial lake.

Sediment cores that were recovered in August 1999 contained millimeter-scale laminations comprised of a thick, light-colored silt and fine sand-sized lamina overlain by a thin, darker clay layer. Together, they represent an annual varve couplet due to the seasonal sedimentation: light (reddish), coarser-grained laminae result from sedimentation during periods of meltwater discharge during spring-summer months, whereas the dark, fine-grained layers form when fine-organic particles settle out due to low-energy conditions during periods of extended ice cover.

The climate record retrieved from Blue Lake spanning the last 2,000 years is the first annually resolved reconstruction from northern Alaska (Bird et al., 2009). In a high latitude climate, seasonal variations in sedimentation and runoff are associated with

meltwaters and spring discharge (Cohen, 2003). Field observations and aerial images from the Blue Lake basin have indicated that meltwater from the glacier contributes a substantial quantity of fine-grained sediment to the lake (Bird et al., 2009). The thickness of clastic varves in glacial lakes has been strongly correlated to sediment discharge that is primarily dependent on summer melt conditions controlled by atmospheric temperature

(Hardy, 1996; Leonard, 1997). Leonard (1997) demonstrated that changes in glacial activity and the frontal position relative to a glacial lake is the primary influence of varve formation. During the summer melt season, the lake is at least partially ice-free, the glacier feeding the lake is melting, the receding glacial front exposes eroded sediment, and sediment can be transported to and deposited within the lake. Additionally, winter precipitation (snowfall) may influence the amount of discharge and, in turn, the amount of sediment transported during the flowing summer melt season (Moore et al., 2001).

Warmer temperatures during the spring/summer melt season produce an influx of terrigenous fine sands and silts as a result of snowmelt and glacier runoff (Hardy, 1996).

The seasonal contrast between summer and winter sedimentation can be accentuated or diminished depending on the differences in mean temperatures experienced in the lake basin (Cohen, 2003).

The varve record retrieved from Blue Lake tracks past changes in summer temperatures, the main control on the physical sedimentation processes in the high latitude basin, with an annual resolution (Fig. 13). Summer temperatures strongly influence the streamflow velocity and suspended sediment load that leads to temperature- dependent variations in the thickness of summer deposited layers, with warmer summers

forming thicker layers and cooler summers resulting in thinner layers. The varve record was also correlated to available instrumental temperature data to further constrain the varve proxy record. Varve measurements from Blue Lake correlate well with regional cooling and warming temperature trends described for the late Holocene from other proxy records across the Arctic. Bird et al. (2009) concluded that summer precipitation is not a significant factor that contributed to the varve formation within Blue Lake through most of the record. Other sites spanning the high latitudes of North America have used multiple proxy indictors to reconstruct terrestrial climate records, including: diatom and pollen assemblages, biogenic-silica and organic content, oxygen-isotopic ratios in diatoms, and variations in ice-raft debris in lakes.

The period between 2000 and 1270 yr BP contained the thickest varve sequences in Blue Lake. This implies that this was the warmest period during the last 2000 years, but other evidence during this interval also indicated that this was a period marked by increased precipitation (Bird et al., 2009) (Fig. 13). Increased temperatures and higher rates of precipitation during a period of glacial retreat (2000-1200 yr BP) would have amplified the formation of varves within the lake. Therefore, the thicker varves during this interval were not exclusively related to summer temperature controlled by the glacial melt and sedimentation within Blue Lake (Bird et al., 2009).

Fig. 13. a) Blue Lake temperature reconstruction since 1270 yr BP (figure modified from Bird et al., 2009). Grey lines denote the upper and lower 95% confidence interval. Note that varve thickness is assumed to be proportional to increasing atmospheric temperature. b) Plot of Greater Yukon aridity as recorded in the δ18O proxy from Marcella Lake (Anderson at al, 2007). Vertical black bars indicated inferred cold intervals in the Brooks Range.

To aid in the interpretation of the Blue Lake varve-thickness and inferred marine sedimentation trends, a comparison was made with a previously published paleoenvironmental moisture balance record (Fig. 14). Moisture variability in the northwest Brooks Range was inferred from a magnetic susceptibility record from Burial

Lake (Abbott at al., 2010). Burial Lake (68.43° N, 159.17° W; 460 m a.s.l.) (Fig. 2) is presented as a representative record for changes in hydrologic variations during the late

Holocene, and it is situated on the north facing side of the Brooks Ranges. The sediment record is characterized by low magnetic susceptibility from 2000-1300 yr BP likely reflecting both warmer and moister conditions. Trends in this lake are similar to those found in other lakes within the region. A sediment cellulose record retrieved from Meli

Lake show more depleted (less positive) δ18O values between ~1000 and 2000 yr BP, indicating that this period in northern Alaska may have been particularly moist (Anderson et al., 2001). Additionally, a calcite δ18O record from Tangled Up Lake also indicated that the climate from 2000-1500 yr BP was warmer and possibly wetter than modern conditions.

Fig. 14. Magnetic susceptibility record from Burial Lake (Abbott et al., 2010), related to the moisture balance, plotted against Blue Lake varve thicknesses. Low magnetic susceptibility values are related to increased aridity.

Summer meltwater from the glacier feeding Blue Lake transports a substantial sediment load into the lake. An increase in summer temperature would therefore lead to longer ice-free conditions and more glacier melt, resulting in more sediment transport to and deposition in Blue Lake and thus, the production of thicker varve sequences. Under winter ice cover, deposition may be reduced to the settling of very fine silt and clay particles that remain in suspension from the previous summer. Additionally, warm winter months, indicative of a shorter ice-cover period, have a significant control on varve thickness as this leads to prolonged deposition of “summer” sediments in the lake.

Mark Abbott, University of Pittsburgh, provided the temperature proxy data retrieved from the Blue Lake varves. This record was smoothed with an infinite impulse response (IIR) low-pass filter to reduce the high frequency noise (< 50 year filter), which highlighted the longer-term trends (Fig. 15). Periods of higher sedimentation, or thicker varves, were generally associated with increased mean summer temperatures and conversely, lower sedimentation rates were generally related to lower atmospheric temperatures. The recent portion of the varve record was well correlated to available summer instrumental temperature records from a nearby weather station (Atigun Pass), approximately 41 km east of the study site (Bird et al., 2009).

Fig. 15. Composite varve-thickness measurements from Blue Lake sediment plotted against varve year (age BP). Orange curve is the 50-year smoothed data with a low-pass filter highlighting the significant variability; grey line is the measured annual varves.

3.3 Correlation of Marine Sedimentation

A direct correlation is presented here between varve deposits during the recent

Holocene (2000 yr BP to the present) and the patterns of marine sedimentation. This correlation is based on high-resolution varve sequences (annual resolution) and a

Varimax-rotated, Principal Component Analysis (VPCA) of marine sediment (decadal to multi-decadal resolution) with robust age constraints. Smoothing and filtering of data are two of the most commonly used time series techniques for removing noise from the underlying data to help reveal important features and overall trends. A 50-year, low-pass

IIR filter was used on the Blue Lake varve record to smooth the data and remove the higher-frequency variability (Fig. 15). This highlighted the overall trends within the data while maintaining the same cycles.

Time series analysis requires a constant sampling interval. The Blue Lake temperature-proxy reconstruction has an annual resolution, whereas the JPC16 sedimentological record has an 18-year resolution from 39 - 895 yr BP and a 35 year sample resolution for the remaining record length. Therefore, prior to any statistical comparison, the entire marine record (JPC16 and MC14D) used in this study was interpolated to a constant sampling interval of 8 cm representing ~ 35 cal yrs. The resulting data for each Principal Component (PC) was interpolated to derive a dataset with a continuous 35 yr sampling interval based on the actual measurements initially collected from the VPCA. The original data was plotted against the interpolated values to visually show that the interpolated points are consistent with the original data (Fig. 16).

This ensured that errors in manipulating the dataset were kept at a minimum. This was completed for each component variable even though only PC-1 is illustrated below.

Fig. 16. Graph depicting the interpolated age measurements for PC-1. Data was interpolated to an 8 cm interval (~35 years). The depth scale of JPC16 has been adjusted downwards by 25 cm by correlating downcore physical properties between JPC16 and MC-14D (Darby et al., 2009).

Plotting the downcore variability of each PC generated by the VPCA against the temperature proxy from Blue Lake provides a better understanding of how marine sedimentation changes in relation to atmospheric climate. When plotted against the temperature proxy data from Blue Lake, PC-1 and PC-3 (both related to sea-ice) appear to be highly correlated with the summer temperature signature, increasing with greater varve thicknesses (temperature) (Fig. 17.1 and 17.2). When the sea-ice components are combined (PC-1 + PC-3), they show a similar relationship with the Blue Lake varve measurements (Fig. 17.3). This indicates that overall sea-ice deposition and/or entrained sediment transport in the western Arctic is likely related to atmospheric climate.

Fig. 17.1. Plot showing the relationships between PC-1 (Anchor Ice) and the varve thickness measurements (blue line) from Blue Lake.

Fig. 17.2. Plot showing the relationships between PC-3 (Suspension Freezing) and the varve thickness measurements (blue line) from Blue Lake.

Fig. 17.3. Plot showing the relationships between the combined component of sea-ice deposition (PC-1 + PC-3) and the varve thickness measurements (blue line) from Blue Lake.

Fig. 17.4. Plot showing the relationships between PC-2 (Winnowed Silt) and the varve thickness measurements (blue line) from Blue Lake.

Fig. 17.5. Plot showing the relationships between PC-4 (Intermittent Suspension) and the varve thickness measurements (blue line) from Blue Lake.

Lagged cross-correlation analysis is a method for estimating the degree of similarity between two time-series as a function of a time-shift applied to one of the datasets; the observations of one series are correlated with the observations of another series at various lags and leads. The cross-correlation analysis was performed to quantify the extent to which marine sedimentation is related to changes in atmospheric climate.

Cross-correlations are useful for determining the phase relationships between two signals.

Here the components of JPC16 sedimentation were compared (lagged) against the Blue

Lake varve measurements. Summaries of the correlations are presented in Table 2. One lag is equal to the sampling interval for the record (35 years). After calculating the cross-

correlation between the two signals, the maximum value of the correlation function indicates the point in time (lag) where the signals are best aligned.

The JPM statistical software program was used to develop the cross-correlation and lagged correlation between the datasets. The correlation coefficient between PC-1 and varve thickness is r = 0.74 (p < 0.01). The maximum lagged correlation shows a significant correlation (r = 0.75) at a lag of -1 (35 years) (Table 2). This suggests that atmospheric temperatures are related to the sedimentation signature from anchor ice. We also found a moderate correlation (r = 0.41, p < 0.05) between the varve thicknesses and the sedimentation signature related to entrained sediment from suspension freezing

(PC-3) (Fig. 17.2). The lagged correlation shows a significant correlation (0.53) at a lag of 1 (Table 2). The lagged correlation for anchor ice lags the temperature record, whereas suspension freezing leads the temperature record. This indicates that the zero lag is the most likely correlation between the records, and that the records of sea-ice sedimentation are likely in-phase with atmospheric temperature records.

When the two sea-ice components are added together, we find a significant correlation (r = 0.68, p < 0.01) between western Arctic sea-ice sedimentation and terrestrial climate (Fig. 17.3, Table 2). The summer atmospheric temperature record is nearly identical to the leading PCs of sea-ice sedimentation. These two components account for a total of 84.4 % of the variance observed within JPC16 record, indicating some direct relationship between sea-ice deposition and warmer atmospheric conditions.

Together, the varve record and the sea-ice sedimentation at the core location support the

occurrence of a warmer interval between 2000 and 1200 yr BP. Table 2 shows the correlation coefficients measured between each JPC16 component and the varve thicknesses. The final two modes from the JPC16 analysis tend to show much weaker relationships with the atmosphere temperature proxy, indicating an influence(s) by other variables. The longer lag of 12 points, which corresponds to 420 years, for PC-2 suggests processes with longer time constants, such as changes in marine conditions.

Table 2. Correlation coefficients for the comparison of the VPCA components with the Blue Lake varve thickness measurements over the last 2,000 years.

Zero Lag Correlation Max Cross-Correlation Lag

Component 1 0.74 0.75 -1 Component 2 - 0.03 - 0.15 12 Component 3 0.41 0.53 1 Component 4 0.16 0.29 1 Combined Sea-Ice 0.68 0.72 1 Components

The correlation analysis shows that marine sedimentation is approximately in phase with atmospheric climate variability during the recent Holocene (Table 2). This suggests that sedimentation in the western Arctic Ocean is possibly related to atmospheric climate, mainly the sedimentological signature from the sea-ice components.

It might be that temperature is an external force influencing sedimentation near the shallow Arctic shelf along the Alaskan coast, or that ocean circulation patterns and

changes in atmospheric pressures influence the climate within the Brooks Range. The latter possibility is likely linked to variations in the AO.

3.4 Periodicities

The temporal variations in the JPC16 sedimentary record and the Blue Lake measurements were analyzed using a wavelet analysis, which identifies non-stationary frequencies within a time series. Wavelet analysis is a method of time series analysis that detects localized variations in power and establishes the dominance and duration of the variations observed within a dataset (Torrence and Compo, 1998). Wavelets are a function of time-frequency, and this analysis was employed to determine the prominent periodicities within each dataset; Torrence and Compo provided the wavelet module that was accessed through the ION website (http://ion.researchsystems.com). Furthermore, this wavelet analysis requires equally spaced time measurements, which was reconciled here by interpolating the sample interval to 8 cm, and assumes that the time interval between consecutive data points is continuous (Torrence and Compo, 1998). This method of spectral analysis uses the wavelet transform of the dataset to recover paleoclimate cycles of several distinct modes of variability (Torrence and Compo, 1998). The Morlet wavelet 6.0 was used here as the “mother wavelet”. By analyzing the JPC16 and Blue

Lake datasets using the wavelet function, cyclical variations in each time series can be extracted and graphed to show time-frequency or phase relationships.

Prior to the spectral analysis, each data set was detrended using a second order fit as a function of age to remove trends with apparent cyclicity greater than one half of the record length, which cannot be adequately resolved by the wavelet analysis. The detrending was conducted on the data after the measurement spacing was reconciled to a constant interval (35 yr). To detrend the data, the interpolated records for each component were plotted and the equation of the best-fit trend line (quadratic equation) was used to find the detrended values. The detrended values were then found by subtracting the fit value from the interpolated value at each depth. This removed the lowest frequency trend in the data while maintaining the higher frequency cycles. This filtering also enhances the power of some of the lower frequency cycles. Plotting the detrended data against the original confirmed that the sign of the cyclicity was not inverted during the detrending subtraction, and that there was no residual trend remaining. A trend line was added, and the detrended data produced a near zero value for the slope of the line. Fig. 18 shows the detrended PC-1 data; each detrended dataset illustrates similar results.

Fig. 18. Detrended PC-1 data, graphed to illustrate the interpolated data (red line) versus the detrended data (blue line). A quadratic fit was used to remove the trend in the data.

After detrending, each JPC16 component time series and the Blue Lake varves underwent wavelet analysis via the online Interactive Wavelets program. This produced the five-wavelet analyses (outputs) shown in Fig. 19.1 through 16.5. These figures illustrate: a) the time series of the detrended data; b) the wavelet power spectrum

(variance in the strength of each period through time); and c) the global wavelet power spectrum (average cycle period for the full dataset). At the border of the wavelet power spectra exist an area (crossed-hatched pattern) called the cone of influence, where short record length relative to the wavelet window length has reduced the variance. Because each time series has a finite-length, the wavelet power spectrum will be distorted near the beginning and end of the time sequence (Torrence and Compo, 1998). Outside the cone of influence, toward shorter periodicities, there is no distortion of the wavelet spectrum.

The global wavelet spectrum provides an unbiased and consistent estimation of the true power spectrum of the analyzed time series, and thus is a simple and robust way

to characterize the variability in the time series. If a peak in the wavelet power spectrum is significantly above the background spectrum for the global wavelet (significant at the

20% level), then it can be assumed to be a true feature with a certain percent confidence.

The wavelet power spectrum (Fig. 19.1b) shows the power (absolute value squared) of the wavelet transform for the component data obtained from the JPC16 record presented in Fig. 19.1a. The squared variance documents the power present in the record for various periods through time. The following figures show the actual oscillations of the individual wavelets, rather than just their magnitude. Observing the wavelet power spectrum for the detrended sea-ice PCs, it is clear that there is more concentration of power between the 256-512 bands between 1200-1000 yr BP. The cyclicity found in PC-4 is centered between 256 and 512 years extending through the entire record (Fig. 19.4). The varve thickness record displayed a weaker power cycle centered between 128-256 years with duration of approximately 1000 years (Fig. 19.5).

Each record displays the 1500-year cyclicity found in other Arctic proxy records, but this periodicity is found outside of the cone of influence. If the record analyzed here was longer, the 1500-year cycle would fall within the cone of influence. The extracted modes related to sea-ice displayed similar variance in the periodicities. These similarities suggest that similar driving mechanisms of the cyclicity are influencing these sedimentary signatures in a similar manner.

Fig. 19.1. (a) Detrended JPC-16 Component 1 data. (b) The wavelet power spectrum using the Morlet mother wavelet. The contour levels are chosen so that 75%, 50%, 25%, and 5% of the wavelet power is above each level, respectively. The cross-hatched region is the cone of influence, where zero padding has reduced the variance. (c) The global wavelet power spectrum (solid black line). The dashed line is the 20% significance level for the global wavelet spectrum, assuming the same significance level and background spectrum as in (b). (Torrence, C. and G. P. Compo, 1998).

Fig. 19.2. (a) Detrended JPC-16 Component 2 data. (b) The wavelet power spectrum using the Morlet mother wavelet. The contour levels are chosen so that 75%, 50%, 25%, and 5% of the wavelet power is above each level, respectively. The cross-hatched region is the cone of influence, where zero padding has reduced the variance. (c) The global wavelet power spectrum (solid black line). The dashed line is the 20% significance level for the global wavelet spectrum, assuming the same significance level and background spectrum as in (b). (Torrence, C. and G. P. Compo, 1998).

Fig. 19.3. (a) Detrended JPC-16 Component 3 data. (b) The wavelet power spectrum using the Morlet mother wavelet. The contour levels are chosen so that 75%, 50%, 25%, and 5% of the wavelet power is above each level, respectively. The cross-hatched region is the cone of influence, where zero padding has reduced the variance. (c) The global wavelet power spectrum (solid black line). The dashed line is the 20% significance level for the global wavelet spectrum, assuming the same significance level and background spectrum as in (b). (Torrence, C. and G. P. Compo, 1998).

Fig. 19.4. (a) Detrended JPC-16 Component 4 data. (b) The wavelet power spectrum using the Morlet mother wavelet. The contour levels are chosen so that 75%, 50%, 25%, and 5% of the wavelet power is above each level, respectively. The cross-hatched region is the cone of influence, where zero padding has reduced the variance. (c) The global wavelet power spectrum (solid black line). The dashed line is the 20% significance level for the global wavelet spectrum, assuming the same significance level and background spectrum as in (b). (Torrence, C. and G. P. Compo, 1998).

Fig. 19.5. (a) Detrended varve data. (b) The wavelet power spectrum using the Morlet mother wavelet. The contour levels are chosen so that 75%, 50%, 25%, and 5% of the wavelet power is above each level, respectively. The cross-hatched region is the cone of influence, where zero padding has reduced the variance. (c) The global wavelet power spectrum (solid black line). The dashed line is the 20% significance level for the global wavelet spectrum, assuming the same significance level and background spectrum as in (b). (Torrence, C. and G. P. Compo, 1998).

3.5 Discussion

Down-core trends in the JPC16 grain-size data indicate that sea-ice processes in the Arctic Ocean are possibly connected to nearby continental atmospheric climate patterns. Comparison of the terrestrial temperature proxy (varve thickness) with JPC16 sediment texture over the last 2,000 years illustrates a negative relationship that shows that varve thickness decreased (cooler conditions) when the overall grain size found in

JPC16 was at its peak (Fig. 20). This suggests that the location of JPC16 received coarser grained material during periods of cooler conditions and possibly an increase in ice coverage, perhaps during the Little Ice Age (LIA) circa ~300 yr BP.

Fig. 20. Comparison of the JPC16 grain-size composition and the Blue Lake proxy record.

The Medieval Warm Period (MWP), also known as the Medieval Climatic

Anomaly, commonly refers to a period when regional temperatures in Europe and the

North Atlantic are believed to have been comparable to those of the late 20th century

(Mann et al., 2002). It is likely that the MWP experienced a strongly positive NAO phase that would have caused stronger winds from the Atlantic to transport warm air towards

Europe (Trouet et al., 2009). Composite temperature records from the Northern

Hemisphere suggest only slightly warmer temperatures during the MWP (1000-700 yr

BP) (Crowley and Lowery, 2000; Mann et al., 2002). Bird et al. (2009) did not discover a distinct warm interval in the Blue Lake record that corresponded with the MWP found in

European proxies, although, the varve thickness record does show two periods of relative warmth that occurred from 380 - 480 yr BP and 550 - 650 yr BP (Fig. 20).

It appears that there is an atmospheric connection between sediment transport in

Blue Lake and at the location of JPC16. When plotted against the summer temperature proxy, variations in the fine fractions associated with sea-ice (anchor ice and suspension

freezing) are highly correlated to atmospheric temperature variability (Fig. 17.1 & 17.2).

As thicker varve sequences are an indicator of warmer conditions, the fine silt fraction in

JPC16 also appears to be elevated. This supports the hypothesis that more fine silt in

JPC16 was deposited as ice melted under warmer conditions. The increase in ice-rafted debris between 2000 and 1200 yr BP (Fig. 17.1 & 17.2) may also be a result of increased annual, first-year sea-ice production related to increased freshwater influx (precipitation) that also occurred during this period (Bird et al., 2009).

Anderson et al. (2005) examined how the Aleutian Low intensified around 1200

BP and shifted towards a more eastern location, promoting cooling in the western Arctic.

Bringué and Rocjon (2012) documented episodic cooling in the western Arctic after

~1300 BP with the most recent cooling associated with the Little Ice Age (LIA; ~300 yr

BP). A decline in the anchor ice signal from 1050-150 yr BP is consistent with a decreased release of entrained sediment related to cooler temperatures and less melting, though this decline in the signal may be associated with a lack of sea-ice influx from

Siberia as a result of a tendency toward more negative AO phase events during this time period. Denton and Karlén (1973) also found glacial advances in the southern Yukon

Territory and Alaska during the LIA and a less-extensive expansion between ~1200-1000 yr BP. Additionally, Oswald et al. (2012) found similar periods of cooler conditions and glacier expansion, with an additional cooling anomaly around 1700 yr BP, from Okpilak

Lake based on organic-matter content, magnetic susceptibility, and pollen proxy records.

These records are consistent with that found in the Blue Lake varve record (Bird et al.,

2009).

The time interval prior to 1200 yr BP was characterized by a prominent positive

AO phase (Darby et al., 2012) that transports ocean storms further north, causing wetter conditions in Alaska. Additionally, there was an increase in the moisture budget just before the onset of the LIA (~ 400 yr BP) with a return to more arid conditions during the

LIA (Fig. 14). The combined data indicate an abrupt regional shift to cooler and drier conditions after 1300 yr BP that included glacial expansion in the Brooks Range, reduced sediment influx into Blue Lake, and increased aridity across northern Alaska. This time interval was also associated with a more negative phase of the AO that would have resulted in a reduced sea-ice influx towards the Alaskan coast from sources within the eastern Arctic Ocean (Darby et al., 2012).

The JPC16 sedimentological parameters visually occur in parallel with variations in the mean summer temperatures over Blue Lake marked with an upturn circa 200 yr

BP. Anderson et al. (2001) described a warm period found in the records from Meli and

Tangled Up Lakes that began following the end of the LIA with maximum warmth since

200 yr BP. A compilation of paleoclimate records from lake sediments, trees, ice cores, and marine sediments (Overpeck et al., 2007) suggest that in the period following the

LIA (early 1800s), the Arctic region experienced unprecedented warming in conjunction with a reduction in the summer sea-ice concentrations.

The period marked by the LIA had an enhanced moisture balance (Clegg and Hu

2010) possibly related to the location of the Aleutian Low in a more western position that produced increased winter precipitation during this period (Anderson et al. 2005;

Chipman et al., 2012). The Aleutian Low is the dominant weather pattern in southern

Alaska with a prevalent high pressure over the Chukchi-Beaufort Seas (Cassano et al.,

2006). Upwelling of Atlantic water onto the Alaskan slope/shelf can be influenced by

Aleutian low storms that produce high pressures in the western Arctic (Pickart et al.,

2011). Pickart et al. (2011) suggested that Barrow Canyon, particularly the eastern flank, would be the most effected location for the wind-driven upwelling during these storms, which become more prevalent during the autumn. These storms may also result in enhanced winds over the open water of the Chukchi Sea, which affects the formation of sea-ice.

A weakened Beaufort Gyre and an Aleutian Low that is slightly weaker and shifted towards a more westerly position creates a persistent northward flow of air into the Beaufort Sea and northern Alaska (Stone et al., 2005). This transports warm, moist

Pacific air into the western Arctic region including northern Alaska (Bartlein et al.,

1991). Variations in snow and ice melt have been attributed to changing atmospheric circulation patterns that affect the temperature regime of northern Alaska. Changes in the annual climate of northern Alaska are attributable to variations in atmospheric circulation that involve the intensification of the Aleutian Low in conjunction with fluctuations of the Beaufort Gyre or the Arctic Oscillation (Stone et al., 2005).

In the south-central Brooks Range, Clegg and Hu (2010) found that winter precipitation and spring melt dominated the water budget of the region during the

Holocene. Additionally, evaporation during ice-free periods is an important control on

the isotopic composition of lake water and is the main process responsible for water loss from closed basin lakes. Variations in δ18O are affected by changes in the moisture balance; wetter conditions (depleted δ18O values) between ~400 and 100 yr BP coinciding to the LIA (Clegg and Hu, 2010). The δ18O record from calcite presented by

Anderson et al. (2001) shows more 18O-depletion, as a result of lower evaporative rates, at ~1700, 1000, and 500-400 BP from the paleoclimate record retrieved from Tangled Up

Lake. This likely indicates cooler and wetter atmospheric conditions over the region.

Other factors likely to contribute to depletion in these values include decreased temperatures or increases in ice cover of lakes that are associated with reductions in evaporative conditions.

Most annual precipitation in northwest Alaska occurs between July and October when the Bering and Chukchi Seas are ice-free and North Pacific storms move northwards into the interior (Moritz, 1979). Depleted δ18O values of inorganic calcite

(CaCO3) during the cold LIA are interpreted as reflecting shifts in atmospheric circulation over the central Alaskan region (Gonyo et al., 2012). The mean summer temperatures in northern Alaska are affected by the displacement of cold air masses of the Arctic Front, which is normally situated along the Brooks Range during the summer months (Rabus and Echelmeyer, 1998). Peak δ18O values of bulk carbonate from Keche

Lake in the northeastern interior of Alaska occurred approximately at ~1800, ~1500, and

~1100 BP; these spikes in isotopic composition have been linked to changes in sediment- depositional processes in the lake basin, likely associated to varying moisture conditions

due to a more westerly position of the Aleutian Low, which transported warm, moist air from the north Pacific (Chipman et al., 2012).

The direct correlation of JPC16 with the Blue Lake record demonstrates the general synchronicity between marine sedimentation patterns attributed to sea-ice and terrestrial responses to atmospheric climate variability during the recent Holocene.

Sediment entrainment via the growth of ice crystals either in the water column

(suspension freezing) or along the ocean floor (anchor ice formation) occurs in the shallow waters of the continental shelf. Studies have suggested that the formation of frazil ice and anchor ice, during the late fall, are the dominant mechanisms for regional entrainment of sediment into sea-ice through the Chukchi-Beaufort Sea (Reimnitz et al.,

1998).

The correlation of sea-ice sedimentation and the atmospheric temperature record shows that most of the warmer periods, during the last 2,000 years, are associated with sea-ice changes in the western Arctic Ocean. After ~2000 BP, the Beaufort Gyre weakened and contracted over the western Arctic (Dyke and Savelle, 2000), increasing the ice export from Siberia towards the shelf along northern Alaska as a result of a positive AO phase (Darby et al., 2012). This is consistent with the sea-ice components from the JPC sediment record. Data from Blue Lake and the JPC16 records are consistent with other paleoclimate records that show variability in effective moisture and temperatures in the western Arctic (Chipman et al. 2012; Clegg and Hu 2010; Mann et al.

2002).

Darby et al. (2012) reconstructed sea-ice drift patterns connected to multi-decadal to millennial-scale fluctuations in the phases of Arctic Oscillation (AO). They found that variations in the paleoclimate system of the Arctic are related to internal oscillations in the climate system with a 1500-year cyclicity, the first study to document a connection between the AO and the 1500-year cycle. The different phases of the AO describe the relative intensity of a low-pressure center over the central Arctic, with winds that circulate to form a vortex over the Beaufort Sea. Model results from Cassano et al. (2006) show that greater air temperature anomalies are associated with circulation patterns that produce strong, southerly air transport towards Barrow, and that cyclonic frequency in the Chukchi-Beaufort Seas increased as a result of low-pressure systems to the north of

Barrow. When the AO index is in a positive phase, the atmospheric pressure over the

Arctic is lower than average; this is associated with cloudier and wetter conditions during the summer, and increased precipitation and temperatures over Alaska in the winter

(Thompson et al., 2000). The atmospheric conditions associated with the +AO are consistent with the marine records from PC-1 and PC-3, and the patterns established in

Fig. 14 based on the varve record from Blue Lake and the magnetic susceptibility record from Burial Lake. Together, these records show a more positive AO between 1200 –

2000 yr BP along with increased temperature and precipitation in the terrestrial proxy records from Northern Alaska. Additionally, the duration of the sea-ice melt season is likely connected to the various modes of the previous winter’s AO index (Stone et al.,

2005; Thompson et al., 2000). Following a more positive AO anomaly, the melt season tends to be longer with an earlier onset of melting and a later autumn freeze-up (Stone et

al., 2005). An increased duration of sea-ice melt would lead to more positive loadings within the sea-ice component records as seen in the records of PC-1 and PC-3 between

2000 – 1200 yr BP. During this time frame we can see the maximum loadings for the sea- ice components. Seasonal changes or phase lags of six months would appear to be instantaneous on the 35-year sampling resolution employed during this study.

3.6 Conclusion

Linking multiple paleoceanographic records to large-scale atmospheric patterns provides an important reference for our understanding of the causal mechanisms for climate change. The high correlation between proxies for summer atmospheric temperature, winter precipitation, and sea-ice sedimentation observed in the Arctic during the late Holocene exhibits a strong relation with long-term trends in the AO (Darby et al.,

2012). The different phases of the AO have affected atmospheric circulation and in turn ocean circulation patterns observed in the western Arctic Ocean.

This study suggests that the atmospheric climate over northern Alaska correlates well with marine sedimentation related to sea-ice drift in the eastern Chukchi Sea during the late Holocene. Temperature appears to be a factor in causing a change in melting rates and the release of entrained sediment from sea-ice. The positive correlation of sea-ice sedimentation with warmer atmospheric temperatures over the lower latitudes suggest that larger temperature gradients produce stronger winds and storms over the Arctic resulting in more release of entrained sediment from sea-ice. Together with regional

paleoenvironmental records, the Blue Lake varve thickness and the JPC16 records shows that the Alaskan Arctic region was warm and wet from 2000 – 1250 yr BP, but after 1250 yr BP shifted to cooler climate. In JPC16 prior to 1300 yr BP, Darby et al. (2012) found evidence of sediment derived and exported from the Kara Sea as a result of more +AO excursions. The +AO phase brings wetter weather into Alaska, which is linked to the increase in varve thickness at Blue Lake prior to 1300 yr BP as a result of increased precipitation as indicated by Bird et al. (2009). The records presented here also show cooler temperatures and ice expansion during the Little Ice Age (350-120 yr BP) followed by post-LIA warming. This is also a period of reduced Kara Fe grains in JPC16 and thus a more negative AO phase.

CHAPTER 4

SUMMARY

A more thorough understanding of regional sea-ice variability and associated climate patterns is needed in order to validate climate models and project future conditions. Higher resolution studies through the recent geologic history will lead to an improved understanding of climate change in Alaska and the western Arctic. The aim of this thesis was to develop a more robust proxy of marine sedimentation by increasing the analyzed-sample interval of JPC16, partition sediment grain-sizes into different transport processes, and to compare this record with variations in atmospheric climate over Alaska during the late Holocene.

Analysis of the JPC16 sediment record (< 45 µm) has revealed a complicated signal of grain-size that has varied considerably during the late Holocene, but which can be interpreted by partitioning the variability in the record through the use of multivariate analysis. A Varimax-rotated, Principal Component Analysis was employed to further resolve the record into various sedimentation mechanisms responsible for the deposition of different grain-size classes. The analysis of core JPC16 presents a high-resolution late

Holocene sedimentary record of environmental variability and sedimentation from the eastern Chukchi Sea. We present a depositional record related to sea-ice melt and ocean

82 83

circulation as well as other paleoclimatic processes going back beyond the timescale of direct instrumental measurements. These results indicate that cores from the western

Arctic Ocean can provide high-resolution temporal information for paleoenvironmental studies. Understanding the connections between the atmosphere and ocean is important for developing better climate models, and in determining the responses of past climate to better comprehend the effects of these changes in the present and future.

This research further compared marine sedimentation records with that of an annually resolved varve record that has been highly correlated to atmospheric temperature with additional influences from precipitation within the lake basin. Samples collected from JPC16 were statistically correlated to a varve record from Blue Lake,

Alaska spanning the last 2,000 years. The results have illustrated the likely relationship between ocean processes and atmospheric climate. It is likely that atmospheric circulation is influencing both the drift of sea-ice within the western Arctic (Beaufort Gyre), and the formation of lake varves through the transport of warm and moist air masses.

Visually, the comparison of the various JPC16 sedimentation records shows a general agreement with the robust Blue Lake temperature reconstruction. The best correlations between temperature and marine deposition occurred in the modes related to sea-ice transport (anchor ice and suspension freezing). Varve thickness and sedimentation of IRD are positively correlated with a high level of significance (anchor ice, r = 0.74; suspension freezing, r = 0.41). Sea-ice sedimentation is a primary mechanism contributing to the variability observed within the marine record, and under warmer

atmospheric conditions the sea-ice signal tends to be stronger. Provided that the concentration of sediment in the ice remains the same annually, sea-ice sedimentation can therefore be associated to changes in melting conditions. Sea-ice sedimentation is also affected by the relative amount of entrainment or transport towards the core location.

Under more positive AO conditions, sea-ice from the Kara and Laptev Seas are transported into the waters of northern Alaska and would provide for a greater sedimentation signature related to sea-ice. Therefore, the stronger component loadings observed within the JPC16 record relating to sea-ice can be attributed to either warmer atmospheric conditions, a strong influx of sea-ice due to conditions within the modes of the Arctic Oscillation, or some combination of the two conditions.

This research has demonstrated that the texture of fine-grained (< 45 µm) marine sediment can provide useful paleoclimatic information within the Arctic. Understanding marine transport and depositional processes in the Arctic is critical for our overall interpretation of climate records. The development of this understanding leads to a better comprehension of the feedbacks and interactions between sea-ice, oceans, land, and the atmosphere as they relate to climate variability. Similar studies on the finer fraction of other marine sediment cores and longer temperature records from other locations throughout the Arctic could broaden the perspective of sea-ice transport and the influence of climate cycles on depositional processes.

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APPENDIX A:

Sample Component Scores

98 99

Downcore component scores for the leading Components extracted by the PCA conducted on the JPC16 grain-size composition.

Adjusted Adjusted Component Component Component Component Midpoint Depth-Age Depth 1 2 3 4 0.5 2.1974 -1.0678 -1.00589 2.6491 -1.2327 1.5 6.5922 -1.43636 0.39817 1.35081 -0.49485 2.5 10.987 -1.76225 -4.18127 1.16907 0.61926 3.5 15.3818 -0.82323 0.91328 3.13388 -1.39614 4.5 19.7766 -0.84202 0.37822 2.33532 -0.67819 5.5 24.1714 -1.21434 -3.13091 1.063 -0.76815 6.5 28.5662 -0.412 0.77764 2.27081 0.73213 7.5 32.961 -0.97432 -0.52733 2.78579 -1.26174 8.5 37.3558 -0.8541 0.46112 1.50841 -0.49428 9.5 41.7506 -0.85389 0.51949 2.88394 0.19923 10.5 46.1454 -0.81692 -2.37093 1.42248 -1.57227 11.5 50.5402 -0.69311 0.69271 2.55527 0.50103 12.5 54.935 -0.64088 0.28129 1.32882 0.40697 13.5 59.3298 -0.55562 1.25135 0.42495 1.12224 14.5 63.7246 -0.6422 0.93215 1.49078 0.58215 15.5 68.1194 -0.84394 0.72012 0.86278 0.74889 16.5 72.5142 -1.0814 1.12301 0.17664 0.52143 17.5 76.909 -0.72817 1.38805 -0.10557 2.12939 18.5 81.3038 -0.29876 1.9244 -0.54292 1.27528 19.5 85.6986 -0.93926 1.50368 0.55511 0.68392 20.5 90.0934 -0.54702 1.3698 0.6883 1.80084 21.5 94.4882 0.45653 1.59938 1.02184 0.94815 22.5 98.883 0.75339 1.70248 0.39277 0.98094 23.5 103.2778 -0.07999 1.27896 1.72812 0.69126 24.5 107.6726 0.03819 1.23942 1.26233 1.26359 25.5 112.0674 -1.10086 1.44297 0.13705 0.83961 26.5 116.4622 -0.75217 0.91229 0.72581 0.67102 27.5 120.857 -0.96726 1.96515 -1.39461 1.5506 28.5 125.2518 -1.08191 0.50439 1.173 0.41178 29.5 129.6466 -1.52513 0.22956 1.11202 0.36283 30.5 134.0414 -1.20893 0.49787 1.25034 0.51856 31.5 138.4362 -1.25854 0.86672 0.20265 0.68366

100

Adjusted Adjusted Component Component Component Component Midpoint Depth-Age Depth 1 2 3 4 32.5 142.831 -1.26871 -0.37935 1.8127 -0.11638 33.5 147.2258 -1.27225 0.14926 1.07374 0.66447 34.5 151.6206 -1.08729 0.43524 0.20692 1.09072 35.5 156.0154 -1.86098 0.55546 0.62359 -0.38556 36.5 160.4102 -1.36506 -1.36747 1.39262 -0.22201 37.5 164.805 -1.47274 -0.9939 0.37962 -0.36133 38.5 169.1998 -1.62382 -1.28527 0.47449 -0.89844 39 171.3972 -1.30153 -0.02859 -0.75829 -0.52624 39.5 173.5946 -1.38772 0.71858 1.13718 -0.35323 40.5 177.9894 -1.32981 -0.63717 0.92629 -1.12823 41.5 182.3842 -1.60963 -0.45323 -0.48248 -0.02981 42.5 186.779 -1.22112 0.6271 -0.58516 0.72891 43.5 191.1738 -0.82715 0.11148 -0.6699 1.1552 44.5 195.5686 -1.61052 0.21893 -0.48197 -0.61123 45.5 199.9634 -1.53314 -0.52098 -0.48002 -0.18154 46.5 204.3582 -1.39548 0.91362 0.79142 -0.24869 53 232.9244 -1.45017 -2.03138 -1.01463 -1.83927 61 268.0828 -1.93846 -1.36698 -0.91572 -1.04028 69 303.2412 -1.39605 -4.33157 -1.18774 -1.07982 77 338.3996 -1.65581 -2.05561 -0.78033 -1.19136 85 373.558 -1.5621 -3.23415 -0.62781 -0.10103 93 408.7164 -1.54407 -3.90479 -1.08895 0.25496 101 443.8748 -1.81788 -1.53689 -0.14101 -1.07813 109 479.0332 -1.75857 -1.06025 -1.87899 3.72613 117 514.1916 -1.79086 0.684 -0.46837 -0.73347 125 549.35 -1.90949 0.54727 -1.47729 -0.7244 133 584.5084 -1.70122 1.17761 -1.84818 -0.92648 141 619.6668 -1.86373 -0.53318 -1.36477 -0.9614 149 654.8252 -1.03138 1.47715 -1.86276 0.43625 157 689.9836 -1.88603 -0.18983 -1.73828 -0.57678 165 725.142 -1.77993 1.17166 -1.47046 -0.89939 173 760.3004 -1.56794 0.8595 -0.95644 -0.72307 189 830.6172 -0.69946 1.85043 -2.26568 -0.90272 197 865.7756 -1.87189 0.63411 -1.1159 -1.04095 205 900.934 -1.54957 -0.74231 -1.42725 -0.78824 213 936.0924 -1.48836 1.60984 -1.77645 1.45166

101

Adjusted Adjusted Component Component Component Component Midpoint Depth-Age Depth 1 2 3 4 221 971.2508 -1.45668 0.85335 -0.04705 0.32751 229 1006.4092 -0.82619 0.24043 -1.21486 -1.09022 233 1023.9884 -0.42761 1.86145 -2.03539 -0.82453 237 1041.5676 -0.08884 1.31583 -1.44048 -1.89716 241 1059.1468 -0.11533 0.70264 -1.22157 0.35972 245 1076.726 -0.11389 0.56424 0.08644 -1.22038 249 1094.3052 -0.1817 1.68167 -1.7472 -0.99999 253 1111.8844 0.57104 0.64581 0.07123 -1.9529 257 1129.4636 0.59114 0.95508 0.14679 -1.13047 261 1147.0428 1.09972 -0.32116 0.72069 -0.54035 269 1182.2012 0.23896 0.11518 0.84984 -2.96856 273 1199.7804 0.27539 1.22935 0.59712 -1.10053 277 1217.3596 0.23763 0.99612 -0.05691 -1.44619 281 1234.9388 0.75736 0.05189 -0.70449 -1.14402 285 1252.518 0.06039 0.53064 -0.70314 -0.43212 289 1270.0972 -0.00559 0.18339 -0.36744 0.30679 293 1287.6764 0.56096 -0.19626 -2.02523 0.15516 297 1305.2556 0.25541 -0.61339 -0.77532 -0.08629 301 1322.8348 -0.02953 -0.15507 -0.24798 -1.47602 305 1340.414 0.91199 -1.80469 -1.46704 0.92427 309 1357.9932 1.08571 -0.56177 -0.81923 -0.09641 317 1393.1516 0.1955 -1.26714 -0.09803 0.96096 321 1410.7308 0.72853 -0.63024 -0.09333 -0.29008 325 1428.31 0.55497 0.20388 -0.05717 -0.34114 329 1445.8892 0.2466 -0.09816 0.13552 -0.26246 333 1463.4684 2.11801 -1.75579 0.77038 1.53221 341 1498.6268 0.65931 -0.74843 0.48068 1.08274 345 1516.206 1.07332 0.18449 0.16963 -1.83238 349 1533.7852 0.92921 0.22265 -0.30249 -1.33114 353 1551.3644 0.98116 -0.19579 0.30948 -1.0732 357 1568.9436 0.95226 -0.39668 0.29951 -0.63883 365 1604.102 0.70791 0.00863 1.55745 -1.62717 369 1621.6812 0.46732 -0.05165 -0.13516 -0.87538 373 1639.2604 1.31258 -0.32441 -0.20394 -1.26078 377 1656.8396 0.91854 0.31124 1.77718 -1.10714

102

Adjusted Adjusted Component Component Component Component Midpoint Depth-Age Depth 1 2 3 4 381 1674.4188 0.47287 -0.2438 0.20829 -0.68093 385 1691.998 1.15261 -0.50371 -0.13723 -1.46541 389 1709.5772 0.35324 0.19961 0.16706 -0.84943 393 1727.1564 0.31879 -0.19188 0.85137 1.16072 397 1744.7356 0.43069 -0.97223 0.38013 1.51673 401 1762.3148 0.72694 -0.58767 0.08236 1.11758 405 1779.894 0.8587 -0.94945 -0.52682 1.06297 413 1815.0524 -0.04834 0.5273 0.27562 1.11521 417 1832.6316 1.13475 0.01282 0.02605 0.33451 421 1850.2108 0.76641 0.23054 -0.34052 0.56226 425 1867.79 0.89685 0.27092 -0.9654 0.171 429 1885.3692 1.49018 -0.23221 0.84815 0.06818 433 1902.9484 -0.165 0.78317 -0.25474 1.04719 437 1920.5276 0.61349 0.52707 1.19819 0.12392 441 1938.1068 0.35343 0.45604 0.22681 0.64625 445 1955.686 0.88105 -0.53507 0.87249 0.15631 449 1973.2652 0.27296 0.46063 0.50246 0.16407 453 1990.8444 0.615 0.79961 -0.34388 -0.24548 461 2026.0028 0.80951 0.79826 0.70615 -0.42893 465 2043.582 0.58186 0.72765 -0.5592 0.46788 469 2061.1612 0.97599 0.07082 0.19306 0.0553 473 2078.7404 0.43493 0.64023 0.72685 0.23997 477 2096.3196 0.59478 0.6425 0.19332 0.50722 481 2113.8988 0.32489 -0.02375 -0.06961 0.01395 485 2131.478 0.68189 -0.24079 0.55646 0.03525 489 2149.0572 -0.30977 0.16252 -0.68548 1.69685 493 2166.6364 -0.24621 0.21662 1.21629 -0.64022 497 2184.2156 -0.03042 -0.00171 1.45502 -0.18239 501 2201.7948 0.80823 -0.11867 0.79323 0.73861 509 2236.9532 -0.03938 0.05591 0.27823 0.88793 513 2254.5324 0.14954 0.56567 0.31652 1.00837 517 2272.1116 0.80063 0.13787 0.38609 0.37085 521 2289.6908 -0.43598 1.18128 -0.64146 0.26107 525 2307.27 -0.12243 1.00337 -0.14107 0.24195 529 2324.8492 -0.19262 0.67677 -0.22785 -0.75875

APPENDIX B:

Comparison of PCA Datasets

103 104

Appendix B

The JPC16 grain-size matrix produced by Malvern analysis at Dr. Dennis

Darby’s lab (Darby et al., 2009) was obtained through Dr. Ortiz. This grain-size data was analyzed in the same way using the PCA statistics as described during this study. This was done to produce four leading components that could be directly compared to the results presented as part of this research. Additionally, the revised age model described previously was applied to the Darby et al. (2009) matrix. The analysis presented here found a nearly constant and slight coarser composition throughout the JPC16 record than the previous analysis by Darby et al. (Fig. B.1).

One explanation for this could be greater sonification of the samples by the Darby lab. However, the VPCA extracted from the two datasets is similar because the results are based on the decomposition of the correlation matrix derived from the individual dataset.

105

Fig. B.1. Comparison of the mean grain-size compositions produced from the analysis of the two datasets

106

Typically, eigenvalues higher than one are assumed to be reasonably important and significant in explaining the variability observed within a dataset.

Here, the eigenvalues were extended to those > 0.4 in order to gain the coarse grained component found in the Darby et al. (2009) analysis of JPC16. Therefore, the first three components with the highest eigenvalues (> 1), plus the forth component with an eigenvalue of approximately 0.4, were retained for the analysis conducted herein although the forth component is much less significant.

By comparing the PCA outputs from Darby et al. (2009) (Table B.1) with the data compiled from this study (Table B.2), the first two components account for remarkably similar percentages regarding the total variance seen within the records. Darby’s Component 1 described 77.36 % of the variance, while the present dataset produced a leading Component with a variance 79.58 % of the total within the grain-size matrix. Darby’s second Component accounted for 12.72 % compared to 13.95 % of the variance from this dataset. The variance from the third and forth

Components established by the Darby PCA accounted for 8.85 % and 0.71 % of the variance, respectively, while the PCA outline here produced a variance of 4.82 % and 0.92 %, respectively.

The leading two modes in the JPC16 PCA produced during this study are very similar in rank and shape to the leading two modes from the Darby et al. (2009) PCA

(Fig. B.2). The amplitudes found in the Darby PCA are slightly greater than the present study’s output. Although the data collected during this research is highly

107

correlated with Darby’s, there are some significant differences that should be taken into consideration.

The leading Component in my PCA exhibits a stronger component loading around 3 µm than the leading Component from the Darby PCA, while the coarse anti-correlated negative peak for both analyses is anchored near the same grain-size class (13.3 vs. 8.45 µm, respectively). The second Component in my PCA has a positive grain-size peak that is slightly greater than that produced from the Darby

PCA (8.45 vs. 6 µm). The strong anti-correlated negative loading in my PCA appears as a trough centered near 47.5 µm, while the Darby PCA shows negative loadings centered around 30 µm. These differences between the two models could be attributed to a number of factors: the use of different Malvern instruments to produce the grain-size matrix, even though the SOP was similar in both, or a function of the smaller sample size in the Darby analyses (26 samples versus 122), for example. The first two Components in the two PCA models are similar enough that they very likely arose from the same processes. The third and fourth modes in each of the two PCA models however are distinct.

108

Fig. B.2. PCA Components from the two datasets; a) Thesis data, b) Darby et al. (2009) data.

The third and fourth modes in the two PCA models are unique to each but appear to be switched in each analysis; the third component in my PCA is similar in some ways to the forth component in the Darby PCA and vice versa. The third

Component of my PCA represents extremely fine grain-sizes, which peak at 0.3 µm.

In the Darby PCA, the third mode represents coarser particles, which peak at 53 µm.

The coarse mode becomes present in the PCA presented in this thesis when a forth component was pulled from the grain-size matrix. This forth Component peaks at 59

109

µm and has an anti-correlated negative trough centered at 23.8 µm whereas the negative loadings in the Darby PCA occurred at 9.5 µm. The only similarity between these two coarse modes is that their peaks both occur > 47.5 µm. The third

Component from my PCA, representing the very-fine fraction, was not found in the initial PCA by Darby et al. (2009). The alternative PCA conducted here on the Darby dataset extracted a forth component that found the very-fine mode in the data. This mode is similar to that found in my PCA as they both peak around 0.3 µm. The very- fine grain mode in my PCA shows two anti-correlated negative peaks at 7.5 and 53

µm, while the Darby PCA has negative peaks centered near 4.75 and 33.5 µm.

The sediment textures from the two different JPC16 datasets appear to be very similar. Additionally, when each Component from the two PCAs is plotted against each other, as shown below (Fig. B.3), the different Components seem to be visually correlated to each other. The fact that each Component seems to be nearly equivalent in both of the PCA results throughout the late Holocene suggests that the sedimentation processes found by Darby et al. (2009) have played an equally important role as those determined by the Component analysis during this study.

110

Fig. B.3. Comparison of the age relationship for the Components produced by the two PCAs.

111

Table B.1. Total variance explained by the PCA from the Darby et al. (2009) dataset.

112

Table B.2. Total variance explained by the PCA from the Thesis dataset.