Recent Extreme Events in a Tropical Stalagmite

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Fall 2006 Recent extreme events in a tropical stalagmite: Multi-proxy records and analysis of ecosystem delta carbon-13 value sensitivity to weak climate forcing Amy E Benoit Frappier University of New Hampshire, Durham

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Recommended Citation Frappier, Amy E Benoit, "Recent extreme events in a tropical stalagmite: Multi-proxy records and analysis of ecosystem delta carbon-13 value sensitivity to weak climate forcing" (2006). Doctoral Dissertations. 338. https://scholars.unh.edu/dissertation/338

This Dissertation is brought to you for free and open access by the Student Scholarship at University of New Hampshire Scholars' Repository. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of University of New Hampshire Scholars' Repository. For more information, please contact [email protected]. RECENT EXTREME EVENTS IN A TROPICAL STALAGMITE:

MULTI-PROXY RECORDS AND

ANALYSIS OF ECOSYSTEM 513C VALUE SENSITIVITY TO WEAK CLIMATE FORCING

AMY E. BENOIT FRAPPIER

B.S. University of Maine, 1999

M.S. University of New Hampshire, 2002

DISSERTATION

Submitted to the University of New Hampshire

in Partial Fulfillment of

the Requirements for the Degree of

Doctor of Philosophy

Earth and Environmental Sciences

September, 2006

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. This dissertation has been examined and approved.

Dissertatiojj^jirector, Dr. Dork L. Sahagian, Professor of Earth & Environmental Sciences, Lehigh University

c XOM*. ______Co-Director, Dr. Cameron P. Wake, Research Associate Professor of Earth Sciences and Earth, Oceans, and Space

Associate Professor of Geology

Dr. Serita D. Frey, Assistant Professor of Soil MicrobiaTEcology

4 A u . j Dr. Luis A. Gonzalez, / ) Associate Professor, The University of Kansas

Date

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ACKNOWLEDGEMENTS

I would like to thank all the many people who helped make this research possible. First,

I must thank my intrepid field assistants, Brian Frappier - who found ATM7 - and Todd Belanger,

and my dependable laboratory assistants Heather Foley and Joshua Blye for many long hours in

the service of science. I also thank Luis Gonzalez, Scott Carpenter, Serita Frey, Mel Knorr, Ruth

Varner, Jeff Miriam, Brian Frappier, Terry Plank, Louise Bolge, Jeff Donnelly, and Jess

Tierney for their hospitality, help, and interest during my visits to their laboratories. Special

thanks go to Jacqui Aitkenhead-Peterson for allowing me to work under her international soil

importation permit, and to Bob Eckert for his kindness in lending me bench space and use of his

laboratory. I appreciate the sustained and last-minute assistance and support I have received from

University of New Hampshire’s Department of Earth Sciences, as well as the Climate Change

Research Center’s outstanding staff: Belinda Camire, Suki Easter, Jennifer Boles, Linda Tibbetts.

I will always remember fondly the companionship and reality checks of my fellow

graduate students, and will continue to treasure the wonderful friendships that grew up along the

way. My parents Paul & Joanne Benoit always enthusiastically supported me throughout my

education. This endeavor would have been impossible without Brian Frappier, and I am ever thank

ful to him for his invaluable dedication, clear perspective, and uncountable actions of support.

My committee members each supported me through this project in many ways. I thank

Wally Bothner for both mentorship and teamwork in College Teaching. Cameron Wake has also

been a special mentor to me, and I appreciate the many opportunities he has sent my way over the

years. Over the years, my advisor Dork Sahagian struck the right balance between freedom and

guidance, allowing me to become a well-rounded scientist. His buoyant optimism, unflagging

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. enthusiasm, and high spirits made laughter the custom as we navigated the shoals of graduate

school. His mentorship is sure to continue long after I launch my career.

The research presented in this dissertation was funded primarily by NASA’s Earth

System Science Fellowship Program with additional support from the U.S. National Science

Foundation, and the Inter-American Institute for Global Change Research.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. TABLE OF CONTENTS

ACKNOWLEDGEMENTS ...... iii

LIST OF TABLES...... viii

LIST OF FIGURES...... ix

ABSTRACT...... xi

CHAPTER PAGE

INTRODUCTION...... 1

I. A THREE-STAGE SCREENING SYSTEM TO SELECT STORM-SENSITIVE

STALAGMITES...... 3

Abstract...... 3

Introduction ...... 3

A Three-stage Screening System to Select Storm-sensitive Stalagmites ...... 7

Potential Problems: Region ...... 8

Potential problems: Cave Configuration and History ...... 10

Potential Problems: Stalagmite ...... 14

Lab Selection Procedures ...... 16

Discussion ...... 18

II. A STALAGMITE STABLE ISOTOPE RECORD OF RECENT TROPICAL CYCLONE

EVENTS...... 24

A bstract...... 24

Introduction ...... 25

Hurricane Stable Isotope Values and Cave Depositional Settings ...... 26

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Methods 27

Identifying and Dating Isotopic Excursions ...... 28

Results and Discussion ...... 28

Investigating Potential Indicators of Storm Intensity ...... 30

Conclusions ...... 31

III. EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS OF MULTIVARIATE

STALAGMITE TRACE ELEMENT DATA: DETECTING THE 1982 EL CHICHON

VOLCANIC ERUPTION AND OTHER ENVIRONMENTAL EXTREMES...... 38

Abstract...... 38

Introduction ...... 39

M ethods ...... 43

Data Analysis: LA-ICPMS ...... 45

Data Analysis: X RF ...... 46

R esults ...... 46

LA-ICPMS...... 46

Scanning XRF ...... 48

Discussion ...... 48

Conclusions ...... 53

IV. ROOTING OUT THE AMPLIFICATION MECHANISM LINKING A WEAK ENSO

TELECONNECTION SIGNAL AND A STRONG STALAGMITE CARBON ISOTOPE

RECORD ...... 63

Introduction ...... 63

M ethods ...... 68

R esults ...... 69

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Discussion ...... 71

Conclusions ...... 74

CONCLUSIONS...... 83

Future Directions ...... 86

APPENDICES...... I l l

APPENDIX A. CLIMATE, ECOLOGY, AND GEOLOGIC SETTING ...... 112

APPENDIX B. MICRO-MILLING AND STABLE ISOTOPE ANALYSIS...... 115

APPENDIX C. AGE MODEL...... 117

Radiometric Dating ...... 118

Layer Counting and Initial Age Model ...... 119

Age Model Changes ...... 120

Tests of the Updated Age Model ...... 122

APPENDIX D. PALEO-HURRICANE INTENSITY PROXY ANALYSIS ...... 125

APPENDIX E. TRACE ELEMENT DATA ...... 127

vii

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF TABLES

Table 1-1. Summary of Screening System Steps ...... 21

Table 2-1. Historical intensity, precipitation, and best track data for Atlantic tropical cyclones

(NOAA Tropical Prediction Center). Intensity categories represent the maximum intensity at or

prior to landfall in Central America, such that 0 indicates tropical storms, and values 1-5 indicate

Saffir-Simpson hurricane intensity categories 1-5. Total rainfall during storm days is reported

from the Central Farm meteorological station, less than 15 km from the cave site ...... 33

Table 4-1. Data Sources ...... 76

Table 4-2. SOI Lag Cross-Correlation Coefficients ...... 77

Table A l. Results of a multiple linear regression to investigate storm and speleothem factors

related to the signal size of measured 8lsO value excursions ...... 126

viii

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF FIGURES

Figure 1-1. Transmission of cyclogenic stable isotope signal in dual porosity system ...... 22

Figure 1-2. Conceptual model of the effect of epikarst thickness on speleothem sensitivity to

brief storm events ...... 23

Figure 2-1. Location map indicating the stalagmite collection site, cave Actun Tunichil Muknal

(star) and nearby tropical cyclone storm tracks (1978 - 2001) ...... 34

Figure 2-2. Photographs of stalagmite ATM7 showing depth of radiometric dating samples and

micro-milling track ...... 35

Figure 2-3. Recent tropical cyclones near Belize with stalagmite 5180 timeseries, 813C timeseries

with SOI, and storm proxy model results ...... 36

Figure 2-4. For locally active hurricane seasons, S18Ovalue detail across corresponding annual

couplets are shown to highlight cyclogenic excursions (A-K) and background isotopic

variability ...... 37

Figure 3-1. AVHRR satellite imagery showing atmospheric transport of El Chichon tephra

imaged on April 5, 1982 ...... 56

Figure 3-2. Photographs of stalagmite ATM7, and trace element sampling tracks ...... 57

Figure 3-3. Chart of LA-ICPMS EOF factor loadings for the first three EOF modes ...... 58

Figure 3-4. Timeseries plot of LA-ICPMS variance in the first three EOF modes ...... 59

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-5. ATM7 Scanning XRF track and EOF results ...... 60

Figure 3-6. Monthly weather observations at Central Farm, Belize, showing water balance

(difference between precipitation and pan evaporation) in gray, and mean maximum temperature

in black. Two hydrological years are highlighted: a veiy wet year 1979 (A), and a slightly dry

year 1981 (B) ...... 61

Figure 3-7. LA-ICPMS EOF results for quiet latter period (1984 - 2001) ...... 62

Figure 4-1. ATM7 carbon isotope record and Southern Oscillation Index (SOI) ...... 81

Figure 4-2. Timeline of Data Sources. Abbreviations as in Table 1 ...... 82

Figure A l. Belize Climatology (Central Farm Meteorological Station, 1973-2005) ...... 113

Figure A2. Seasonality of Belize Hurricanes (1883 -2005) ...... 113

Figure A3. ATM7 137Cs activity profile ...... 119

Figure A4. ATM7 LA-ICPMS species concentrationsATM7 LA-ICPMS species concentrations

(counts per second normalized toCa) ...... 128

Figure A5. ATM7 LA-ICPMS data comparison with first three EOF modes (y-axis). EOF

modes are plotted as loading factors; P31, Na23, and Sr88 are plotted as counts per second

normalized to Ca ...... 133

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ABSTRACT

RECENT EXTREME EVENTS IN A TROPICAL STALAGMITE: MULTI-PROXY

RECORDS AND ANALYSIS OF ECOSYSTEM 513C VALUE SENSITIVITY TO WEAK

CLIMATE FORCING

Amy E. Benoit Frappier

University of New Hampshire, September, 2006

Speleothems are emerging as important and detailed chronological records of environmental

change. Integrating exploration of modem speleothem records of environmental variability,

related forcing factors, and proxy biogeochemical characteristics reveals gaps in current

understanding and suggests new potential proxies. This dissertation demonstrates the potential

for developing novel proxy records of past regional environmental extremes, such as tropical

cyclones, explosive volcanism, and enhanced seasonal contrasts from speleothems that are

sensitive to transient infiltration events through very high-resolution, multi-parameter analysis of

a rapidly growing, fracture-fed tropical stalagmite. A series of sample screening steps were

developed prior to stalagmite collection in the field in order to increase the likelihood of

collecting suitable samples sensitive to tropical cyclone precipitation events. Once the correct

annual dating was established for a recent period (2001-1978), a weekly-monthly record of

stalagmite stable isotope ratios showed low stable oxygen isotope excursions that corresponded to

the historical record of tropical storm strikes in the region. Furthermore, excursion amplitude was

related to the maximum intensity of storms prior to landfall. The same stalagmite contains a trace

element record of the El Chichon eruption, validated by both Laser Ablation Inductively Coupled

Plasma Mass Spectrometry (LA-ICPMS) and Scanning X-ray Fluorescence (S-XRF). A

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. dominant, broad spectrum trace element perturbation was recorded by both methods at 1982,

coincident with a major explosive eruption of the nearby trachy-andesite El Chichon volcano in

Chiapas, Mexico in April of that year. This result demonstrates the ability of stalagmites to

record proxy evidence of a major regional tephra fallout event. The LA-ICPMS technique

showed greater discriminating power between volcanic and extensive rainfall signals. A lag

correlation analysis of weak El Nino forcing in Belize using a large suite of meteorological and

satellite datasets required removal of seasonal variance in order to detect any El Nino response.

Individual variables responded independently to El Nino, and no coherent lag timescale was

evident. Analysis of biological factors including the belowground community will be required to

assess the non-linear processes linking small changes in temperature and moisture extremes to

large carbon isotope variations. Promising new proxies for tropical cyclone activity and low-

latitude explosive volcanism may emerge from this work.

xii

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. INTRODUCTION

As Earth’s climate system comes into better focus, it is increasingly clear that solutions to

many outstanding scientific problems are predicated on a more complete understanding of

variability and feedback mechanisms within the Earth system. Of particular concern is the fact

that climate models that simulate the “correct” recent climate for incorrect reasons may yield an

inaccurate view of future behavior of the climate system. Potentially critical feedback effects are

not now included in global circulation models, such as the tropical cyclone effect on ocean

mixing and heat transport (Emanuel, 2002). Low-latitude volcanic forcing produces different

effects than high-latitude eruptions, yet tropical volcanic activity is not well constrained. Proxy

records that constrain such feedbacks, forcings, and variability are positioned to advance the state

of the science.

However, not all proxy records are equally well-positioned to improve current

understanding of climate system dynamics. When seen through the lens of paleoclimatology, our

view of past climate system behavior is inverted with respect to the modem climate system

viewed through the lens of surface- and satellite-based observational networks. Our view of pre

historic climate is dominated by annual to millennial-scale marine and polar perspectives, but our

view of modern climate is dominated by synoptic terrestrial meteorological observations from the

tropical regions and northern hemisphere mid-latitudes. Similarly, the understanding of current

climate variability has been shaped primarily by daily land-based observations; mobile shipboard

observations are relatively less common. In contrast, the current understanding of paleoclimate

variability has been shaped primarily by low-resolution marine sediment cores, plus a handful of

high-resolution ice core proxy records from the polar and high altitude regions. Only very

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. recently has global satellite remote sensing technology provided comprehensive coverage of

many environmental parameters of interest. Modem and paleo perspectives on the climate

system can be brought into better alignment by generating multi-proxy paleoclimate records with

annual or better temporal resolution from terrestrial lowlands, particularly in the tropical and sub

tropical regions (Briffa et al., 1995; Crowley, 2000; Jones et al., 2001; Mann et al., 1999).

Stalagmites are rapidly emerging as sources of high quality proxy records of low-latitude

terrestrial environmental change (Bertaux et al., 2002; Bums et al., 2002; Lachniet et al., 2004a;

Lachniet et al., 2004b; Lundblad and Holmgren, 2005; Wang et al., 2001; Yadava and Ramesh,

2005). A number of advantages make speleothems particularly sought after, including spatial

distribution, relative ease of precise radiometric dating, high temporal resolution, and duration of

record. Stalagmites are able to record seasonal and sub-seasonal variations over long periods of

time, like small paleo-weather stations (Bertaux et al., 2002; Brook et al., 1999; Fairchild et al.,

2001; Fairchild et al., 2000; Frappier et al., 2002a; Frisia et al., 2003; Hellstrom and McCulloch,

2000; Hou et al., 2002; Huang et al., 2001; Ihlenfeld et al., 2003; Kong et al., 2003; McMillan et

al., 2005; Pan, 1999; Qin et al., 2000; Roberts et al., 1998; Treble et al., 2003; Treble et al., 2005;

Treble et al., 2002). Many different signals can be measured in the same samples, providing a

rich view of climatic and biogeochemical events and trends over time. Stalagmites are thus

increasingly viewed as the “ice cores” of the tropical lowlands.

However, stalagmite proxy interpretation is still maturing. The complex biogeochemical

dynamics of caves are less well understood compared to many other more established, simpler

and more direct proxy systems. Despite relatively early recognition of their potential value

(Bogli, 1980; Broecker et al., 1960; Dreybrodt, 1980, 1981; Harmon et al., 1979; Hendy, 1971;

Pan, 1999), development of speleothem proxies was delayed in part by the complexity of the

spelean environment. Cave dripwater delivery rates often vary across a range of timescales, and

the chemical and isotopic composition of cave seepage water is spatially and temporally variable.

Extension rates are often slow, requiring very high-resolution analytical procedures and dating

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. techniques to obtain better-than-decadal sampling. As speleothem exploration and modem

calibration studies have expanded, understanding of speleothem depositional processes and links

to the epikarst and surface environment have also grown (Ayalon et al., 1998; Baker and

Brunsdon, 2003; Baskaran and Krishnamurthy, 1993; Dorale et al., 1992; Fairchild et al., 2000;

Frisia et al., 2000; Genty et al., 2001; Johnson and Ingram, 2004; Kaufmann, 2003; Kaufmann

and Dreybrodt, 2004; McMillan et al., 2005; Railsback et al., 1994; Sancho et al., 2004; Swart et

al., 1991; Tooth and Fairchild, 2003; Zhang et al., 2004). Interpretation of carbon isotope ratios

and trace element variations in speleothems remains especially problematic (Fairchild et al.,

2006; McDermott, 2004; Mickler et al., 2004a). In particular, higher-resolution studies show

great promise for uncovering indicators that enable investigators to distinguish between various

controls on proxy variations, delimiting cases in which various controls cannot be distinguished,

and constraining sensitivity to various forcing factors.

Integrated studies of speleothem stratigraphic records, environmental forcing and extreme

events, as well as forest-soil-cave biogeochemical dynamics, are poised to expand the range of

questions that speleothem records can address. Each of the following chapters addresses a

different aspect of an integrated investigation of the relations between sub-annual to decadal

proxy records from a tropical stalagmite and the involvement of forcing factors and cave-forest

biogeochemical system. Two studies describe the development of new high-resolution modem

calibration-based proxies of environmental extremes, and two studies investigate the importance

of environmental forcing and epikarst channel filter characteristics to the resulting stalagmite

proxy records. Together, this work points toward a number of new directions for ongoing

research on speleothem proxy systematics and paleoenvironmental variability. It is anticipated

that high-resolution speleothem records will contribute valuable new insights to paleoclimatology

and Earth systems science.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER I

A THREE-STAGE SCREENING SYSTEM TO SELECT STORM-SENSITIVE

STALAGMITES

Abstract

We describe the field and laboratory speleothem screening process applied by Frappier

and co-workers in a successful effort to select a stalagmite suitable for high resolution stable

isotope paleotempestology. Field and laboratory criteria were combined in series, to screen out

candidates whose characteristics indicated lower sensitivity. Together, this screening system is

expected to increase the likelihood that the selected stalagmite would contain large, measurable

stable isotope anomalies related to tropical cyclone precipitation. This particular screening

process is an important methodological consideration for researchers attempting to replicate the

original speleothem stable isotope paleotempestology study, or to apply the technique elsewhere.

Furthermore, the overall approach to stalagmite selection presented here can be adapted readily

by other investigators in support of different scientific goals.

Introduction

Paleo-environmental research is based upon connecting the impact of environmental factors of

interest with measurable properties of a particular proxy archive. Any proxy signal of interest to

paleo-environmental investigators is transmitted to the proxy record through a channel

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. consisting of a series of intervening processes (Brown, 1987). The conduit pathway serves as a

filter that can amplify, attenuate, interfere with, add noise to, eliminate, and/or simply the original

environmental signals. A scientific instrument (in this case, a proxy) can provide an unobstructed

view of the items acting on it despite the characteristics of the proxy archive itself and related

channel attributes (Dretske 1981, in Brown, 1987). The common process of instrument

calibration is a physical application of this concept. Proxy signals are most reliable where the

translating channel is simple and stable with a high signal to noise ratio. The resulting proxy

transfer functions are valid so long as the channel remains unchanged through time, facilitating

reliable paleo-environmental reconstruction. The assumption of system stationarity is central to

paleoclimate reconstruction in general. Replication studies of some proxy systems have

established broad applicability of proxy signal-channel transfer functions across a range of

locations and ages (Cuffey and Steig, 1998; Grootes et al., 1993; Mann, 2002). On the other

hand, rare but valuable quantitative studies of interruptions and distortions found in the proxy

record (Cuffey et al., 1994; Lohmann, 1987; Lohmann, 1988; Shackleton and Opdyke, 1973;

Spero et al., 1997) have illuminated the understanding of “ancient mechanisms of environmental

change, metabolism, or diagenesis encoded in proxy signals” (Weedon, 2003).

Multi-proxy paleo-environmental archives from secondary carbonate mineral deposits in

caves, or speleothems, are being developed rapidly (Fairchild et al., 2006; McDermott, 2004). In

cave depositional settings, the signal channel is in part, quite literally a conduit that transmits

seepage water along pathways from the surface to the cave. As a result of spatial heterogeneities

in conduit hydrology, bedrock, surface topography, soil properties, ecosystem structure, cave

geometry and microclimate, and seepage water chemical composition, each conduit has a unique

combination of vadose/kinetic processes that has the potential to induce considerable variations

between speleothem records (Dorale et al., 2002b). A replication test should result in essentially

identical proxy records only in the special case where differences in kinetic/vadose zone

processes, or “noise”, are negligible relative to the proxy signal of interest (Dorale et al., 2002b).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Replication of proxy records thus lends confidence to interpretation as indicative of primary

environmental variables of interest (Wang et al., 2001).

Existing replication studies have found both congruence and idiosyncrasy in speleothem

records. Similar stalagmites cannot always be identified in every cave, and speleothem

replication has often been limited by practical, ethical, and resource considerations (Lundblad and

Holmgren, 2005). In the field, speleothem collection is typically subject to severe restrictions

from conservation ethics, consideration of cave aesthetics, and/or permits. The quality of internal

speleothem stratigraphy cannot generally be determined in the field, with the inevitable result that

some collected samples turn out to be useless for paleo-environmental studies. Furthermore, age

differences often play a role when the target of the study is ancient, inactive speleothems. It is

not unusual for the few promising samples remaining in a collection to differ widely in age,

making replication impossible without additional field sampling.

Even when fairly well-replicated records are present, contemporary stalagmite records

from the same cave also can reveal different proxy signals. Lack of convergence has been taken

by some as a sign that speleothems may be unreliable for paleo-environmental studies (e.g.

Betancourt et al., 2002). In this view, individual stalagmites may have rather idiosyncratic

sensitivities, and replication of paleo-environmental records is hindered by the very complexity of

the spelean depositional environment.

Yet, a growing number of studies have derived valuable insights into paleo-

environmental change from attributing differences between speleothem records to surface or

vadose zone effects. In some cases, proxy record differences can be attributed to field

relationships such different hill slope aspect above the chambers where stalagmites were collected

(Denniston et al., 1999). Denniston and colleagues contrasted two stalagmite records, utilizing

the water balance differences between stalagmites from cave chambers with different surface

aspect in order to distinguish different climatic regimes. Genty and co-workers found that soil

richness (or soil respiration rate) controlled 513C value amplitude in a suite of speleothems from

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. France (2003). Another group tracked the relations between precipitation and cave dripwater

stable isotope composition, finding that two types of vadose pathways feeding different cave

drips were sensitive to different aspects of weather variability (Ayalon et al., 1998). Other studies

have found dripwater variability related to land-use differences above a cave system, seasonal

cave ventilation shifts, soil composition, and distance from cave entrance (Baker, 2002; Baker

and Brunsdon, 2003; Musgrove and Banner, 2004; Spotl et al., 2005). In other cases, non

equilibrium factors have been implicated in the origin of idiosyncrasies in individual speleothem

records that diverge from signals common to other formations (Mickler et al., 2004a). Thus,

detailed observations of field relations and/or modern cave system process studies provide critical

aids to proxy interpretation.

As the biogeochemical factors driving stalagmite differences become better understood,

this diversity of sensitivity may turn out to be a great strength, when detailed field observations

are available. Failure of the replication test can thus indicate less desirable stalagmite types to

avoid in future analysis. Reports relating field conditions to speleothem characteristics provide a

means for distinguishing between stalagmite types that have conduits sensitive to different

aspects of paleoclimate variability (Ayalon et al., 1999; Ayalon et al., 1998; Tooth and Fairchild,

2003). Different kinds of speleothems thus provide different perspectives on the same history,

much the same as different species, lake types, and glaciers have been found to exhibit

sensitivities to particular environmental factors. In this view, the observed variation between

stalagmites presents an opportunity for more precise paleoclimatology - if each type can be

identified unambiguously.

Indeed, one expects formations that share specific signal properties to share certain

related channel characteristics. If strong links can be established between field characteristics and

proxy signal outcomes through cave monitoring and experimental studies, then more efficient

speleothem sample collection would advance the scientific value of speleothem proxy records.

Such information can be applied in the field in order to 1) avoid collecting unsuitable samples for

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. investigating the environmental variable of interest, and 2) enhance sensitivity of the target proxy

record to the phenomena of interest given the practical limitations of sampling and analytical

techniques. Similarly, using non-destructive or minimally destructive methods to target

sectioning and analytical foci can be particularly useful in the pre- and post-collection phase

(Mickler et al., 2004a; Mickler et al., 2004b). The following section describes a three-stage

stalagmite screening system that was used successfully to select a stalagmite sensitive to tropical

cyclone precipitation events.

A Three-stage Screening System to Select Storm-sensitive Stalagmites

We wished to develop a screening system to select, from the large number of stalagmites

available in the field, a small number of samples suitable for analysis as the basis for a paleo-

hurricane research study. Here, a case study is presented that outlines a three-stage screening

method used to select stalagmites used to develop a proxy record of individual tropical cyclone

rainfall events (Chapter 2). The stepped stalagmite screening approach presented here is a

variation on one commonly applied by paleoclimatologists to most expeditiously meet scientific

objectives, in which field sites are screened prior to and during fieldwork, and collected materials

are further screened in the laboratory. Prior to sample collection, a series of stalagmite screening

steps were designed to maximize the likelihood of recovering samples containing tropical cyclone

proxy signals that would be measurable using available analytical techniques. In developing the

sample screening system, related studies were used to link 1) the tropical cyclone precipitation

signal to seepage water, 2) the cave-epikarst system to seepage water chemistry, and 3) the cave-

epikarst system to speleothem proxy records. The result of this study (see Chapter 2) supports the

importance of the screening system described below for speleothem paleotempestology

applications.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. On the basis of existing research linking field relations to cave hydrology and spelean

geochemistry, a series of factors were developed that should have increased the likelihood of

recovering samples containing the target signal, in this case the 8lsO anomaly of tropical cyclone

precipitation. The screening approach was chosen to filter out candidate stalagmites with lower

sensitivity. We screened candidates at three steps: karst regions, cave sites, and finally individual

stalagmites, both in the field and after initial analytical assessment. A sequential list of contra-

indicators was developed in order to reject less-qualified candidates, and candidates that passed

each stage were further subjected to the next step in the screening process. Using the

considerations described below as a guide, I selected a region, cave, and stalagmite that resulted

in a proxy record that in fact contained the target hurricane proxy records (Chapter 6).

A number of factors were recognized that could affect our ability to resolve the expected

tropical cyclone precipitation signal given the limitations of our selected analytical methods. For

each problem, we developed criteria to reject less promising candidates, described below. To

guide site and sample selection, the potential problems and criteria for each screening step was

organized in three practical stages: region, cave, and stalagmite (Table 1-1).

Potential Problems: Region

1. The most basic issue is that a speleothem proxy for tropical cyclone events can only be

found where caves and hurricanes co-exist. Thus, the first consideration was to reject karst

regions outside the hurricane belt. We favored karst regions in areas where tropical

cyclone strikes were high during recent decades, such as Florida, the Yucatan Peninsula,

Puerto Rico, Hispaniola, and Cuba.

2. We expected proxy signals from individual hurricane precipitation events to persist only

briefly. Earlier studies that analyzed speleothems at seasonal to annual resolution were

with permission of the copyright owner. Further reproduction prohibited without permission. unable to resolve individual storm signals (Malmquist, 1997; Schwehr, 1998). Tropical

cyclones can precipitate a week to a month’s equivalent rainfall. In order to meet our

target calcite micro-sampling rate of 50 samples per year with the available 20 micron

sampling technique, we required a relatively rapid average stalagmite extension rate of

~lmm yr'1. The relatively rapid chemical weathering rates in the tropics increased the

likelihood of collecting rapidly growing stalagmites from lower latitude caves as compared

to karst regions at higher latitude. Furthermore, snow has a low stable isotope composition

similar to tropical cyclone precipitation (Lawrence and Gedzelman, 1998). Avoiding

temperate zone karst regions also enabled us to avoid potential confusion between the

tropical cyclone and wintertime precipitation. On this basis, we rejected karst regions in

the temperate zone for our initial study.

3. We required stalagmites with annual layering in order to accurately date stable isotope

variations. A seasonal water deficit seems to contribute to annual calcite banding; thus we

rejected stalagmites from regions without a dry season.

4. Stormwater from within the rain shield of a tropical cyclone has a distinct isotopic

signature. A study of the isotopic composition of summer precipitation in Texas showed

that the precipitation stable isotope anomaly produced by tropical cyclones is large (~6 %o)

relative to the analytical precision of modem mass spectrometry (<0.1 %o) (Lawrence,

1998; Lawrence and Gedzelman, 1996; Lawrence et al., 2002; Lawrence et al., 1998). The

amount of SlsO value variation from tropical cyclones is comparable to typical seasonal

variability in tropical precipitation in Central America, where data has been collected

(Lachniet and Patterson, 2002). Using stoichiometric reasoning based upon speleothem

calcite precipitation equations (Hendy, 1971), the calcite 5lsO anomaly produced by a -6% o

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. precipitation anomaly would be about -2%o, still easily resolved by a Kiel carbonate device

and Finnigan MAT252 gas source stable isotope mass spectrometer. However,

transmission of tropical cyclone water to the cave could be affected by rainfall amount and

stormwater infiltration. David Malmquist (personal communication) indicated that tropical

cyclones typically produced very little precipitation on the low-lying island of Bermuda.

We determined that a record of tropical cyclone precipitation events was more likely in

regions where storm rainfall totals are large, i.e. areas of orographic relief (Roe, 2005).

5. Soil thickness is an important factor affecting the chemical and isotopic composition of

infiltrating groundwater. Meteoric water passing through thicker soils is more altered than

water that has passed through only thin soil cover. The soils of most limestone regions of

Florida, Central America, and the Caribbean are relatively thin; thus, these regions are

good candidates for high-resolution isotopic investigations (Ford and Williams, 1989).

We selected karst regions for fieldwork by combining the above criteria with the list of

areas for which we could obtain access and speleothem collection permits as well as export

permits. Using these criteria, we selected the Boundary Fault Karst region in central Belize for

our initial project. The region has a distinct dry season, is located in the foothills of the Maya

mountains, many large caves, and a historical tropical cyclone recurrence interval of about 3

years.

Potential problems: Cave Configuration and History

6. Tropical cyclone water infiltration and the soil buffer. For our purposes, where sufficient

precipitation is generated, stormwater must infiltrate in sufficient quantity to produce a

measurable calcite layer on speleothems. The clay-rich soils typical in karst regions often

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. transmit water slowly and reduce infiltration. However, soils are thinner in upland areas

with steep slopes (Brenner, 1978). We determined that a record of tropical cyclone

precipitation events was more likely in caves under thin soils, rejecting caves in low-lying

areas with low slope angles.

7. Cave entrance effects. Although deep cave environments are very stable, light levels,

relative humidity, air motion, and temperature may vary considerably near the cave

entrance (Mickler et al., 2004a; Spotl et al., 2005). Variations in relative humidity and

temperature in the cave can lead to kinetic non-equilibrium effects that interfere with the

speleothem record of stable isotopic composition of seepage water (Hendy, 1971). In

general, cave environments are stable deeper than 10m below the surface and greater than

50 m from entrances; however, this can vary with regional climate and cave morphology

(Hill and Forti, 1997). Large caves with interior rooms were thus preferred, and small

caves were rejected to avoid stalagmites sensitive to unstable cave environmental

conditions.

8. Flooding. Caves and cave rooms subject to flooding are generally avoided for speleothem

paleo-environmental studies (Hill and Forti, 1997). Abrasion and corrosion from

floodwaters can erode previously deposited calcite. Even when physical damage is

minimal, no dripwater deposition can take place while formations are submerged. After

floodwaters drain away, deposition may be further delayed until mud and debris are

washed from the speleothem surface. Floodwaters can also deliver residual clays with high

Th concentrations to stalagmite surfaces, making U-Th dating more difficult. Flood

evidence is often easily recognized in the field, including mud layers, ripple marks,

fragments of carbonate and organic debris draped over surfaces, horizontal bathtub-like

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. rings on the walls, and objects lodged into crevices by flowing water. We avoided any

caves and cave rooms with evidence of recent flooding.

9. Evaporation and wind. High humidity inhibits evaporation in cave chambers and promotes

isotopic equilibrium calcite deposition (Hendy, 1972). Internal cave rooms located far

from entrances, and with standing water represent favorable humidity conditions. A

hygrometer was used to quantitatively measure relative humidity in candidate caves. We

planned to reject cave rooms with relative humidity < 85-90%. However, this criterion

was of limited use in this case because such dry conditions were not encountered in any

candidate caves during this project. Similarly, air currents in caves can increase

evaporation and related kinetic effects in speleothem records (Hill and Forti, 1997).

Constricted passageways often inhibit air flow. Air currents may not be apparent to cave

visitors if they appear only seasonally, at a certain time of day, or associated with particular

weather patterns (Spotl et al., 2005). However, the presence of air currents that

substantially affect calcite deposition can be recognized often in the field (Hill and Forti,

1997). Although we felt no air movement in most caves, we avoided sampling in cave

rooms with speleothem evidence of air currents, particularly asymmetric forms (Hill and

Forti, 1997).

10. Vandalism. Although we did not have to reject any caves on this basis, we also planned to

avoid caves with substantial vandalism, as this may have reduced the number of available

stalagmites.

11. Epikarst conduit hydrology. We also wished to avoid two problems related to hydrologic

pathways through the epikarst. If stormwater travels a short distance through large

conduits and reaches the cave too quickly, it can be undersaturated with respect to calcite,

leaving behind no measurable stable isotope record. On the other hand, while stormwater

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. that takes a long and circuitous route through the bedrock’s primary pore network will

certainly deposit spelean calcite upon reaching the cave, the stormwater will be extensively

homogenized with other vadose groundwaters (Ayalon et ah, 1998). In this case, the

stormwater will leave behind a highly attenuated stable isotope record that lacks individual

storm events, but that nevertheless reflects long-term storm activity. Where feasible, it is

desirable to obtain long-term seepage water monitoring data from several candidate drip

sites prior to speleothem collection. However, in many instances, dripwater monitoring

data is not obtainable prior to collection because of funding limitations and the logistical

complications that arise from working in remote cave areas. Because long-term seepage

water monitoring data was neither available nor practical in this study, a field observations-

based approach (See #10 and 13, below) was designed to select stalagmites with seepage

pathways that that avoid both extremes.

12. Conduit hydrologic pathway: Calcite undersaturation. Following a significant storm event,

the increased height of the groundwater column is expected to cause cave drip rates to

increase within hours. A measurable tropical cyclone precipitation signal requires that the

storm water percolate slowly enough to reach supersaturation with respect to calcite before

reaching the cave. Drips that are undersaturated during periods of high flow (such as storm

events) can dissolve calcite in the top of previously-deposited stalagmites, forming a drill

hole morphology (Hill and Forti, 1997). While exploring a number of candidate caves in

the field, we noted that shallower caves with thin limestone roofs were more likely to

contain stalagmites with drill cups, whereas this morphology was absent in deeper caves.

The correlation between epikarst bedrock thickness and conduit infiltration time is not

absolute. Nevertheless, in general, as cave overburden thickness increases, seepage water

residence time and calcite saturation state both increase, and waters infiltrating from

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. different precipitation events and seasons becomes increasingly homogenized (Ayalon et

al., 1998). Note that the issue of epikarst thickness is separate from that of soil thickness,

described above. The presence of stalagmite drill cups indicated undesirable shallow caves

where dripwater was likely to be corrosive during periods of high flow, such as hurricane

events. However, it is important to distinguish between drill holes caused by

undersaturated drips and splash cups related to the height of the drop and rate of the drip

(Fig. 1-1).

We used the above criteria to select the cave Actun Tunichil Muknal for sample

collection. The air in the upper level cavern where speleothems were collected for this summer

had a temperature of 25°C and relative humidity of -90%. Stalagmites are ideally located far

from cave entrances in rooms with restricted air-flow to avoid associated complications in

mineral deposition and isotopic fractionation (Hill and Forti, 1997). An advantage is that this

cave site has been mapped, and has been visited regularly for the past several years, so there is

some local knowledge of environmental variability.

Potential Problems: Stalagmite

Only stalagmites were considered for this study. Speleothems appear in many forms, but

stalagmites are preferred for paleo-environmental studies because of their relatively simple

stratigraphy (Hill and Forti, 1997). Although a stalagmite’s internal growth form cannot

generally be assessed in the field, the pool of available stalagmites can be reduced somewhat

through observations of external characteristics.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 13. Irregular growth form. A stable growth form tends to result from a stable seepage conduit.

Highly asymmetrical forms may indicate air motion or an unstable drip location that could

affect the proxy record. Stalagmites with irregular form are to be avoided.

14. Inactive stalagmites. Ancient stalagmites were not the target of this modem test-of-

concept study. Stalagmites without wet surfaces indicating active formation were excluded

due to interest in recovering modem material deposited during a period for which the

history of storm strikes is known.

15. Conduit hydrologic pathway: stormwater homogenization. A measurable tropical cyclone

precipitation signal also requires that the storm water reach the cave as a coherent slug of

isotopically distinct water. Limestone bedrock usually has “double porosity” hydrology,

meaning that some groundwater travels relatively rapidly through fractures, while the rest

moves slowly and circuitously through unfractured permeable bedrock (Ford and Williams,

1989). Different residence times and different dripwater conduit pathways produce

differences in the isotopic composition of the groundwater. Ayalon et al. (1998) found that

fast-drip water that entered a cave through fractures (after brief bedrock residence times)

retained isotopic identity related to the source precipitation. In contrast, slow-drip cave

water (with longer residence times) had a more homogenous isotopic composition, related

to the average accumulated seasonal stable isotope values. In an Israeli cave system,

mixing of water derived from these various sources within the top 40-50 m of the vadose

zone determines the isotopic composition of the groundwater reservoir (Ayalon et al.,

1998). Fracture-fed stalagmites are thus more likely to be sensitive to brief storm events

(Fig. 1-2). Fortunately, recognizing fracture-fed stalagmites is a relatively simple matter in

the field. Visible lineation traces on the ceiling and linear arrays of stalactites and/or

stalagmites indicated the presence of fractures. We rejected stalagmites without evidence

with permission of the copyright owner. Further reproduction prohibited without permission. of a fracture source for seepage water. Of the fracture-fed stalagmites available, those with

relatively slow drip rates were less desirable, indicating a less direct hydrologic connection

to the surface.

16. Dirty calcite. As discussed in the section on flooding, above, clays can contaminate calcite

with excess initial Th. So-called “dirty calcite” is more difficult to date precisely using U-

Th techniques. The presence of clays can often color speleothem calcite. Although many

other impurities can also affect calcite color, white stalagmites are thought to be less likely

to contain problematic clays.

17. Re-crystallization. Smaller crystals are more desirable than large crystals, which can

indicate re-crystallization or weak alteration of the speleothem (Kendall and Broughton,

1978).

For this study, six stalagmites were chosen on the basis of morphology, mineralogy, crystal

texture, drip water source, location in cave, soundness of structure, and effect of harvesting on

cave aesthetics. Of the six candidates that were collected, these were screened using simple

laboratory tests to select one candidate for micromilling and high-resolution stable isotope

analysis.

Lab Selection Procedures

18. Mineralogy. X-ray Diffraction (XRD) was used to detect the presence of aragonite, whose

stable isotope systematics differ from that of calcite. The chemical composition and

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. mineral structure of powdered stalagmite samples was determined by performing X-ray

diffraction using a Siemens D 5000 powder X-ray diffractometer. Powdered samples were

adhered to a quartz mounting plate using petroleum jelly. Each sample was rotated

through an appropriate 2-theta angle. The resulting XRD profiles were compared to

spectra from samples of known composition to test for mineralogical composition. In this

case, mineralogy was not very helpful in selecting formations because all speleothems

collected were composed entirely of calcite.

19. Stratigraphy. After stalagmites were halved and polished, the stratigraphy was described.

Speleothems with obvious hiatuses, or irregular growth form were not good candidates for

micro-milling.

20. Equilibrium test. Hendy tests were performed to screen for stalagmites deposited outside

of isotopic equilibrium with their source fluid (Hendy, 1971), and although the efficacy of

this test has been called into question (Mickler et al., 2004a), the test is likely valid in the

case of the ultimately selected stalagmite ATM-7 because of its wide bands, a result of

relatively rapid recent growth (see below).

21. Growth rate. Radiometric dating was used to measure the vertical extension rate of the

stalagmites. Because our samples were modem, we chose to analyze I37Cs and 210Pb.

Stalagmites with higher growth rates were preferred in order to maximize the temporal

sampling rate we could achieve with the available microsampling system.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Stalagmite ATM7 was selected for high-resolution analysis. This stalagmite had the

highest drip rate in the field, and was located in a relatively inaccessible section of the upper

passage. ATM7 had smooth and relatively even layering, compact calcite with small crystals,

and a rapid growth rate in the most recent few centimeters of deposition, making it an excellent

candidate for our pilot study. Table 1-1 summarizes the screening system presented in this case

study.

Discussion

The first stalagmite selected using this screening system and analyzed at appropriate

temporal resolution successfully recorded recent tropical cyclone precipitation stable isotope

signals (Chapter 2). This initial success suggests that this screening approach is an important part

of the speleothem paleotempestology tool presented in Chapter 2. Not all stalagmites have the

appropriate characteristics to record tropical cyclone precipitation amount. Although the

stalagmite screening system presented here remains to be validated for different stalagmites and

cave sites, we strongly suggest that future efforts to replicate or apply this method for

paleotempestology should include the appropriate screening steps described above. In the future,

we hope to replicate this work using another stalagmite selected from the same cave site, Actun

Tunichil Muknal in central Belize.

More generally, while replication is a strong indicator of excellent proxy records (Dorale

et al., 2002b), it requires collecting a set of similar stalagmites. Some speleothem records with

different intrinsic sensitivities have the potential to contribute valuable new perspectives to paleo-

environmental studies. Applying explicit field and laboratory sample screening may improve the

odds of collecting stalagmites with sensitivity to the desired environmental variable(s), and

advance replication efforts. To achieve different scientific goals, other screening procedures

could be applied to target an appropriate subset of stalagmites.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Furthermore, field observations can be critical to later interpretation of speleothem proxy

records, and it is difficult to over-emphasize the importance of field documentation. In some

cases, the overall physical configuration of the cave site, its history and climate may be relatively

easy to determine after the fact. However, careful attention should be paid in the field to

describing the exact speleothem location and micro-environment in detail and broad-brush,

because some observations (such as the rate of water flow on different sides of a stalagmite, any

water contributions from adjacent drips) cannot be easily re-constructed after removing the

formation. The scientific value of many speleothems in existing collections may be compromised

by incomplete descriptions and a lack of field provenance information. Where documentation is

lacking, any available information should be developed or compiled as soon as possible, in

cooperation with the individuals involved in fieldwork when possible, especially for speleothems

collected in past decades.

The reasoning behind field and laboratory stalagmite sample selection should be

evaluated in advance with respect to scientific goals. Although different investigators and groups

have developed rubrics and helpful observations used during field sample selection, these are

rarely published. Sharing selection approaches would advance speleothem paleo-environmental

studies in both a forward sense, with regard to sensitivity selection, as well as in an inverse sense,

with regard to record interpretation. Such information can also help to bring together parallel

efforts by paleo-environmental proxy interpretation experts and groups studying applicable

modem processes in cave, vadose hydrology, and soil systems.

Speleothems are a very promising source of high-resolution, multi-proxy continental

paleo-environmental records. Ultimately, information linking field relations to proxy records can

provide rich sources of new hypotheses for cave systems research. Sharing sample collection

approaches should advance ongoing stalagmite collection, replication, and interpretation efforts

by the broader scientific community while supporting cave conservation efforts. Attention to the

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. potential value of speleothems with different intrinsic sensitivities suggests a number of

promising avenues for future paleo-environmental research.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 1-1. Summary of Screening System Steps.

Step Factors Observations

Tropical karst region (bedrock maps) High TC landfall frequency NOAA National Hurricane Center Annual layering Climatology (Seasonal water deficit) High TC rainfall amount Orographic effect (Topographic maps)

Existing cave information Cave maps, reports, and other historical documents Vandalism Field observations and interviews Cave entrance effects depth of cave (cave maps, if available) RH hygrometer Flooding mud, debris, washing of formations Air currents formation deflection, constricted passageways Dual Porosity bedrock Field observations of fractures and decorations Epikarst thickness Field observations, map if available Soil thickness Field measurements

Actively growing Field observations (wet surfaces) Fracture-fed Field observations (fracture traces, linear arrays of stalactites) Shape, color, crystal size Field observations (broomstick or cone, white, small crystal size) Stratigraphy Polished cross-section. Mineralogy XRD Analysis 5180 and 513C analysis Dental Drill and Stable isotope ratio analysis 210Pb and 137Cs dating y-ray detector Hendy Tests Dental Drill and Stable isotope ratio analysis

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 1-1. Transmission of cyclogenic stable isotope signal in dual porosity system.

Blue indicates vadose water with background isotopic composition, red indicates tropical cyclone water. Purple indicates intermediate compositions.

Time 1 > 2 > 3 Background Storm Signal Storm Signal Signal Breakthrough Decay

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 1-2. Conceptual model of the effect of epikarst thickness (i.e. transmission rate or flow pathway) on speleothem sensitivity to brief storm events.

Bedrock

Fractu *es

Too thick * Bedrock Ceiling Thickness > Too thin Too slow < Infiltration time > Too fast Stormjii§rTcU M easurable Non-deposition [homogenized Storm Signal opdt^solution

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER II

A STALAGMITE STABLE ISOTOPE RECORD OF RECENT TROPICAL CYCLONE

EVENTS

Abstract

A 23-year tropical stalagmite record of recent stable isotope variations (1978 - 2001) at

monthly-weekly temporal resolution contains abrupt low excursions in the stable oxygen isotope

ratio (5lsO value) of calcite that correspond temporally with recent historical tropical cyclones in

the vicinity of the cave. A newly developed logistic discriminant model can reliably identify

tropical cyclone proxy signals using the measurable parameters 5180 value, 513C value, and single

point changes in S180 value. The logistic model correctly identified 80% of low 5,80 value

excursions as storm events and incorrectly classified only one of nearly 1200 non-storm sampling

points. In addition to enabling high-resolution tropical cyclone frequency reconstruction, this

geologic proxy also reveals information about the intensity of individual storm events. A multiple

regression predicted storm intensify (R2 = 0.48 p=0.030) using sampling frequency and excursion

amplitude. Consistent with previous low-resolution studies, we found that the decadal average

5180 value was lower during the 1990s when several tropical cyclones produced rainfall in the

area and conversely, was higher during the 1980s when only one storm struck. Longer,

accurately-dated, high-resolution speleothem stable isotope records thus may contribute a useful

new tool for paleotempestology to clarify associations between highly variable tropical cyclone

activity and the dynamic range of Quaternary climate.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Introduction

Tropical cyclones, including hurricanes, cyclones, and typhoons, are among the most

deadly and costly natural hazards. Tropical cyclone intensity, frequency, spatial distribution, and

economic damages are highly variable and respond to an array of global and regional climatic

factors including the El Nino-Southern Oscillation System, or ENSO (Klotzbach and Gray, 2004)

and references therein, Landsea, 2000; Pielke et al., 1999; Tartaglione, et al. 2003). Conversely,

these storms may be an important yet poorly understood feedback in the climate system, possibly

controlling rates of marine poleward heat transport and thermohaline circulation (Emanuel,

2001). Despite the ongoing research effort to understand Earth’s climate system, associations

remain controversial between highly variable tropical cyclone activity and the dynamic range of

Quaternary climates (Emanuel, 2005; Giorgi et al., 2001; Elenderson-Sellers et al., 1998; Pielke et

al., 2005; Walsh, 2004; Walsh et al., 2004). Historical records from most tropical cyclone regions

contain few examples of the most catastrophic storms. Paleo-tempestological evidence of past

storms preserved in sedimentary and biogeochemical archives (Donnelly et al., 2001a; Donnelly

et al., 2001b; Liu and Feam, 1993, 2000, 2002; Nott and Hayne, 2001; Nott, 2003) has already

illuminated centennial-millennial variations in tropical cyclone activity (Eisner and Liu, 2003;

Eisner et al., 2000; Nott, 2004; Nott, 2003). Paleotempest proxies with annual-decadal resolution

could clarify the controversial problem of tropical cyclone hazard climatology and offer an

independent test of posited feedbacks among the climate system, thermohaline circulation, and

tropical cyclone activity (Emanuel, 2005). Toward that end, we present a new, high-resolution

tool for paleotempestology that uses the proxy record of past tropical cyclone rainfall preserved in

a calcite stalagmite, a type of speleothem or cave formation.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Hurricane Stable Isotope Values and Cave Depositional Settings

The 5lsO value of average tropical cyclone precipitation is ~6%o lower than other summer

season rainwater (Gedzelman et al., 2003; Gedzelman and Lawrence, 1982; Lawrence, 1998;

Lawrence and Gedzelman, 1996; Lawrence et al., 2002), and perturbs the stable isotopic

composition of groundwater and streams in the storm’s wake (Lawrence, 1998). Recognition of

this natural isotopic tracer ‘spike’ by Lawrence (1998) led to exploration of potential paleo-

hurricane archives in corals (Cohen, 2001), fish otoliths (Patterson, 1998), tree rings (Miller et al.,

2003), and speleothems (Malmquist, 1997; Schwehr, 1998). Early attempts to develop a

speleothem isotope proxy for individual tropical cyclones were frustrated by low sampling

resolution (-annual). Nevertheless, these studies established significant decadal/multidecadal

anti-correlations between storm frequency and average speleothem calcite 5180 values, indicating

that intervals of enhanced tropical cyclone precipitation can depress speleothem isotopic

composition in affected regions on decadal timescales. A high-resolution speleothem isotope-

based paleotempest proxy would lend a number of complementary advantages to established

coastal paleotempest proxies, including relative ease of radiometric dating, high temporal

resolution, continuous deposition, storm intensity indicators, a detailed background climatic

record, and a stable depositional environment independent of Quaternary sea level variability.

Only tropical cyclones that produced significant rainfall could be recorded, and the proxy would

be limited to karst regions. Advances in micro-sampling technology for isotopic analysis of

carbonates (Carpenter, 1996; Frappier et al., 2002b) now enable us to examine the speleothem

record at sufficiently high resolution to detect proxy evidence of individual tropical cyclones.

A hurricane rainfall event over a cave results in a slug of low 5180 value water infiltrating

through soil and karst bedrock overburden; ultimately, an isotopic “spike” is recorded by growing

speleothems as a low §l80 value excursion. Inter-storm meteoric water with more typical, higher

§180 values provides the requisite isotopic contrast. To precipitate a measurable isotopic

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. excursion in speleothem calcite, the hydrologic pathway through the epikarst must 1) allow

infiltrating tropical cyclone water to reach supersaturation with respect to calcite, and 2) avoid

excessive homogenization of tropical cyclone water with other soil- and groundwaters. Even

absent excessive mixing, diffusion and dispersion during infiltration cause some tropical cyclone

water to be released slowly, affecting the isotopic composition of dripwater and calcite for some

time after the primary isotopic ‘spike’ passes through the conduit system. For individual storms,

the size of precipitation 5isO value anomalies increases with tropical cyclone intensity, and

decays with distance from the eye of the storm (Lawrence and Gedzelman, 1996; Lawrence et al.,

1998). The size of SlsO value anomalies at any site may reflect the “apparent local intensity” of

the storm, raising the possibility that the amplitude of individual stalagmite S180 excursions may

contain paleotempest intensity information. In contrast, tropical cyclones are unlikely to

substantially perturb dripwater S13C values. To investigate the suitability of speleothems as a

proxy archive for paleotempestology, we analyzed a stalagmite stable isotope record from Belize

over a recent 23-year period for which the history of nearby storm tracks and intensity is known

(Fig. 2-2-1, Table 2-1).

Methods

In January, 2001 we collected several actively growing stalagmites from the cave Actun

Tunichil Muknal in central Belize (additional site information is included in Appendix 1).

Stalagmite ATM7 was sectioned longitudinally, polished, and dated. Micro-sampling and stable

isotope analyses were performed at the Paul H. Nelson Stable Isotope Laboratory at University of

Iowa (Frappier et al., 2002, Appendix 2). Microsamples (approximately 0.02 to 0.05 mg of

CaCCL for each sample) were milled along the growth axis continuously at 20 pm resolution

(Fig. 2-2) using a computer-controlled microsampling device (Appendix 2). Analytical precision

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. on standards was better than ± 0 .1 %o for both S lsO and 8 13C values. All results are reported in per

mil (%o) relative to V-PDB.

Identifying and Dating Isotopic Excursions

We visually inspected the stable isotope record for the largest brief, low excursions in

5180 value not also associated with substantial declines in 813C value. It was necessary to account

for covariation between 8180 values and 813C values in order to distinguish the isotopic signature

of tropical cyclone precipitation from ENSO-related inter-annual climatic and ecological

variability (Frappier et al. 2002). We also updated and verified a previously developed age model

(Frappier et al., 2002b) to reflect new recognition of sub-annual layers (Appendix 3). We used the

resulting age model to identify the year in which each low 8lsO value excursion was deposited.

Results and Discussion

The ATM7 oxygen isotope record contains 11 large (> ~ 0.5 % o), short-lived, low

excursions in 8lsO values that are not coupled with similar decreases in S13C values (< 0.2 % o) and

are correlated with historical tropical cyclones near the cave (Fig. 2-3A, B). No similar SlsO value

excursions are evident during years lacking nearby tropical cyclones. These 8lsO value

excursions are associated with historical storms ranging in intensity from Tropical Storm to

Catastrophic Flurricane, and with storm tracks whose eyes passed the cave at distances of 40 to

370 km. The record also reflects inter-storm deposition, enabling accurate estimates of between-

storm intervals (Fig. 2-3 A, B). Multiple S180 value excursions occurred in years with multiple

storm strikes, as well as 1998 when Hurricane Mitch re-intensified as it passed the site a second

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. time (Fig. 2-2-1). In 1996, when Dolly and Kyle struck two months apart, the two excursions are

separated by several samples (Fig. 2-4). In 1995, when Opal and Roxanne struck two weeks

apart, the two excursions are separated by a single intervening point (Fig. 2-4). Hurricane Keith’s

stormwater signal was not recorded in 2000 as ATM7 was collected only three months after Keith

made landfall while the tropical cyclone-derived, or “cyclogenic” water was most likely still

moving down through the epikarst toward the cave. In agreement with previous studies

(Malmquist, 1997; Schwehr, 1998), decadal average 8lsO values in ATM7 were lower (higher) by

1.87%o ±0.19%o during the 1990’s (1980’s), a decade when many (few) tropical cyclones affected

the region (Fig. 2-3B).

To test the suitability of high-resolution stalagmite stable isotope records for detecting

unknown pre-historic tropical cyclones, we applied a simple numerical technique to distinguish

the proxy record of cyclogenic excursions from background variability in this dataset. Within the

11 historical excursions we identified as cyclogenic, we coded the sampling point with the lowest

5180 value as a storm event (1); all other sampling points were coded as non-storm (0). During

Hurricane Mitch’s long traverse across this region, the storm made landfall in both Honduras and

Yucatan; thus, we coded the two largest low S180 excursions in 1998 as storm events. A binary

logistic regression of sampling points identified as storm/non-storm with the predictor variables

oc* 18 Od (the difference in 8 18 O value between adjacent samples), 5 O 18 value, and 8 C value, 13

resulted in a probability function to quantify the likelihood that any sample represents a storm

event:

(1) Probability = -11.01 * 5,8Od- 1.85 * S180 ± 0.51 * SI3C - 12.86

Because of adjacent missing data due to a machine error on one side of the dual inlet

system, a cyclogenic excursion in 1993 was not included in this model as the 518Od could not be

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. calculated. Using equation (1), logistic probability values greater than 0.5 captured excursions

related to 8 of the 10 remaining isotopic excursions included in the model (Fig. 2-3D). Of over

1200 non-storm data points, the logistic probability model identified as a cyclogenic signal only

one sample that we coded as non-storm, with a logistic probability cut-off value greater than 0.5.

A third low SlsO excursion in 1998 identified by the model was also associated with Hurricane

Mitch. We can thus infer that multiple excursions can be produced by a single storm system that

produces multiple local precipitation events. Overall, this model reliably identified tropical

cyclone signals in an isotopic record with substantial background variability, and will serve as a

testable model for future speleothem paleotempestology records.

Investigating Potential Indicators of Storm Intensity

We examined relations between the proxy (low 5lsO value excursion amplitude) and the

characteristics of recorded storm events. We postulated that annual sampling frequency (S, #

stable isotope samples per year) could be an important secondary control on the amplitude of

measured excursions as a result of inter-annual growth rate fluctuations (Appendix 4). A standard

multiple regression using maximum storm intensity(I), proximal storm track distance (D), and S

as the independent variables explained 56.6% of variation in 5180 value excursion amplitude

(p<0.031) (Appendix, Table Al). The most important predictors of excursion amplitude were I

(semi-partial R2= 0.354) and S (semi-partial R2= 0.382), indicating that this application is limited

by microsampling technology. Surprisingly, excursion amplitude was not substantially related to

D (semi-partial R2= 0.025). We interpret these results to indicate that in this dataset, excursion

amplitude was primarily related to tropical cyclone intensity and was not substantially

confounded by storm track distance. This finding is surprising given observed radial stable

isotope gradients in tropical cyclones, but consistent with the observation that tropical cyclone

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. rainfall 5180 value is an integrated signal of the storm’s history (Lawrence et al., 1998). Storm

intensity was a better predictor of excursion amplitude than local precipitation amount (Appendix

4). Toward reconstructing tropical cyclone intensity from measurable predictor variables (S and

excursion amplitude), we performed a linear regression that explained 48.0% of variance in

maximum storm intensity (p<0.030). High-resolution pre-historic speleothem proxy datasets can

thus provide the basis for accurately reconstructing paleo-hurricane intensities. However, a robust

test of the models presented here for reconstructing paleotempest incidence and intensity was

precluded by the absence of an independent dataset of similar quality. A larger number of

recorded historical-era tropical cyclone events will be required to fully test the sensitivity of this

stable isotope proxy to various storm characteristics identified by Lawrence and co-workers

(1998) (e.g. various storm track distance and intensity measures, storm duration or short-term

intensity changes, storm radius, storm quadrant affecting the site).

Conclusions

The low 5lsO value excursions identified in this stalagmite record represent an accurate,

measurable high-resolution proxy for past tropical cyclone precipitation events, opening the door

to more detailed records of past hurricane frequency and intensity. The overall success of simple

statistical tools to distinguish cyclogenic b180 value excursions from a relatively noisy

background gives us confidence that individual tropical cyclone events can be reliably identified

in pre-historic speleothems. Additional independent stable isotope records and longer calibration-

period records are needed to test the proxies for tropical cyclone activity presented here. Close

agreement between this speleothem proxy archive and the historical record suggests that this tool

can bridge the gap between historical/meteorological hurricane observations (synoptic to multi-

decadal scale) and low-resolution (centennial to millennial scale) paleotempest proxy records.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. The speleothem proxy complements existing coastal paleotempestology proxies, lending

advantages related to ease of dating, independence from Holocene sea-level changes, and

potential applicability throughout the Quaternary. The proxy is limited by the availability of

storm-sensitive speleothems, stable isotope sampling frequency, and potential interference in

temperate regions from snow-melt infiltration(Dansgaard, 1964a). Only tropical cyclones that

produce significant rainfall in karst regions could be recorded. Between 1851 and 2004, this c

site alone was affected by 6% of major hurricanes and 8% of all named landfalling tropical

cyclones in the Atlantic Basin. Thus, several cave sites from karst regions across the Atlantic

Basin may may yield a representative record that reflects seasonal to centennial variation in

overall Atlantic paleotempest activity during Quaternary intervals of paleoclimatic interest. A

spatially and temporally distributed network of high-resolution paleotempest proxy records in

multiple tropical cyclone basins may ultimately contribute to coastal risk assessment and

detection/attribution programs aimed at investigating potential impacts of climate change on

tropical cyclone activity (Nott, 2004).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 2-1. Historical intensity, precipitation, and best track data for Atlantic tropical cyclones (NOAA Tropical Prediction Center). Intensity categories represent the maximum intensity at or prior to landfall in Central America, such that 0 indicates tropical storms, and values 1-5 indicate Saffir-Simpson hurricane intensity categories 1-5. Total rainfall during storm days is reported from the Central Farm meteorological station, less than 15 km from the cave site. * Combined rainfall from both Hurricane Mitch landfall events.

Landfall Distance between storm Storm Precipitation, Storm Date Intensity track and cave (km) Central Farm (mm)

Keith Oct. 2000 4 120 264

Katrina Oct. 1999 0 158 15

Mitch 2 Nov. 1998 0 369 *

Mitch Oct. 1998 5 282 246*

Kyle Oct. 1996 0 147 59

Dolly Aug. 1996 1 233 51

Roxanne Oct. 1995 3 311 43

Opal Sep.1995 0 248 68

Gert Sep.1993 0 86 24

Diana Aug. 1990 0 259 24

Hermine Sep. 1980 0 138 131

Greta Sep.1978 4 42 179

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2-1. Location map indicating the stalagmite collection site, cave Actun Tunichil Muknal (star) and nearby tropical cyclone storm tracks (1978 - 2001). Black storm tracks indicate hurricanes; gray storm tracks indicate tropical storms (NOAA National Hurricane Center).

90 W 85° W ATLANTIC I !' Gulf of ...OCEAN I Mexico

20 N ' .Belfzer-f Caribbean Sea

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2-2. Photographs of stalagmite ATM7 showing depth of radiometric dating samples, and micro-milling track. White circles show the depth of y-activity samples with positive l37Cs activity; gray circles indicate zero (undetectable) 137Cs activity. We interpret he onset of 137Cs y-activity to indicate local deposition of global fallout from atmospheric thermonuclear bomb testing after 1953. The polished cross section of the top portion of ATM7 shows the stable isotope micromilling track (blue outline), which was positioned to maintain alignment with the growth center and perpendicularity to growth banding throughout sampling (1978 -2001).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 2-3. Recent tropical cyclones near Belize with stalagmite SlsO timeseries, S13C timeseries with SOI, and storm proxy model results. Horizontal axes for A, B, and D are identical (1978-2001); C is offset by a 1.5 year lead. Dating uncertainty for ATM7 is a few weeks to months; thus, events named in A and highlighted in B should not be expected to match exactly. A. Historical records of storm distance (black bars) from cave to nearby tropical cyclone storm tracks and local maximum intensity [red bars indicate Saflir- Simpson intensity categories TS (tropical storm) to H5 (Category 5 Hurricane)]. Blue circles denote storm rainfall totals from three meteorological stations near the cave site. B. ATM7 5180 and S13C values are shown in black and blue, respectively. Cyclogenic low 5lsO value excursions (A-K) are highlighted. Note the different vertical scales for §lsO values and S13C values. C. The Southern Oscillation Index (SOI) is shown inverted. Major El Nino events (vertical bars) during the period of record are associated with high 513C and 5lsO values in ATM7. D. Logistic regression model results to discriminate cyclogenic excursions from background isotopic variation and the amplitude of isotopic excursions. Solid circles denote excursions the model classified correctly as cyclogenic. Open circles (1980, 1995) denote excursions classified incorrectly as non-cyclogenic. One data point the model classified as cyclogenic (black) was associated with Mitch, but not a unique historical storm.

300 - Distance (km) Diana 2 0 0 - Gtsls P SfE KatrinaKeilh 100 - TS - H I - H2 - (-20D % H 3 - H 4 - "3 BS J Intensity 40D a

b -3 - *0 -j, --9 m

1S60 19Q5 1995

-30

1960 19B5 51 1980 1 9B 5 199D 1 9 9 5 2000 0 ■3 s TJ~T M £ >

2 Cl.E

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

1998.5 1999 •4 CM to 1991 1997.5 1990.5 •5 •6 •4 ■5 •4 to 1981 k 1997 1980.5 1996.5 ■5 •3 ■4 1996 1995.5 1978.5 1979 Q.

_ •3 •2 •4 -6 to >! ^ O CO Figure 2-4. Forlocally active hurricane seasons, 8lsO valuecyclogenic detail across excursions corresponding (A-K) annualand coupletsbackground are isotopicshown variability. to highlight U) ' j

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER III

EMPIRICAL ORTHOGONAL FUNCTION ANALYSIS OF MULTIVARIATE STALAGMITE

TRACE ELEMENT DATA: DETECTING THE 1982 EL CHICHON VOLCANIC ERUPTION

AND OTHER ENVIRONMENTAL EXTREMES

Abstract

We used Empirical Orthogonal Function (EOF) analysis to evaluate a recent Belize

stalagmite record of trace element concentrations obtained using Laser Ablation-Inductively

Coupled Plasma Mass Spectrometry (LA-ICPMS) and Scanning X-Ray Fluorescence (XRF). A

major perturbation in dripwater geochemistry occurred in 1982, coincident with local ash fallout

from Mexico’s El Chichon volcano. Tephra deposition had a marked effect on the trace element

geochemistry of this stalagmite that was not otherwise evident in visible stratigraphy or stable

isotope ratios. A different LA-ICPMS trace element fingerprint in 1979 is associated with a

distinct color change and an extended period of low stable oxygen isotope values, coincident with

extremely high rainfall during that year. The XRF series included some species not included in

the LA-ICPMS analysis, such as S, K, and Cl; likewise, LA-ICPMS series include species not

analyzed by XRF, such as Na. As a result, XRF was more sensitive to major volcanic events, and

LA-ICPMS showed greater discriminating power between volcanic events and years of high

precipitation. Applying Empirical Orthogonal Function (EOF) analysis to a smaller, more recent

window of LA-ICPMS data without extreme events (1984 - 2001) revealed an additional mode of

Sr:Mg anticorrelation that may reflect seasonal to interannual changes in drip-rate. The dominant

EOF modes extracted depend on the environmental controls that dominated during different

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. intervals; major environmental events can dominate brief trace element records, overshadowing

the weaker seasonal to interannual signals. EOF is a promising data analysis technique for

distinguishing between different modes of geochemical variability in long multivariate

speleothem records, and also providing a means to distinguish between environmental triggers.

High-resolution, multivariate trace element analysis revealed stalagmite sensitivity to various

environmental events, including major regional volcanic eruptions and extensive precipitation

anomalies. These observations suggest new potential speleothem proxies for extreme events,

including low-latitude tephrochronology.

Introduction

It has been long established that major volcanic eruptions can introduce large quantities

of sulfate into the stratosphere, with widespread climatic effects (Briffa et al., 1998; Lamb, 1970;

Robock, 2000; Stott et al., 2000). Stratospheric sulfuric acid aerosols derived from volcanic S02

backscatter incoming solar radiation and trap longwave radiation, cooling the troposphere and

warming the stratosphere for years after an eruption (Toon and Pollack, 1980). Global coverage

of the direct effects of aerosol loading began with satellite observations in 1979 (Bluth et al.,

1993; McCormick et al., 1993). Since then, the global effects of two major eruptions, Pinatubo

(1991) and El Chichon (1982) have been studied extensively. However, understanding the

climate system response to past variations in volcanic forcing is restricted by incomplete

knowledge of the history of past eruptions, as well as the sulfate loading produced by individual

volcanic episodes (Bradley and Jones, 1992).

Historical records of volcanic eruptions and related climatic anomalies have been

extended by development of volcanic eruptive records based on geologic evidence (e.g.

(Sigurdsson et al., 1984) and/or ice core records (Clausen et al., 1997; Delmas et al., 1992; Lyons

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. et al., 1990; Palmer, 2001; Thompson et al., 1986; Yalcin et al., 2003; Zielinski et al., 1996;

Zielinski et al., 1994). The vagaries of atmospheric transport ensure that multiple ice cores from

different regions are needed to provide a more complete record of major volcanic eruptions in the

past (Clausen and Hammer, 1988; Delmas et al., 1985; Zielinski et al., 1997). The atmospheric

transport and climatic effects differ considerably between tropical and high-latitude volcanoes

(Stendel et al., 2006), and comparison between Greenland and Antarctic records has been used to

differentiate between N. Hemisphere, S. Hemisphere, and equatorial volcanic eruptions.

Relatively few annually-resolved tropical records of past volcanism have been produced, and

dating tropical eruptions using eruption products has comparatively, low precision (103 yrs) (Bluth

et al., 1997; Thompson et al., 1986), leaving the essential quantities of the timing and latitudinal

resolution of volcanic aerosol pulses poorly known (Stendel et al., 2006).

The first major volcanic plumes observed by satellites were the 1982 trachy-andesite

eruptions of El Chichon (17°21"N, 93°13' W, 1150 m elevation), classified as V.E.I. 5 on the

Volcano Explosivity Index (Newhall and Self, 1982). Tephra deposits were produced in three

phases: a major Plinian eruption on March 28th (V.E.I. 4+) was followed by two larger eruptions

on April 4th (V.E.I. 5) (Tilling et al., 1984; Varekamp et al., 1984). The eruption column reached

stratospheric altitudes of at least 26 km, and within weeks the aerosols were transported

completely around the equatorial belt (Robock and Matson, 1983). El Chichon (VEI 5) is about

400 km west of the cave site. AVHRR and TOMS satellite imagery of the tephra and S02 clouds

shows that ash was transported over central Belize on April 3rd-6th (Figure 3-1) (Bluth et al.,

1997). Rampino and Self (1984) showed that El Chichon is a source of unusually large quantities

of sulfate aerosol. El Chichon is the source of with one of the largest and most climatically active

volcanic eruptions in the entire Holocene (Palais et al., 1992). The volcano’s history is also of

interest to anthropologists because earlier eruptions are associated with the decline of Maya

civilization, including an eruption -1250 A.D. that has been linked to a major volcanic horizon in

Greenland ice cores in 1259 A.D. (Gill, 2000; Oppenheimer, 2003). Although the hypothesized

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. impact of volcanoes on the El Nino-Southern Oscillation System (ENSO) has long been a source

of controversy (Robock and Free, 1995; Self et al., 1997), Adams and co-workers (Adams et al.,

2003) showed that large ash plumes from equatorial volcanoes such as El Chichon can double the

chances of an El Nino event the following winter.

Recent developments in speleothem research have led to a number of precisely dated,

high-resolution, multi-proxy speleothem records of environmental change (Genty et al., 2003;

Wang et al., 2001). Although ice core and tree-ring records are the primary annually-resolved

volcanic proxy records, there is some suggestion that speleothems (cave mineral deposits such as

stalagmites) may record volcanic signals. The spatial distribution of speleothem records is

different from ice cores and centered in the tropics. Establishing reliable speleothem proxy

records of regional volcanic episodes could lead to more complete records of their timing and

location, particularly in the low-latitude belt and in regions with few glaciers. A Scottish

stalagmite recorded a dramatic four-year cooling episode that has been tentatively linked to the

Icelandic Hekkla eruption in 1104 (Baker, 1999; Baker et al., 1995). Atmospheric sulfate signals

related to volcanism have recently been identified in stalagmites from northern Italy, yielding

sulfate deposition records comparable to ice cores (Frisia et al., 2005). However, direct tephra

fallout has not yet been identified in speleothems, and ice core work has shown that multiple

chemical species in ash must be analyzed in order to fingerprint the source volcano. We had

available a well-dated modem stalagmite of opportunity, ATM7, (Frappier et al., 2002a) from a

region that experienced fallout from the 1982 El Chichon eruption in Chiapas, Mexico. During

the 1982 El Chichon eruption, Terry McCloskey (Louisiana State University) was farming in the

village of Santa Marta in eastern Belize, not far from the Actun Tunichil Muknal cave site. Dust

fallout on crops was noticeable for some time after the eruption (McCloskey, 2006, personal

communication). McCloskey’s local experience provides ground truth for the expectation of

tephra fallout derived from remote sensing of the tephra cloud was actually deposited in central

Belize (Schneider et al., 1999). The goal of our investigation was thus to test the potential to

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. measure stalagmite tephrochronology signals by analyzing the multivariate trace element

“fingerprint” of the 1982 El Chichon eruption.

Unlike glaciers, where post-depositional chemical transformations of aerosols are

relatively small, atmospheric deposition in karst regions is filtered by the overlying ecosystem

and soil biogeochemical systems before any signal is transmitted by infiltrating water to growing

speleothem surfaces (Fairchild et al., 2006). Speleothem geochemistry may be perturbed in a

number of ways by volcanic eruptions, primarily controlled by soil zone processes. Wet

deposition of volcanic sulfate may alter soil water geochemistry, leading to rapid leaching toward

the underlying cave (Frisia et al., 2005). However, strong wind shear during the 1982 El Chichon

eruptions separated the S02 gas cloud from the silicate tephra cloud (Schneider et al., 1995). The

tephra cloud was carried eastward over Central Belize (Fig. 3-1), but the stratospheric sulfate

precursors traveled further to the west. Thus, we expect most of the geochemical effects of this

volcanic event to be driven primarily by tephra; most sulfate delivered to Central Belize in the

initial plume phase was probably adsorbed by tephra particles and deposited in dry form (Rose,

1977). The effects of direct sulfate deposition were probably secondary and related to slow

fallout of stratospheric sulfate months to years after the eruption (Bluth et al., 1997). Fresh tephra

is rapidly weathered on contact with water (Varekamp et al., 1984), and can be expected to have

multiple effects on soil geochemistry, with complex interactions with organic matter and clay

mineral phases present. Leaching products of tephra include metallic ions common in silicates

(Varekamp et al., 1984).

In general, investigations of inter-annual to sub-annual relationships between trace

element proxies and their controlling variables have been reported by a number of groups,

(Fairchild et al., 2001; McMillan et al., 2005; Treble et al., 2003). It is frequently the case that

multiple factors affect individual measurable trace element species and modem calibration is

required to establish any causal relationships between a speleothem record and volcanic

eruptions. The soil and epikarst filter makes it unlikely that tephra particles will be found in

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. stalagmites. Instead, we explored the stalagmite for evidence of geochemical perturbations

triggered by chemical weathering of tephra and associated secondary biogeochemical effects. To

distinguish the signature of event types which have affected the area (e.g. hurricanes, heavy

rainfall, tephra fallout), we analyzed a large suite of trace elements and applied principle

component analysis to derive common modes of variability. Here we report the results of our

exploration of a very high-resolution modem trace element record measured in stalagmite ATM7.

Methods

Actively growing stalagmites were collected from Actun Tunichil Muknal in January,

2001 (Frappier et al., 2002a). After sectioning stalagmite ATM7 vertically, a thick section was

cut from the upper portion and polished. The stalagmite was dated using layer counting after

testing for annual growth patterns using the onset of 137Cs activity (Chapter 2).

A smaller piece was cut to approximately 25 mm x 20 mm in order to fit the LA-ICPMS

sample cell mount. Laser ablation was carried out at the LA-ICPMS facility at the Department of

Geological Sciences at Boston University. We followed the methodology of Treble et al. (2003),

analyzing multiple trace element concentrations collected in rapid sequence during slow scans;

however, we made a few modifications. The instrument consists of a VG Plasma ExCell

Quadrupole ICPMS fitted with a VG Merchantek LUV MP II frequency-quintupled 213 nm Nd-

YAG laser ablation microprobe. The laser energy profile was flat at 101 J/cm2. The laser was

operated with a spot size of 40 pm and 50% output at 10Hz. Analyses were conducted while

scanning the sample mounted on a motorized stage moving at 3 mm/min to create a continuous

transect. The surface was pre-cleaned by scanned ablation using a larger laser spot size (300 pm

spot size and 30% power at 10 Hz) prior to data collection on each transect section in order to

remove surface impurities. We analyzed 11 parallel transects at 50 micron spacing (Fig. 3-2).

Microscopic observation showed that the width of each resulting ablation line was approximately

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 40 pm. A stream of Argon gas carried the laser-ablated sample material into the plasma source

for ionization. Nineteen species were measured, each with an integration time of 0.05 s (nB,

23Na, 26Mg, 27A1, 29Si, 31P, 43Ca, 47Ti, 49Ti, 55Mn, 57Fe, 85Rb, 88Sr, 89Y, 90Zr, 138Ba, 140Ce, 232Th, and

238U). This method produces approximately 100 data points per mm of transect, although

smoothing of the series is produced by partial overlapping of successive measurements.

Each transect was collected in three overlapping sections, which were spliced to create

each continuous transect. Background counts (carrier gas background with the laser off) were

collected before and after each section. Before and after each transect, a NIST612 glass standard

was also analyzed (Pearce et al., 1996). Although this silicate glass standard is not ideal for this

work, a widely accepted calcite standard with the range of species found in NIST 612 is not

available as an alternative. The raw data was processed by subtracting background counts, taking

the ratio of each species relative to the Calcium signal, and standardizing with respect to the

NIST612 analyses. Following Treble et al. (2003), all 11 parallel transects were stacked prior to

data analysis in order to assimilate between-track variability. We correlated the LA-ICPMS

transect to the adjacent, well-dated stable isotope transect from ATM7 (Chapter 2) in order to

determine the vertical extension rate along the LA-ICPMS transect (Fig. 3-2). Age model error is

on the order of a few months.

Scanning XRF analysis was carried out at Woods Hole Oceanographic Institute.

Scanning XRF is a rapid, non-destructive analytical technique that captures a series of broadband

emissions spectra from a sample surface after excitation with a focused x-ray beam as the sample

is translated under the beam. Spectral peaks can be analyzed to quantify the chemical

composition of the sample surface at relatively high resolution. We scanned 23 elements (Al, Si,

P, S, Cl, Ar, Ca, K, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Br, Rb, Sr, Ba, W, Pb) along a single

sampling track (Fig. 3-2). The scanning XRF system is designed to analyze sediment cores, and

the x-ray beam is a slit with a fixed width of 5mm and adjustable length (20 microns to 500

microns). A polished slab of ATM7 was mounted on the scanning track, and translated through

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. the scanner at 30 s timesteps with the beam length set at 200 pm. This yields an average of 6

sampling points per year.

Data Analysis: LA-ICPMS

In order to determine the principle modes of variability present in the entire 18-species

trace element series, we performed Empirical Orthogonal Function (EOF) analysis, a method of

principle component analysis (Meeker et al., 1995; Peixoto and Oort, 1992). The EOF technique

partitions the total variance present in the data set into uncorrelated, orthogonal patterns, termed

empirical orthogonal functions, eigenvectors, or modes. The resulting modes describe the

uncorrelated linear combinations of observations that are most efficient in explaining the variance

in the data. The first mode explains the largest percentage of variance in the dataset. Each

successive mode explains decreasing amounts of variance, and any two modes are independent

and uncorrelated. Each eigenvector is “loaded” with a combination of variables, where the factor

loading for each species is the correlation coefficient between the mode and that variable. The

different modes thus presumably represent different kinds of variability, each driven by a

different process. The ice core community has applied this technique widely in order to

differentiate between sources and transport mechanisms through interpreting the relationships

between individual species described by each mode (Mayewski et al., 1994). Each mode thus

may reveal information about how the biogeochemical system of a stalagmite is controlled by a

different environmental parameter. EOF analysis was performed using L. D. Meeker’s Matlab

script toolbox (Meeker et al., 1995). Eigenvector factor loading and variance were recorded, and

EOF modes were plotted over time for comparison to the local historical record.

We also re-analyzed the EOF modes from 1983 - 2001 to explore smaller-amplitude

events during a more stable period of deposition. The late-period EOF modes showed that trace

elements were fairly stable throughout this interval, except for a few events deposited in 1996-

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1997. The character of the 1996 and 1997 events were chemically similar to the 1982 and 1981

events, although considerably smaller in amplitude.

Data Analysis: XRF

23 species were analyzed in order to determine whether this method was also able to

capture volcanic signals. In particular, we were interested in a few species that could not be

analyzed using LA-ICPMS, including S, K, and Cl. A few species of interest were only rarely

detectable using this method (P, Cu, Cl). The data were normalized to Ca. Reported XRF

concentrations are relative, because no calcite standard materials were available for analysis. We

hope to obtain reference materials in the future to report more quantitative values using this new

method. Nevertheless, this rapid, non-destructive trace element analytical method produced well-

behaved data with considerable structure as described below. As before, we performed EOF

analysis on the XRF series. Eigenvector factor loadings and variance were recorded, and EOF

modes were plotted for comparison to the local historical record and LA-ICPMS data.

Results

LA-ICPMS

All measured species show significant high-frequency variability (Fig. A-4). A few

species show long-term trends and inter-annual variability (Mg, Sr). Although additional

information about biogeochemical variability can be revealed through time-series analysis of

individual EOF modes, this application is limited by the short length of the ATM7 dataset.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Without measured chlorine concentrations we were unable to correct for variations in cyclic salt

concentrations.

The first three EOF modes accounted for nearly 60% of the total variance in the trace

element dataset (Fig. 3-3, Fig. A-4). The remaining modes had low explanatory power (<~5%

variance), and are not reported here. Timeseries plots (Fig. 3-4) show how each mode, or multi

variate fingerprint, varied over the last few decades. The factor loadings and temporal variability

provide the basis for interpreting the most likely environmental factors that underlie each mode.

Variance was partitioned evenly across the first two EOF modes for Sr and Si; thus EOF is not a

particularly diagnostic tool for investigating either Si or Sr in this dataset.

EOF1 explains 37.1% of the total variance in the dataset, and is positively loaded with all

measured species. Several species are loaded at greater than 50% variance (Ti, Mn, Rb, Y, Ba,

Ce, Th, and U), while Ba, Na, and Fe were loaded at less than 25%. EOF1 variability is

concentrated in a single major event positive around 1982, with two lesser events centered at

1979 and 1981. EOF2 contains 14.5% of total variance, with a more complex chemical pattern.

The factor loadings of anticorrelated species have opposite sign, such that the concentrations of

species are high when those species loadings are negative, as in EOF2. The species B, Mg, Sr,

and Al are anticorrelated with Fe. EOF 2 is not loaded on Na, P, Ti49, Mn, Rb, Y, Zr, Ba, Ce, Th,

U. Figure 3-4 shows that EOF2 has a slight downward trend throughout the series, driven

primarily by Mg (Fig. 3-5). Positive events in EOF2 occurred -1976 and in 1979. In 1982, a

major negative event in EOF2 is concurrent with a positive event in EOF1. In 1981, EOF2 shows

an increase in variability without a clear signal or trend. EOF3 incorporates 7.9% of total

variance, primarily related to high-frequency P and Fe variations. EOF3 shows a single positive

spike in 1981 (Fig. 3-4; See also Fig. A-5).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Scanning XRF

All measured species show significant high-frequency variability. A few species are

detectable in a minority of samples (K, Cu, Ar). The first EOF pattern explains 28.7% of the total

variance in the dataset, and is positively loaded with most measured species (Fig. 3-5). As for the

LA-ICPMS results, EOF1 variability is concentrated in a single major event around 1982.

Another large event occurred prior to the mid-1970s. The XRF EOF2 is somewhat different from

the second mode identified in LA-ICPMS. Here, EOF2 contains 15.7% of total variance, where

the transition metals (K, Ti, Fe, Cu, Zn) vary opposite to Al. The XRF EOF2 also shows major

events around 1982 and the early 1970s. The first two XRF modes are both dominated by two

events: 1982 and the early 1970s; although the modes are positively correlated for the early event

and anticorrelated during the latter event. These differences in loadings between modes indicate

that the two events are chemically different.

Discussion

The chemical and temporal fingerprint of each EOF pattern can be interpreted with

respect to the most likely controlling processes that could generate the observed mode. Tropical

cyclones striking the vicinity in 1978, 1980, 1990, 1993, 1995 (twice), 1996 (twice), 1998, 1999,

and 2001resulted in brief 5I80 value excursions (Chapter 2). No major chemical perturbations

related to tropical cyclones have been observed in this record using either LA-ICPMS or

Scanning XRF analytical techniques. This suggests that the high-resolution stable isotope proxy

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. demonstrated by Frappier and colleagues is thus far the only demonstrated speleothem proxy that

can distinguish tropical cyclones from background environmental variability.

For both LA-ICPMS data and XRF data, EOF1 represents a general flux of trace

elements into the groundwater system, likely related to extended periods of extreme wetness,

external input of silicate material, and/or changes in soil pH/redox conditions. The brief dataset

and lack of sea-salt correction in the LA-ICPMS data prevents us from distinguishing entirely

between these potential drivers. High La-ICPMS EOF2 values represent input of silicate

aluminosilicate minerals (clays) varying opposite to Fe, pointing to a physical flushing control

washing clays into the system. P and Fe are primarily loaded on LA-ICPMS EOF3. For the XRF

data, the additional loading from S in the XRF EOF1 mode suggests a stronger volcanic

signature, particularly during the 1982 event. The loadings of the second XRF mode strongly

suggest that it is also related to silicate minerals varying opposite to transition metals. If both

XRF EOFs are related to volcanic tephra, the second mode may represent differences in

composition between events. We now can interpret each trace element event (early 1970s -

2001).

Around 1978-1979, the stalagmite shows a striking increase in LA-ICPMS EOF2 and

EOF 1 that is coincident with color changes in stalagmite stratigraphy, stalagmite stable isotope

perturbations, and major historical rainfall events (Fig. 3-4C). Local meteorological records for

1979 show that rainfall exceeded three standard deviations above the climatological mean (Figure

3-6). At this time, the stalagmite shows a distinct rusty-colored layer in stalagmite stratigraphy

and an extended period of very low stable oxygen isotope ratio (low 5lsO values) (Chapter 2).

The positive EOF2 anomaly in 1979 reflects increased flushing of clays from the soil and into the

cave conduit system; this is consistent with that year’s extreme rainfall season. However, this

event does not seem to have affected EOF 3 significantly, indicating that P and Fe leaching were

not affected especially by this period of wetness. Because P is generally insoluble, the lack of a

response in EOF3 in 1979 is also consistent with control by an extended period of extreme

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. wetness. The XRF data shows no major event at this time, suggesting that species analyzed only

by LA-ICPMS may be the primary driver behind this geochemical event (e.g. Ba, Mg).

An explanation for the 1981 excursion in all LA-ICPMS EOFs is not immediately

apparent; however, the unique spike in EOF3 at the onset of this event may hold the key to

understanding the driver of this event (Fig. 3-4B). No environmental extreme or event was

reported that year in the region, although local meteorological observations indicate that rainfall

was somewhat lower than normal (Fig. 3-6). A rather speculative explanation is that the 1981

event represents the influence of fire. Although this cave site is located in the Tapir Mountain

Nature Preserve, at that time the preserve had only recently been established. Local subsistence

farmers living in the area practiced slash-and-bum agriculture in the riparian floodplain nearby,

traditionally setting fires just prior to the onset of the summer monsoon. The presence of large

trees at the cave site shows that no major forest clearing or major canopy fires took place in

recent decades. Understory biomass burning is one plausible explanation for the sudden leaching

of all species in 1981, particularly the release of biologically important species such as P and Fe.

The mineral form of P is particularly insoluble in the soil, while soluble P is tightly cycled within

the ecosystem (???). Fires combust organic matter, leaving soluble P behind in the ash,

contributing to short-lived post-burn P leaching (Hernandez et al., 1997; McColl and Grigal,

1975). Available P is quickly absorbed by primary producers, leached by infiltrating

groundwater, and converted to insoluble carbonates and phosphates (Ewel et al., 1981). Fe is also

limiting in many forest environments, and available Fe is quickly absorbed by organisms; thus the

Fe response is similar to P. Thus, the brief duration of the 1981 EOF2 event is consistent with a

fire source. If the 1981 stalagmite signal was pyrogenic, the XRF analysis should reveal increased

K and reduced S deposition at this time, because K is highly soluble and fire volatilizes S (Ewel

et al., 1981). An alternative interpretation is that the 1981 event in EOF1 and EOF2 is a volcanic

tephra signal, but a smaller event from a different volcanic center with a different geochemical

fingerprint from El Chichon’s signal, described below. If this represents a volcanic signal, we

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. would expect XRF to reveal an increase in S from tephra deposition (Frisia et al., 2005).

However, the XRF data shows no 1981 event, and no conclusive interpretation can be made for

this event given the available data.

The largest element perturbation in both the LA-ICPMS and XRF data also occurred

around 1982 (Fig. 3-4A). Although the ash cloud from El Chichon covered the cave site in 1982,

no visible changes in the stalagmite stratigraphy were observed. On the other hand, stable isotope

perturbations were evident in 1982: both S180 and Si3C values dropped sharply by about 0.2 per

mil (Chapter 2). This discontinuity in an otherwise remarkably continuous dataset points to a

period of non-deposition. The small stable isotope record offset and lack of stratigraphic

manifestation indicates that the hiatus was brief (on the order of weeks to months). For LA-

ICPMS, EOF1 and EOF2 show an inverse relation during the 1982 event, indicating greatly

increased leaching of all species from the soil (EOF1), but with enhanced Fe and P and without

flushing of clays (EOF2). We interpret broad leaching spectrum as a tephra weathering product

signal from El Chichon (Varekamp et al., 1984). The first two XRF modes suggest tephra

deposition with proportionally reduced (but still very concentrations high) of K and Fe and

enhanced proportion of Ar and Cl. This event is the most striking geochemical signal in the

entire period of record. The EOF fingerprint of this event points to the influence of tephra from

El Chichon.

The second largest XRF event occurred in the early 1970s, prior to the start of the LA-

ICPMS laser track. EOF1 shows that the early 1970s event was generally similar to, but

considerably smaller than, the 1982 event. This similarity suggests a possible volcanic origin for

the early 1970s event. In addition, this event is more heavily loaded on EOF2, an indicator of

alumino-silicate minerals with more Fe and transition metals. Thus, the early 1970s event can be

interpreted as a volcanic event with higher levels of Fe and other transition metals compared to El

Chichon. This pattern is consistent with ash deposition from the largest volcanic eruption in the

Neotropics during the 1970s, the late 1974 VEI 4 eruption of Fuego near Antigua, Guatemala.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Fuego is a large stratovolcano volcano about 350 km from the cave site. Calc-alkaline basaltic

ash from the 1974 eruption produced volcanic sunsets across the northern hemisphere (Rose et

al., 1978). Thus, the greater Fe and transition metal content in the 1974 event compared to 1982

is consistent with the more mafic eruptive content of Fuego compared to El Chichon. No

similarly large geochemical perturbations are visible in ATM7 either around 1992-93, related to

the Pinatubo eruption in the Philippines, or around 1996-97, related to the Caribbean eruption of

Montserrat. No other major tropical eruptions occurred in Central America during the period of

record. The XRF series appears to be dominated by major volcanic eruptions in Central America.

Thus, the ATM7 stalagmite is highly sensitive to recording regional tephra fallout from VEI 4 or

greater eruptions.

Since 1983, the ATM7 trace element record has been relatively quiet. Results of a second

LA-ICPMS EOF analysis on the more recent period (1983 - 2001) show smaller events in 1996-

1997 with some similarities to both El Chichon ash fall and to the high rainfall period of 1979

(Fig. 3-7, EOF1 and EOF2). Local rainfall was in the normal range in 1997 and although the

previous two years had somewhat strong dry seasons, this is not particularly unusual (Fig. 3-5).

We can thus have little confidence in attributing the 1997 trace element event to unusual

seasonality. Another explanation for the geochemical character of the late 1990s trace element

events, biomass burning immediately over the cave, can be ruled out by reports from locals and

anthropologists working at the site during this period. Regional drought and fires in 1998 related

to El Nino may be a source of aerosol particles, however, the EOF analysis does not show a clear

biogenic signal. Alternatively, moderate Central American volcanic eruptions of at least VEI 3 in

the late 1990s could be related to the observed signal. Regional eruptions during this period of at

least VEI 3 include Popocatepetl, Mexico in June 1997 (-1000 km west of the cave site), and

Papaya, Guatemala in May 1998 (-300 km south of the cave site). Tephra aerosol trajectories for

these eruptions could rule out this explanation, although these are not readily available for the

listed eruptions. A definitive interpretation for the late 1990s trace element events remains

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. elusive, although these events are orders of magnitude smaller than the 1982 event. In order to

distinguish biomass burning events from tephra deposition, it may be helpful to analyze more

distinguishing species, such as K and S, in addition to considering a much larger number of

events of both types. Analyzing speleothem material from a site with a known history of bums

could contribute to resolving this problem. EOF 3 represents changes in the ratio of Sr + Ba:Mg,

which vaiy on inter-annual and annual time-scales. Anticorrelation between Mg and Sr indicates

control by crystal growth on the stalagmite surface, related to dripwater chemistry and/or drip rate

(Fairchild et al., 2006; Huang et al., 2001). To assess intervals not dominated by volcanic

influence, it is evident that controlling for sea-salt input will be important for assessing non

marine influences.

This analysis illustrates one way in which stalagmite geochemical records of regional

volcanic eruptions may be distinguished from other sources of geochemical change. Speleothem-

based records of explosive volcanism in the surrounding region would open the door to improved

understanding of tropical volcanic forcing and related climatic impacts. Furthermore, TIMS

dating error bars are large for speleothems with low Uranium content, such that identifiable

tephra events could provide absolutely dated horizons for additional age control. Although this

stalagmite was selected for its tendency to record brief infiltration events such as tropical

cyclones, ATM7 also recorded the geochemical signature of the 1982 El Chichon tephra cloud;

however, the brief hiatus associated with the eruption suggests that a longer infdtration time

might be more appropriate for the purpose of detecting volcanic events.

Conclusions

A rapidly growing, modem tropical stalagmite recorded the multivariate influence of a

number of environmental factors from 1978-2001. Applying EOF analysis to an exploratory LA-

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. ICPMS multivariate stalagmite trace element dataset effectively separated the geochemical

signatures of several different geochemical factors, including the 1982 El Chichon eruption,

extremely wet conditions during 1979, and some smaller events that will require additional

analyses to interpret with confidence. No geochemical signal was linked specifically to historical

tropical cyclone events, indicating that stable isotope analysis remains the sole speleothem

tropical cyclone proxy identified to date. However, this interpretation was hampered by lack of

Cl analysis, which would enable a sea-salt signature to be identified. The geochemical fingerprint

of the 1982 El Chichon eruption was positively identified by both LA-ICPMS and XRF data

analysis, and we further suggest that a similar signature in the early 1970s reflects the 1974 Fuego

eruption. Furthermore, during periods when extreme events do not dominate the geochemical

signature of the stalagmite, seasonal to inter-annual fluctuations of Sr+Ba:Mg ratios appear to be

driven by growth rate as well as other dripwater chemistry changes. Additional species, such as S

and K, may prove diagnostic for distinguishing between potential drivers such as local biomass

burning and tephra fallout.

Tephra deposition over a cave site can result in distinctive and overwhelming

geochemical perturbations that persist for months after the fallout event, even when S is not

included in the analysis. The rapid, non-destructive Scanning XRF technique readily identified

volcanic signatures in ATM7, verifying the LA-ICPMS analysis. We find that Scanning XRF

seems to be a rapid and straightforward technique for identifying volcanic events in stalagmite

stratigraphy; however, several analyses per year are required to resolve the -annual-scale

volcanic signals. Scanning XRF has a number of critical advantages over LA-ICPMS, including

the ability to place entire stalagmites into the instrument and rapid, high-resolution multiple

species analytical capability. However, for speleothems with slow extension rates or growth

laminae that are asymmetrical, Scanning XRF is limited by the x-ray beam size and single-axis

scanning. Longer records with a larger number of volcanic signals will be required to further test

the sensitivity of a potential stalagmite proxy for tephra deposition, and to determine what species

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. are most useful for discerning volcanic signals from other geochemical event types. A larger

number of spatially distributed speleothem records around a volcanic region such as Central

America will enable the spatial sensitivity of speleothem tephrochronology records. Pursuing

potential speleothem records of volcanism could ultimately enhance the understanding of

volcanic forcing in the climate system, and contribute to global climate model validation studies.

In regions where speleothem uranium concentrations are low, tephra layer recognition may

provide additional marker horizons useful for dating. Elsewhere, it may be possible to use more

precisely-dated speleothem records of ashfall events to investigate the history of low-latitude

explosive volcanism.

EOF analysis of multiparameter stalagmite records through decomposition of dataset

variability is a powerful method for investigating the underlying mechanisms of biogeochemical

change in these sensitive geologic recorders. The EOF modes reveal the common responses of

the cave-epikarst biogeochemical system to various forcing factors, geochemical fingerprints

whose dynamics can be traced through time. Although annual-scale trace element variations have

been identified in several stalagmite studies (Fairchild et al., 2001; Fairchild et al., 2000; Huang

et al., 2001; Treble et al., 2003; Treble et al., 2002), we find that it may be inappropriate to use

the EOF technique to identify annual variations in very short records containing large volcanic

events that overwhelm background variability, such as the ATM7 record presented here. Longer

records may enable better separation of small annual geochemical variations from intermittent

extreme events. The EOF analysis approach to multivariate speleothem proxy data may prove

fruitful in the ongoing effort to understand interactions between the atmosphere, hydrosphere,

biosphere, and rock record preserved in caves.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-1. AVHRR satellite imagery showing atmospheric transport of El Chichon tephra imaged on April 5, 1982. Colors of ash plume denote ash concentrations. Note eastward transport of large quantities of tephra from the volcano (©) toward Belize and the cave site (£2). The location of Fuego is also shown (®). Bar graph shows dates of ash and sulfate satellite observations (gray and yellow triangles) after the two eruptions on 4/4. Modified from Schneider et al., 1999.

Volcanic Ash Mass (kg/km2) 400 1,000 3,000 8,000 i i i i i 11______I___i__i i i i i i

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-2. Photographs of stalagmite ATM7. Polished upper cross-section shows the location of the laser ablation tracks (green) and XRF track (black) relative to the stable isotope micro-milling track (blue).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-3. Chart of LA-ICPMS EOF factor loadings for the first three modes. The total variance explained by each mode of variance (EOF) is indicated. Positive loadings indicate positive correlation of a chemical species with mode; negative loadings indicate an inverse correlation of species with mode. Species with loadings of opposite sign on the same mode are anti-correlated. Species with small loadings, explaining less than 10% of variance, are not particularly important or well described by the mode in question.

EOF 1: 37.9 % variance 70 60 50 40 30 20 10

EOF 2: 11.1 % variance

B Mg Ti 00Mn 7 Na 27Al 31P Ti Fe ^ 88Sr 90Zr ^°Ce 238U

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-4. Timeseries plot of LA-ICPMS variance in the first three EOFs. Y-axes are standardized variance values for each mode. Events in 1979 (A), 1981 (B), and 1982 (C) are highlighted. Event A (EOFs 1 and 2) is coincident with the 1982 El Chichon eruption. Event B (EOF 1, 2, 3) marks a suspected local fire or smaller volcanic event in the region. Event C (EOF 1 and 2) is coincident with a rusty-colored layer, low 5lsO values, and extreme precipitation throughout most of 1979 when local rainfall was greater than three standard deviations above the norm. The first three EOF modes show relatively little variability from 1983 - 2001.

EOF 1 Time Series 0 .3

0.2

0.1

1 1 Ay ' l-.i-.L-.iiJ., . i ...

- 0.1

EOF 2 Time Series 0.1

0 .0 5

- 0 .0 5

- 0.1

EOF 3 Time Series

1 9 8 0 1985 1990 1995 2000

s_ v 01 u IBR

1—F f a .1—m < —1 H

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without prohibited reproduction Further owner. copyright the of permission with Reproduced Figure 3-5. ATM7 Scanning XRF EOF results. Note major events A (1982), B (1979), and (1979), B (1982), A events major Note results. EOF XRF Scanning ATM7 3-5. Figure C (1974?). C Factor Loadinas -0.5 - 0.5 0 0.6 0.4 0.2 0.2

8 r r - 2000 2000 EOF1: 28.7% variance 28.7% EOF1: EOF2: 15.7 %variance 15.7 EOF2: 901980 1990 901980 1990 1970 1970 60

Temperature (C) Precipitation (mm) and pan evaporation) in gray, and mean maximum temperature in black. Two hydrological years are highlighted: a very wet Figure 3-6. Monthly weather observations at Central Farm, Belize, showing water balance (difference between precipitation year 1979 (A), and a slightly dry year 1981 (B).

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 3-7. LA-ICPMS EOF results for quiet latter period (1984 - 2001). Factor Loadings and timeseries plots of the first three EOF modes. Note that in the absence of volcanic dominance, lower-amplitude trends are apparent (see EOF3). Here, EOF 3 represents changes in the ratio of Sr + Ba:Mg, which vary on inter-annual and annual time-scales. The event in 1996-1997 (EOF1 and 2) is similar in character to the volcanic signals in the overall record, but much smaller in amplitude.

EOF 1 : 24.2015% Total Var.

EOF 2 : 13.4247% Tota Var. (/} o> c T 3 CO O

O o 03 EOF 3: 10.1681% Total Var.

Mn Fe Rb Sr Y Zr Ba Ce Th U

EOF 1 Time Series

- 0.5 EOF 2 Time Series 0.2

0.1

- 0.1

- 0.2 EOF 3 Time Series 0.2

0.1

- 0.1

- 0.2 '1984 1 9 8 6 1 98 8 1990 1 9 9 2 1 9 9 4 1 9 9 6 1 9 9 8 2000

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CHAPTER IV

ROOTING OUT THE AMPLIFICATION MECHANISM

LINKING A WEAK ENSO TELECONNECTION SIGNAL

AND A STRONG STALAGMITE CARBON ISOTOPE RECORD

Introduction

The El Nino-Southern Oscillation (ENSO) system is a primary driver of variations in

global weather conditions on interannual time scales (Philander, 1990). The ENSO system

oscillates chaotically between warm phases (El Nino events) and cold phases (La Nina events).

Regions with strong ENSO teleconnections typically experience major changes in average

rainfall and/or temperature regimes during strong El Nino events (Diaz and Kiladis, 1992). Some

regions, such as central Belize, show weak or instrumentally un-detectable ENSO teleconnection

patterns (Belize National Meteorological Service). We were thus surprised to find a strong record

of El Nino events in the carbon isotopic record of a Belize stalagmite (Frappier et al., 2002).

Stalagmites (mineral deposits typically composed of calcium carbonate) are formed in

caves by slowly dripping groundwater. The ultimate source of this water is precipitation;

however, the carbon isotopic ratios and dissolved ionic composition of rainwater is altered by

nteractions with the overlying biogeochemical system before reaching the cave. Stalagmite

carbon (C) comes from three primary sources: local bedrock, atmospheric carbon dioxide (C02),

and C 02 produced by soil respiration. Carbon dissolved in percolating drip water is the

iproximate carbon source to the growing stalagmite. Thus, if the local bedrock carbon source is

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. invariant, variations in stalagmite carbon isotopic ratios (513C values) reflect changes in the

overlying ecosystem’s carbon usage. However, large variations in groundwater carbon isotopic

ratios on inter-annual and shorter time scales had not been measured previously (Figure 4-1).

The ATM7 stalagmite 513C record shows a marked rise in 513C values corresponding to

recent El Nino events which occur with negative values on the Southern Oscillation Index (SOI),

a key indicator of ENSO phase (Troup, 1965). The amplitude of the recorded 513C excursions

varies between individual El Nino events, from about 5 to 10 per mil. Stalagmite stable carbon

isotopic ratio variations have been used as a proxy for ancient biome variations over caves (e.g.

C-3 forests vs. C-4 prairies) at century to millennial time scales (e.g. Denniston et al., 1999). In

this record, the variation occurs across a 6-month period, yet the amplitude range is similar to that

reported between forest and prairie biomes (Tieszen et al., 1989). Previous biome-based

interpretations of speleothem records are not called into question by the work of Frappier et al.,

(2002), because the isotopic changes occur over sufficiently long periods of time to allow soil

carbon turnover, and because they are consistent with lower-resolution regional pollen records.

However, on short, interannual timescales, there is no theoretical prediction of a relationship

between ENSO and carbon isotopes in terrestrial ecosystems, groundwater, or stalagmites - even

though biospheric carbon fluxes are closely tied to ENSO (e.g. Jones et al., 2001). The ATM7

record revealed the operation of some poorly constrained carbon cycle process(es) (Frappier et

al., 2002).

The discovery of a relationship between ENSO and carbon isotopic fractionation within

terrestrial ecosystems is particularly puzzling in light of the lack of significant local temperature

or precipitation anomalies during El Nino and La Nina intervals (Frappier et al., 2002). During

the past few decades, local meteorological variables had no significant variation in the interannual

(ENSO) band. A multivariate analysis showed that weather anomalies in Belize are also not

significantly related to ENSO. The ATM7 record indicates that the magnitude of El Nino

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. teleconnections at this site has apparently been amplified by the ecosystem carbon cycle with

respect to local weather variations. This result raises a series of important questions:

• How does ENSO affect ecosystem carbon fluxes in regions without

significant weather teleconnections?

• What is the relative importance of physical processes (e.g. barometric

pressure) compared to biological processes (e.g. soil respiration rates)?

• How does the carbon biogeochemical system amplify the apparent local

magnitude of ENSO forcing?

• What are the most appropriate variables to capture ENSO’s impact on

terrestrial ecosystems?

• What are the implications of these processes for our current understanding

of terrestrial carbon cycle dynamics?

In the absence of significant ENSO-related variations in daily meteorological

observations, the teleconnection between ENSO and Belize must be controlled by variables that

are not reported by the Belize Meteorological Service and/or distributed among multiple

environmental factors (Taylor et al., 2002). One way to explore the nature of the ENSO-Belize

teleconnection is through modeling. Another approach that is the subject of this paper is remote

sensing. NASA’s archive of remote sensing products enabled us to explore various

environmental factors for significant relationships to ENSO and Belize during recent decades.

Interannual variability in carbon fluxes from terrestrial ecosystems is more than twice

that of marine sources (Bousquet et al., 2000). ENSO-driven variability in terrestrial ecosystem

carbon cycle dynamics from regions without obvious teleconnections may constitute an important

contribution to global C 02 fluxes (Frappier et al., 2002). Identifying the specific carbon cycle

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. processes and their climatic controls is important for projecting future atmospheric greenhouse

gas concentrations. It is thus essential to determine how this stable carbon isotope record was

produced to understand the implications (if any) of this discovery for climate-carbon cycle

interactions.

Given the problem of identifying the local ENSO teleconnection driver(s), a key

observation to consider is the relative amplitude of the annual cycle compared to the inter-annual

signal. The annual cycle dominates local weather records; yet although the stalagmite contains

approximately annual layers, the ENSO signal clearly dominates the stable isotope record. We

expected to identify strong interannual variability in some local factors. Any proposed

mechanisms must be capable of producing the rapid and continuous response of groundwater

carbon isotopes to ENSO that are implied by the dynamical details of the SOI captured by the

ATM7 record. We expected the local teleconnection factor or factors involved to show a

relatively strong correlation or lag correlation with the SOI. Furthermore, the unknown

teleconnection factor or factors that drive this system may be dominated by physical (climatic)

processes or could be dominated by non-linear vegetation responses to physical forcing. For

example, forest canopy openness and soil respiration rates can affect the carbon isotopic

composition of soil gas (Amundson et al., 1998; Cerling et al., 1991; Sternberg et al., 1997).

Candidate drivers for the ENSO teleconnection must be characterized by temporal variations in

Belize with maximum power in the interannual band, correlate with ENSO phase, and be

consistent with local meteorological data. The behavior of these variables during ENSO phase

transitions may also be particularly critical (Behrenfeld et al., 2001).

In order to understand the links connecting El Nino events and ATM7 813C values, two

major issues must be addressed: 1) the teleconnection factor(s) by which ENSO affects this

ecosystem, and 2) the biogeochemical dynamics that transfer the ENSO signal to groundwater

8I3C values. This study focuses on former, which can be addressed by a analyzing a variety of

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. remotely sensed environmental parameters for Belize during past ENSO cycles. The objective of

this study was thus to explore the nature of the ENSO teleconnection in central Belize using a

wide array of available observations, including local ground-based and remotely sensed

measurements of physical and biological factors. A set of hypotheses have been generated to

address the relative importance of the most likely teleconnection factors linking ENSO and the

local carbon cycle that can be explored by analyzing patterns in local weather and remote sensing

datasets:

H I: ENSO affects weather variables not measured by local meteorological stations.

H2: ENSO has a coherent effect that is distributed across a set of atmospheric parameters.

H3: ENSO has a weak effect on local weather, amplified by ecosystem changes in the forest

canopy.

H4: ENSO has a weak effect on local weather, amplified by ecosystem changes below the forest

canopy.

This paper explores variations in atmospheric and forest canopy parameters in relation to

the SOI in order to examine what support there is for any of the forementioned hypotheses. We

explore variables readily available in the scientific literature that might provide indications

regarding the links between ENSO and the carbon cycle at the cave site in central Belize.

Parameters of interest include barometric pressure, surface temperature, atmospheric C 02

concentration, dust, cloud amount and optical thickness, potential evaporation, relative humidity,

precipitation, soil moisture, canopy openness, and leaf color indices (Graham et al., 2003;

Schaefer et al., 2002). The first and second hypotheses can be tested by incorporating a larger

number of remotely sensed parameters into a time-series lag correlation matrix using the SOI as

the predictor variable. The third hypothesis can be tested by comparing the SOI and local

weather teleconnections to remotely sensed forest canopy measures. The fourth hypothesis falls

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. outside the scope of this paper, but could be addressed by monitoring the micrometeorology and

carbon cycle dynamics at the forest-cave site across an ENSO cycle.

Methods

To investigate the relationships between the Southern Oscillation and the local weather

and forest ecosystem response, we collected several long-term remote sensing and meteorological

datasets describing weather, atmosphere, and vegetation near the cave site (Table 4-1). The

Trenberth Southern Oscillation Index (SOI) served as our primary ENSO descriptor (Trenberth,

1984). As SOI is a monthly index, we also sought descriptors of Belizean weather, atmosphere,

and vegetation at monthly temporal resolution. The Belize Meteorological Service supplied daily

weather observations of temperature, precipitation, and evaporation from the nearby Central Farm

station (17°11’ N lat., 89° W long., elevation 64 m). We compiled these daily observations into

monthly indices describing the maximum, minimum, mean, and in some cases totals within a

month, for each daily temperature, precipitation, sunshine, and pan evaporation (Table 4-1).

In all cases, the datasets containing the satellite-derived descriptors covered relatively

large areas measured in fractions of degrees. Thus, for all remote-sensing datasets, we used the

highest resolution grid datasets available (see Table 4-1) and queried the parameter value of the

pixel best centered on the cave site at 17.1167 N lat., 88.8833 W long., 100 m elevation. We

queried all MODIS, TOMS, and TRMM datasets using the GES-DISC Interactive Online

Visualization ANd aNalysis Infrastructure (Giovanni) web-based data archive query tool at

NASA's Goddard Earth Sciences (GES) Data and Information Services Center (DISC); these

datasets can be found at NASA’s permanent data archive center Distributed Active Archive

Center (DAAC, 2006). Our measure of vegetation amount over the cave was the Fourier-

Adjusted, Solar-zenith-angle corrected, Interpolated, Reconstructed (FASIR) Normalized

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Difference Vegetation Index (NDVI) available as monthly observations from 1983-1998 (Hall et

al., 2005). Again, we selected for each month the quarter degree pixel best centered over the cave

site.

Most of the variables showed strong seasonal trends that were much stronger than any

interannual variations. We removed seasonal signals from each parameter by calculating monthly

means for each parameter and subtracting the monthly means from each observation in their

respective months. We then calculated time-series cross-correlations between the SOI and the

monthly variables describing local surface weather, atmospheric conditions, and vegetation

(Table 4-1). Many satellite-derived parameters were only available for short time periods,

covering only one or two ENSO events (Fig. 4-1). We calculated correlation lags of between 0

and 24 months, with the SOI as the leading indicator, for the weather variables from the Belize

Meteorological service and the FASIR-NDVI. For all other remote-sensing derived variables

(primarily the satellite-derived atmospheric descriptors), we calculated only 12 month lags as the

data were available for only 5-6 years (Fig. 4-2); longer lags would have resulted in too small a

sample size to adequately calculate correlation coefficients. Confidence intervals were calculated

for all of the cross-correlations to determine the likelihood the correlation coefficients were noise.

Correlations beyond the 95% confidence intervals we considered likely to be real. All statistical

analyses were performed using R 2.0.1 (R et al., 2004).

Results

Most weather variables showed cross-correlations with the SOI beyond the 95%

confidence intervals at some lag between 0 and 24 months (Table 4-2). However, the maximum

correlation coefficient (R value) was 0.368 (Fatent heat at 4-5 km, Table 4-2) indicating that the

univariate cross-correlations between SOI and weather and atmospheric properties in Belize were

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. weak. This finding is consistent with results from a more simple lag correlation analysis using

only local weather data (Frappier et al., 2002a). In addition, there were no clear patterns in the

lag periods; variables seem to respond independently to ENSO forcing (Table 4-2).

Examining the variables separately, the strongest links to SOI were related to

atmospheric moisture. SOI seemed to be positively correlated (correlation coefficients between

0.270 and 0.368) with latent heat at between the surface and 6 km above the Earth’s surface 5

months later (Table 4-2). The SOI was negatively related to atmospheric column water vapor

content between lags of 0 and 6 months (correlation coefficients between 0.261 and 0.292; Table

4-2) . SOI was positively related to the maximum monthly evaporation with lags of 0 to 7 months

(R between 0.191 and 0.289; Table 4-2). However, we cannot infer that significant, longer lags

do not exist in the short-term variables. Because of the brief length of these series, lack of

evidence for long lags is not evidence of their absence.

A few variables showed weaker correlations that appeared much later; not surprisingly,

these longer lags were apparent in the longer observational series. El Nino did not affect average

temperature or precipitation amount, but increased the monthly extreme maximum daily

temperature by about +1.6 °C, about 13-14 months after SOI changes in the Pacific. The ENSO

effect is weak but significant in a multivariate 13-month regression, including maximum daily

temperature (both extreme daily max and min), maximum daily precipitation, hours of sunshine

(reflective of cloudiness), and evaporation.

To investigate the potential response of the forest canopy to ENSO, we also conducted a

lag correlation analysis on NDVI data. Like most other variables included in this analysis, the

seasonal NDVI variations are large compared to the interannual mode. Similar to the weather

observations and remotely sensed data described above, ENSO correlations were not significant

at the 95% confidence level unless seasonal variations were removed. NDVI analysis of a 0.25

degree grid cell containing the field site in Belize showed that while the ENSO effect is

significant, it is rather weak compared to seasonal effects. This NDVI effect size comparison is

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. consistent with the difference in the amplitude of climate forcing in Belize at the seasonal vs.

interannual scales. The NDVI lag was significant at 14-15 months. Interestingly, the longest

NDVI lag follows changes in local temperature extremes by only about 1 -2 months, but follows

the most widespread weather changes by about 6 months.

Discussion

We cast a wide net with the goal of capturing the teleconnection mechanism linking

ENSO variability in the Pacific with central Belize environmental conditions. Toward that end,

we combined data series of different length. This approach had the advantages of including more

variables and El Nino events, but also made it inherently more difficult to assess longer-period

lags. Thus, finding short-period ENSO lags in the brief satellite data series does not rule out the

possibility that atmospheric variables may also play a role over a longer term. This result is

consistent with previous work showing that the lag response of terrestrial ecosystem productivity

to relatively small ENSO-driven temperature change is biome-specific, and varies in strength and

even sign at different lag periods from zero to three years (Braswell et al., 1997). As the satellite

series grow over time, the links may become more clear between regional atmospheric conditions

over Central America and the Caribbean and local weather extremes in Belize. Some weather

variables not measured by local meteorological stations show an ENSO response (e.g. latent heat,

aerosols, cloudiness, and vegetation NDVI), lending some support to HI.

Each potential local forcing factor seems to respond independently. While no ENSO lag

correlations were found when seasonal variations were not removed (Frappier et al., 2002), the

present analysis of seasonally de-trended weather data found weak lags at 0-8 months and 13-14

months, distributed across several variables with respect to the SOI. The lack of a coherent lag

pattern distributed across several weather patterns refutes H2.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Both Belize surface weather observations and remotely sensed parameters show the same

overall pattern: weak interannual variations superimposed upon very strong annual cycles. This

contrasts with the stalagmite stable isotope record which is dominated by ENSO, with a weaker

seasonal cycle. The present analysis confirms the presence of some integration or amplification

of the interannual signal and/or damping of the seasonal signal, operating in the gap between the

local teleconnection and the stalagmite surface. Some non-linear process(es) of carbon isotope

dynamics involved in amplifying the interannual effect of ENSO remain unexplained.

Nevertheless, the present analysis does point toward some interesting aspects of the local

manifestation of ENSO. Stronger lag correlations are associated with weak anomalies in daily

weather extremes (e.g. precipitation intensity, extreme daily temperatures), suggesting that daily

weather anomalies are involved in driving the stalagmite ENSO signal. Atmospheric moisture

also seems to be involved, though only short lags have been identified (5 months for latent heat in

the lower troposphere; 0-7 months for ground-based maximum daily evaporation). The

mechanism(s) through which these anomalies in moisture and daily extremes are translated into

carbon isotope dynamics is a critical part of this puzzle that requires further investigation using

different methods.

However, the lower-amplitude ENSO signal in the ATM7 stable oxygen isotope ratio

(5180 value) record provides some direction (Chapter 2, Fig. 3). This related proxy indicates that

local weather is affected by ENSO through one or more of the following mechanisms:

precipitation or evaporation amount, storm tracks, or the isotopic composition of the water vapor

source (organization of the tropical atmosphere) (Dansgaard, 1964b; Lawrence et al., 2004). In

particular, the extended period of increased evaporative extremes at lag periods of 0-7 months

could have important impacts on soil microbial activity. Simple changes in storm tracks or the

isotopic composition in local precipitation are unlikely to perturb 813C values. There is no

evidence for substantial changes in local precipitation amount related to ENSO, although El Nino

increases precipitation variability by contributing a slight increase to the intensity of daily rainfall

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. at 0-3 months. Thus, we infer that the local effect of ENSO is probably a temperature and

moisture signal delivered primarily through changes in evaporation and precipitation intensity.

Similar to the weather and remote sensing analysis, ENSO signals are not apparent in

NDVI data when seasonal patterns are not removed. ENSO has a small effect on the forest

canopy superimposed on a stronger seasonal cycle. The canopy responds weakly to a similarly

weak ENSO teleconnection distributed across an array of lags and a number of weather and

atmospheric variables. The seasonally de-trended NDVI lag analysis shows a long lag of 13-15

months, following the 11-13 month SOI lag in temperature extremes by 1-2 months. Research

supports variable canopy response timescales (Braswell et al., 1997). This suggests that the local

vegetation may be responding to either the ~l-year local temperature lag, or the shorter

atmospheric moisture/precipitation lag of 0-6 months found in the remote sensing series.

It is important to note that NDVI is a fairly coarse measure of vegetation cover, and may

not be the most appropriate measure for exploring ENSO-ecosystem links. Vegetation may

participate in the ENSO signal transmission through three primary factors: canopy openness (C 02

recycling), 513C values of respired C 0 2j and 8i3C values of photosynthetic products (Farquhar et

al., 1989). We suggest that a more conclusive study of canopy involvement could be conducted

using LANDSAT data. LANDSAT coverages are more spatially-resolved and contain a range of

spectral bands that can be queried in many combinations in order to focus on particular signals,

such as water stress, leaf area, and nutrient limitation (ERDAS, 1999; Lauer et al., 1997). A more

detailed analysis of LANDSAT data could provide a more quantitative assessment of the role

played by the forest canopy.

Ecological systems are notoriously non-linear, and can be extraordinarily sensitive to

small environmental changes at critical control points (Burkett et al., 2005). Belowground

communities in particular can respond very rapidly to changes in environmental extremes as well

as mean conditions (Wardle, 2002). Furthermore, related studies have not shown ENSO

variability in speleothem growth rate or trace element concentrations (Chapters 2 and 3). We

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. have not ruled out a physical controlling process. If the latent heat changes (5 month lag) and

barometric pressure variability (no lag) are related to changes in atmospheric turbulence, any soil

C 02 efflux response would change the 513Cvalue of soil C 02 (Malhi et al., 1998). However, two

lines of evidence in particular make it difficult to envision a purely physical driver for the carbon

isotopic manifestation of ENSO and point toward some biological amplification mechanism: 1)

the apparent weakness of the interannual local manifestation of ENSO compared to the seasonal

pattern, and 2) the confinement of the ENSO signal to the stable isotope ratios.

Conclusions

The ENSO teleconnection to Belize is only apparent after correcting for seasonal

variations. The effect of ENSO on Belize weather is weak, distributed amongst several weather

variables. The most important teleconnection factors linking ENSO to central Belize’s ENSO

appear to be anomalous moisture and temperature/precipitation extremes. While some remotely

sensed variables respond with a lag timescale of zero to six months, a more coherent set of

weather variables respond with a lag of about 13-14 months. The ENSO teleconnection in Belize

lags behind the equatorial Pacific by several months to a year, consistent with recent studies

showing a lag for the Caribbean region (Chiang and Sobel, 2002; Giannini et al., 2001; Giannini

et al., 2000).

A weak El Nino signal in the NDVI data was found to most closely follow the local

temperature lag. This pattern points toward a sub-canopy amplification mechanism; however,

canopy involvement cannot be ruled out on the basis of relatively coarse NDVI data alone. We

have also argued that the biogeochemical amplifying factors of the local ENSO teleconnection

are most likely non-linear biological processes sensitive to small changes in moisture and

temperature extremes.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Future analyses could narrow the list of potential teleconnection drivers by exploring

forest canopy involvement using more sensitive LANDSAT coverages, and by assessing possible

longer ENSO lag signals distributed among remotely sensed atmospheric parameters. Remote

sensing techniques cannot detect sub-canopy or belowground linkages between ENSO forcing

and local carbon cycle. Rather, the transformation of a weak ENSO teleconnection signal into a

strong groundwater signal can be investigated most effectively through real-time monitoring of

local micrometeorology, ecosystem carbon processing, cave dripwater chemistry, and direct

measurements of local carbon reservoirs, carbon fluxes, and microbial activity. Monitoring

across an ENSO cycle will be vital to identifying the mysterious processes involved in the ATM7

record. The data obtained in such an effort may also contribute to the thorny disciplinary

problem of 513C value interpretation in speleothem records.

While this analysis has advanced our understanding of the nature of the ENSO

teleconnection in central Belize, many outstanding questions remain and the local manifestation

of ENSO remains incompletely understood. Any assessment of the potential implications of the

813C signal for the current understanding of the interactions between climate variability and

terrestrial carbon cycling, must be postponed until the proxy systematics that underpin the ATM7

record can be unraveled through higher resolution analyses and sub-canopy investigations. From

the perspective of paleoenvironmental research, it remains troubling that a sedimentary proxy

record should reflect local environmental factors in such a biased manner. The apparent Gordian

knot posed by the ATM7 record remains to be solved by future research.

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Table 4-1. Data Sources. Original Spatial Resolution Data Parameter Temporal (Lat x Long) Sou ret Resolution Trenberth Southern Oscillation Index (SOI) Monthly Southern Pacific Basin 1 Daily MaximumTemperature, Monthly Maximum (deg) Daily Site 2 Daily Maximum Temperature, Monthly Minimum (deg) Daily Site 2 Maximum Temperature, Monthly Mean (deg) Daily Site 2 Daily Minimum Temperature, Monthly Maximum (deg) Daily Site 2 Daily Minimum Temperature, Monthly Minimum (deg) Daily Site 2 Minimum Temperature, Monthly Mean (deg) Daily Site 2 Daily Precipitation, Monthly Maximum (mm) Daily Site 2 Daily Precipitation, Monthly Minimum (mm) Daily Site 2 Total Precipitation, Monthly (mm) Daily Site 2 Mean Precipitation, Monthly (mm) Daily Site 2 Rain Days per Month (days) Daily Site 2 Mean Monthly Precipitation on Rain Days (mm) Daily Site 2 Daily Sunshine, Monthly Maximum (hrs) Daily Site 2 Daily Sunshine, Monthly Minimum (hrs) Daily Site 2 Total Sunshine, Monthly (hrs) Daily Site 2 Mean Sunshine, Monthly (hrs/day) Daily Site 2 Daily Evaporation, Maximum Monthly (mm) Daily Site 2 Daily Evaporation, Minimum Monthly (mm) Daily Site 2 Total Evaporation, Monthly (mm) Daily Site 2 Mean Evaporation, Monthly (mm/day) Daily Site 2 Aerosol Optical Depth at 0.55 Micron Monthly l°x 1° 3 Aerosol Fine Mode Fraction ' Monthly l°x 1° 3 Cirrus Fraction NIR Method Monthly l°x 1° 3 Cirrus Reflectance Monthly l°x 1° 3 Cloud Fraction Daytime IR Method Monthly l°x 1° 3 Cloud Effective Radius Combined Phase (microns) Monthly l°x 1° 3 Cloud Effective Radius Ice Phase (microns) Monthly l°x 1° 3 Cloud Effective Radius Water Phase (microns) Monthly 10 x 1° 3 Cloud Optical Thickness Combined Phase Monthly 1° x 1° 3 Cloud Optical Thickness Ice Phase Monthly l°x 1° 3 Cloud Optical Thickness Water Phase Monthly l°x 1° 3 Cloud Top Pressure (hPa) Monthly l°x 1° 3 Cloud Top Temperature (K) Monthly l°x 1° 3 Water Vapor Clear Sky NIR Method (cm) Monthly l°x 1° 3 Water Vapor Above Cloud NIR Method (cm) Monthly l°x 1° 3 Water Vapor Column IR Method (cm) Monthly l°x 1° 3 Accumulated Rain (mm) Monthly 0.5° x 0.5° 4 Convective Rainrate (mm/hr) Monthly 0.5° x 0.5° 4 Stratiform Rainrate (mm/hr) Monthly 0.5° x 0.5° 4 Surface RainRate (mm/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-1 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-1-2 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-2-3 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-3-4 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-4-5 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-5-6 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-6-7 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-7-8 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-8-9 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface-9-10 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface 11-12 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface 12-14 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface 14-16 km (deg/hr) Monthly 0.5° x 0.5° 4 Latent heat surface 16-18 km (deg/hr) Monthly 0.5° x 0.5° 4 FASIR Normalized Difference Vegetation Index Monthly 0.25° x 0.25° 5 1 University Corporation for Atmospheric Research, National Center for Atmospheric Research (Trenberth, 1984). 2 Belize Meteorological Service (BMS) Daily Weather Observations, Central Farm Station (17° 1T N lat., 89° W long., 64 m elevation). 3 MODIS/Terra Atmosphere Monthly Global Product (MOD08 M3), NASA GES DISC (DAAC, 2006). 4 TRMM V6 Monthly TMI rain, latent heat, cloud liquid water profiles (3A12 V6), NASA GES DISC (DAAC, 2006). 5 Monthly FASIR-NDVI; The International Satellite Land-Surface Climatology Project, Initiative II Data Archive (ISLSCP II).

Table 4-2. SOI Lag Cross-Correlation Coefficients. Items in bold were significant at the 95% confidence interval. 2 § CU H o £ 3 o o s o- CQ o Po t- o t- I & •I 2 Q c2 | # o l § Q .§ *S s < ? GA cd GO5 J3 § C a - ^ g *►? 0 0 2 r r GO 6 s 3 s > CD cd p ® Q u x h I * •I *1H /"} — /—J / —H - < 2^ 2^ £ H * r —Hr o 3 H < z i a q J Q 1 .8. .1 2 & b a> vt-i cd GG GO O O O s q M a *-12 1 ® r* 6

vs so NCN CN d d d d d d d d d d d d r-H GO 00 © CN xt d OS CN d d d o p os Xt O O o sr- vs p xt r-H © o «—«o r-H so d o d CN r- d Os O vs r-H f SO d V) rH d o H 1 ». ■ i i i i CN os vs 1—4 00 00 o o o © so vs OC OC CO CO SO CO CO d Tfr r-H o Nr CN r- CN SO CO d O d Os d r-H vs - r-H 1-H d fr T r-H o SO d FH CN ,i i i ■ i 1 cNcoxfrvssor-ooON CO CO CN so Ss so so OS o o OS o o o vs O CN CN CN o o O d d n Os d CN o co © r-H d r-H f—H d p d o O o o d Os so ©\ o o o NC CN o CN CN d o o < ■ i i 1 i ( n d d d o d d tj CN CO NCO CN - 1—4 H i r-H CN © p o p t-- CN d CO txt xt d CN vs OO d N f N so © 1-H o o d V) o r CN d 1-H « H i i i • « h i - r- O r—4 p o o d d d xj- d o o O O 0Os 00 1-H CN d O d o o r-H © VS © so CN O vs VS o o d O OS o o 1—1 d 1-H vs 00 ,-H r i i 1 i i n n o CN d 1-H CO CO t- vs CN O SO © © OCO CO d CO o o o d d d OS 1-H o so CN d xt o O o r— 1—4 O d • t i 1 n 00 Xf Os CN 1—4 1-H so Os o Oot- o CO r-H So OS o d 0ooo o o 00 d o d —Hr vs d p O d p O sOs Os d 1—4 o d o o 1-H 1-H 1-H sSO Os © d I/O • i 1 i ■ CN o V sr OS r- vs r- - 1-H 1-H xr r- i d d © © so xt r- d vs d r-H o O os CO d d d CN CN o 00 WH 0o 00 o o © d d - h • i i i CN d 1-H CN o NCN CN o d CN ^H O d d d d d r- © tOS xt d o O O p p o o xt d d P o r- Os 00 CN p o p VO o 1—H o — ^ n M ro rr d 1-H CN o CN 0Os 00 WH o o d CN d d so CN d 00 to xt p d xt Oo SO o p O o o -oo r- © o 00 O O O © r-H 1—H i i i i 1 i 1-H d d o CN r- d d o d o 00 o d rj* r- d p o d d oo CN CN d r-H o p d CO o d d © r—H CN co o O © vs i i t d 1—4 r- p Sso VS 00 Os o O p C"" Os F-< d vs o o CO Oso CO d o 1—i CN V to vs 1-H 00 o os o so CN o O d d d d 1—1 ’ o d o r-H o d oo r- 00 d c- oo p p o o p p O p i t CO V d CO CN © xf xf oo CN O d o to o d d d d CN so *3- V OS CN p CO N* V o p O O o d d d 1-H oo ooso o so os so d o co Oxt SO o CN o d d d d o i , xj- co d d d d d d X* d d d © ,-H oCN so os o 1-H vs o d 00 CN o I— os O*^ CO so so r-H 00 o -r- r- o p o r- 0r- 00 1-H CO o p o i CN os d CO vs NCN CN d o Ot o t- SO o d CN o Ovs CO d d d 1-H O0 OCO SO 00 CO CN r-H p o o o O oo r** 1-H o 1-H d d d d d vs <—i t * d 1-H oo OO 1-H o o o o © CO Oi> CO d 1—4 Os d d d d 1—H OO CO OS CO p Os o o o CO o o O o o d o r- o o p o OS r-H p i i i sv oo vs Os vs o o 1—H 00 d d vs VS NCN CN ^H d d d - r-H 1-H d d d o NCN CN p o o o p 0co 00 o Os CO o d d d V d d o d V CO ■ CN Ovs CO 1-H iH CO o d p o d so CN xt o r- 1-H d 00 CN i-H o d d tso xt i CO d 1-H so © p o O d H CO so V 1—H oo TJ- Os V) CO p CN vs d d o r o — 1 h n 77 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without prohibited reproduction Further owner. copyright the of permission with Reproduced

Table 4-2. Continued. s . IS T*-< 5 w 5 9 . GO 2 S cd . o r> o c c O i_ cd X cd o o S 3 o c > 4) bA s_ cd a> w\J o p o J H

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~~78 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without prohibited reproduction Further owner. copyright the of permission with Reproduced~~

~~Table 4-2. Continued. H3 U XJ v O !H £ H o 3 O O o- 0> u "O 0 T3 -a W O 0 -O 3 U u U H tC § o o cd O o c/i s_ cd o G OI 3 > v 3 o\ h h~~

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~~79 Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. without prohibited reproduction Further owner. copyright the of permission with Reproduced~~

Table 4-2. Continued. 33 13) J3 "S c3 ■*—* /H, bOO W r- 1—1 J CN i S j tj cn CN s _ j x 1— CN O on s O - r OO - c vo to >^4 cn i'.iK oo VO to CN ts p B B B c P 5 P B B - 5 B § s P * 1 t ■ i i i 1 t 1 ■ h j - o © p o cn o o cn oo p p ON o CN vo O o O VO - i T p n c r-H o © o o o o o Tf* oo o o o - r © o o C- o p 00 r-H CN VO vo © o* o r-~ o' o VO cn o o o r-H o 1 1 1 n o oVOCNto rj- CN © O OOo p to CN - r o cn o o o CN CN o © CN rj- 00 o r-H o ^t* VO O r- o o o o ooo00r- o o to © CN CN CN © o t > l © 1 1 1 1 'CNm^mvot—ooON' T— © r— o OO o o oCNvo cn o o

~~Date of ATM7 calcite deposition~~

~~2000 1995 1990 1985 1980~~

~~--6 -40 5 13C (per mil) V-PDB~~

~~-20 ~-10~~

~~o - ■--14~~

~~2000 1995 1990 1985 1980 SOI date~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure 4-2. Timeline of Data Sources. Abbreviations as in Table 4-1.~~

~~1 - SOI~~

~~2 - BMS Observations~~

~~3 - FASIR-NDVI~~

~~4 - MODIS/Terra Atmosphere (MOD08_M3)|~~

~~5 - TRMM V6 Monthly TMI (3A12 V6); ISLSCP II ______~~

~~I I I i I I I I 1970 1975 1980 1985 1990 1995 2000 2005~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. CONCLUSIONS~~

~~In the preceding chapters, I have demonstrated the sensitivity of a rapidly-growing,~~

~~fracture-fed tropical stalagmite record to regional tropical cyclone activity, major regional~~

~~volcanic tephra deposition events, and weak El Nino teleconnections. The implications of this~~

~~research are exciting, as this work has generated far more questions than it has answered. In~~

~~particular, this work forms the basis for new approaches to paleotempestology, and contributes to~~

~~an emerging effort to generate speleothem-based records of explosive volcanic activity in the~~

~~past. The field of speleothem paleoclimatology is in its infancy, and there is great potential for~~

~~scientific discoveries in this area of inquiry. The results discussed herein form a foundation upon~~

~~which to develop new tools for paleo-environmental research that will enable the broader~~

~~research community to address outstanding fundamental questions in new ways, for example:~~

~~Do field sample selection methods actually result in more sensitive stalagmite~~

~~proxy records?~~

~~What conditions must be satisfied for speleothem proxy records to be~~

~~reproducible?~~

~~How common are stalagmites that are sensitive recorders of tropical cyclone~~

~~rainfall?~~

~~What is the return period for catastrophic tropical cyclone strikes in regions~~

~~without extensive historical records?~~

~~What was hurricane activity like during intervals of paleoclimatic interest, such as~~

~~the Last Glacial Maximum and the Eemian interglacial?~~

~~What are the relations between tropical cyclone landfalls and large-scale climate~~

~~boundary conditions?~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. How does weak hurricane activity during pre-historic “hyperactive” tropical~~

~~cyclone periods compare to the 20th century (e.g. Liu, 2000)?~~

~~Can stalagmite trace element spectra distinguish between tephra from different~~

~~volcanoes?~~

~~How sensitive are stalagmites to regional and pan-tropical volcanic fallout?~~

~~Do major volcanic eruptions generate marker horizons recognizable in stalagmites~~

~~across a region?~~

~~Can stalagmites provide annually resolved records of tropical volcanic forcing~~

~~akin to polar ice core records of high-latitude volcanism?~~

~~By what mechanism(s) are weak local teleconnections amplified into strong~~

~~carbon isotope proxy signals?~~

~~The fidelity and sensitivity with which different speleothems record proxy evidence of~~

~~various environmental factors depends fundamentally on the epikarst channel that acts as a signal~~

~~filter between the aboveground environment and an active stalagmite surface. The amplitude of a~~

~~proxy may not reflect the amplitude of the local forcing mechanism (Chapter 2, Chapter 4).~~

~~Stalagmites that are ideally sensitive to one factor (such as hurricane infiltration events) may be~~

~~less faithful recorders of other factors of interest. Trade-offs abound; for example, stalagmites~~

~~selected for their responsiveness to brief, flashy infiltration may also be more likely to contain~~

~~visible layering related to sub-annual events that must be carefully distinguished from annual~~

~~layers (Chapter 2).~~

~~The internal cave environment is often very stable; yet, stochastic events can cause~~

~~singularities that must be distinguished from environmental controls. Coherence between~~

~~multiple records is strong evidence of reliable proxy signals (Dorale et al., 2002a). Although~~

~~analyzing multiple stalagmites was beyond the scope of the present work, replication of~~

~~stalagmite records is a necessary next step in the development of paleo-tempest and paleo-~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. volcanism proxies. For the potential of speleothem approaches to Earth System Science research~~

~~to be realized, existing singular records must be replicated.~~

~~Developing new, multi-proxy records at the highest possible resolution is one avenue that~~

~~leads effectively toward greater insight into the mechanisms and history implied by the stalagmite~~

~~records. Comparing the behavior of different parameters can provide greater insight into system~~

~~dynamics. Consider the findings that no ENSO events are evident in the trace element record,~~

~~and that no volcanic signals are found in the stable isotope record. Even when the mechanisms~~

~~are incompletely known, speleothem record exploration can reveal processes of interest occurring~~

~~in the cave, ecosystem, or regional environment (Chapters 1-4).~~

~~It is critical to conduct modem calibration of stalagmite proxy signals at all study sites as~~

~~soon as it is feasible to deploy the monitoring equipment, particularly in cases where the exact~~

~~mechanism of signal emplacement is unknown. Thus, a second line of inquiry that is likely to~~

~~revolutionize our understanding of the possibilities and pitfalls of speleothem records involves~~

~~modem process studies. This approach can integrate basic observational, experimental, and~~

~~modeling approaches, and has been used successfully by the ice core community (Dibb, 1989;~~

~~Wake et al., 1993; Wake et al., 2002). Few tropical cave sites are subject to systematic~~

~~monitoring (Mickler et al., 2004a; Sondag et al., 2003). At this time, advancing in the state of the~~

~~science particularly requires systematic, real-time studies of the links between modem cave~~

~~systems, the overlying above- and below-ground ecosystem that acts as a biogeochemical signal~~

~~processor, and external forcing factors from precipitation, climatic variability, aerosol deposition.~~

~~Field relations indicating epikarst pathway characteristics and likely sensitivities as well as~~

~~sensitivity trade-offs should be included in field sample collection and laboratory selection~~

~~procedures in order to focus analytical resources on samples (e.g. Chapter 1),~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Future Directions~~

~~My current research track has two rails: terrestrial carbon cycling and paleo-~~

~~tempestology, the emerging science of past hurricanes. Through the former, I hope to shed light~~

~~on the relation between climate variability and the natural/perturbed carbon cycle. Through the~~

~~latter, I hope to quantify the relations between climate regimes and hurricane activity, a matter~~

~~only beginning to come to the attention of the general public. Going forward, I am dedicated to~~

~~contributing key datasets that could be used to address fundamental scientific questions with~~

~~societal relevance, such as:~~

~~• Beyond the instrumental record, how is hurricane activity related to climate factors such as~~

~~ocean temperature and ENSO?~~

~~• What links exist between hurricane activity and thermohaline circulation?~~

~~• How important are the tropics in controlling climate change?~~

~~• What triggers rapid changes in tropical moisture (e.g. Mega-droughts)?~~

~~• How extensively do weak climatic variations perturb the terrestrial carbon cycle?~~

~~• How have seasonal climate variability/weather extremes affected ecosystems/human~~

~~culture? For example, in Central America, the relative importance of socioeconomic vs.~~

~~environmental factors in precipitating cultural change will remain unresolved until the~~

~~history of climatic and ecological changes are better constrained.~~

~~The initial stalagmite tropical cyclone proxy record was validated against the historical~~

~~record (Frappier et al., 2002; Chapter 2, Chapter 3). Based on our initial stalagmite record, we~~

~~developed and calibrated a quantitative proxy model to reconstruct tropical cyclone frequency~~

~~and intensity. However, to further validate the approach, it will be necessary to obtain additional~~

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. speleothem records on which to apply the model to determine its universal applicability so that it

~~can be subsequently applied to times prior to observational records. Once the proposed paleo-~~

~~hurricane proxy technique has been fully tested, it should be possible for paleotempestologists to~~

~~quantify variations in tropical cyclone landfall frequency and intensity in any tropical cyclone~~

~~basin during intervals of paleoclimatic interest throughout the Holocene and in the more distant~~

~~past. Toward that end, I am pursuing a follow-up project to replicate and further validate the new~~

~~speleothem paleo-Hurricane proxy. I have proposed to develop and test the proxy by producing~~

~~three century-long high-resolution stalagmite stable isotope records and then apply it to an~~

~~ancient century-long time interval for which we have no instrumental record.~~

~~In collaboration with my colleagues from a variety of institutions, I will continue to~~

~~pursue paleotempestology and the modem and ancient expressions of terrestrial biogeochemical~~

~~change. Despite their devastating impact on societies throughout history, we know very little~~

~~about the interactions between tropical cyclones and the climate system. I plan to apply my~~

~~newly-developed tool to investigate hurricane frequency and intensity in the past during different~~

~~climate regimes, which will provide new and valuable perspectives to inform modeling efforts~~

~~and advance understanding of the consequences of global climate change for hurricane activity.~~

~~Developing a spatially distributed network of cave sites is central to addressing the questions~~

~~posed above. I recently helped establish a new international Collaborative Research Network for~~

~~Paleotempestology. The Inter-American Institute for Global Change Research (IAI) has just~~

~~awarded our group a major five-year grant to develop a pan-Caribbean network of paleo-~~

~~hurricane records and to translate our findings into a regional risk GIS database for policy-makers~~

~~and other stakeholders. My role in the network will be to lead the effort to sharpen the new~~

~~speleothem hurricane proxy into a broadly applicable scientific tool.~~

~~I am also developing relationships with ecologists in order to investigate the impact of~~

~~the area’s weak El Nino teleconnections on carbon fluxes and ecosystem function in more detail,~~

~~from a systems perspective. I hope to advance this research in cooperation with local Belizean~~

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. scientists by establishing a regular sampling program for water and soil carbon, and outfitting the

~~site with an array of datalogging sensors. This will lay the groundwork for future terrestrial~~

~~paleoecological and carbon cycling studies.~~

~~This dissertation research has demonstrated that speleothems are a very promising source~~

~~of high-resolution, multi-proxy paleo-environmental data. Speleothems are indeed emerging as~~

~~the ‘ice cores of the lowland tropics’. In particular, I am optimistic about the potential for~~

~~tropical speleothem records to revolutionize the current understanding of key climate system~~

~~interactions, principally volcanic climate forcing, tropical cyclone-climate interactions, and the~~

~~links between land-use history and terrestrial carbon cycling. Our understanding of past, present,~~

~~and future interactions within the Earth System will grow as speleothem-based paleoclimatology~~

~~matures. I anticipate contributing to this effort by building on the lines of inquiry initiated during~~

~~this dissertation research.~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. LIST OF REFERENCES~~

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Ayalon, A., Bar-Matthews, M., and Kaufman, A., 1999, Petrography, strontium, barium and uranium concentrations, and strontium and uranium isotope ratios in speleothems as palaeoclimatic proxies: Soreq Cave, Israel: Holocene, v. 9, p. 715- 722.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Dorale, J.A., Gonzalez, L.A., Reagan, M.K., Pickett, D.A., and et al., 1992, A high- resolution record of Holocene climate change in speleothem calcite from Cold Water Cave, Northeast Iowa: Science, v. 258, p. 1626-1630.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Fairchild, I.J., Borsato, A., Tooth, A.F., Frisia, S., Hawkesworth, C.J., Huang, Y., McDermott, F., and Spiro, B., 2000, Controls on trace element (Sr-Mg) compositions of carbonate cave waters: implications for speleothem climatic records: Chemical Geology, v. 166, p. 255-269.

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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Gedzelman, S., Lawrence, J., Gamache, J., Black, M., Hindman, E., Black, R., Dunion, J., Willoughby, H., and Zhang, X.P., 2003, Probing hurricanes with stable isotopes of rain and water vapor: Monthly Weather Review, v. 131, p. 1112-1127.

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~~Weedon, G.P., 2003, Time-series analysis and cyclostratigraphy: Examining stratigraphic records of environmental cycles, Cambridge University Press, 259 p.~~

~~Yadava, M.G., and Ramesh, R., 2005, Monsoon reconstruction from radiocarbon dated tropical Indian speleothems: Holocene, v. 15, p. 48-59.~~

Yalcin, K., Wake, C.P., and Germani, M.S., 2003, A 100-year record of North Pacific volcanism in an ice core from Eclipse Icefield, Yukon Territory, Canada: Journal of Geophysical Research-Atmospheres, v. 108.

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~~109~~

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Zielinski, G.A., Dibb, J.E., Yang, Q.Z., Mayewski, P.A., Whitlow, S., Twickler, M.S., and Germani, M.S., 1997, Assessment of the record of the 1982 El Chichon eruption as preserved in Greenland snow: Journal of Geophysical Research- Atmospheres, v. 102, p. 30031-30045.

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~~110~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDICES~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX A~~

~~CLIMATE, ECOLOGY, AND GEOLOGIC SETTING~~

~~Climate in Belize is tropical to subtropical. The average annual temperature in Central~~

~~Belize is -25 °C with about 1600 mm of annual rainfall (Frutos, 2006). Central Belize has a~~

~~seasonal water deficit during the dry season (early March through late May), followed by summer~~

~~monsoon rainfall (Fig. A l). The wet season extends from June through January, transitioning to~~

~~cold front precipitation by November. Precipitation is moderate in August during the so-called~~

~~“little dry” period. Tropical cyclones have made landfall in Belize from June through November,~~

~~although September is the most active month (Fig. A2) (Frutos, 2006). Tropical cyclone events~~

~~are bracketed by other frontal and convective precipitation. Tropical cyclones bring heavy rainfall~~

~~and flooding to Belize with a recurrence interval o f-2.5 years, contributing approximately 2.5%~~

~~of annual precipitation (calculated for 1978 - 2001). Climate data is courtesy of the Belize~~

~~Meteorological Service’s Central Farm meteorological station, located less than 15 km from the~~

~~field site.~~

~~Located between true rainforest of Southern Belize and the Yucatan tropical scrub to the~~

~~north, the dominant forest type at the site is mature subtropical moist semi-evergreen broadleaf~~

~~forest (Vreugdenhil et al., 2002). The cave is located in the Tapir Mountain Nature Reserve,~~

~~which was donated to the Belizean government in 1975 and formally designated as a preserve in~~

~~1986. Common tree species include Swietenia macrophilla (mahogany), Manilkara zapota~~

~~(chicle), Brosimum alicastrum (Ramon breadnut), Orbigyna cohune (cohune palm), Cryosophila~~

~~stauracantha (“Give-and-Take Palm ”), Agonandra .s/?.(guinweo vine), Ficus crassiuscula~~

~~(strangling fig), Cedrela odorata (Spanish “cedar”).~~

~~112~~

Monthly Precipitation and - 9 ^ E CO 12 - 15 - 18 ae blne peiiain vprto)s eaie from negative evaporation)is - (precipitation Meteorological balance Farm Water (Central Climatology Belize l. A Figure February through May. through February vprto (ahd ie, n ma tmeaue bl rd line). red (bold temperature mean and line), (dashed evaporation Station, 1973-2005), including monthly precipitation (thin blue line), blue (thin precipitation monthly including 1973-2005), Station, iue 2 Saoaiy f eie urcns 18 - 20) After 2005). - (1883 season. Hurricanes wet the of middle the in occur Belize cyclones Tropical of 2006. Frutos, Seasonality A2. Figure 3 - 0

~~a Fb a Ar a Jn u Ag e Ot v Dec ov N Oct Sep Aug Jul Jun May Apr Mar Feb Jan~~

~~en ep (C) Temp Mean 113 Limestones and dolomites of Late Cretaceous age and Paleozoic limestones make up the~~

~~bulk of local bedrock (Miller, 1996). The cave site used in this study, Actun Tunichil Muknal~~

~~(ATM), is developed in a massive pink limestone breccia. This bedrock unit correlates with a~~

~~locally described rock unit in Central Belize known as the Albion formation or “Teakettle~~

~~Diamictite,” which has been identified as the K-T boundary in central Belize (King and Jr., 1996;~~

~~Pope and Ocampo, 2000). This breccia unit, common in the Boundary Fault Karst region of~~

~~Belize, has low primary porosity with major speleogenesis occurring in conjunction with~~

~~fractures (Miller, 1996).~~

~~ATM is an active, multi-level phreatic cave with an underground river flowing through~~

~~low passages (Miller, 1990). The river enters ATM through a sink in a different drainage basin,~~

~~flowing underground for 5 km and ultimately emptying into Roaring Creek about 100 m from the~~

~~resurgence. The keyhole-shaped lower cave entrance shows clear geomorphic evidence of the~~

~~downcutting origin of multiple upper-level passages. Elevated cave passages in ATM are heavily~~

~~decorated with speleothems, and were used for ceremonial purposes by the Maya civilization c.~~

~~400-900 CE (Miller, 1990). Some speleothems are actively forming, while others appear~~

~~desiccated. An upper level passage, about 500 m from the cave entrance, where speleothems were~~

~~collected for this study has a stable temperature of 25°C and relative humidity of >90%. Bedrock~~

~~overburden above this portion of the cave is at least 10 m in thickness, but may extend a few tens~~

~~of meters. Soil cores taken on the hill above the cave yielded thin, clay-rich soils (5-30 cm depth)~~

~~with a minimal organic horizon and a thin covering of leaf litter. Carbonate bedrock outcrops also~~

~~attest to the thin upland soils over ATM.~~

~~114~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX B~~

~~MICRO-MILLING AND STABLE ISOTOPE ANALYSIS~~

~~Micro-samples were milled continuously from the surface of ATM7 the polished~~

~~stalagmite slab at 20 pm intervals (Pig. A2) with a CM-1 micro-milling system custom-built by~~

~~Scott J. Carpenter. The system combines a fixed drill (Brasseler UP 200 controller and a UG 12~~

~~handpiece fitted with a 0.3 mm tungsten carbide dental bur), computer-controlled stage, and~~

~~observation under a Nikon SMZU microscope. A rotational stage allowed us to rotate the sample~~

~~relative to the drilling axis as milling progressed in order to maintain alignment with the growth~~

~~axis (Fig. A2).~~

~~The width of the continuously-milled micromilling track is shown as a blue outline in~~

~~Fig. A2. The depth of all micro- samples from the surface of the slab was approximately 0.5 mm.~~

~~The first 37 samples (two annual layers in the uppermost 1.48 mm) were milled at 40 micron~~

~~intervals, and 5-6 mm in width. It was clear at this point that a smaller drilling track would~~

~~generate sufficient material for stable isotope analysis. The remaining micro-samples were milled~~

~~at 20 micron intervals, where the track was approximately 2.5 mm across. Individual calcite~~

~~powder samples were transferred using a stainless steel scalpel from the milled surface to~~

~~individual stainless steel-sample containers. Static electricity enables the calcite powder to cling~~

~~to the scalpel so that samples can be recovered reliably. Between samples, the drill bit and milling~~

~~surface were cleaned with a stream of compressed air. Each 20 micron micro-sample represents~~

~~one to several weeks of deposition. Some commercially available micro-milling equipment can~~

~~be used to the same effect.~~

~~115~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Stable Isotope Analysis was performed at the Paul H. Nelson Stable Isotope Laboratory at~~

~~University of Iowa. Calcite powders were roasted in vacuo at 380°C for one hour to remove~~

~~volatile contaminants and then desiccated. We analyzed -1300 samples using a Finnigan-MAT~~

~~252 IRMS with a Kiel III automated carbonate device. Powdered calcite samples (approximately~~

~~0.02 to 0.05 mg of CaC03 for each sample) were reacted with 2 drops of anhydrous phosphoric~~

~~acid at 75°C. Daily analysis ofNIST powdered carbonate standards (NBS-18, 19, 20) and several~~

~~in-house standards were conducted. Analytical precision on these standards was better than ±0.1~~

~~%o for both S180 and S13C values. All results are reported in per mil (%o) relative to V-PDB.~~

~~116~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX C~~

~~AGE M ODEL~~

~~We have updated the age model initially published for this stalagmite record to correct~~

~~counting errors in a previously published dataset (Frappier et al, 2002). We show that these~~

~~counting errors are related to the direct hydrological effects of heavy tropical cyclone~~

~~precipitation. It is important to note that while the low 5180 excursions were important in~~

~~identifying counting errors, the stable isotope excursions themselves are not used as a dating tool.~~

~~We avoid a circular argument for dating the low S180 excursions by using independent lines of~~

~~evidence to test the updated age model, outlined below. Thus verified, the updated age model can~~

~~be used to determine the year in which low 5lsO excursions were deposited and compare those~~

~~dates with the historical record of tropical cyclone activity in the area. In this section, we describe~~

~~the initial age model development and justify the changes in the present work.~~

~~Major groundwater flushing events can result in double bands or couplets of speleothem~~

~~calcite, representing a single annual period (e.g. Asmerom and Polyak, 2004) and references~~

~~therein; (Baker et al., 1993; Kaufmann and Dreybrodt, 2004; Proctor et al., 2000). The oscillation~~

~~between seasons with positive and negative water balance triggers seasonal shifts in dripwater~~

~~volume and isotopic composition, color, luminescence, trace element concentrations, and/or~~

~~crystal fabric. Central Belize experiences an annual water deficit from March through May~~

~~followed by onset of the summer monsoon (Fig. A l), conditions amenable to generating annual~~

~~variations in dripwater chemistry and calcite morphology. The initial and updated age models are~~

~~based on our interpretation of visible band pairs as annual deposits. After describing age model~~

~~development and changes below, we show that both age models are supported by 137Cs dating,~~

~~117~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. and that the updated age model is further supported by independent stratigraphic, isotopic, and~~

~~trace element evidence.~~

~~Radiometric Dating~~

~~The 137Cs activity depth profile was used to test whether layer counting was consistent~~

~~with an annual pattern of calcite deposition (Fig. A3). Although no classic radiometric decay~~

~~curve is evident in the data, the onset of 137Cs activity is clearly demarcated. It is important to~~

~~note that a classical decay curve is not necessarily predicted in this depositional setting. Cesium~~

~~substitutes strongly for the macronutrient Potassium; as a result, in tropical ecosystems 137Cs is~~

~~not simply transmitted to the cave but is tightly cycled within the overlying ecosystem and soil~~

~~(Dorr and Miinnich, 1989; Ritchie and McHenry, 1990; Robison et al., 1997; Walker et al., 1997).~~

~~However, the depth of 137Cs activity onset provides sufficient age control to test the presence or~~

~~absence of annual banding in this stalagmite. Using this approach, the error in 137Cs dating is~~

~~primarily related to the spatial resolution of l37Cs samples and the assumptions used to derive~~

~~ages from the depth of 137Cs activity onset.~~

~~The onset of 137Cs activity at a depth between 48 mm and 39 mm marks the start of~~

~~global atmospheric fallout from thermonuclear weapons testing (Robinson et al., 2002) (Fig. A3).~~

~~We bracketed growth rates for the upper portion of ATM7 with three different assumptions:~~

~~1. The highest stratigraphic level at which zero 137Cs activity was measured (-48 mm)~~

~~reflects pre-atmospheric weapons testing conditions that prevailed in 1953 or earlier. This~~

~~assumption yields a maximum growth rate of 1.02 mm-yr'1.~~

~~2. The depth of 137Cs activity onset (-39 mm) may represent the onset of atmospheric~~

~~fallout as early as 1954. This assumption yields a minimum growth rate of 0.85 mm-yr"1.~~

~~118~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 1 0 0 -I~~

~~'a- CO E 40 (0 O 20~~

~~O 0 1 2 3 4 5 6 Depth (cm)~~

~~Figure A3. ATM7 137Cs activity profile. Error bars are 1 standard deviation. Note that the activity of 137Cs exceeds zero above 4.8 mm.~~

~~3. An early maximum of 137Cs activity (-39 mm) may also represent the peak of~~

~~thermonuclear fallout in 1963. This assumption yields a growth rate of 1.05 mm-yr"1.~~

~~The average of these three radiometrically-constrained growth rates was 1.03 mm-yr"1 ± 0.08.~~

~~Laver Counting and Initial Age Model~~

~~Paired visible couplets of clear and opaque calcite laminae in ATM7 are thought to~~

~~correspond to the dry and wet seasons during the March - February hydrological year (Frappier et~~

~~al., 2002a). The average radiometrically constrained growth rate estimate for the upper 60 mm~~

~~(1.03 mm-yr"1 ± 0.08) was indistinguishable from the layer-counting growth rate calculated for the~~

~~119~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. same section (1.04 mm-yr '). However, we observed that layer thickness was greater in the upper~~

~~-tw o dozen couplets, the portion of ATM7 subjected to high-resolution stable isotope analysis~~

~~(Fig. 2). We surmised that the growth rate was higher than average in the uppermost portion of~~

~~ATM7. The layer-counting-based growth rate in the micro-sampled portion was slightly higher,~~

~~1.15 mm-yr1. The radiometric age model and the more detailed annual layer-counting-based age~~

~~model are consistent with one another.~~

~~Having established consistency between ATM7 stratigraphic patterns and !37Cs dating,~~

~~we concluded that opaque-clear couplets represent annual depositional cycles. We assigned a~~

~~linear growth function to each annual couplet to generate a refined age model with a March date~~

~~for the base of each hydrological year (Frappier et al. 2002). In the absence of counting errors, the~~

~~simplifying assumption of linear growth within each hydrological year results in variable dating~~

~~error for individual stable isotope samples on the order of a few weeks to a few months.~~

~~Age Model Changes~~

~~We identified counting errors in the original age model through recognition of stratigraphic~~

~~associations between a few visible layers and large tropical cyclone-related 5lsO value~~

~~excursions. In addition to annual banding generated by strong seasonal water balance variations,~~

~~smaller rainfall events could likewise perturb drip rates and calcite morphology. In fact, we~~

~~observed many minor fluctuations in opacity within the clear portions of annual band couplets.~~

~~Annual layer counting was not affected by most lesser rainfall events, which apparently do not~~

~~perturb speleothem growth extensively enough to cause confusion with annual layers. However,~~

~~infiltration from particularly intense rain events (e.g. Hurricane Keith produced over 26 cm of~~

~~rain) would rapidly raise the hydraulic head of the drip water source conduit. The resulting~~

~~sudden change in drip rate could temporarily perturb speleothem deposition to a greater degree~~

~~120~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. than more frequent but smaller storms. It is important to note that in the case of a major~~

~~precipitation event, drip rate in the cave would be affected immediately upon stormwater~~

~~infiltration, but water isotopic composition at the speleothem surface would not reflect the storm~~

~~event until after enough time had passed to allow the isotopically-distinct stormwater to infiltrate~~

~~from the surface to the cave site.Thus, we expected any physical changes in crystal opacity~~

~~resulting from tropical cyclone events to occur stratigraphically below any measured stable~~

~~isotopic excursions.~~

~~In ATM7, one visible couplet that we originally classified as annual was deposited a few~~

~~months before stalagmite collection, about the time that major Hurricane Keith struck Belize. We~~

~~originally classified three additional visible couplets as annual deposits that we later found to be~~

~~located stratigraphically below associated low 5lsO value excursions. On further examination,~~

~~compared to other band pairs that we classified as annual, these four low 5180 value excursion-~~

~~related layers, or “storm couplets” were more akin to the minor sub-annual opacity variations~~

~~than to other annual band pairs: storm couplets were typically much thinner, less distinct, and~~

~~morphologically rougher and more angular. As a result of our present analysis, we now recognize~~

~~these storm couplets as sub-annual events generated by tropical cyclone rainfall rather than~~

~~complete annual periods. Although weak fluctuations in opacity were also associated with other~~

~~low S180 value excursions, apparently, most tropical cyclone precipitation events and other major~~

~~storms did not affect speleothem stratigraphy enough to affect the initial layer-counting process.~~

~~Importantly, the high-resolution stable isotope record enabled us to distinguish clearly between~~

~~the few cyclogenic storm couplets and annual couplets within the visible banding pattern. This~~

~~discovery enabled us to refine the annual layer counting in the age model used in our previous~~

~~analysis of this stalagmite (Frappier et al., 2002).~~

~~After accounting for the storm couplets, we re-assigned a linear growth function to each~~

~~annual couplet as before to generate an updated age model with a March date for the base of each~~

~~hydrological year (Fig 2-2). The storm couplet at the top of the stalagmite (presumably generated~~

~~121~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. by stormwater from Hurricane Keith) shifted the entire record forward by several months, and~~

~~three older layers associated with low 5lsO value excursions (1990, 1980, and 1978) further~~

~~shifted the lower part of the record.~~

~~Tests of the Updated Age Model~~

~~When plotted using the updated age model, ATM7 stable isotope, stratigraphic, and trace~~

~~element variations are very consistent with regional climatology and local meteorological~~

~~observations. Three lines of evidence validate the updated age model:~~

~~1. The ENSO teleconnection correlation previously identified in ATM7 (Frappier et al. 2002)~~

~~remains robust and stable in the updated age model (Fig. 3). Previously, we showed a total~~

~~correlation offset (or proxy record lag) between the ATM7 stable isotope record and the SOI of~~

~~approximately 1.5 years (Frappier et al. 2002). This lag has two components, comprised of the~~

~~teleconnection time (time between changes in the core ENSO region of the equatorial Pacific and~~

~~subsequent changes in Belize), plus an infiltration or recharge component (time for meteoric~~

~~water to percolate through the epikarst to the stalagmite surface). A total lag of about 1.5 years is~~

~~also inferred from the updated age model. We now infer that the teleconnection lag component is~~

~~approximately 1 year, based on studies showing that Caribbean weather responds to ENSO~~

~~forcing after a lag of approximately one year (Tourre and White, 1995, 2005).~~

~~The remainder of the correlation offset between the SOI and ATM7 stable isotope record~~

~~(~0.5 years) also enables us to estimate the percolation portion of the total time lag at~~

~~approximately 6 months. While we have not yet been able to conduct a long-term tracer test to~~

~~quantify the exact timescale of recharge, additional information enables us to bracket the~~

~~infiltration time to 3-6 months. The updated age model gives dates for each low 5lsO value~~

~~122~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. excursion in the ATM7 record that match years of local tropical cyclone activity (Figure 2-3).~~

~~Another constraint comes from the lack of observation of any excursion from Hurricane Keith,~~

~~which struck Belize just three months before the stalagmite was collected in January 2001. Given~~

~~the excellent fidelity of the proxy record to earlier tropical cyclone events, we surmise that~~

~~cyclogenic water from Hurricane Keith was still percolating down through the epikarst at the time~~

~~of collection, and the infiltration time must be at least 3 months. An infiltration time of 3-6~~

~~months is reasonable for this site, and is consistent with the duration of the stratigraphic offset~~

~~between storm couplets and associated low 8lsO value excursions. The consistency between the~~

~~ATM7 record, 1-year regional teleconnection lag, and infiltration time estimate, combined with~~

~~the stability and clarity of the ENSO correlation together provide strong evidence that the updated~~

~~age model is correct.~~

~~2. The extended low 5180 value interval near the base of the ATM7 record (located between K~~

~~and J in Fig. 3) now dates to 1979, a year when local precipitation exceeded 2856 mm, more than~~

~~three standard deviations (lcr= 323 mm-yr'1) above the climatological average (1618 mm-yr"1).~~

~~This extended period of low 5lsO values is thus an expression of the “amount effect” and is~~

~~unrelated to tropical cyclone precipitation events (Rozanski et al., 1997). Visible stratigraphy also~~

~~reflects the extreme wetness of 1979. A distinct, rusty-colored layer apparent in the polished~~

~~cross-section (Fig. 2) and trace element event (described below) is embedded within this extended~~

~~period of low S180 values. This isotopic, trace element, and stratigraphic horizon constitutes an~~

~~event horizon that is best explained by the combined effects of the extremely wet weather~~

~~conditions that prevailed in central Belize during 1979.~~

~~3. Two different, major trace element perturbations in this stalagmite are dated to 1979 and 1982~~

~~using the updated age model (Chapter 2, see also Figure A-4 and A-5). The 1979 trace element~~

~~event corresponds to the wet year and associated stratigraphic and isotopic changes in the ATM7~~

~~123~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. record described above. The 1982 trace element event represents an even greater disruption of the~~

~~site’s biogeochemistry, and is unique in the record (2001 - 1978). The 1982 trace element event~~

~~is significant because it occurred in a year when clouds of tephra from the eruption of the El~~

~~Chichon volcano in nearby Chiapas, Mexico were observed to cover the study area (Chapter 2).~~

~~This 1982 trace element event thus constitutes a marker horizon of known age that confirms the~~

~~validity of the ATM7 age model update presented here.~~

~~Together, these three independent and consistent lines of evidence provide a rigorous test~~

~~of the updated age model. Isotopic, stratigraphic, and trace element evidence consistently support~~

~~the updated age model, enabling us to avoid the circular use of the low 8lsO value excursions to~~

~~date the stalagmite itself. The excellent temporal match observed between the history of storm~~

~~events in this area and low 8lsO value excursions in ATM7 is thus an application of the updated~~

~~age model, and not a support for that age model. However, this is discussed in further detail~~

~~above. The remaining age model error is related to the assumption of linear deposition within~~

~~each annual couplet, indicating dating uncertainty for individual stable isotope samples of a few~~

~~weeks to a few months.~~

~~124~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX D~~

~~PALEO-HURRICANE INTENSITY PROXY ANALYSIS~~

~~To investigate the relations between signal amplitude and storm characteristics, we~~

~~performed a standard multiple linear regression for the eleven storm signals we identified as~~

~~cyclogenic. The four independent variables were storm maximum intensity at or prior to landfall~~

~~(integer Saffir-Simpson intensity categories, Table 1) (I), minimum distance between storm track~~

~~and cave site (km) ( D), mean storm precipitation recorded at three nearby meteorological stations~~

~~(mm) (P), and sampling frequency (number of micro-samples per year) (5).~~

~~Sampling frequency could exert a strong control on the ability of this technique to resolve~~

~~the isotopic signature from very brief individual storm infiltration events. Given a constant~~

~~sampling interval of 20 (im, inter-annual and sub-annual variations in stalagmite growth rate~~

~~would control extent of contamination in storm micro-samples by adjacent, isotopically “normal”~~

~~background calcite. Variations in the relative thickness of micro-samples compared to annual~~

~~layers may modulate the resolving power of the stable isotope record for detecting storm events~~

~~and/or intensity (Chapter 2). Although seasonal growth rate variations may be large, the tropical~~

~~cyclone events in this dataset all occur within the rainy season (Fig. A2). Sampling rate is thus~~

~~likely to be relatively stable for different storm events occurring within the same season. For~~

~~multiple tropical cyclones in the same year, the measured amplitude of excursions is more likely~~

~~to be controlled by differences in precipitation 5180 values. In contrast, growth rate differences~~

~~between years has a relatively large effect on the amount of time represented by each sample, and~~

~~thus could substantially affect the measured amplitude of different cyclogenic excursions. The~~

~~125~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. parameter sampling frequency varies with annual growth rate, but remains constant for multiple~~

~~storm strikes during a single year, in keeping with our understanding of the relations among~~

~~sampling, growth rate, and measured excursion amplitude. For example, within the multiple strike~~

~~years 1995 and 1996, sampling frequency was stable and more intense storms were associated~~

~~with larger excursions.~~

~~The overall regression model results (Adj. R2= 0.653, p=0.031) are explained in Results,~~

~~and tabulated below (Table Al). Distance to storm track was not a significant contributor to the~~

~~overall model, and was not correlated with any other independent variables. Not surprisingly,~~

~~local rainfall was highly correlated with storm maximum intensity. The correlation between storm~~

~~maximum intensity and local precipitation means that reconstructions of past storm intensity also~~

~~closely reflect the amount of local precipitation generated by those storm events. Interestingly,~~

~~local storm precipitation is not significantly correlated with 5180 signal size, suggesting that given~~

~~sufficient sampling resolution §180 signal size is controlled substantially by the maximum~~

~~intensity reached by the storm while producing rainfall at the cave site, and is not a direct~~

~~expression of the local “amount effect”.~~

~~Table A l. Results of a multiple linear regression to investigate storm and speleothem factors related to the signal size of measured 5lsO value excursions.~~

Coefficients correlations toleranc Factor unstandard Standard zero- semi- sig. e ized ized order partial Constant 0.364 ---- 0.168 Maximum Intensity 0.241 1.275 0.582 0.595 0.218 0.019 Resolution 0.007 0.662 0.381 0.618 0.872 0.016 (samples/ yr) Distance to Storm 0.001 0.169 0.247 0.158 0.877 0.428 Track (km) Local Precipitation -0.003 -0.639 0.383 -0.303 0.225 0.155 (mm)

~~126~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. APPENDIX E~~

~~TRACE ELEMENT DATA~~

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure A-4. ATM7 LA-ICPMS species concentrations (counts per second normalized to Ca and NIST 612).

~~10-1 B 11~~

~~1 -~~

~~0.1~~

~~0.01 1980 1985 1990 1995 2000~~

~~1000 -I Na 23~~

~~100 -~~

~~100 Mg 26~~

~~10000 1 Al 27~~

~~1000~~

~~100 t 10~~

~~1 1980 1985 1990 1995 2000~~

~~198~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Si 29~~

~~1980 1985 1990 1995 2000~~

~~1000 P 31 100~~

~~0.1~~

~~100 Ti 47~~

~~10 -~~

~~1 -~~

~~0.1 -~~

~~0.01 -~~

~~0.001~~

~~Ti 49~~

~~0.01~~

~~0.001 1980 1985 1990 1995 2000~~

~~190~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. 100 1 Mn 55~~

~~• U ■ •~~

~~0.1 1980 1985 1990 1995 2000~~

~~60 Fe 57 50 40 30 20 10~~

~~100 ! Rb 85 10~~

~~1 -\~~

~~0.1~~

~~0.01 -\~~

~~0.001~~

~~100 i Sr 88~~

~~10 -~~

~~1980 1985 1990 1995 2000~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Y 89~~

~~1 H~~

~~0.1~~

•I .•*

~~0.01 -~~

~~0.001 1980 1985 1990 1995 2000~~

~~1 0 -1 Zr 90~~

~~0.1 -~~

~~0.01~~

~~100 n Ce 140~~

~~10 -~~

~~1 -~~

~~0.1~~

~~100 Ba 138 10 1 0.1 0.01 0.001 0.0001 1980 1985 1990 1995 2000~~

~~131~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Th 232~~

~~0.001~~

~~0.0004- 1980 1985 1990 1995 2000~~

~~U 238~~

~~0.001~~

~~0.0001~~

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission. Figure A-5. ATM7 LA-ICPMS data comparison with first three EOF modes (y-axis). EOF modes are plotted as loading factors; P31, Na23, and Sr88 are plotted as counts per second normalized to Ca. EOF 1 Time Series 0 .3

~~0.2~~

~~0.1 B~~

~~0 lit— ...... 1 .. v .. »i.'i >HL~~

~~- 0.1~~

~~H O F 2 T i m e S e r i e s 0.1~~

~~0 .0 5~~

~~- 0 .0 5~~

~~- 0.1~~

~~EOF 3 Time Series 0.2~~

~~0.1~~

~~- 0.1~~

~~- 0.2 1 9 8 0 1 9 8 5 1 9 9 0 1 9 9 5 2000~~

~~10 0 0 P 31 1 0 0 1* i 1 1 0 - I~~

~~0.1 10 0 0 Na 23~~

~~Sr 88~~

~~133~~

~~Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.~~