Investigating the Usage of Transpacific Ice Cores As a Proxy for El Niño-Southern

Investigating the usage of transpacific ice cores as a proxy for El Niño-Southern

Oscillation dynamics

THESIS

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the Graduate School of The Ohio State University

Katelyn Marie Johnson

Graduate Program in Geological Sciences

The Ohio State University

2012

Master's Examination Committee:

Professor Lonnie G. Thompson, Advisor

Professor W. Berry Lyons

Professor Jialin Lin

Katelyn Marie Johnson

2012

Abstract

The El Niño-Southern Oscillation (ENSO) is a quasi-periodic climate anomaly with atmospheric and oceanic properties. Paleoclimate proxies provide valuable information regarding the dynamics and behavior of the poorly understood ENSO, which is often associated with the failure of the Asian Monsoon. Two data sets examined for

ENSO signatures are annually resolved 500-year climate histories extracted in 2003 from the Quelccaya Ice Cap in the southern Andes of Peru and in 1997 from the Dasuopu glacier in the south-central Himalayas. The derived ENSO proxies are based on the insoluble dust content, oxygen isotopic ratios, and major ionic concentrations. While ice cores have been used to study ENSO in the past, our approach differs as we employ two cores located on opposite sides of the Pacific Ocean in an effort to better understand the impacts of ENSO as an event progresses through time and space. We also employ meteorological and sea surface temperature data to better understand how ENSO is recorded on Quelccaya Ice Cap. Oxygen isotopic ratios are determined to be the best recorders of ENSO behavior on Quelccaya ice cap, while Dasuopu does not have one, distinct ENSO signal. Future research should focus on creating better, multi-proxy reconstructions to further investigate the ENSO-monsoon relationship.

Dedication

I would like to dedicate this to my family who has given me nothing but unconditional

love and support throughout all of these years. To my parents, who always went above

and beyond for me, whether it meant waking up at five in the morning to go to a

gymnastics meet or moving me three different times in one summer. To my siblings, despite the first ten years of my life, you are my best friends and greatest role models. To my dog Meika, who has provided nothing but happiness these past twelve years. Last, I would like to thank my friends, who were always there to make me laugh on the toughest

of days. Clichés aside, without any of you, I would not be where I am today.

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Acknowledgments

Finishing this thesis has been an arduous, yet extremely rewarding experience that

I could not have done without the help of many. First, I am forever grateful for the guidance, advice, and knowledge of Drs. Lonnie G. Thompson and Ellen Mosley-

Thompson throughout this entire process. I would also like to thank the members of the

Ice Core Paleoclimatology Research Group for offering suggestions and support on this project; their help has been indispensable. In addition, all of the individuals who worked so very hard to collect, process, and analyze the cores from Quelccaya Ice Cap and

Dasuopu glacier. Without the hard work of so many individuals at Byrd Polar Research

Center and abroad, this project would not have been possible. I would also like to thank my committee members Drs. W. Berry Lyons and Jialin Lin for their tireless support. I want to express my appreciation to Dr. Steve Naber of the Department of Statistics at The

Ohio State University for providing support in the statistical analysis of this project. I would also like to thank, Dr. Doug Hardy of the University of Massachusetts, for providing the meteorological data from Quelccaya Ice Cap, the TAO Project Office of

NOAA/PMEL for providing sea surface temperature data for the TAO/PIRATA arrays, and NOAA/NWS/CPC for providing the Niño 1+2, Niño 3, Niño 3.4, and Niño 4 data sets. I would also like to express my sincerest regards to all of my professors, The Ohio

State University, and Byrd Polar Research Center for an extremely rewarding last two

iv years. Last, I would like to thank the National Science Foundation Paleoclimate Program and The Ohio State University for providing funding; without it, this project could not have been completed.

Vita

December 12, 1988 ...... Born – Thousand Oaks, CA

2007-2010 ...... B.S. Meteorology, Texas A&M University

2010- Present ...... Graduate Research Associate, School of

...... Earth Sciences, Byrd Polar Research Center,

...... The Ohio State University

Field Experience

2010...... Lick Creek Park, College Station, Texas

2011...... Expedition to the Quelccaya Ice Cap, Peru

...... June 30- July 17, 2011

Presentations

“Characteristics and Impacts of the aerosols over Oklahoma determined from airborne measurements during the RACORO campaign,” K. Johnson, C.D. McClure, D.R. Collins, H. Jonsson, R. K. Woods, J. Ogren, and B. Andrews. American Meteorological Society 90th, annual meeting, Atlanta, GA, January16-21, 2010.

“Characterizing El Niño-Southern Oscillation signatures in annually resolved ice cores from Quelccaya Ice Cap (Peru) and Dasuopu Ice Cap (Chinese Himalayas) over the last 500 years,” K. Johnson, L.G. Thompson, and E. Mosley-Thompson. Geological Society of America North-Central Section 46th, annual meeting, Dayton, OH, April 23-24, 2012.

Fields of Study

Major Field: Geological Sciences

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Table of Contents

Abstract ...... ii

Dedication ...... iii

Acknowledgments...... iv

Vita ...... vi

Table of Contents ...... viii

List of Tables ...... xi

List of Figures ...... xii

Chapter 1: Introduction ...... 1

1.1 Problem Statement and Research Questions ...... 1

1.2 Significance of Research ...... 3

Chapter 2: Background ...... 5

2.1 El Niño-Southern Oscillation (ENSO) ...... 5

2.1.1 History ...... 5

2.1.2 ENSO Mechanisms and Monsoon Connection ...... 11

2.1.3 ENSO Reconstructions ...... 14

2.2 Previous Work ...... 17 viii

Chapter 3: Study Area, Data, and Methodology ...... 19

3.1 Study Area ...... 19

3.1.1 Quelccaya Ice Cap ...... 19

3.1.2 Dasuopu Glacier ...... 22

3.2 Data and Methodology ...... 24

3.2.1 Ice Cores ...... 24

3.2.2 Weather Station Data ...... 28

3.2.3 Lake Level Data ...... 28

3.2.4 Buoy Data ...... 28

Chapter 4 Results and Analysis ...... 31

4.1 Sea Surface and Weather Station Data ...... 31

4.2 General Ice Core Temporal Analyses ...... 34

4.2.1. Quelccaya Ice Cap ...... 34

4.2.2 Dasuopu Glacier and Quelccaya Ice Cap ...... 41

4.3 ENSO Analysis ...... 46

4.3.1 Net Accumulation ...... 47

4.3.2 δ18O ...... 48

4.3.3 Chloride ...... 52

4.3.4 Lake levels and other parameters ...... 56

4.3.5 Reconstructing ENSO...... 61

Chapter 5 Conclusions and Discussions ...... 77

5.1 Research Question 1 ...... 77

5.2 Research Question 2 ...... 78

5.3 Research Question 3 ...... 79

5.4 Research Question 4 ...... 80

5.5 Limitations and Implications for Future Research ...... 80

References ...... 82

Appendix A: ENSO thresholds and reconstructions ...... 89

Appendix B: Determined El Niño and La Niña years on Quelccaya Ice Cap ...... 100

Appendix C: Graphical Representation of Determined El Niño and La Niña years on

Quelccaya Ice Cap ...... 105

List of Tables

Table 1: Correlations between Lake Titicaca levels and chloride, and Lake Titicaca levels and deuterium excess during very strong to extreme ENSO events...... 58

Table 2: Correlations between Lake Tititcaca levels and chloride, and Lake Titicaca levels and deuterium excess with ENSO events of all strengths...... 60

Table 3: δ18O thresholds for El Niño events on QIC ...... 63

Table 4: Natural log of chloride thresholds for El Niño events on DG ...... 65

Table 5: Revised natural log of chloride thresholds for El Niño events on DG ...... 66

Table 6: Natural log of dust thresholds for El Niño events on DG ...... 67

Table 7: δ18O thresholds for El Niño events on DG ...... 68

Table 8: δ18O thresholds for La Niña events on QIC ...... 70

Table 9: List of cold tongue and warm pool El Niño events from the 1960s to the present.

...... 73

Table 10: El Niño years on QIC with concurrent El Niño signals on DG...... 75

Table 11: El Niño years on QIC (as indicated by our record) with concurrent El Niño signatures on DG and in other reconstructions...... 76

Table 12: Detailed record of all ENSO years determined by thresholds and reconstructions...... 90

Table 13: Quelccaya ENSO reconstruction...... 101

List of Figures

Figure 1: Schematic of the Walker circulation...... 6

Figure 2: Map of El Niño impacts from (Top) Dec-Feb (Bottom) June-Aug...... 8

Figure 3: Map showing the location of Quelccaya Ice Cap and Lake Titicaca ...... 19

Figure 4: General circulation and precipitation patterns around Quelccaya Ice Cap...... 21

Figure 5: Map of Dasuopu and inset of drilling locations and elevations of the Tibetan

Plateau ...... 23

Figure 6: Location of the TAO array ...... 29

Figure 7: Location of PIRATA buoys...... 30

Figure 8: Map of the Niño SST regions in relation to Quelccaya Ice Cap...... 33

Figure 9: Temporal plots of Quelccaya air temperature and the Niño SST regions...... 34

Figure 10: δ18O per mil, comparison of Summit and North Dome core on QIC ...... 35

Figure 11: δD per mil, comparison of Summit and North Dome core on QIC ...... 36

Figure 12: Deuterium excess per mil, comparison of Summit and North Dome core on

QIC ...... 37

Figure 13: Accumulation (m.w.e/yr), comparison of Summit and North Dome core on

QIC ...... 38

Figure 14: Dust (0.63µm/ml), comparison of Summit and North Dome core on QIC ..... 38

Figure 15: Sodium (ppb), comparison of Summit and North Dome core on QIC...... 39

xii

Figure 16: Chloride (ppb), comparison of Summit and North Dome core on QIC ...... 40

Figure 17: Standardized net accumulation comparison between QIC and DG ...... 41

Figure 18: Standardized δ18O comparison between QIC and DG ...... 42

Figure 19: Standardized deuterium excess comparison between QIC and DG ...... 43

Figure 20: Natural log of chloride comparison between QIC and DG ...... 44

Figure 21: Natural log of dust comparison between QIC and DG ...... 45

Figure 22: Comparison of net accumulation between QIC and DG with half-year leads and lags ...... 48

Figure 23: Comparison of δ18O between QIC and DG with half-year leads and lags ...... 49

Figure 24: δ18O, removal of 500-year trend on QIC ...... 50

Figure 25: δ18O, removal of trend since 1850 A.D. on QIC ...... 50

Figure 26: δ18O, removal of 500-year trend on DG ...... 51

Figure 27: δ18O, removal of trend since 1850 A.D. on DG ...... 52

Figure 28: Comparison of chloride between QIC and DG with half-year leads and lags 53

Figure 29: Chloride, detrended since 1850 and 1970 A.D. on QIC...... 54

Figure 30: Chloride detrended since 1500 A.D. on DG ...... 55

Figure 31: Chloride detrended since 1850 A.D. on DG ...... 55

Figure 32: Comparison between QIC accumulation (3-yr std. avg) and lake levels from

Lake Titicaca ...... 57

Figure 33: Graphical representation of determined El Niño and La Niña years on

Quelccaya Ice Cap ...... 106

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Chapter 1: Introduction

1.1 Problem Statement and Research Questions

The El Niño-Southern Oscillation (ENSO) is a quasiperiodic climate anomaly with atmospheric and oceanic properties. Typically, an ENSO event occurs every 2-7 years.

Although this anomaly and its effects are most notably seen in Peru, the effects are global. Droughts and flooding in Peru, droughts in Australia, the failure of the Asian

Monsoon, and increased precipitation in the southwestern United States, are just a few of the global impacts. Furthermore, the change in climate impacts the fisheries in Peru and agricultural yields around the world (Garnett and Khandekar, 1992; Chen et al., 2001).

Despite the far-reaching nature of ENSO, interest in ENSO only grew dramatically after the 1982-83 ENSO event, when instrumentation was present to gather accurate measurements (Neelin, 2011). By the time the 1997-1998 ENSO occurred, scientists were able to predict the event, but failed to forecast the severity.

Since world populations and economies are severely impacted by strong ENSO events, it is necessary to understand the periodicity and recognize the precursors of such events. However, the mechanisms behind ENSO are still poorly understood; multiple theories about ENSO dynamics exist (Wang and Picaut, 2004). One thing that is certain is that the movement of sea surface temperatures is paramount in El Niño and La Niña events. Observational analysis has also indicated that El Niño is a driver in transporting

1 heat poleward in the tropical Pacific (Wyrtki, 1985; Sun and Trenberth, 1998; Sun,

2003). Observational data have also shown that the transport and movement of sea surface temperature anomalies has also been shown to redistribute areas of convection

(Sun and Trenberth, 1998). If observational data are able to capture this phenomenon, then it is suggested that climate proxies should be able to capture it as well. High- resolution ice cores should capture changes in precipitation brought on by the movement of sea surface temperatures and areas of deep convection.

First, the relationship between meteorology on Quelccaya Ice Cap and sea surface temperatures will be explored. In order to better understand how meteorological variables are recorded on the Quelccaya Ice Cap, meteorological data from Quelccaya Ice

Cap will be used to investigate the relationship between temperatures recorded on the ice cap and sea surface temperatures recorded from TAO and PIRATA buoys, as well as sea surface temperatures from the predefined Climate Prediction Center/NOAA Niño sea surface temperature (SST) regions. Determining the relationship between SSTs and temperatures recorded on the ice cap is important because observations have shown that the pooling of warm water is typical in an El Niño event and that there is a subsequent transport of heat poleward; leads and lags of El Niño would be highly dependent on the movement of SSTs anomalies.

Using ice cores from Quelccaya Ice Cap and Dasuopu glacier, both of which have annual resolution, a proxy of ENSO events over the past five hundred years will be developed. Unlike past reconstructions, this one will utilize cores from opposite ends of the ENSO signal. It is hoped that by viewing ENSO from both ends of the phenomenon

2 it may be possible to capture a lead and lag in ENSO signal caused by the evolving nature of ENSO.

Ultimately, the goals of this study are: 1) Establish a relationship between SSTs and temperatures recorded on Quelccaya Ice Cap, 2) Establish a 500-year ENSO record from

Quelccaya Ice Cap and Dasuopu glacier, 3) Examine the nature, if any, leads and lags of

ENSO dynamics are recorded in the cores, 4) Clarify the ENSO-Monsoon relationship and possible decoupling since the 1970s (Kumar et al., 1999).

1.2 Significance of Research

As stated previously, ENSO can have a significant impact on world economies and humans. It is unknown how ENSO variability will change with increasing global temperatures. Trenberth and Hoar (1997) were some of the first to suggest changing frequency and intensity of ENSO was not due to natural variability, and that further research was needed to determine the impact climate change and rising greenhouse gases was having on El Niño variability. The 2007 IPCC reported that model predictions on how ENSO would change in a future climate were uncertain (Meehl et al., 2007). With regards to the ENSO-Monsoon connection, while it varies due to natural variability in the climate system, models show that the system may decouple in a warmer climate (Meehl et al., 2007). Reconstructions of ENSO are especially important because properly modeling these scenarios will likely improve the models use for future projections

(Gergis and Fowler, 2009). This study will add to the current knowledge of ENSO behavior, as well as examining further the decoupling of the ENSO-Monsoon system,

3 both of which are crucial to producing better forecasts, and to allow for better modeling of this climate anomaly.

Chapter 2: Background

2.1 El Niño-Southern Oscillation (ENSO)

2.1.1 History

The failure of the Indian Monsoon is what initially motivated research into the cause of the El Niño-Southern Oscillation (Philander, 1990a). Sir Gilbert Walker wanted to continue the research of scientists before him, namely Sir John Eliot, in terms of predicting the Indian Monsoon and in doing so named the Southern Oscillation (Eliot

1905; Walker 1923, 1924; Philander, 1990a; Allan, 2000). The Southern Oscillation is a term used to describe the out-of-phase atmospheric pressure fluctuations over the central

Indo-Pacific region and southeastern tropical Pacific Ocean (Walker 1923, 1924).

However, data were limited and Walker’s work was not without skeptics making it far less impactful during that time (Philander, 1990a). During the International Geophysical

Year in the late 1950’s, scientists noted that both atmospheric pressure fluctuations and oceanic properties were anomalous and Professor Bjerknes proposed that this was not coincidence; these events were linked together (Philander, 1990a). In doing so, he introduced the term and explanation for the Walker Circulation (Bjerknes, 1969).

Essentially the Walker Circulation describes the sinking motion of dry air over the colder waters off the coast of South America. The high pressure at the surface of the southeastern tropical Pacific drives air westward towards the Indo-Pacific region,

5 generating the trade winds in this area. The air progressively gets warmer as it flows over warmer waters, ultimately leading towards convection in the western tropical

Pacific. Bjerknes proposed that it was the position of sea surface temperatures that drove the circulation (Bjerknes, 1969). Furthermore, Bjerknes described a positive feedback in which changes to the ocean temperature could drive the meteorology of the affected area, leading to an enhancement of the original ocean changes (Bjerknes 1969, 1972).

Figure 1: Schematic of the Walker circulation during normal conditions and the associated SST anomalies during El Niño conditions. Adapted from Collins et al., 2010.

These enhancements are known as El Niño or La Niña. In the case of El Niño, the trade winds slacken and the upwelling of cold, nutrient rich water in the eastern tropical

Pacific is reduced. As the differences between sea surface temperatures in the southeastern tropical Pacific and Indo-Pacific Region lessen, so does the atmospheric pressure differences, which in turn affect the strength of the winds creating the positive feedback loop Bjerknes proposed. El Niño typically reaches its peak intensity during the

Northern (Southern) Hemisphere Winter (summer), in December-January-February (DJF) and lasts for 18-24 months (Rasmusson and Carpenter, 1982; Trenberth et al., 1998;).

This is when tropical forcing of higher latitude teleconnections is typically strongest

(Trenberth et al., 1998). Teleconnections of ENSO occur globally because of the Hadley cell circulation (Allan, 2000; Gergis et al., 2006). In the case of La Niña, the opposite occurs. Trade winds strengthen and there is an enhancement, as well as an extension of cooler water, in the eastern tropical Pacific. As the temperature gradient increases, the trade winds continue to strengthen, and precipitation in the western Pacific region increases. Philander argues that the idea of a ‘normal’ climatic state in this region is incorrect; the Pacific sits in a constant seesaw of La Niña and El Niño states, despite the fact that a ‘normal’ condition can be found statistically (Philander, 1990b). A schematic of the surface measurement –based teleconnection patterns during El Niño events is shown in Figure 2.

Figure 2: Map of El Niño impacts from (Top) Dec-Feb (Bottom) June-Aug. Adapted from: NOAA/NWS, 2012b.

Following Bjerknes hypothesis, observations of meteorological and oceanographic variables increased in order to gain a better understanding of the evolution

8 and range of ENSO characteristics (Wallace et al., 1998). In 1975, Wyrtki focused on the

1957-58, 1965-66, and 1972-73 years to develop a new theory on El Niño (Wyrtki,

1975). Wyrtki proposed that El Niño is caused by a sudden decrease of unusually strong easterlies in the south Pacific; the unusually strong easterlies, which exist in the years prior to an event, cause water to build up in the western Pacific, changing the slope of the sea level. When the easterlies suddenly decrease, the waters flow back to the east, increasing the slope of the thermocline and accumulation of warm water off the coast of

South America (Wyrtki, 1975). Wyrtki’s study cast light on a ‘buildup phase’ of El

Niño, a phase that could allow for the forecasting of an event prior to the failure of the easterly trade winds (Wallace et al., 1998).

Unfortunately, characterizing ENSO events are not as easy as one would assume.

Each event presents itself differently, and at different intensities (Philander, 1990b). For example, the 1982-83 event proved difficult to forecast as it did not follow the typical evolution Wyrtki proposed; the atypical warming initiated in the central Pacific, rather than off the coast of South America (Wallace et al., 1998).

The lack of consistency and understanding of the variability of El Niño development reinforced a need for real time ocean-atmosphere data. The Tropical

Ocean-Global Atmosphere Program (TOGA) was a 10-year program designed to attain these real-time data with hopes of the ability to forecast El Niño events (McPhaden et al.

1998). The international mission of TOGA was to provide long-term in situ data and information about high-frequency fluctuations that would also be used to improve the models (McPhaden et al., 1998).

McPhaden and others (1998), note that most of the original data requirements were met in the 8°S and 8°N region of the Pacific Ocean, but not in Pacific regions outside of this latitude, nor in the Indian and Atlantic Oceans. In meeting these data requirements, the Tropical Atmosphere Ocean (TAO) array was deployed over the course of the TOGA program (McPhaden et al., 1998). The TAO array provides highly resolved data on sea surface temperatures, winds, and other climatic variables. These data have been used to depict the evolving subsurface temperatures for the 1997-1998 El Niño

(Neelin, 2011). In addition, heat transport and convection described in Sun and

Trenberth (1998) and Sun (2003), as a result of El Niño, have also been explored; not only could El Niño potentially be forecasted, but its movement could also be tracked.

While data and knowledge regarding ENSO has greatly increased, it has not allowed researchers to determine if or how ENSO varies on decadal and century time scales. Other important factors that researchers need to understand is how other fluctuations in the climate system impact the intensity and duration of ENSO. Globally, instrumental records have only been available for the past 150 years, and ocean records are even more limited. The lack of records make understanding the nature of ENSO difficult, and therefore, the use of paleoarchives are imperative for future research.

However, the very issue reconstructions try to resolve is the same issue that makes accurate reconstructions difficult: ENSO events can vary greatly in their characteristics (Gergis et al., 2006). In order to accurately capture ENSO in reconstructions, it is necessary to include proxies from a variety of regions to ensure that the entire event is captured (Gergis et al., 2006).

2.1.2 ENSO Mechanisms and Monsoon Connection

As mentioned before, the El Niño-Southern Oscillation involves a series of ocean- atmosphere feedbacks in which trade winds weaken, causing warm water to flow eastward, deepening the thermocline and subsequently preventing the upwelling of cold water off the coast of Peru (Bjerknes, 1969; Neelin, 2011). While the warm water spreads eastward and inhibits upwelling, the thermocline in the west shoals as cold subsurface anomalies take over (Neelin, 2011). These cold anomalies begin to make their way eastward and overtake the warm anomalies, which weaken El Niño, and begin the process of initiating La Niña conditions (Neelin, 2011).

However, the exact mechanisms of the cold subsurface anomalies/negative feedback and reversal of an El Niño event is poorly understood (Wang et al., 2012). The dynamics of ENSO can typically be categorized into two theories. One theory regards

ENSO as a stable system, one that continually exists in a neutral or cold phase (La Niña) and is interrupted by distinct warm phases caused by outside random forces or noises in the climate system (Wang et al., 2012). The second theory regards the ENSO system as one that is a natural oscillatory mode of the ocean-atmosphere system (Wang et al.,

2012). El Niño is just one phase of this unstable, self-propagating oscillation (Wang et al., 2012). Each of these categories have their own subset of dynamic models which have theorized how the positive feedback known to El Niño is stopped and reversed, but are outside the scope of this study (Wang et al., 2012). Understanding the transition between

El Niño and La Niña conditions is further inhibited because every transition is different;

11 the strength of the event and changing background meteorological conditions affect the phenomenon (Neelin, 2011).

Since El Niño impacts climate globally, it has far-reaching socioeconomic and ecological impacts. El Niño and La Niña teleconnections have both positive and negative consequences depending on the alteration of the climate at that particular location.

Negative impacts are focused on in this discussion since positive impacts or maintaining the status quo is not as critical as negative impacts. Referring back to Figure 2, El Niño teleconnections are greatest during the austral winter (Rasmussen and Carpenter, 1982;

Diaz and Kiladis, 1992).

In the United States, both phases of ENSO can have positive and negative impacts on certain parts of the country; however, the overall impact is negative due to reduced crop yields resulting in losses of billions of US dollars (Adams et al., 1999). Meanwhile, northern Peru experiences wetter and warmer conditions during the austral summer. The disruption in the Walker circulation inhibits upwelling off the coast of Peru, and certain fish populations move to deeper or more southern waters where temperatures are cooler

(Fiedler, 2002). The Peruvian Anchoveta population declines tremendously due to lack of food, causing the industry serious strain (Fiedler, 2002). The wetter conditions also lead to disastrous flooding in northern South America (Thompson et al., 2000a). The

Indo-Pacific region typically experiences warmer and drier conditions during an El Niño event. In places where the slash-and-burn method for clearing land is still used, uncontrollable forest fires may result (Kovats et al., 2003). Lastly, the increased risk of

12 droughts and floods in certain areas leaves populations at increased risk for diseases such as malaria and cholera (Kovats et al., 2003).

Furthermore, anomalies in the Southern Oscillation have been linked to changes in the Indian Monsoon and El Niño is often associated with the failure of the Indian

Monsoon (Shukla and Paolino, 1983; Kumar et al., 2006). Sea surface temperatures in the Pacific have also shown to be strongly linked to monsoon rains (Kumar et al., 2006).

This has caused ENSO to be used as a predictor for monsoon rains in India. However, the exact relationship between ENSO and monsoon rainfall is unknown (Shukla and

Paolino, 1983; Kumar et al., 2006). There have been great variances in rainfall amounts during moderate-strong El Niño conditions (Kumar et al., 2006). For example, using

Quinn et al. (1987) as a reference for strong El Niño events, Kripalani and Kulkarni

(1998) determined that there were no drought episodes after these years. The El Niño of

1997 is considered the century’s strongest, and yet, there was no drought after this event either (Kumar et al., 2006). These anomalies and the presence of strong droughts during weaker El Niño events further obscure the relationship.

Two theories exist as to why there is not an exact, direct relationship between El

Niño episodes and subsequent monsoon failure (Kumar et al., 2006). One is that the monsoon system is highly variable, and this variability can mask the El Niño teleconnection (Kumar et al., 2006). The second is that monsoon system is responsive to the location of the sea surface temperature anomalies in the eastern tropical Pacific

(Kumar et al., 2006). El Niño is thought to shift the location of the areas of convergence in the Walker Circulation; warmer sea surface temperatures cause increased rainfall,

13 whereas low surface pressure anomalies enhance descending motion and inhibit rising motion needed for convection (Kousky et al., 1984; Kumar et al., 2006). Recently, this second theory has initiated the categorization of El Niño events. These two categories are the cold tongue El Niño and the warm pool El Niño (Kug et al., 2009). Most El Niño events fall into the cold tongue category, where sea surface temperature anomalies are greatest in the Niño 3 Region (5°S–5°N, 150°–90°W) and slight anomalies exist in the

Niño 4 Region (5°S–5°N, 160°E–150°W) (Kug et al., 2009). Meanwhile, the warm pool

El Niño is categorized by a greater and isolated warming in the Niño 4 Region (Kug et al., 2009). An investigation of the second theory showed that the warm pool El Niño reduces Indian monsoon rainfall far more than the cold tongue El Niño (Kumar et al.,

2006). In other words, the more westward the development of warm sea surface temperatures in the Pacific, the more likely the monsoon is to fail.

2.1.3 ENSO Reconstructions

Reconstructions of the El Niño-Southern Oscillation are imperative for quantifying changes in ENSO activity over time. However, many impacts of El Niño or

La Niña events are still not well understood. The issue with reconstructing ENSO is the fact that the assumption of a linear relationship between impacts and ENSO activity is often made (Gergis and Fowler, 2006). Reconstructions need to be looked at with a critical eye to distinguish the true ENSO forcings from background noise. Using proxies from multiple locations can reduce the role of background noise in the reconstruction

(Ortlieb, 2000). However, since every El Niño event is different, reconstructions may not

14 include all events, or include too many, as well as misconstruing the strength of an event.

Regardless, it is still useful to attempt to create a record for the non-instrumental period if these limitations are recognized.

One of the most prominent studies of El Niño events is Quinn and others (1987), which looked at event occurrences from the early 1500s to the late 20th century. This study mainly focused on northern South America where wetter conditions exist during an event, and used numerous sources including historical records, many of which were written in languages other than English (Quinn et al., 1987). Using information available at the time, Quinn and others (1987) were able to distinguish ‘strong’ and ‘very strong’ events from 1525 A.D. to the present. ‘Moderate’ events were included from 1806 A.D. to 1987 A.D. when more instrumental data was available (Quinn et al., 1987). The methods in which Quinn and others (1987) determined the intensity and certainty of the event are described further in the paper. One key finding that is absent in other reconstructions is an unusually active period from 1812-1832 A.D. with moderate El

Niño events (Quinn et al., 1987). Quinn later modified and added records from the opposite side of the Pacific Ocean to his original reconstruction in the early 1990s

(Quinn, 1992). The original 1987 record became a ‘regional’ El Niño chronology, while the modified versions were known as a complete chronology of the El Niño-Southern

Oscillation.

While Quinn had modified this reconstruction in the early 1990s, in 2000, Ortlieb reviewed the reconstruction and added to it using other proxies and reexamining some of the historical materials used in the 1987 document (Ortlieb, 2000). Ortlieb also included

15 other notable reconstructions such as Whetten and Rutherford (1994), which reconstructed droughts as they related to ENSO. Quinn’s record was largely seen as the basis for proxy calibrations for El Niño studies. One of the issues Ortlieb (2000) took with the Quinn and others (1987) reconstruction is that some of the proposed El Niño events had been based on impacts in which the relationship with that impact and El Niño is not clear. Ortlieb acknowledged that while Quinn seemed more apprehensive about his reconstruction than others, most were using it without question, and therefore, a more in depth look at the quality of the historical records was necessary (Ortlieb, 2000). In addition, Ortlieb suggested that conclusions about changes in El Niño activity over time, such as from the Little Ice Age to the post-Little Ice Age period might need to be reexamined and that these conclusions may change because of the revision to Quinn and others reconstruction (Ortlieb, 2000).

In 2005, Gergis and Fowler emphasized the need in considering both the oceanic and atmospheric components of ENSO when creating a reconstruction record (Gergis and

Fowler, 2005). In addition, La Niña activity needed to be reconstructed as it was neglected in other studies (Gergis and Fowler, 2005). Therefore, in 2006, Gergis and

Fowler created a multi-proxy reconstruction from A.D. 1525-2002 that included both El

Niño and La Niña events. Using percentile analysis, events reported in this reconstruction were considered very strong to extreme events (Gergis and Fowler, 2006).

This reconstruction had determined that peaks in La Niña activity occurred from the

1500s to the mid-1600s, and in the 1900s (Gergis and Fowler, 2006). El Niño activity peaked during the 20th century, which may be considered abnormal in the entire 500-year

16 record (Gergis and Fowler, 2006). In 2009, Gergis and Fowler added to this reconstruction, and came up with ninety-two El Niño events and eighty-two La Niña events, once again using multiple proxies. This reconstruction was far more detailed, classifying events as weak, moderate, strong, very strong, and extreme (Gergis and

Fowler, 2009). The list of years also included how many proxies captured the event, as well as events classified as ‘protracted’, lasting three or more years (Gergis and Fowler,

2009). Due to its completeness, and inclusion of many proxies from both sides of the

Pacific Ocean, the 2009 Gergis and Fowler reconstruction years were used as the reference years in this reconstruction.

2.2 Previous Work

There have been multiple expeditions to Quelccaya Ice Cap. While studies began in the mid-1970s, the first drilling to bedrock occurred in the boreal summer of 1983

(Thompson et al., 1985, 1986). The first cores provided 1500 years of accumulation, dust, and oxygen isotopic records (Thompson et al., 1985, 1986; Grootes et al., 1989).

These records showed clear seasonal signals in precipitation, as well as the effects of other climatic anomalies such as the El Niño-Southern Oscillation (Thompson et al.,

1984; Grootes et al., 1989). Early investigations showed that lowered accumulation may indicate El Niño events, and that lower mass balances were positively correlated with positive sea surface temperature anomalies, which is indicative of El Niño events

(Thompson et al., 1984). Furthermore, the accumulation records have been used in other

El Niño reconstructions (Gergis and Fowler, 2006, 2009). In addition, δ18O values on

Quelccaya Ice Cap are positively correlated to sea surface temperatures, which is promising for use as an El Niño record (Thompson et al., 1992). Early on, the connection between Dasuopu, monsoon intensity, and ENSO was explored (Thompson et al., 2000a).

Since the majority of the precipitation at Dasuopu is the result of the monsoon, Dasuopu provides an annual record of the South-Asian monsoon (Thompson et al., 2000b). As shown, the reliability of these high-resolution records as recorders of large-scale climate is well established.

Chapter 3: Study Area, Data, and Methodology

3.1 Study Area

3.1.1 Quelccaya Ice Cap

Quelccaya Ice Cap is located in the southeastern Peruvian Andes (13°56’S, 70°50’W) with an elevation of 5,670 meters as shown in Figure 3 (Thompson and Mosley-

Thompson, 1987).

Figure 3: Map showing the location of Quelccaya Ice Cap and Lake Titicaca

Due to the elevation of Quelccaya Ice Cap, precipitation falls as snow. As of the late

1980’s, the average annual temperature was -3°C (Thompson and Mosley-Thompson,

1987).

Air circulation around Quelccaya Ice Cap shows large seasonal variability.

Easterly trade winds dominate circulation at this latitude; however, the Andes Mountains act as a barrier for near-surface atmospheric processes. During the Southern Hemisphere summer, a low pressure system develops near northwestern (southeastern) Argentina

(Bolivia), causing the wind to circulate southward along the mountains towards the low, instead of continuing in a westward fashion (Grootes et al., 1989; Garreaud et al., 2008).

The southerly flow of winds brings moist Amazonian air (gathering moisture from the

Atlantic Ocean) along the eastern side of the Andes, causing precipitation there and on most of the South American continent (Grootes et al., 1989; Garreaud et al., 2008). In the upper atmosphere, the rising air and release of latent heat from all of the convection causes an upper level high, aptly named the ‘Bolivian High’, to form (Grootes et al.,

1989; Lenters and Cook, 1997; Garreaud et al., 2008). The upper level high causes winds to flow counter-clockwise and eastward across Quelccaya Ice Cap. The circulation and precipitation described above is shown in Figure 4a and 4c from Garreaud and others

(2008).

In the Southern Hemisphere winter, the precipitation moves northward as the

Intertropical Convergence Zone (ITCZ) shifts to higher latitudes for the Northern

Hemisphere summer. A small high-pressure cell at the surface replaces the upper level

Bolivian High (Grootes et al., 1989). This keeps most of the precipitation away from

Quelccaya during the winter months (Grootes et al., 1989). Westerly upper level winds dominate during the winter (Garreaud et al, 2008). Figures 4b and 4d from Garreaud and others (2008) show the precipitation and wind patterns during the Southern-Hemisphere winter.

Figure 4: General circulation and precipitation patterns around Quelccaya Ice Cap. Shading indicates long-term mean Climate Prediction Center Merged Analysis of Precipitation (CMAP) precipitation (scale to the right) 925 hPa wind vectors (scale at the bottom) for (a) January and (b) July, and 300 hPa streamlines for (c) January, and (b) July. Red triangle indicates location of QIC. Adapted from Garreaud et al., 2008.

Interestingly enough, there are relatively high correlations between sea surface temperatures and tropospheric temperatures in this region, and off the coast of Peru where El Niño temperature anomalies are typically large (Ramaswamy et al., 2006).

Quelccaya and surrounding areas are unique since these high correlations do not hold true for other tropical continents or in the western tropical Pacific (Ramaswamy et al., 2006).

This correlation implies that changes due to El Niño are very much recorded on

Quelccaya. El Niño tends to peak during the austral winter, changing circulation patterns around Quelccaya Ice Cap. During an El Niño event, Quelccaya receives far less precipitation due to the change in circulation; this is indicated by especially low accumulation rates (Thompson et al., 1984).

3.1.2 Dasuopu Glacier

Dasuopu glacier is located on the Tibetan Plateau near the border of Nepal

(28°23'N, 85°43'E). The elevation at this site is 7200 m (Thompson et al., 2000). Due to the remoteness of the region, long-term meteorological observations are sparse. The majority of the precipitation that falls on the Himalayas is caused by the Asian Monsoon, which arrives during the Northern Hemisphere summer (Thompson et al., 2000). This precipitation is sourced from the Indian Ocean, as the predominately westerly winds shift towards the northwest (Thompson et al., 2000; Barros and Lang, 2003). During other times of the year, eastward flow brings moisture from the Atlantic Ocean and

Mediterranean Sea (Thompson et al., 2000).

Very strong ENSO events often coincide with monsoon failure, leading to very dry conditions across the Himalayas, and lower accumulation rates near the drilling sites

(Thompson et al., 2000).

Figure 5: Map of Dasuopu and inset of drilling locations and elevations of the Tibetan Plateau. Inset from Thompson et al., 2000.

3.2 Data and Methodology

3.2.1 Ice Cores

The ice core data presented here had already been drilled and analyzed. All ice samples are decontaminated prior to analyses, as explained in Dai and others (1995).

Sample decontamination and preparation is conducted on a clean-air bench in a -10 oC work room (Dai et al., 1995). Twenty millimeters of firn is removed from the outside of firn samples using a pre-cleaned band saw; firn is extremely porous and it is necessary to remove the outside portion since it has come in contact with the drill and the shipping container (Dai et al., 1995; Thompson, 1977). Ice samples handled with glass-tipped tongs are rinsed with Millipore Milli-Q ultra-pure deionized water and the outside two millimeters of ice is removed from the sample (Dai et al., 1995; Mosley-Thompson et al.,

1991; Thompson 1977). Once samples are decontaminated, they are placed in sealed pre- rinsed plastic (polyethylene) bottles, melted at room temperature, and immediately analyzed upon melting (Dai et al., 1995).

Insoluble dust content is analyzed in a Class 100 clean room (Thompson, 1977;

Thompson and Mosley-Thompson, 1989). Particles are electronically separated into 15 size ranges between 0.4 and 16µm diameter using Model TA II Coulter Counters

(Thompson and Mosley-Thompson, 1989). In order to make the sample conductive, an

NaCl solution is added to the sample until the solution is two-percent NaCl; just prior to entering the coulter counter, the solution is stirred with a precleaned dropper (Thompson,

1977). Insoluble dust content is represented as the total particle concentration with diameters from 0.63-20 µm per milliliter (ml) sample.

Major ionic concentrations are measured using ion chromatography on a Dionex

2010i ion chromatograph (Henderson et al., 1999). For anion concentrations, an AS4A anion separator column and micro-membrane suppressor column are used (Mosley-

Thompson et al., 1991). The membrane suppressor column requires a carbonate/bicarbonate (0.0012/0.00038 mol l-1) solvent for elution, as well as a 0.1 mol l-1

H2SO4 regenerant (Mosley-Thompson et al., 1991). As explained in Mosley-Thompson and others (1991) this method improves analytical sensitivity and precision over previous methods, while still preserving desired detection limits. Procedural blanks and stock solutions are used throughout sample preparation and analysis to ensure that there is no sample contamination (Mosley-Thompson et al., 1991).

Stable isotopic ratios of oxygen and hydrogen are analyzed using a Finnigan-

MAT mass spectrometer at Byrd Polar Research Center. Oxygen isotopic ratios are reported as δ18O per mil (‰), and are calculated as a departure from the Standard Mean

Ocean Water (SMOW). The equation is as follows:

18 18 16 18 16 δ Oice = ( O/ O)sample – ( O/ O)SMOW x 1000 ‰ (Eq. 3.1). (18O/16O)SMOW

Hydrogen isotopic ratios are calculated similarly and are reported as δD per mil (‰):

2 1 2 1 δDice = ( H/ H)sample – ( H/ H)SMOW x 1000 ‰ (Eq. 3.2). (2H/1H)SMOW

Lastly, deuterium excess (dice) can be calculated using the following equation:

18 dice = δDice – 8 x δ Oice (Eq. 3.3).

3.2.1.1 Quelccaya

There have been multiple drilling campaigns on Quelccaya Ice Cap, but the data used in this study were extracted from ice cores drilled in 2003. Two ice cores were drilled to bedrock and returned to the Byrd Polar Research Center completely frozen and stored at -30o C. The Summit Dome core was 168.68 meters in length and drilled at

5,670 masl. The North Dome core was 128.57 meters long and drilled at 5,600 masl,

1.92 km from the Summit Dome core. The ice cap maintains a temperature of about

-3oC, while the ice/bedrock contact is at the pressure melting point. The firn-ice transition on Quelccaya Ice Cap is at a depth of 20 m (Grootes et al., 1989).

The Summit Dome core was split into 6,802 samples and then analyzed for isotopic ratios (δ18O and δD), insoluble dust content (measured as 0.63 - 20µm/ per mil), and major ion concentrations.

Due to the high accumulation rates, as well as a known rainy-season, the seasonal variation in the core allowed for accurate counting of the annual layers. Annual resolution is available up to 698 AD in Quelccaya, with absolute certainty for the topmost

600 years (Thompson et al., submitted). Net balance was calculated based on a constant rate of change relationship. Using the thickness layer at the surface, the remaining net

26 balance calculations were based on the idea that the layer thickness would decrease constantly as it moved down in the ice column (Thompson et al., submitted).

3.2.1.2 Dasuopu

The data from Dasuopu were drilled in 1997. Three cores were drilled using an electromechanical drill (Thompson et al., 2000). In this analysis, data from Core 3 were used. Core 3, which was 167.7 m long, was located 7,200 masl, and was drilled to bedrock (Thompson et al., 2000). It was determined that Dasuopu glacier was frozen to bedrock due to borehole temperature measurements of -16.0˚C at a depth of 10 m and

-13.8˚C at the ice-bedrock interface (Thompson et al., 2000). Core 3 was brought to

Byrd Polar Research Center completely frozen to be analyzed (Thompson et al., 2000).

For δ18O and δD, 6,903 samples were cut, while 6,419 samples were cut for insoluble

- -2 - dust and chloride (Cl ), Sufate (SO4 ), and nitrate (NO3 ). Beta radioactivity made calibration of annual layers possible by having a known time horizon. At 42.2 m, 1963 was dated due to the presence of beta from radioactive tests in the Arctic in 1962

(Thompson et al., 2000). Annual resolution was maintained until about 1440 A.D., well within the 500 years needed for this study (Thompson et al., 2000). Since Dasuopu receives ample precipitation annually, the ice core record yielded an annual resolution for the last 560 years (Thompson et al., 2000).

3.2.2 Weather Station Data

In 2003 an Automatic Weather Station (AWS) was installed on Quelccaya Ice Cap. I obtained daily averages of non-aspirated temperature, non-aspirated relative humidity, standard atmospheric pressure, wind speed, wind direction, and snowfall from the principle investigator, Dr. Doug Hardy at the University of Massachusetts. I created monthly averages of these variables, which were more useful in comparison with long- term sea surface temperature variations. Air temperature measurements are primarily used in this study. The sensors used on the AWS are identical to those used for the US

Climate Reference Network.

3.2.3 Lake Level Data

Monthly, historical, lake-level data from Lake Titicaca are available until 1914 A.D.

These measurements are taken simply using a marker as a gauge for the lake level. The monthly lake level data were averaged in two ways. The first, was a typical January –

December average, while the second, was from July – June. The purpose of the second average was to match the lake level data with the rainy season and the season in which

Quelccaya Ice Cap records climatic signals.

3.2.4 Buoy Data

Sea surface temperature data from Tropical Atmosphere Ocean (TAO) and Predicted and

Research Moored Array in the Atlantic (PIRATA) buoys provided the first level of sea surface temperature analysis. These data are measured at a depth of 1 m. Each data set

28 provided a quality code for the measurements. Data with quality codes 0 and 5 were removed from the analysis, as these are representative of missing data and failed sensors, respectively. Monthly averages are only provided for months that have at least sixteen daily measurements taken. I took a swath of data at 8°S in the Pacific Ocean because this is the closest latitude to the location of Quelccaya Ice Cap that buoys in the TAO array are available. Measurements from 165°E, 180°W, 170°W, 155°W, 125°W and 95°W were compared against the monthly averaged temperature measurements from the automatic weather station and checked for correlations at the 95% significance level. The location of these buoys is shown in red in Figure 6. Air temperature measurements were also lagged one to eleven months when checking for correlations to see the monthly offset of sea surface temperatures to those on the ice cap. These measurements were also placed on a time series plot to clarify the interpretations of the correlations calculated.

Figure 6: Location of the TAO array. Buoys used are highlighted in red. Adapted from NOAA/PMEL, 2012c.

The same was done for a swath of measurements from the PIRATA buoys located in the

Atlantic Ocean. From the equator, data from 10°W, 25°W and 35°W were acquired. I

29 also acquired a swath of measurements from the 38°W longitude line, at 4°N, 8°N, 12°N, and 15°N. The locations of these buoys are highlighted in red in Figure 7.

Figure 7: Location of PIRATA buoys. Buoys used are highlighted in red. Adapted from NOAA/PMEL, 2012d.

General atmospheric circulation patterns show that this area is indicative of weather patterns on Quelccaya Ice Cap after the air mass flows over the Amazon Basin. Lastly, the predetermined Climate Prediction Center/NOAA Niño SST regions were also used as the Niño 4 sea surface temperature index has been previously shown to be the most representative of Quelccaya (Thompson et al., 2011).

Chapter 4 Results and Analysis

Many correlations were performed throughout this analysis. All correlations given are r-values at the 95% significance level, unless stated otherwise.

4.1 Sea Surface and Weather Station Data

The Tropical Atmosphere Ocean (TAO) buoys located at 165°E, 180°W, 170°W, and 155°W showed less seasonal variation in temperatures than buoys located near the coast of Peru. This is consistent with the region where warm ocean currents dominate.

Seasonal variability increased the closer the buoy was to the Peruvian coast, with 95°W showing the greatest seasonal variability. When air temperature measurements from

Quelccaya were compared with the sea surface temperatures, it appeared that air temperatures on Quelccaya most closely correlated to those temperatures recorded by the

95°W buoy, due to the high level of seasonal variation. The highest positive correlation is seen between the 95°W buoy and the three month lagged air temperature, at a value of

0.79. The highest negative correlation seen is between the 95°W buoy and the air temperature lagging by nine months with a value of -0.82. This is intuitive since the six months between the highest positive and negative correlation accounts for the seasonal change.

The 38°W, 15°N temperatures from the Predicted and Research Moored Array in the Atlantic (PIRATA) swath were the most highly correlated temperatures in the

Atlantic with air temperatures on Quelccaya. With QIC temperatures lagging by three months, there was a negative correlation of 0.78. The negative correlation with a three- month lag is logical because these temperatures are on opposite sides of the equator, and therefore, have opposite seasonal cycles. While airflow from this area does impact QIC temperatures, weather patterns near the 95°W buoy should also be noted, since they are also highly correlated with QIC.

Air temperatures on Quelccaya Ice Cap were also compared to the predetermined

Niño SST Regions, which use the mean temperature in a specific region to define an event. The Niño 3.4 region is most often used as the SST index for predicting El Niño and La Niña events. Typically, sea surface temperatures greater than (less than) 0.5°C the normal temperature for five consecutive months indicate an El Niño (La Niña) event.

The Niño SST regions are shown in Figure 8.

Unsurprisingly, air temperatures on Quelccaya were most highly correlated to the Niño 1+2 region. Air temperatures on Quelccaya lagged two (eight) months had a correlation of 0.76 (-0.80). This is agreeable to the correlations with the

Quelccaya weather station and 95°W TAO buoy. Seasonal variability in this region is high and despite its location, use of this region for determining El Niño conditions is problematic. The Niño 1+2 region is not very responsive to La Niña events, and is also the most likely to miss El Niño events or capture non-existent events (Hanley et al.,

2003).

Figure 8: Map of the Niño SST regions in relation to Quelccaya Ice Cap. Niño 1+2 (yellow): 0°-10°S, 90°W-80°W, Niño 3(Red): 5°N-5°S, 150°W-90°W, Niño 3.4 (purple overlay): 5°N-5°S, 170°-120°W, and Niño 4(Blue) 5°N-5°S, 160°E-150°W. Adapted from NOAA/NCDC, 2012a.

Quelccaya temperatures with a three (nine) month lag have a correlation with the

Niño 3 region of 0.63 (-0.64). This region has less variable sea surface temperatures, which makes it a better indicator of activity in the Pacific as a whole. The Niño 3.4 region has a correlation of 0.37 (-0.39) with a five (eleven) month lag, while the Niño 4 region has a correlation of 0.37 (-0.34) with a seven (thirteen) month lag. Even though

Quelccaya air temperatures are most highly correlated with Niño 1+2 region, there are still significant and strong correlations with the remaining regions. These findings suggest that the ice core record from Quelccaya would be a good proxy for El Niño events. Temporal plots of temperatures from QIC and the SST Niño regions are shown in

Figure 9. 33

Figure 9: Temporal plots of Quelccaya air temperature and the Niño SST regions. From left to right: Niño 1+2, Niño 3, Niño 3.4, Niño 4.

4.2 General Ice Core Temporal Analyses

Initial investigation into the isotopic composition of the ice cores led to simple time-series plots of each variable. Variables from each core were compared to look at overall trends, variations, and differences in the means to determine how ENSO events were recorded in the ice, if at all.

4.2.1. Quelccaya Ice Cap

The 2003 expedition to Quelccaya Ice Cap rendered two annually resolved cores which were then analyzed at Byrd Polar Research Center. Here, the second core from the

North Dome, which was located 1.9 km north of the Summit Dome core, is used to determine how the parameters focused on in this study varied spatially across the ice cap.

If there was a great deal of variability, a composite of the cores was to be made so as not to influence the El Niño–Southern Oscillation reconstruction and general interpretation of the core. See graphs below for further information.

-14

-16

-18 O O (per mil)

18 -20 ð

-22

Summit Dome -24 North Dome

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 10: δ18O per mil, comparison of Summit and North Dome core on QIC

The annually resolved δ18O isotope data is extremely similar in both cores as seen in

Figure 10. There are few years in which the isotope measurements from one core are much higher or lower than the isotope measurements from the other core. This is unlikely to impact the ENSO reconstruction over a 500-year period.

The annually resolved δD isotope data is extremely similar to that of δ18O, which is to be expected, as these two isotopes track closely together; the variations between

δ18O and δD, are used to calculate deuterium excess. If δ18O and δD varied greatly across the ice cap, using one core for the deuterium excess calculation would not be acceptable.

However, there is little variation seen in Figure 11.

-100

-120

-140 ðD (per (per ðD mil)

-160

Summit Dome North Dome

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 11: δD per mil, comparison of Summit and North Dome core on QIC

The results of the deuterium excess calculation are shown in Figure 12. Deuterium excess is fairly similar in both cores, with the greatest difference occurring around the onset of the Little Ice Age. However, towards the middle of the core the values are better aligned. There are also a few places where deuterium excess spikes, but these are isolated, and would not impact an entire reconstruction.

Summit Dome North Dome 35

Deuterium Excess (per mil) Excess Deuterium 15

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 12: Deuterium excess per mil, comparison of Summit and North Dome core on QIC

Net accumulation measurements for the two cores are also very similar as seen in

Figure 13. The North Dome core has slightly higher measurements in some cases, but due to the location of the North Dome core, this could be due to blowing snow.

Quelccaya Ice Cap has higher accumulation during the onset of the Little Ice Age, but declines towards 1850 A.D. After 1850 A.D., accumulation slowly starts increasing, becoming much more variable from 1975 A.D. onward.

Dust increases slightly during periods of low net accumulation. This is seen during the 1750 1850 A.D. period in Figure 14.

Summit Dome 3 North Dome

2 Accumulation (m.w.e/yr) Accumulation 1

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 13: Net Accumulation (m.w.e/yr), comparison of Summit and North Dome core on QIC

3 100x10 Summit Dome Huaynaputina Eruption North Dome (1600 A.D.)

40 Dust (0.63-20 Dust um/mL)

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 14: Dust (0.63µm/ml), comparison of Summit and North Dome core on QIC

Dust is also similar in both cores. Both cores show increased dust deposition after the

Huaynaputina eruption in 1600 A.D. Like the other parameters, dust increases from 1975

A.D. onward, despite the increase in net accumulation during this period.

The sodium profiles from each core are extremely similar in Figure 15, with all major increases and decreases occurring simultaneously throughout the 500-year period.

From 1975 A.D. onward, sodium concentrations increase greatly.

Summit Dome North Dome 300

200 Sodium (ppb) Sodium

100

0 1500 1600 1700 1800 1900 2000 Thermal Year

Figure 15: Sodium (ppb), comparison of Summit and North Dome core on QIC

Chloride is quite variable over the 500-year period, but both cores record fairly similar signals in Figure 16. The largest spike occurs during the late 1700s, a period that corresponds to a large drought in India. However, we do not see a similar rise in sodium, suggesting that this is not sea salt. Chloride concentrations also begin increasing during 39 the late 1970s. There is one incongruous spike in the North Dome core that does not occur in the Summit Dome, but since it is anomalous with the rest of the record, it is not representative of the two cores recording differing amounts of chloride.

Summit Dome North Dome 80

40 Chloride (ppb) Chloride

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 16: Chloride (ppb), comparison of Summit and North Dome core on QIC

Since the parameters do not vary greatly and the location of Summit Dome is optimal, only values of Summit Dome are being used in the remaining analysis. Summit

Dome offers the better record of variability on Quelccaya Ice Cap and is representative of the entire area.

4.2.2 Dasuopu Glacier and Quelccaya Ice Cap

The data from the Summit Dome core from Quelccaya Ice Cap (5,670 m) was then compared with the data from Dasuopu glacier (7,200 m) to see how the parameters matched up. The variables were standardized in order to make comparisons easier to quantify. Below are several graphical representations of the parameters.

In Figure 17, the net accumulation record on Quelccaya shows an increase in net accumulation during the Little Ice Age. The largest increase is during the late 1500s and early 1600s, but net accumulation rates are higher than normal up until the early 1800s.

After this point, net accumulation on Dasuopu overtakes that of Quelccaya. This increased net accumulation persists up until about 1875 A.D. and is abnormal for the 500- year period studied.

Dasuopu 4 Quelccaya

-2 Net accumulation (Z-score) accumulation Net

-4

1500 1600 1700 1800 1900 2000 Thermal Year Figure 17: Standardized net accumulation comparison between QIC and DG

The apparent strengthening of the Asian Monsoon during this time period is also reflected in the Lake Victoria (Africa) water levels (Nicholson, 1998). After this period, net accumulation rates between the two sites are similar; however, Quelccaya’s net accumulation begins to be slightly higher during the mid-1900s, and also shows much more variability between wet and dry seasons in the last few decades than does Dasuopu.

The standardized δ18O data shown in Figure 18 have a similar shape to net accumulation, but the deviations from the mean are smaller.

Dasuopu 4 Quelccaya

O O (Z-score)

18 ð -2

-4

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 18: Standardized δ18O comparison between QIC and DG

On Quelccaya, δ18O is higher than on Dasuopu throughout most of the record and both cores show enrichment towards the present day. Quelccaya’s overall isotopic enrichment over Dasuopu is due to elevation differences; Dasuopu is 1,530 m higher than Quelccaya.

The average δ18O value for Quelccaya over the entire period from 1500 to 2007 A.D. is 42

-18.28 per mil and seasonal fluctuations are visible throughout the entire record. This value is slightly lower during the Little Ice Age (defined here as 1520 -1880 A.D.) when the average δ18O value is -18.54 per mil. While Quelccaya usually has higher values than

Dasuopu, the records appear to converge and overlap during the mid-1600s. This overlap is more prominent towards the present day because of the enrichment seen in both cores.

Dasuopu has an average δ18O value of -19.84 per mil over the 1500 to 1996 A.D. period.

During the Little Ice Age, the average value of δ18O is -20.23 per mil on Dasuopu.

Dasuopu appears to have a steeper upward slope towards the present day, but Quelccaya shows greater variation.

Differences between δ18O and δD in the ice cores are more easily seen in standardized deuterium excess plots in Figure 19.

-2 Deuterium Excess (Z-score) Excess Deuterium

-4 Dasuopu Quelccaya

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 19: Standardized deuterium excess comparison between QIC and DG

During the first part of this record, deuterium excess is higher than average on Dasuopu and lower than average on Quelccaya. However, aside from a few large deviations, the records converge around their average value until the late 1700s. Dasuopu experiences a large drop in deuterium excess around 1800 A.D.; this occurs directly after a long drought. Both records decrease until 1825 A.D., and then begin to increase till the end of the record. Deuterium excess on Quelccaya is much more variable than on Dasuopu during the more recent decades.

Chloride does not have a normal distribution in either core. Therefore, a natural logarithmic scale was applied to the data instead of standardizing the data.

6 Dasuopu Quelccaya

2 Ln (Chloride) Ln 0

-2

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 20: Natural log of chloride comparison between QIC and DG

On average, chloride levels are higher on Quelccaya than on Dasuopu. Levels of chloride on Quelccaya are especially higher during the Little Ice Age, and there is a noticeable 44 increase in concentrations towards the present day. From the raw data, Quelccaya spikes higher than 40 ppb, from 1790-1802, 1987-88, and 2000 A.D. However, the 1987-88

A.D, and 2000 A.D. spikes are not as noticeable on a log scale since the 1790-1802 A.D. event is so large. Dasuopu has chloride spikes higher than 40 ppb in 1581, 1626, 1790-

93, 1794-95, 1987, and 1994 A.D. Dasuopu also shows increasing concentrations in chloride from about 1950 A.D. onward.

Dust was another parameter that was not distributed normally and was best viewed on a natural logarithmic scale.

Dasuopu Huaynaputina Eruption Quelccaya 14 (1600 A.D.)

12 Ln(Dust)

8 1500 1600 1700 1800 1900 2000 Thermal Year

Figure 21: Natural log of dust comparison between QIC and DG

Dust on Dasuopu is much more variable and oscillatory in nature than on

Quelccaya, and on average, is much higher. This is more indicative of the monsoonal climate system Dasuopu is responsive to and located in. The Huaynaputina signature is 45 very much present on Quelccaya, with dust levels spiking in the years after the 1600 A.D. eruption; this signal dominates the Quelccaya record. Huaynaputina was located in southern Peru, and therefore, the signature is not present on Dasuopu. Dust levels on

Dasuopu begin increasing in 1925 A.D., with a much greater acceleration in the last few decades. Quelccaya shows a marked increase in dust starting in 1975 A.D., which could be the result of biomass burning in the Amazon basin.

In closing, it should be noted that in certain time periods, such as the accumulation period from 1585-1600 A.D., the two cores appear to mirror each other but are slightly offset. Shifting the dates of these cores so that peaks and valleys in accumulation matched up only offset other sections. The cores were offset by one to two years, but neither of these provided a better record. Correlations of the data lagged by one to two years confirmed what was observed visually: the record was not improved.

4.3 ENSO Analysis

In order to complete an ENSO analysis, it was necessary to determine which parameters varied significantly from event to non-event years. As a preliminary step to determine which variables were most promising, I consulted Gergis and Fowler

(2006), which listed the extreme and very strong El Niño and La Niña events from 1525

A.D. to the 2002 A.D. These years where imported to JMP Pro 9.0 statistical software, with El Niño years assigned a value of 1, La Niña years assigned a value of 2, and the remaining years between 1500 and 2007 A.D. (1996 A.D. for Dasuopu) assigned a value of 0. A simple one-way analysis of variance test (one-way ANOVA) was performed

46 using the Fit Model Platform in JMP Pro 9.0 statistical software to determine which parameters varied significantly from El Niño, La Niña, and non-event years. Later on, this method was performed a second time using all events, regardless of their strengths, from the Gergis and Fowler (2009) reconstruction. The parameters that stood out initially as recorders of El Niño years were δ18O on Quelccaya Ice Cap and the natural log of chloride on Dasuopu glacier. Quelccaya Ice Cap and Dasuopu glacier have rainy seasons that are out of phase with each other. Quelccaya Ice Cap receives most of its rain in the austral summer, which means that it records the year from July to June. Dasuopu, on the other hand, receives most of its rainfall from the Asian monsoon during the Northern

Hemisphere summer. Since these cores record at different times, it was necessary to see if there was a seasonal lag that would affect the ENSO reconstruction using these parameters. This meant lagging the cores by 0.5 years, instead of one to two, which was done previously. It was also necessary to determine if the increasing trends seen in these parameters would affect the ENSO reconstruction.

4.3.1 Net Accumulation

Since the accumulation periods for each ice cap are out of phase with each other, the net accumulation measurements for the 1900-1950 A.D. periods were compared. The data from Quelccaya were shifted one half-year forward and backward in Figure 22 to see if the seasonal differences could be minimized. Despite these ice cores recording different seasons, the half-year shifting does not meliorate the alignment of the records.

While precipitation decreases during drought-like conditions or when the monsoon fails,

47 net accumulation was not determined to be a good indicator of an El Niño or La Niña event in either core. Therefore, the half-year shifting was done on the remaining parameters that were good indicators of El Niño and La Niña events.

2.0

1.5

1.0

Accumulation (m.w.e/yr) Accumulation 0.5 Dasuopu Quelccaya Quelccaya +.5yr Quelccaya -.5yr 0.0 1900 1910 1920 1930 1940 1950 Thermal Year

Figure 22: Comparison of accumulation between QIC and DG with half-year leads and lags

4.3.2 δ18O

Since δ18O is indicative of precipitation and temperature changes, it is possible that this parameter is out of phase in the ice cores. However, Figure 23 shows that this is not the case. While there are some years in which the shift does appear to align the parameters better, it makes other years worse. Therefore, shifting δ18O one half year forward or backward was not done.

-12

-14

-16

-18

O O (per mil)

18 ð -20

Quelccaya -22 Dasuopu Quelccaya + 0.5yr Quelccaya - 0.5yr -24 1900 1910 1920 1930 1940 1950 Thermal Year

Figure 23: Comparison of δ18O between QIC and DG with half-year leads and lags

δ18O varies significantly on Quelccaya Ice Cap during El Niño events and therefore, trends on Quelccaya Ice Cap need to be assessed to ensure that it does not affect the producibility of an El Niño reconstruction. δ18O had significant positive trends over the 500-year period, from 1850 A.D. onward, and from 1975 A.D. onward. The trend became more severe when the shorter time periods were assessed. Below, in Figure

24, is a graph of how the raw data compares to the data with the 500-year trend removed.

The raw data does not differ greatly from the detrended 500-year data. Therefore, the removal of this trend is not necessary for an unbiased ENSO reconstruction. If looking solely at the anthropogenic period (1850 A.D.-present), δ18O has a significant and greater trend than the 500-year trend, as seen in Figure 25.

0 Z-Score -2

-4

Detrended since 1500 A.D. 500-year AVG removed -6 1500 1600 1700 1800 1900 2000 Thermal Year

Figure 24: δ18O, removal of 500-year trend on QIC

Detrended since 1850 A.D. Quelccaya, 500-yr Z-score

0 Z-Score

-2

1860 1880 1900 1920 1940 1960 1980 2000 Thermal Year

Figure 25: δ18O, removal of trend since 1850 A.D. on QIC

If we remove the trend from 1850 A.D. to the present, the general shape of the graph remains the same up until the 1970s. While the 1970s to 1990s are decidedly different, this short period of time is unlikely to affect the El Niño reconstruction as a whole.

δ18O is also used on Dasuopu as an ENSO recorder; however, it does not perform as well as the δ18O record on Quelccaya. Regardless, trends in δ18O on Dasuopu also need to be evaluated. Only the 500-year trend and the trend from 1850 A.D. to present on Dasuopu were significant; the effects of these trends are shown in Figures 26 and 27.

Neither the 500-year trend nor the 1850 A.D.-present trend drastically skew the data, and both appear to be far smaller than the δ18O trends on Quelccaya. Neither of these trends will be removed in the analysis since they are unlikely to impact the reconstruction.

Z-score Z-score

-2

Detrended since 1500 A.D. Dasuopu, 500-year Z-score

1500 1600 1700 1800 1900 2000 Thermal Year

Figure 26: δ18O, removal of 500-year trend on DG

0 Z-score

-2

Detrended since 1850 A.D. Dasuopu, 500-yr Z-score

1860 1880 1900 1920 1940 1960 1980 2000 Thermal Year

Figure 27: δ18O, removal of trend since 1850 A.D. on DG

4.3.3 Chloride

Chloride varies significantly on Dasuopu during El Niño years, but this is not seen on Quelccaya Ice Cap. Despite the half-year shift not improving the other parameters, it is still necessary to check with chloride, since it is used in the El Niño reconstruction.

Again, the half-year shift did not improve the alignment of the records in Figure

28. Therefore, it is unlikely that the lack of significant change in chloride during an El

Niño event on Quelccaya is due to a seasonal shift in recording.

Chloride (ppb) Chloride 10

Dasuopu Quelccaya Quelccaya + 0.5yr Quelccaya - 0.5yr 0 1900 1910 1920 1930 1940 1950 Thermal Year

Figure 28: Comparison of chloride between QIC and DG with half-year leads and lags

There is also an issue of recent enrichment in chloride on Dasuopu and

Quelccaya. Unlike δ18O, there is not a significant trend over the entire 500-year period on Quelccaya. The trends are apparent and significant towards the industrial period, namely the post-1850 A.D. period. In Figure 29, the detrended data of chloride on

Quelccaya Ice Cap is shown. The removal of the trend since 1850 A.D. does not change the structure of the data much as this trend is very slight. The removal of the trend from

1970 A.D. onward has a larger impact. The slope of this trend was 0.6, versus the 0.05 slope for the trend starting at 1850 A.D. The trend from 1970 A.D. onward is larger, likely because of land-use changes in the Amazon basin.

50 Normal Data Detrended since 1850 A.D. Detrended since 1970 A.D. 40

10 Chloride (ppb) Chloride 0

-10

-20 1860 1880 1900 1920 1940 1960 1980 2000 Thermal Year

Figure 29: Chloride, detrended since 1850 and 1970 on QIC

Regardless, this is too short a time period to have much of an impact over the 500-year reconstruction. In addition, chloride is not used as recorder for El Niño activity on

Quelccaya and so the trend does not need to be removed.

The trends in chloride on Dasuopu are slightly different. Chloride has significant positive trends over the entire 500-year period and since 1850 A.D., but if we isolate

1970 A.D. onward, the trend is not significant. This is unexpected since graphically, the increase of chloride on Dasuopu appears large. The trends over the longer time periods are slight, with slopes of 0.01 and 0.07 for the 500-year trend and 1850 A.D. to present trend, respectively. While Chloride does not follow a normal distribution when standardized, standardizing was used to compare data with and without the trends removed in Figures 30 and 31.

8 Detrended since 1500 A.D. Chloride, 500-yr Z-score

Z-Score 2

-2 1500 1600 1700 1800 1900 2000 Thermal Year

Figure 30: Chloride detrended since 1500 A.D. on DG

Detrended since 1850 A.D. Chloride, 500-yr Z-score 4

2 Z-Score Z-Score

-2 1860 1880 1900 1920 1940 1960 1980 2000 Thermal Year

Figure 31: Chloride detrended since 1850 A.D. on DG

The 500-year trend is very slight and does not affect the data much. The raw data is lower in the 1500s, and higher from about 1925 onward, than the detrended data. The trend since 1850 A.D. is also slight, but the impact is clear from the 1920s to the present day in Figure 31. Despite this difference, this short time period affects little; if ENSO events during these years are scrutinized, it is not necessary to remove the trend.

4.3.4 Lake levels and other parameters

In addition to determining which parameters varied due to ENSO events in each core, various pairwise correlations were also completed to see if the relationship between parameters from each ice core varied due to El Niño or La Niña events. The stronger events from Gergis and Fowler (2006) were used to define the event from non-event years over the entire 500-year period. Results indicated that chloride levels in Dasuopu and Quelccaya are significantly correlated during non-event and strong El Niño years, but not for La Niña years. The r-value at a significance level of 95% during non-event years was 0.35, but rose to 0.53 for El Niño years. Therefore, chloride was investigated further as a possible link between the two cores for an ENSO reconstruction, even though chloride was not an indicator of El Niño years on Quelccaya on its own.

One source of chloride during El Niño years could be from lakes that dry up due to reduced rainfall. Historical lake level data were acquired from Lake Titicaca (15°45′S

69°25′W), which sits on the border between Peru and Bolivia. The annually resolved lake levels were standardized and compared to a three-year standardized running average

56 of net accumulation on Quelccaya Ice Cap to ensure there was a relationship between the ice cap and the lake before any other comparisons were made.

0 Z-Score

-2 Quelccaya Accumulation (3-yr AVG) Lake Levels -0.5 year Lake Levels - 1 year 1920 1930 1940 1950 1960 1970 1980 1990 Thermal Year

Figure 32: Comparison between QIC net accumulation (3-yr std. avg) and lake levels from Lake Titicaca

The lake levels seem to respond to changes on the ice cap within about one year, as indicated by the green line. Since there appears to be a relationship, the lake levels were then compared to chloride concentrations in the ice cores, and also to deuterium excess which can be used to infer relative humidity and evaporation rates.

Initially, the years for very strong to extreme El Niño and La Niña events were used to distinguish normal years from years with events, in the hopes of correlating changes in chloride and deuterium excess with that of lake levels during event years.

This was the same subset of years used in the initial pairwise correlations. However, this gave us a very small sample size to work with since the lake level data started in 1914

A.D.; there were only nine to eleven events in either category with which to run the pairwise correlations. The resulting r-values at the 95% significance level are detailed in

Table 1 below.

Table 1: Correlations between Lake Titicaca levels and chloride, and Lake Titicaca levels and deuterium excess during very strong to extreme ENSO events. Colors indicate how the average lake levels were calculated and whether or not they had a lag. Pink (orange): Jan-Dec(July-June), no lag; green(blue): Jan-Dec(July-June), 1-year lag.

Non-event years El Niño Years La Niña Years

Chloride (QIC) None None 0.67; 0.88; 0.75; 0.83 Chloride None None 0.75; 0.84 (Dasuopu) Deuterium Excess (QIC) None None 0.70; 0.77

Deuterium Excess None -0.78;-0.75 None (Dasuopu)

The small sample size (nine) indicated that chloride levels on Quelccaya Ice Cap were related to the yearly lake levels (averaged from monthly data from January-

December), that were offset one-year, during La Niña years. The correlation was 0.88.

Unsurprisingly, this also meant that yearly lake levels (averaged from July-June) lagged one-year and also the yearly lake levels not lagged one-year were correlated with chloride on Quelccaya Ice Cap during La Niña years. However, there were no correlations during

El Niño or non-event years.

The data also indicated that chloride levels on Dasuopu glacier were related to the yearly lake levels (both averaged Jan-Dec, and July-June) lagged one-year, with a correlation of 0.75 and 0.84 respectively, during La Niña events.

Deuterium excess on QIC was correlated to lake levels (July-June) lagged one- year during La Niña events with a correlation of 0.70. Deuterium excess on Dasuopu was also correlated to lake levels (July-June; Jan-Dec) lagged one year during El Niño events with a correlation of -0.78 and -0.75 respectively, but there were no correlations during La Niña years.

However, with such small sample sizes, the pairwise correlations were possibly misleading. Therefore, the entire Gergis and Fowler (2009), El Niño and La Niña lists were used in a second set of correlations. The issue with using both lists is that nine years from 1914 A.D. onward had both El Niño and La Niña events and there was no way to differentiate which event the ice cores had recorded. Therefore, three event classifications were created to run the correlations with the larger sample sets. One classification left out the years in which both events occurred and treated these years as

‘non-event’ years. The second classification treated all the years in which both events occurred as El Niño years, and the last classification treated all the years in which both events occurred as La Niña years. The correlations were run under all three of these classifications, creating a much higher sample size of event years (29-43), but depleted the sample size of ‘non-event years’ for the second and third classifications. The resulting r-values at the 95% significance level are detailed in the Table 2 below.

Table 2: Correlations between Lake Titicaca levels and chloride, and Lake Titicaca levels and deuterium excess with ENSO events of all strengths. Colors indicate how the average lake levels were calculated and whether or not they had a lag. Orange: July-June, no lag; green(blue): Jan-Dec(July-June), 1-year lag.

Duplicate (Non-Event) Duplicate ( El Niño) Duplicate (La Niña) Non- El La Non- El La Non- El La event Niño Niña event Niño Niña event Niño Niña years Years Years years Years Years Years Years Years Chloride None None 0.38 None None 0.38 None None 0.38, (QIC) 0.40 Chloride None None None None -0.31 None None None None (Dasuopu) Deuterium 0.47 None None None None None None None None Excess (QIC) Deuterium None None None None None None None None None Excess (Dasuopu)

Clearly the larger sample sizes produce a more complicated picture. It is fairly certain that lake levels lagged one-year are related to chloride during La Niña events on

Quelccaya Ice Cap. This means that as chloride levels increase, lake levels increase the year after La Niña events. Potentially, one may assume that this occurs because La Niña events are typically preceded by El Niño events, where chloride levels may rise because of increased dryness, but a significant correlation between chloride and lake levels during

El Niño years is not seen. There must be other, more dominant factors affecting chloride in this region. Unfortunately, data from lakes in India or Tibet were inaccessible and this relationship could not be explored using the Dasuopu record.

4.3.5 Reconstructing ENSO

4.3.5.1 Determining Thresholds

To develop an El Niño reconstruction using the Quelccaya and Dasuopu ice cores, thresholds were chosen for variables that seemed to be indicative of strong El Niño events. The initial method for determining these parameters is explained at the beginning of section 4.3; for Quelccaya, δ18O increased significantly during El Niño events, while the natural log of chloride increased significantly (however, the significance was 93%) during El Niño events on Dasuopu. Using the means and standard deviations of these variables during event and non-event years over the entire 500-year period as guidance, I chose various values to act as thresholds for capturing El Niño events. Of course these thresholds are subjective, and using a different reconstruction may produce different results.

Nevertheless, for δ18O on Quelccaya, the first threshold chosen was -17.00 per mil, the second -16.85 per mil, and the third -16.70 per mil. These values were chosen because they are different enough from the mean value of δ18O over the 500-year period that they should only occur during years where there is less precipitation, such as a drought, or an El Niño event.

The threshold values were then used to sort the data to select the years that measurements were greater than the threshold. In the case of Quelccaya, values below thresholds 1, 2, and 3 were eliminated. The years left after eliminating the unwanted values were compared with Gergis and Fowler (2009), an El Niño reconstruction based on multiple proxies with each event rated on a scale from Weak, Moderate, Strong, Very

Strong, and Extreme. Gergis and Fowler (2009) captured a total of 189 El Niño years and 211 La Niña years over the period from 1525 A.D. to 2002 A.D.

For threshold 1 on Quelccaya, with a value of -17.00 per mil, a total of 105 years had δ18O values higher than -17.00 per mil. The average δ18O value for the 105 years was -16.08 per mil with a standard deviation of 0.74. Of these 105 years, fifty-nine of those years were El Niño years and twenty-five were La Niña years based on the reconstruction, including seven years in each of those calculations in which both events occurred. In other words, fifty-six percent of the years were El Niño years and twenty- four percent were La Niña years.

For threshold 2, with a value of -16.85 per mil, a total of 93 years had δ18O values higher than the threshold; the 93 years had a mean of -15.97 per mil and standard deviation of 0.72. Of these 93 years, fifty-four were El Niño years and twenty-one were

La Niña years based on the reconstruction, including seven years in each of those calculations in which both events occurred. Fifty-eight percent of the years captured were El Niño years, and twenty-three percent were La Niña years.

For threshold 3, with a value of -16.70 per mil, a total of eighty-two years had

δ18O values higher than the threshold. The mean δ18O value of the eighty-two years was

-15.87 per mil with a standard deviation of 0.70. Of these eighty-two years, forty-eight of those years were El Niño years and seventeen were La Niña years based on the reconstruction, including six years in each of those calculations in which both events occurred. Fifty-nine percent of the years captured were El Niño and twenty-one percent were La Niña years. If we remove the five years captured before 1525 A.D. (when the

Gergis & Fowler (2009) reconstruction started) our percentages increase to sixty-two percent El Niño years and twenty-two percent La Niña years. If we also consider that

Quelccaya captured the El Niño event in which both events occurred in one year, the percentage of La Niña years is reduced to fourteen percent.

Also, as some of the reconstructions have events that differ by one or two years, a threshold comparison was done on the three-year average of δ18O values on QIC. δ18O appears to be a decent recorder of El Niño years, justifying the reasoning behind using this parameter to test the three-year average. If an El Niño year occurred during a year close to an event, but not labeled by Gergis and Fowler (2009), the three-year average would work to minimize this error. However, the differences in years captured in the three-year average scenario were not large; it was therefore deemed not useful. Please see Table 3 for results.

Table 3: δ18O thresholds for El Niño events on QIC

δ18O Threshold 1 Threshold 2 Threshold 3 3-Yr Avg Quelccaya (-17.00 per mil) (-16.85 per mil) (-16.70 per mil) (-17.00 per mil) Total Years 105 93 82 71 Captured El Niño Events 59 54 48 38

La Niña Events 25 21 17 19

Both Events1,2,3,4 7 7 6 6

1, 2 1583, 1747, 1748, 1904, 1957, 1958, 1968 3 1583, 1747, 1748, 1904, 1957, 1968 4 1635, 1748, 1904, 1957, 1958, 1968

Since chloride followed a more normal distribution on a natural logarithmic scale, this scale was used for determining the mean value and standard deviation of chloride.

Chloride concentrations increased on Dasuopu during El Niño years, therefore values higher than the mean value of chloride over the 500-year period were chosen. The first threshold chosen was 2.25, the second 2.45, and the third 2.65; the results are compiled in

Table 4.

For threshold 1, with a value of 2.25, a total of 151 years had chloride concentrations higher than 2.25. The mean of these years was 2.74 with a standard deviation of 0.43. Of these 151 years, seventy-four were El Niño years and seventy-two were La Niña years based on the reconstruction, including twenty-one years in each of those calculations in which both events occurred. This threshold essentially captured the same number of La Niña and El Niño events.

For threshold 2, with a value of 2.45, a total of 103 years, with a mean of 2.91 and standard deviation 0.40, had chloride concentrations higher than the threshold. Of these

103 years, fifty-one of those years were El Niño years and forty-six were La Niña years based on the reconstruction, including fourteen years in each of those calculations in which both events occurred.

For threshold 3, with a value of 2.65, a total of seventy-six years had chloride concentrations higher than the threshold. The mean value of the seventy-six years was

3.06 with a standard deviation of 0.38. Of these seventy-six years, thirty-eight of those years were El Niño years based and thirty-three were La Niña years based on the

64 reconstruction, including ten years in each of those calculations in which both events occurred.

A comparison was also done for the Dasuopu chloride thresholds lagged one year after the El Niño years from the ENSO reconstruction. The years captured did not increase, and it was determined that the chloride did not spike solely after the year of an

El Niño event.

Table 4: Natural log of chloride thresholds for El Niño events on DG

Ln(Chloride) Threshold 1 Threshold 2 Threshold 3 Threshold 3 Dasuopu (2.25) (2.45) (2.65 per mil) (Lagged 1 year) Total Years 151 103 76 76 Captured El Niño Events 74 51 38 36

La Niña Events 72 46 33 34

Both Events1,2,3,4 21 14 10 8

1 1544, 1583, 1597, 1635, 1639, 1641, 1964, 1648, 1709, 1747, 1748, 1864, 1864, 1874, 1918, 1951, 1957, 1958, 1959, 1968, 1972, 1973 2 1583, 1639, 1641, 1648, 1709, 1747, 1748, 1864, 1951, 1951, 1957, 1958, 1968, 1972, 1973 3 1593, 1648, 1747, 1748, 1864, 1951, 1957, 1958, 1972, 1973 4 1584, 1748, 1847, 1896, 1957, 1958, 1959, 1968

Additional calculations were carried out for chloride. A number of the years included in the ‘La Niña’ captured category occurred directly before and after El Niño events, suggesting that chloride may increase before an El Niño event, and may remain high for a number of years after an El Niño event occurs. Of the La Niña years captured,

11 occurred after El Niño event, four occurred two years after an El Niño event, and seven occurred in the year before an El Niño event. If these surrounding years are added 65 into the El Niño events, and taken out of the La Niña events, the following adjustments are made in Table 5:

Table 5: Revised natural log of chloride thresholds for El Niño events on DG

Ln(Chloride) Year After Two Years After Year Before and Threshold 3 Dasuopu (2.45) (2.45) Two Years After (Lagged 1 year) (2.45 per mil) Total Years 103 103 103 76 Captured El Niño Events 62 66 73 36 La Niña Events 35 31 24 34 Both Events1,2,3,4 14 14 14 8

1 1583, 1639, 1641, 1648, 1709, 1747, 1748, 1864, 1951, 1951, 1957, 1958, 1968, 1972, 1973 2 1583, 1639, 1641, 1648, 1709, 1747, 1748, 1864, 1951, 1951, 1957, 1958, 1968, 1972, 1973 31583, 1639, 1641, 1648, 1709, 1747, 1748, 1864, 1951, 1951, 1957, 1958, 1968, 1972, 1973 4 1584, 1748, 1847, 1896, 1957, 1958, 1959, 1968

Following the theory that chloride remains elevated on Dasuopu after an El Niño event, three-year running averages were also calculated to see if this enhanced the El Niño signal, but it did not. Regardless, including these surrounding La Niña years improves the El Niño capture to sixty to seventy-one percent from the dismal fifty percent capture of both events in Table 4.

Since chloride on Dasuopu was not as great of a recorder as δ18O on Quelccaya, other parameters were investigated to see if they could help clarify the climatic signal in the ice core. Dust and δ18O proved useful and could be used in conjunction with chloride to bolster the reconstruction. Both dust and δ18O values increased during an El Niño event.

For Dasuopu, the dust thresholds are detailed in Table 6. The best threshold for dust was 11.02. A threshold value of 10.85 captured too many non-event years, and a threshold of 11.09 did not greatly improve the percentages of events captured. Threshold

2 was the best choice so as not to lose too many El Niño events by being too restrictive in the threshold value. For threshold 2, with a value of 11.02, a total of 136 years were captured. Of these 136 years, seventy-nine of those years were El Niño years and fifty were La Niña years based on the reconstruction, including twenty years in each of those calculations in which both events occurred. This means that fifty-eight percent of the years captured were El Niño events and thirty-seven percent were La Niña events.

However, if we assume that the years where both events occurred captured El Niño years, this decreases the percentage of La Niña events to twenty-two percent. While these percentages are not stellar, when used in conjunction with other Dasuopu proxies they are helpful.

Table 6: Natural log of dust thresholds for El Niño events on DG

Ln(Dust) Threshold 1 Threshold 2 Threshold 3 Dasuopu (10.85) (11.02) (11.09) Total Years 185 136 123 Captured El Niño Events 99 79 72 La Niña Events 77 50 45 Both Events1,2,3,4 25 20 17

1 1540, 1597, 1630, 1635, 1638, 1641, 1642, 1648, 1709, 1747, 1748, 1847, 1848, 1864, 1874, 1891, 1896, 1918, 1951, 1957, 1958, 1959, 1968, 1972, 1973 2 1597, 1635, 1641, 1648, 1709, 1747, 1748, 1847, 1864, 1874, 1891, 1896, 1918, 1951, 1957, 1958, 1959, 1968, 1972, 1973 3 1648, 1709, 1747, 1748, 1847, 1864, 1874, 1891, 1896, 1918, 1951, 1957, 1958, 1959, 1968, 1972, 1973

For δ18O, a threshold of -18.60 per mil was chosen from Table 7. Lower thresholds were investigated, but in being more restrictive, many events were not captured. Therefore, -18.60 per mil appeared to be the best choice. For threshold 1, a total of 107 years were captured. Of these 107 years, sixty of those years were El Niño events and forty-two were La Niña events based on the reconstruction, including sixteen years in each of those calculations in which both events occurred. This means that fifty- six percent of the years captured were El Niño years and thirty-nine percent were La Niña events. However, if we assume that the years where both events occurred captured El

Niño years, this decreases the percentage of La Niña events to twenty-four percent. The percent captured for δ18O is even worse than for dust, but again, when used in conjunction with dust and chloride, it proves useful.

Table 7: δ18O thresholds for El Niño events on DG

δ18O Threshold 1 Threshold 2 Threshold 3 Dasuopu (-18.60 per mil) (-18.05 per mil) (-18.00 per mil) Total Years 107 74 71 Captured

El Niño events 60 43 41 La Niña Events 42 27 27 Both Events1,2,3 16 9 9 1 1630, 1635, 1642, 1709, 1798, 1847, 1864, 1868, 1896, 1904, 1918, 1951, 1957, 1968, 1972, 1973 2 1798, 1847, 1864, 1868, 1951, 1957, 1968, 1972, 1973 3 1798, 1847, 1864, 1868, 1951, 1957, 1968, 1972, 1973

Overall, the events captured decrease as the thresholds become more rigorous, and fewer events are captured as both recorders are included; this effect is also seen in

68 previous studies (Gergis and Fowler, 2009). The event capture and correlation with other reconstructions improves toward the present day. This is perhaps due to the enrichment of both chloride and δ18O towards the present day and the fact that the ENSO reconstructions are better towards the present day due to increased awareness and availability of instrumental records. If the enrichment does impact the event-capture, it would take less climatic variability from ENSO to cause the recorders to surpass the threshold limits.

With regards to La Niña, Gergis and Fowler (2009) suggested that QIC was not very sensitive to La Niña. I employed another threshold comparison using δ18O in order to test their claim. I chose -19.00 per mil, -19.43 per mil, and -19.85 per mil for thresholds 1, 2, and 3, respectively. On the whole, Quelccaya is able to capture La Niña events about fifty percent of the time. However, as the threshold levels become more severe, the amount captured decreases, as well as the number of El Niño events captured.

Threshold 2 appears to be the best recorder, capturing fifty-seven percent of La Niña events, with the relationship breaking down in Threshold 3, which only captures fifty- three percent of La Niña events, as seen in Table 8. Gergis and Fowler (2009) used net accumulation records to determine that Quelccaya was not very sensitive to La Niña events; the analysis of δ18O in this study is the first to suggest otherwise. While

Quelccaya is better at capturing El Niño events, the potential for reconstructing La Niña events should not be overlooked.

Table 8: δ18O thresholds for La Niña events on QIC

δ18O Threshold 1 Threshold 2 Threshold 3 Quelccaya (-19.00 per mil) (-19.43 per mil) (-19.85 per mil) (La Niña) Total Years 168 127 85 Captured El Niño events 47 32 21 La Niña Events 96 73 45 Both Events1,2,3 18 12 8

11540, 1544, 1584 1601, 1630, 1639, 1642, 1724, 1782, 1798, 1848, 1860, 1866, 1874, 1879, 1918, 1972, 1973 21540, 1544, 1584, 1601, 1639, 1642, 1724, 1798, 1848, 1860, 1874, 1973 31540, 1544, 1642, 1724, 1798, 1848, 1860, 1874

4.3.5.2 Threshold Discrepancies

All of the best threshold values and the years they ‘captured’ were compiled, along with previous reconstructions into Table 12 in Appendix A. The best threshold values include: 2.45 (without the additional El Niño years described in Table 5) for chloride on Dasuopu, -16.7 per mil for δ18O on QIC (El Niño), -18.6 per mil for δ18O on

Dasuopu (El Niño), -19.43 per mil for δ18O on QIC (La Niña), and 11.02 for dust on

Dasuopu (El Niño). In Table 12, a few discrepancies are apparent. There were thirty- three years where the δ18O value on Quelccaya indicated a La Niña event, while there were El Niño signals on Dasuopu. A breakdown of these years and the comparison between Gergis and Fowler (2009) is provided below.

Seven of the thirty-three years occurred in years that Gergis and Fowler (2009) also considered El Niño years. This included A.D. 1567, 1659, 1729, 1742, 1768, 1815, and 1920. All of these years were bordered by low δ18O values that would not meet the

El Nino threshold of (-16.70 per mil), except for the year 1659 A.D. In 1660 A.D., the

δ18O measurement is -14.34, suggesting that there may be a temporal issue with regards to the reconstruction record or the documented record is off by one year.

There were also seven years (A.D. 1640, 1677, 1688, 1704, 1705, 1796, and

1843) in which there were neither a La Niña nor El Niño classification from Gergis and

Fowler, 2009. Many of these years were preceded by low δ18O values, and had low δ18O values the year after. These years could be true La Niña events, or the product of the

Little Ice Age signal on Quelccaya which results in depleted δ18O values. However, 1834

A.D. was bordered by years with δ18O values that qualified as El Niño years. Since the signal on Dasuopu is not as clear, and El Niño thresholds on Dasuopu tend to lag, perhaps these stronger El Niños which bordered 1834 A.D. masked the La Niña event that occurred in between on Dasuopu.

Five years, A.D. 1639, 1642, 1798, 1874, and 1973 were years in which both events occurred according to Gergis and Fowler (2009). The El Niño events for these years were classified as weak. Perhaps the location of these events in the Pacific Ocean occurred in such a way where it was not recorded on Quelccaya, and the Indo-Pacific region was impacted the most. Another possibility is that these elevated Dasuopu signals occurred because of drought in that region, which was not the result of ENSO.

The remaining thirty-three years that indicated both a La Niña event on Quelccaya

Ice Cap and an El Niño signature on Dasuopu, can be explained in several ways. The most obvious is that it is possible for droughts to exist on Dasuopu without an El Niño event. Another explanation is that there could have been preceding El Niño events that

71 caused drought conditions which translated to elevated dust and chloride levels after the fact. Many of the El Niño events bordering these elevated levels on Dasuopu are considered weak. However, if these events developed in the central Pacific, rather than off the coast of Peru, it may be that these events are less likely to be accurately recorded in terms of their strength and magnitude, which would explain the mismatch between the

Gergis and Fowler (2009) record, and this reconstruction.

In fact, one theory as to why the Dasuopu and Quelccaya records did not have a clear ENSO relationship is that one type of El Niño was more likely to cause monsoon failure than the other. As mentioned, Kumar and others (2006) stated that warm pool El

Niño events were more likely to have a negative impact on the monsoon. In 2009, Yeh and others created a list of El Niño events and their types from 1854 A.D. to the present using raw and detrended sea surface temperatures. Multiple examinations of sea surface temperatures in the Pacific led them to the same conclusion that the frequency of warm pool El Niño events (referred to as central Pacific (CP) in Yeh et al., 2009) were becoming increasingly common as cold tongue (referred to as eastern Pacifc (EP) in Yeh et al., 2009) were becoming less frequent. No warm pool El Niño events occurred prior to 1960 A.D.; the breakdown of events from 1960 A.D. onward is shown below in Table

9, using the raw SST data from the supplementary Table 1 of the Yeh and others (2009) paper.

Table 9: List of cold tongue and warm pool El Niño events from the 1960s to the present. Adapted from Yeh et al., 2009.

Years Raw Sea Surface Temperature Cold Tongue Warm Pool 1960s 1963, 1965, 1969 1968 1970s 1972, 1976, 1979 1977 1980s 1982, 1986, 1987 1990s 1991, 1997 1990, 1992, 1994 2000s 2002, 2003, 2006 2001, 2004

These years are highlighted on Table 12 in Appendix A. Red (gold) highlights signify warm pool (cold tongue) El Niño events. Unfortunately, there are few warm pool events and data from Dasuopu does not extend into the 2000s; this makes it very difficult to draw any conclusion as to whether the failure of the monsoon due to the warm pool El

Niño shows up in the ice core record.

4.3.5.2 Final Reconstruction

Ultimately, since Quelccaya is more responsive to El Niño and the relationship between El Niño and the monsoon cannot be easily discerned, a reconstruction using only

Quelccaya is deemed best. Since δ18O is the best recorder of both El Niño and La Niña events on Quelccaya Ice Cap, it was used as the basis for the final reconstruction on

Quelccaya Ice Cap. In Appendix B, a table lists the El Niño and La Niña years captured using the δ18O thresholds of -16.7 per mil for El Niño and -19.43 per mil for La Niña.

The highlighted years represent those years that can be verified by at least one other reconstruction, keeping in mind that there is only one La Niña reconstruction available for comparison in this project. Years before 1525 A.D. are also difficult to verify since

73 most of the reconstructions begin at 1525 A.D. Strictly based on the reconstructions used, seventy-three (fifty-seven) percent of El Niño (La Niña) events could be verified.

More recent years need to be looked at since most reconstructions end before the end of our core and there are a few noticeable differences from the instrumental record and this record. Ironically, 1987 is not captured, but 1988 does meet the El Niño threshold. It is well known that there was a persistent El Niño event from 1990-1995

A.D. (Allan and D’Arragio, 1999). This prolonged event is recorded in Quelccaya with an El Niño signal occurring from 1990 -1997 A.D., with the exception of 1991 A.D.

However, the Mt. Pinatubo (Philippines) eruption, which was the largest volcanic eruption in the 20th century, was responsible for global cooling; this alone could explain the absence of 1991 A.D. from our El Niño record. While 1996 is not regarded as an El

Niño event, it is sandwiched between the 1990-1995 protracted event and the very strong

1997 event, perhaps explaining why it is captured. Also, 2004 is considered to be a weak

El Niño event. For La Niña, 1999 A.D. is known to have been a strong La Niña event.

Despite not including Dasuopu in the reconstructions, several of the years where the oxygen isotopic values captured El Niño on Quelccaya Ice Cap also captured drought-like conditions on Dasuopu. The natural log of dust, the natural log of chloride and δ18O values on Dasuopu was considered indicators of drought conditions. The years in which these conditions were concurrent with El Niño events on Quelccaya are detailed in the Table 10.

Table 10: El Niño years on QIC with concurrent El Niño signals on DG. Amount of concurrent signals from Dasuopu is listed on left.

Amount Years A.D. 3 1721, 1792, 1799, 1846, 1905, 1939, 1942, 1944, 1956, 1957, 1966, 1967, 1968, 1992, 1993, 1994 2 1722, 1747, 1748, 1825, 1833, 1895, 1937, 1941, 1965, 1988 1 1530, 1583, 1593, 1594, 1634, 1804, 1843, 1877, 1899, 1904, 1906, 1914, 1925, 1926, 1936, 1940, 1990, 1996

Furthermore, if we consider all of the El Niño proxies (Dasuopu thresholds included) and reconstructions (Gergis and Fowler, 2009; Quinn et al., 1987; Quinn 1992

(regional and large-scale); Whetton and Rutherford, 2004; Ortlieb, 2000) when the δ18O value on Quelccaya indicates an El Niño event, multiple years have multiple indicators of an El Niño event. The breakdown for the total number of proxies and reconstructions indicating an El Niño event for that year is depicted in Table 11.

Here, ‘0’ represents years in which only the δ18O threshold was met on Quelccaya and no other reconstruction or proxy indicated an event. Overall, it appears the records agree more from 1825 A.D. onward, as the occurrence of El Niño events appears more frequent. However, this apparent increase in activity could be due to better instrumental records. Again, anything occurring before 1525 A.D. and after 2000 A.D. must be scrutinized due to the constraints of the other reconstructions and the Dasuopu ice core record which only extends to 1997 A.D. Regardless, most of the years captured on

Quelccaya can be verified by at least one other source.

Table 11: El Niño years on QIC (as indicated by our record) with concurrent El Niño signatures on DG and in other reconstructions. Amount of DG thresholds and reconstructions in agreement is listed on left.

Amount Years A.D. 8 1747, 1905, 1939 7 1877, 1899, 1957, 1965 6 1804, 1846, 1925, 1966, 1968 5 1724, 1748, 1792, 1799, 1833, 1904, 1914, 1926, 1940, 1944 4 1608, 1721, 1783, 1828, 1887, 1912, 1942, 1967, 1992 3 1565, 1594, 1660, 1708, 1722, 1723, 1825, 1865, 1937, 1956, 1993, 1994 2 1634, 1655, 1793, 1895, 1906, 1964, 1988, 1990 1 1521, 1527, 1530, 1609, 1646, 1712, 1835, 1843, 1855, 1924, 1936, 1938, 1996, 1997 0 1512, 1522, 1524, 1550, 1569, 1633, 1682, 1690, 1693, 1995, 2001, 2004

Chapter 5 Conclusions and Discussions

5.1 Research Question 1

Quelccaya Ice Cap is a very good recorder of El Niño events. Potentially, it is also a very good recorder of La Niña events, but since there are far fewer historical La Niña records and reconstructions than El Niño records and reconstructions, this is hard to say with certainty. Oxygen isotopes appear to be the best proxy for El Niño and La Niña reconstructions on Quelccaya. A graphical representation of the El Niño and La Niña reconstructions using oxygen isotopes on Quelccaya is seen in Appendix C. While net accumulation has been used in the past and in other reconstructions (i. e. Gergis and

Fowler, 2006, 2009) the data has typically been filtered to remove short-term variability.

On an annual, unfiltered basis, net accumulation is not a good recorder over the entire five hundred year period. As a cautionary note, this was determined using the Gergis and

Fowler (2009) reconstruction, and therefore, other reconstructions could show something different for net accumulation on an annual, unfiltered basis. Also, precipitation can be highly variable over a short distance, which may be why unsmoothed (where outside noise is not taken out) accumulation data does not work on an annual scale.

Initially this study set out to determine the ENSO-monsoon connection using the

Dasuopu ice core. While certain parameters (chloride, dust, δ18O) on Dasuopu coincided with El Niño events, the relationship was not very strong. Even though the monsoon

77 system is the dominant source of moisture for Dasuopu, the ENSO connection was not the dominant climatic signal. In addition, Dasuopu is not recording El Niño, but the aftermath of an El Niño event, which is typically drought; this also explains why the relationship was not as strong. El Niño events are not the only cause of droughts, and therefore, distinguishing the cause of the drought is difficult when the signals on Dasuopu are the same. Lastly, the factors that signal drought on Dasuopu remain elevated for quite some years after the drought may be over, further masking the interpretation of an annually-resolved reconstruction. Regardless, the instances in which drought conditions coincided with El Niño years are detailed in chapter four. Dasuopu may not be part of the main reconstruction, but the information may help future studies.

Determining whether or not El Niño events are increasing compared to earlier centuries is also difficult. The reconstruction certainly indicates more El Niño events in recent decades. However, this may be due to better instrumental records and greater certainty of El Niño events in this period in the reconstructions.

5.2 Research Question 2

Leads and lags in the ice cores were not explicitly seen. Chloride levels on Dasuopu remain elevated after El Niño events, however it is not likely that this is exclusive to droughts caused by El Niño events; other droughts may also have persistent, elevated levels of chloride. In addition, El Niño does not seem to be the principle climate anomaly affecting Dasuopu. On Quelccaya, leads and lags with any significance were not seen.

Therefore, it is determined that the resolution of the cores are not high enough to see leads and lags in ENSO development; only developed events are recorded.

5.3 Research Question 3

Using air temperature measurements from the AWS installed on top of Quelccaya Ice

Cap, buoy data, and predefined SST regions, relationships between Quelccaya air temperatures and SSTs were determined. Quelccaya temperatures were most closely correlated with the 95°W TAO buoy and the Niño 1+2 region, with three (two) month lags respectively. These were also the same locations that had the highest negative correlations six months after the highest positive correlations, corresponding to the changing seasons. In terms of the PIRATA buoys located in the Atlantic Ocean, the

15°N, 38°W sea surface temperatures were the most highly correlated with Quelccaya air temperatures. This agreed with the general circulation patterns outlined in chapter three.

Quelccaya temperatures also had strong correlations with the remaining Niño SST regions. Overall, the observations that the sea surface temperatures in the eastern tropical

Pacific correlated well with the Quelccaya temperatures agree with the more general findings of previous studies of these regions, that the sea surface temperatures relate well to temperatures in the upper troposphere (Ramaswamy et al., 2006). This high degree of significant correlations between the sea surface temperatures and what is happening on the ice cap further support the use of the Quelccaya ice cores for ENSO reconstructions.

5.4 Research Question 4

About fifty-four percent of the ‘El Niño’ years as determined from the oxygen isotopic ratios on Quelccaya Ice Cap, also coincided with drought-like conditions on Dasuopu.

Again, chloride levels increased significantly on Dasuopu during El Niño years.

However, there were many El Niño years that had no impact on Dasuopu. Several studies have suggested that the failure of the Asian monsoon is connected to the type of

El Niño present (Kumar et al., 2006; Kug et al., 2009; Yeh et al., 2009). Using a list from

Yeh and others (2009), this hypothesis was tested to see if there was a connection between drought-like conditions on Dasuopu and warm-pool El Niño years. However, there was simply not enough warm-pool El Niño years (and the Dasuopu record ends in

1997) to determine whether or not this phenomenon turned up in the ice core record. The majority of El Niño years that were concurrent with drought like conditions on Dasuopu occurred during the 1900s. While this would suggest that the El Niño and monsoon connection is not decoupling, this could simply be the result of better records available for the latter part of the reconstruction, and the 1997 cut off of the Dasuopu record.

These factors do not make it possible to say with certainty whether or not the monsoon is decoupling from ENSO.

5.5 Limitations and Implications for Future Research

The shortness of the instrumental record, as well as the uniqueness of all ENSO events makes reconstruction of past occurrences and variability especially difficult.

Comparisons between reconstructions and actual data are only as reliable as the

80 reconstructions themselves. Dasuopu may be too far removed from the ENSO system to provide an excellent record. The future of ENSO reconstructions should lie in improving the understanding of ENSO signals in well-dated proxies. In addition, like Gergis and

Fowler (2009), combining multiple records from various mediums should be done to look at wide-scale ENSO events. In terms of ice core analysis, the recently drilled Indonesian ice core should be examined to determine if it could provide a better picture of the

ENSO-monsoon connection, and to also get a better look at how the two types of El Niño events differ in the ice core record, if at all. Lastly, this analysis of Quelccaya should be used as a stepping stone to a complete ENSO index using all high-resolution cores recovered from the Andes Mountains.

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Appendix A: ENSO thresholds and reconstructions

Table 6: Detailed record of all ENSO years determined by thresholds and reconstructions. G&F EL and G&F LA refer to Gergis and Fowler (2009) El Niño and La Niña reconstructions, respectively. Q. et al., 1987 refers to Quinn, et al., 1987. Q., 1992L and Q., 1992R refer to Quinn’s 1992 large scale and regional scale reconstructions. W&R, 1994 refers to Whetton and Rutherford’s 1994 drought reconstruction. O., 2000, refers to Ortlieb’s 2000 reconstruction. Each column details the events and relative strengths of each event as determined by the individual authors.

Ln(dust)DIC DIC δ18O QIC δ18O EL Ln(chloride) DIC QIC δ18O LA G&F El G&F LA Q. et al., 1987 Q., 1992L Q., 1992R W&R, 1994 O., 2000 1500 EL Y 1501 1502 LA 1503 1504 1505 1506 1507 1508 1509 Y 1510 M 1511 1512 EL 1513 1514 1515 EL 1516 EL 1517 1518 M+ 1519 1520 EL M Y 1521 EL Y 1522 EL 1523 1524 EL 1525 VS S M M 1526 VS S M M 1527 EL M 1528 E 1529 1530 EL EL 1531 VS S M M 1532 VS S M M 1533 EL E 1534 1535 S M+ M+ 1536 1537 1538 S 1539 M M/S S M/S 1540 LA S S M/S S M/S Y 1541 VS M/S S M/S Y 1542 S Y 1543 1544 LA VS S M+ M+ 1545 EL 1546 S S S M? 1547 LA S S M? 1548 LA VS 1549 S 1550 EL

Continued 90

Table 12 continued

1551 1552 LA S S S 1553 S 1554 S Y 1555 Y 1556 VS Y 1557 1558 M S M/S 1559 LA VS S M/S 1560 EL VS S M/S 1561 LA S S M/S 1562 1563 M 1564 1565 EL VS M+ M+ 1566 EL S 1567 EL LA M S+ S+ S+ 1568 S+ S+ S+ 1569 EL 1570 LA 1571 LA S 1572 E 1573 E 1574 LA VS S S S M? 1575 LA 1576 LA S Y 1577 LA S Y 1578 S VS S VS VS 1579 S S VS 1580 S 1581 EL S M+ M+ 1582 EL M+ M+ 1583 EL EL M S 1584 LA M VS 1585 VS M M+ 1586 LA 1587 1588 1589 S M/S 1590 EL M S M/S 1591 S S S M/S 1592 VS S Y 1593 EL EL VS M? 1594 EL EL S Y 1595 Y 1596 S M M+ Y S 1597 EL M M Y 1598 EL EL Y

Continued

Table 12 continued

1599 LA 1600 LA VS S S 1601 LA M M S 1602 M 1603 LA M 1604 M S M+ 1605 M 1606 1607 VS S S S M? 1608 EL VS S S M? 1609 EL W 1610 LA 1611 S 1612 LA M 1613 Y 1614 W S S S Y 1615 Y 1616 1617 1618 VS S M S Y M? 1619 S S M S Y M? 1620 W 1621 LA W S M+ 1622 S M? 1623 EL VS Y 1624 LA VS S+ M+ S+ M? 1625 EL M 1626 EL EL LA VS 1627 LA W 1628 W 1629 LA S Y 1630 EL M S S+ M Y 1631 VS S+ Y 1632 E 1633 EL 1634 EL EL S 1635 EL EL M W M S 1636 EL 1637 LA S 1638 W M 1639 EL LA W M 1640 EL LA M 1641 EL EL W VS S+ M 1642 EL LA W S 1643 EL EL 1644 S 1645 E 1646 EL W 1647 EL M M+ 1648 EL EL M W Y

Continued

Table 12 continued

1649 EL EL LA W 1650 E S+ M Y 1651 W 1652 W S+ M S+ 1653 LA 1654 VS 1655 EL M M 1656 1657 EL EL 1658 M 1659 EL LA M Y 1660 EL VS S Y 1661 LA S VS S Y 1662 LA 1663 LA VS 1664 1665 W 1666 LA 1667 1668 M 1669 W 1670 EL 1671 LA S M+ S 1672 EL 1673 EL EL 1674 LA 1675 M 1676 S 1677 EL LA 1678 EL LA M M? 1679 1680 1681 LA S S S 1682 EL 1683 M+ 1684 EL W M+ M+ 1685 EL LA S Y 1686 M M? 1687 EL VS S+ S S+ Y M? 1688 EL LA S+ S M? 1689 LA 1690 EL M 1691 LA 1692 EL W M+ S 1693 EL 1694 S VS 1695 W VS M 1696 EL LA VS S M? 1697 LA M M+ 1698 LA

Continued

Table 12 continued

1699 EL EL EL 1700 LA W 1701 M S+ M S+ S 1702 EL M Y 1703 LA S Y 1704 EL LA S M Y 1705 EL LA 1706 1707 LA W S M M/S 1708 EL S S M M/S 1709 EL EL EL W M M M/S Y 1710 EL EL W 1711 1712 EL W 1713 M M+ M 1714 W S M+ 1715 LA S S S+ S 1716 LA S S+ S 1717 LA 1718 E M M+ Y M? 1719 S 1720 EL EL EL W S+ M+ VS S 1721 EL EL EL EL W 1722 EL EL EL M 1723 EL E S M+ 1724 LA W M 1725 LA M M 1726 EL EL W 1727 1728 EL S VS M VS VS 1729 EL LA W 1730 EL LA W 1731 LA M M+ 1732 LA M 1733 LA VS Y 1734 W M M 1735 EL EL W 1736 W 1737 E S S Y 1738 W 1739 VS Y 1740 EL EL EL VS 1741 EL EL S 1742 EL LA E 1743 LA VS 1744 EL W M+ M+ Y 1745 EL LA S 1746 LA W Y 1747 EL EL EL M W S S S+ Y S 1748 EL EL EL M W S S

Continued

Table 12 continued

1749 1750 S 1751 LA M M+ M+ 1752 LA VS Y 1753 LA S 1754 W M S M 1755 S S M 1756 EL LA M 1757 LA M 1758 EL EL LA W M 1759 EL 1760 LA 1761 LA M S S S M? 1762 S 1763 S 1764 1765 S M+ M 1766 EL W M+ 1767 LA 1768 EL EL LA W M+ M 1769 EL EL M M+ Y 1770 EL EL EL VS Y 1771 EL 1772 M M M 1773 W M 1774 LA 1775 LA S 1776 LA W M+ S 1777 W M+ S 1778 LA W M+ S 1779 LA S 1780 S 1781 1782 W W VS S Y 1783 EL M VS S Y 1784 EL W VS 1785 LA W S M+ M+ M 1786 LA S S M+ M+ 1787 EL EL S 1788 LA VS 1789 EL EL LA W 1790 EL EL S VS 1791 EL EL VS VS VS VS Y S 1792 EL EL EL EL W VS 1793 EL M VS 1794 EL EL M M+ 1795 EL EL EL W M+ 1796 EL EL EL LA M+ 1797 LA M M+ 1798 EL LA W W

Continued

Table 12 continued

1799 EL EL EL EL S M 1800 EL EL 1801 LA VS 1802 VS S+ Y 1803 S S+ S+ S+ Y S 1804 EL EL W S+ S+ S+ S 1805 VS 1806 LA VS M M M Y 1807 S M M M 1808 LA VS 1809 LA S 1810 LA S M M 1811 S 1812 LA S M M+ M+ Y 1813 M 1814 LA W S S S M 1815 EL LA W 1816 LA W 1817 LA S M+ M+ M+ M 1818 LA M 1819 LA S M+ M+ M+ M 1820 S 1821 M M+ M+ M 1822 EL 1823 LA M Y 1824 M M S M+ Y M? 1825 EL EL EL M S 1826 EL 1827 EL EL S+ 1828 EL VS S+ VS VS 1829 EL W 1830 M M 1831 EL EL 1832 EL W M S+ M+ Y M 1833 EL EL EL S S+ Y 1834 EL LA 1835 EL M 1836 M 1837 LA S M S M+ Y M 1838 W S Y 1839 EL EL S 1840 W 1841 W 1842 1843 EL EL W 1844 EL M S+ VS S Y S? 1845 VS S+ VS S S? 1846 EL EL EL EL W VS S 1847 EL EL W S 1848 LA W W

Continued

Table 12 continued

1849 M 1850 EL EL LA W M S M W? 1851 EL M 1852 EL EL W M M W? 1853 EL VS M M Y 1854 W/M S M W? 1855 EL S 1856 M 1857 M W M+ M+ M M? 1858 EL M M+ M+ M 1859 LA M+ 1860 LA W VS M M M Y 1861 VS W? 1862 EL M M- M- W? 1863 LA VS 1864 EL EL EL W W S S+ S Y S? 1865 EL M M+ Y 1866 VS M M M+ M+ Y M? 1867 LA S M S+ M+ 1868 EL VS S M S+ M+ Y 1869 S+ 1870 VS 1871 EL LA VS S+ M S+ Y A 1872 EL EL LA M 1873 LA VS M+ 1874 EL LA W S M M+ 1875 S M 1876 EL EL W VS Y 1877 EL EL VS VS VS VS Y VS 1878 EL M W VS VS VS VS 1879 EL E 1880 EL EL VS M M+ M W? 1881 M M+ 1882 EL EL EL 1883 1884 EL W S+ M+ S+ S 1885 S M+ 1886 M 1887 EL VS W/M S M W? 1888 EL EL EL VS W/M S M W? 1889 W W/M S M 1890 S 1891 EL VS W VS M VS Y VS 1892 LA S 1893 LA VS 1894 EL E 1895 EL EL EL 1896 EL EL M W M+ M+ 1897 EL S M+ M+ M+ M 1898

Continued

Table 12 continued

1899 EL EL S S VS S Y S? 1900 EL VS S VS S 1901 EL EL EL S S+ Y 1902 EL EL VS M+ S+ M+ 1903 EL W 1904 EL EL W W S M- Y 1905 EL EL EL EL E W/M S M- Y 1906 EL EL W 1907 EL M M M+ M+ Y 1908 EL EL EL S 1909 VS 1910 LA VS M+ 1911 M S M+ M Y 1912 EL VS S M+ M 1913 VS S+ Y 1914 EL EL VS M+ S+ M+ 1915 EL VS S+ M+ Y 1916 S 1917 VS S M+ 1918 EL EL VS S W/M S+ M Y 1919 EL EL S W/M S+ M 1920 EL EL LA W S+ Y 1921 EL EL LA W 1922 S 1923 W M M M 1924 EL W 1925 EL EL S VS S VS Y 1926 EL EL E VS S VS 1927 EL EL 1928 EL 1929 M+ 1930 EL EL M W/M M+ M 1931 LA S W/M M+ M 1932 EL EL EL LA W S M+ S 1933 EL EL EL W 1934 EL W 1935 W 1936 EL EL 1937 EL EL EL W 1938 EL M 1939 EL EL EL EL M M+ M M+ Y 1940 EL EL VS S VS S 1941 EL EL EL E S VS S Y 1942 EL EL EL EL E 1943 EL W M+ M M+ 1944 EL EL EL EL S M 1945 EL EL EL W 1946 EL EL EL S 1947 EL EL EL W 1948 EL EL

Continued

Table 12 continued

1949 EL EL W 1950 EL EL EL E 1951 EL EL EL M M W/M M+ M- Y 1952 EL EL EL W M+ Y 1953 LA E M+ M+ M+ 1954 EL 1955 S 1956 EL EL EL EL VS 1957 EL EL EL EL S W S S S 1958 EL EL M M S S S 1959 EL W W 1960 W 1961 EL EL EL 1962 EL W 1963 EL EL W W 1964 EL S W 1965 EL EL EL S M+ S M+ Y 1966 EL EL EL EL M S Y 1967 EL EL EL EL W 1968 EL EL EL EL S W M- Y 1969 EL EL S M- M- 1970 EL M 1971 EL VS 1972 EL EL EL M W S S+ S Y 1973 EL EL EL LA W W S S+ S 1974 E VW Y 1975 EL S VW 1976 EL EL EL M M M M 1977 EL W M 1978 EL EL 1979 EL EL EL M W Y 1980 M W 1981 EL EL EL 1982 EL EL EL E VS VS VS Y 1983 EL EL E VS VS VS 1984 M 1985 W W 1986 EL M M 1987 EL EL EL VS M M M 1988 EL EL EL M 1989 EL EL VS 1990 EL EL VS VW 1991 EL EL EL VS 1992 EL EL EL EL VS 1993 EL EL EL EL 1994 EL EL EL EL 1995 EL S 1996 EL EL M 1997 EL W 1998 E 1999 LA 2000 S 2001 EL 2002 E 2003 2004 EL 2005 2006 2007

Appendix B: Determined El Niño and La Niña years on Quelccaya Ice Cap

100

Table 7: Quelccaya ENSO reconstruction. Shaded cells are verifiable by at least one other source.

Quelccaya El Niño Quelccaya La Niña 1512 1502 1520 1540 1521 1544 1522 1547 1524 1548 1527 1552 1530 1559 1550 1561 1565 1567 1569 1570 1582 1571 1583 1574 1593 1575 1594 1576 1608 1577 1609 1584 1633 1586 1634 1599 1646 1600 1655 1601 1660 1603 1682 1610 1690 1612 1693 1621 1708 1624 1712 1626 1721 1627 1722 1629 1723 1637 1728 1639 1747 1640 1748 1642 1783 1649 1792 1653

Continued 101

Table 13 continued

1793 1659 1799 1661 1804 1662 1825 1663 1828 1666 1833 1671 1835 1674 1843 1677 1846 1678 1855 1681 1865 1685 1877 1688 1887 1689 1895 1691 1899 1696 1904 1697 1905 1698 1906 1700 1912 1703 1914 1704 1924 1705 1925 1707 1926 1715 1936 1716 1937 1717 1938 1724 1939 1725 1940 1729 1941 1730 1942 1731 1944 1732 1956 1733 1957 1742 1964 1743 1965 1745

Continued

102

Table 13 continued

1966 1746 1967 1751 1968 1752 1988 1753 1990 1756 1992 1757 1993 1758 1994 1760 1995 1761 1996 1767 1997 1768 2001 1774 2004 1775 1776 1778 1779 1785 1786 1788 1789 1796 1797 1798 1801 1806 1808 1809 1810 1812 1814 1815 1816 1817 1818 1819 1823

Continued

103

Table 13 continued

1834 1837 1848 1850 1859 1860 1863 1867 1871 1872 1873 1874 1892 1893 1910 1920 1921 1931 1932 1953 1973 1999

104

Appendix C: Graphical Representation of Determined El Niño and La Niña years on

Quelccaya Ice Cap

105

Figure 33: Graphical representation of determined El Niño and La Niña years on Quelccaya Ice Cap. Red bars indicate El Niño events, while green bars indicate La Niña events. Events outlined in black are years verified by other reconstructions (this corresponds to the shaded values in Table 13 in Appendix B).

106

Figure 33 Continued

107

Figure 33 continued

Continued

108

Figure 33 continued

Continued

109

Figure 33 continued

Continued

110

Figure 33 continued

111