Glacier Sensitivity Along the Andes: Implication for Paleoclimatic

Glacier sensitivity along the Andes: Implications for paleoclimatic reconstructions of the Little Ice Age

By Esteban A. Sagredo M.S. Universidad de Chile, 2007 B.S. Pontificia Universidad Católica de Chile, 2002

A dissertation submitted in partial fulfillment of the Requirements for the degree of Doctor of Philosophy (in Geology)

Graduate School University of Cincinnati May, 2012

Advisory Committee:

Thomas V. Lowell (primary advisor) Warren Huff Lewis A. Owen Michael R. Kaplan Patricio I. Moreno

Abstract

Accompanying the drastic increase of global temperatures observed since the end of the nineteenth century, and particularly during the last decades, glaciers worldwide have experienced rapid retreating trend. Considering the magnitude of the climate change projected for the next decades, and the potential impacts of glacier retreat on human livelihood, a thorough comprehension of climate-glacier interaction is critical in order to i) predict the response of glaciers to the different scenarios of climate change and ii) reconstruct the climatic conditions associated with former glacial fluctuations, which in turn could provide important background information for the study of both natural cycles and human impacts on climate change. This study explores the magnitude of response of the equilibrium line altitude (ELA) to different scenarios of climate change, along the climatically diverse Andes range, and its applicability to reconstruct paleoclimates.

A statistical analysis of the climatic conditions at 234 glacier sites permits to classify the climate that host present-day Andean glaciers into seven groups. These groups have a distinctive geographical distribution. It has been suggested that glaciers located in different climates could respond with different magnitude to similar climatic perturbations. Here, a full-surface energy and mass balance (SEMB) model was applied to quantify the ELA sensitivity to climate across glaciated Andean regions. The results suggest that there is spatial variability in the magnitude of response of the ELA to uniform changes in temperature and precipitation, and that the spatial pattern of this variability has a general correspondence with the climatic groups identified along the Andes. The most sensitive areas to changes in temperature are the inner tropics, whereas precipitation sensitivities are relatively greater in the subtropics and northernmost mid-latitudes.

It is suggested that the variability in the ELA sensitivity has implications for the reconstruction of paleoclimates across large areas. Based on an approach that combine the geomorphic reconstruction of ELA of

Andean glaciers and the application of the SEMB model, different scenarios of climatic conditions for the maximum glacial advance occurred during the Little Ice Age (LIA, sensu lato AD 1300-1850) are suggested. To conduct this

experiment, three glacial sites (located in different climatic regimes) were selected: Cordillera Vilcanota (13°S),

Cipreses glacier (34°S) and Tranquilo glacier (47°S). The results consist of a set of combination of temperature and precipitation anomalies that can account for ELA changes from the maximum glacial advance that occurred during the LIA to the present for each site. Assuming no changes in precipitation, the ELA fluctuation since the LIA could be explained by a cooling of at least: -0.7°C at Vilcanota, -0.5°C at Cipreses and -1.3°C at Tranquilo glacier. Assuming no changes in temperature, on the other hand, the ELA changes could be explained by an increase in the precipitation greater than 63% at Vilcanota, 21% at Cipreses and 62% at Tranquilo glacier.

Finally, it is expected that the integration of these analysis provides a framework to understand former episodes of glacial fluctuation, as well as to predict the response of glacier to different scenarios of climate change.

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Acknowledgements

I would like to thank my committee members, Thomas Lowell (U. of Cincinnati), Warren Huff (U. of

Cincinnati), Lewis Owen (U. of Cincinnati), Michael Kaplan (Lamont-Doherty Earth Observatory, Columbia University) and Patricio Moreno (Universidad de Chile), for their help, support and constant advice. Each of them contributed in different ways in order to help me to complete this dissertation. I would like to extend many thanks to Tom, my primary advisor, for constantly challenging me, letting me think, and make mistakes in order to succeed.

It would not be fair for me to neglect to thank the Department of Geology at the University of Cincinnati for supporting this study as part of the doctoral research program. I would like to acknowledge the fine people in UC

Geology as well: faculty members, staff and fellow graduate students for their support over the last four years.

I would like to extend my gratitude to Juan Carlos Aravena (CEQUA) for introducing me to Mt. San Lorenzo; a jewel for the study of Quaternary glacial fluctuations. My sincere appreciation is especially due to Meredith Kelly

(Dartmouth College) for allowing me to use her lab and for the numerous insights she has shared with me regarding cosmogenic exposure age dating. And last, but certainly not the least, my deepest and most sincere gratitude is for

Summer Rupper (BYU) for introducing me to the modeling world; without her expertise, this dissertation may not have been possible.

I would like to acknowledge the Fulbright-Conicyt Doctoral Fellowship for financial support and assistance during my stay in the United States. This research was funded by: GSA Graduate Student Research Grants,

Department of Geology, Fondecyt 1080320 (JCA), NSF Grants EAR-1003072 (TVL) and EAR-1003460 (MK).

I would also like to thank to CONAF and particularly to Reserva Nacional Río los Cipreses, for the permission to work in the protected area. My especial appreciation goes to Tomás Gómez and Jesús Soto, the two most loyal field assistants ever! I would like to acknowledge the Crispín family, Fundación Altiplano MSV, Centro de

Estudios Avanzados en Zonas Áridas and Centro de Estudios del Cuaternario for logistic support.

Finally I would like to thank to all the “Andinos”, for preserving our heritage and keeping it alive… and of course, to my friends and family for always reminding me that life is, and always will be, more than science.

Table of Contents

Introductory remarks ……………………………………………………………………………………………………. 1

Chapter I: Climatology of Andean glaciers: a framework to understand glacier response to climate change…………………………………………………………………………………………………….. 10 Abstract…………………………………………………………………………………………………..... 10 Introduction………………………………………………………………………………………………... 11 Methods………………………………………………………………………………………………….... 12 Results…………………………………………………………………………………………………...... 14 Discussion………………………………………………………………………………………………... 18 Summary and final remarks…………………………………………………………………………...… 21 Acknowledgements………………………………………………………………………….…………… 21 References………………………………………………………………………………………………... 22 Tables and Figures……………………………………………………………………………………..… 26

Chapter II: Sensitivities of the equilibrium line altitude to temperature and precipitation changes along the Andes…………………………………………………………………………………………. 36 Abstract…………………………………………………………………………………………………..... 36 Introduction………………………………………………………………………………………………... 37 Methods………………………………………………………………………………………………….... 38 Results…………………………………………………………………………………………………...... 40 Discussion………………………………………………………………………………………………... 45 Summary and final remarks……………………………………………………………………………... 48 Acknowledgements………………………………………………………………………………………. 49 References………………………………………………………………………………………………... 49 Tables and Figures………………………………………………………………………………………. 52

Chapter III: Little Ice Age equilibrium line altitude along the Andes: paleoclimatic implications………. 61 Abstract…………………………………………………………………………………………………..... 61 Introduction………………………………………………………………………………………………... 62 Study areas………………………………………………………………………………………………... 63

Methods………………………………………………………………………………………………….... 64 Results…………………………………………………………………………………………………...... 69 Discussion……………………………………………………………………………………………….... 74 Summary and final remarks……………………………………………………………………………... 78 Acknowledgements………………………………………………………………………………………. 79 References………………………………………………………………………………………………... 79 Tables and Figures……………………………………………………………………………………….. 88

Final Remarks………………………………………………………………………………...…………………………… 100

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List of tables and figures

Introductory remarks Figure 1………………………………………………………………………………………………...... 24 Figure 2………………………………………………………………………………………………...... 25

Chapter I: Climatology of Andean glaciers: a framework to understand glacier response to climate change Table 1……………………………………………………………………………………………….…..... 26 Table 2……………………………………………………………………………………………………... 27 Table 3…………………………………………………………………………………………………...... 28 Figure 1………………………………………………………………………………………………...... 29 Figure 2………………………………………………………………………………………………...... 30 Figure 3…………………………………………………………………………...……………………….. 31 Figure 4………………………………………………………………………….………………………… 32 Figure 5………………………………………………………………………………………………...... 33 Figure 6……………………………………………………………………………………..……………... 34 Figure 7……………………………………………………………………………………..……………... 35

Chapter II: Sensitivities of the equilibrium line altitude to temperature and precipitation changes along the Andes Table 1……………………………………………………………………………………………….…..... 52 Figure 1………………………………………………………………………………………………...... 53 Figure 2………………………………………………………………………………………………...... 54 Figure 3…………………………………………………………………………...……………………….. 55 Figure 4………………………………………………………………………….………………………… 56 Figure 5………………………………………………………………………………………………...... 57 Figure 6……………………………………………………………………………………..……………... 58 Figure 7……………………………………………………………………………………..……………... 59 Figure 8……………………………………………………………………………………..……………... 60

Chapter III: Little Ice Age equilibrium line altitude along the Andes: paleoclimatic implications Table 1……………………………………………………………………………………………….…..... 88

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Table 2……………………………………………………………………………………………………... 89 Table 3…………………………………………………………………………………………………...... 90 Table 4…………………………………………………………………………………………………...... 91 Table 5…………………………………………………………………………………………………...... 92 Figure 1………………………………………………………………………………………………...... 93 Figure 2………………………………………………………………………………………………...... 94 Figure 3…………………………………………………………………………...……………………….. 95 Figure 4………………………………………………………………………….………………………… 96 Figure 5………………………………………………………………………………………………...... 97 Figure 6……………………………………………………………………………………..……………... 98 Figure 7...…………………………………………………………………………………………….…..... 99

Final Remarks Figure 1………………………………………………………………………………………………...... 106 Figure 2………………………………………………………………………………………………...... 107 Figure 3…………………………………………………………………………...……………………….. 108 Figure 4………………………………………………………………………….………………………… 109

Introductory remarks

Since the end of the nineteenth century, the Earth has experienced a drastic increase in global temperatures. Recent studies have shown that this trend will likely continue during the next century, with estimations of temperature increase that go from 0.6°C up to 4.0°C 1 (IPCC, 2007). In response to this change in the climate conditions, glaciers worldwide have undergone a generalized retreat and thinning (Dyurgerov and Meier, 2000;

Oerlemans, 2005). This trend has been particularly enhanced during recent decades (Casassa et al., 2007).

It has been recognized that the accelerate glacier melt could have important impacts on the environment and its inhabitants. Among the potential effects are: i) sea level rise (e.g., Meier, 1984; Jacob et al., 2012), ii) changes in water availability for human consumption, agriculture and energy generation (e.g., Vergara et al., 2009;

Mark et al., 2010), iii) substantial change in biota on glacier forefield ecosystems (Cannone et al., 2008), iv) increase in the possibility of potentially hazardous interactions between climatic and geological processes (e.g., outburst floods) (Clarke, 1982).

Considering the magnitude of the climate change projected for the next decades, and the potential

(catastrophic) consequences of glacier retreat, a thorough comprehension of climate-glacier interaction is critical in order to predict the response of glaciers to the different scenarios of climate change. Understanding the climate- glacier interaction is also crucial to reconstruct the climatic conditions associated with former glacial fluctuations, which in turn could provide important background information for the study of both natural cycles and human impacts on climate change (Matthews and Briffa, 2005; Solomina et al., 2008).

It is well established that glaciers a very sensitive indicator of climate changes, and that their dynamic is mainly controlled by changes in temperatures and precipitation (Oerlemans and Fortuin, 1992; Oerlemans, 1994;

1 Projections vary depending on the different scenarios of concentration of greenhouse gases (GHGs) and aerosols used. The lower bracket (0.6°C) represents a scenario that assumes that the concentration of GHGs and aerosols would remain constant since the year 2000.

Braithwaite and Zhang, 2000; Lowell, 2000; Oerlemans, 2005; Raper and Braithwaite, 2006; Fujita, 2008). However, disentangling the contribution of both factors in former glacial fluctuation has proven difficult (Smith et al., 2008), because it has been recognized that the relative impact of changes in temperature and precipitation may vary across different climatic regimes (Kaser, 2001; Rupper and Roe, 2008). This regional variability of glacier sensitivity to climate change could become extremely relevant in climatically diverse areas such as the Andes.

The Andes, along 9000 km of western South America, span a broad range of both latitude and altitude. This dictates a large range of temperature and precipitation conditions, resulting in an significant climatic diversity

(Garreaud et al., 2009). In the northern portion of the Andes (12°N to 23.5°S), climate is influenced by tropical

(Atlantic) circulation patterns. South of 23.5°S the tropical influence fades out and gives way to more Pacific influence. The area south of 31°S lies in the westerly circulation domain (see Rodbell et al., 2009 for a review of the climatic and glaciological conditions in each domain) (Fig. 1).

The Andes, with its diverse mosaic of climates, hosts numerous glaciers (Fig. 2). From the Equator to the southern tip of the continent (~55°S), the Andes exhibit an estimated glaciated area of 26,000 km2 (IAHS

(ICSI)/UNEP/UNESCO, 1988). These glaciers lie along the mountain crests and may extend down to sea level. The only exception corresponds to the area between 18°30’ and 27°S where, despite altitudes above 6000 m.a.s.l, the terrain is barren of glaciers due to its extreme aridity (Vuille and Ammann, 1997; Ammann et al., 2001). During the last 100 years Andean glaciers have followed a retreat trend similar to the rest of the glaciers worldwide. Although the estimated contribution to sea level rise of the complete melting of all Andean glaciers is comparatively minor (~3 cm) (Rignot et al., 2003), its impact on human livelihood is larger. Rural communities as well as urban centers across the Andes depend on water associated with runoff from glacierized basins for agriculture, potable water and power generation. For example, it has been calculated that 30 to 40% of potable water in La Paz, the capital city of Bolivia

(population 2.3 million), comes from glacier meltwater (Vergara et al., 2009).

This study aims to understand glacier sensitivity across the Andes and its implications for the interpretation of the geological record and paleoclimatic reconstruction. Toward that end, this dissertation is a compilation of three manuscripts (chapters), each one corresponding to a separate study.

- Chapter I:”Climatology of Andean glaciers: a framework to understand glacier response to climate change.”

Given that the Andes host an intricate mosaic of climates, one question to ask is “what are the climatic

conditions where present-day Andean glaciers occur?” This chapter systematizes, classifies, and identifies the

spatial distribution of the climates that permit the occurrence of present-day glaciers in the climatically diverse

Andes. A cluster analysis and a principal component analysis of the climatic variables (temperature, precipitation

and humidity) reveal that Andean glaciers occur under seven distinctive climate regimes. This climatic

classification of Andean glaciers provides a framework to test hypotheses about spatial variability of glacier

sensitivity to climate.

- Chapter II: “Sensitivities of the equilibrium line altitude to temperature and precipitation changes along the

Andes”. The equilibrium line altitude is where accumulation of snow exactly balances ablation (Porter, 1975;

Benn and Gemmell, 1997). Fluctuations of this metric produce changes in the mass balance of a glacier which,

in turn, responds to changes in climate (Porter, 1975; Porter, 2001; Benn et al., 2005). Hence, variations of the

equilibrium lines altitudes (ELA) are key to understanding and quantifying the magnitude of glacier fluctuation in

response to climate change. This chapter explores the response of the equilibrium line altitude to temperature

and precipitation changes throughout different climatic regimes across the Andes. The application of a full

surface energy mass balance (SEMB) model reveals a spatial variability in the magnitude of ELA changes to

temperature and precipitation perturbations across the glaciated regions of South America. The estimated

variability has a general correspondence with present climate settings throughout the Andes.

- Chapter III: “Little Ice Age equilibrium line altitudes along the Andes: paleoclimatic implications”. The Little Ice

Age (LIA, sensu lato AD 1300-1850) represents a time of generalize (worldwide) glacier expansion occurred

during the late Holocene (Porter and Denton, 1967). Identifying the climatic conditions during the LIA will not only

contribute to understanding the causes and mechanisms underlying this glacial event, but it will also provide the

basis for understanding the current climate change (Matthews and Briffa, 2005). This chapter explores the

timing, magnitude and climate of the LIA maxima across different climatic regimes along the Andes. First, the

change in the equilibrium line altitudes (ELAs) since the most extensive advance occurred during the LIA to the

present was estimated in three glacier sites. Then, a full surface energy mass balance model was applied in

order to reconstruct different scenarios of the climatic conditions (temperature and precipitation anomalies) that

accommodate the observed ELA fluctuations. The results reveal that the climate change following the LIA maxima

was not uniform throughout the study areas.

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FIGURE 1: Climatic domains, major wind patterns, and present-day location of the ITCZ across South America

(modified from Maslin et al., 2000).

FIGURE 2: Map of glacier distribution in South America, modified from Casassa et al. (2007). Crosses denote individual glaciers. Dark gray polygons represent major glaciers bodies.

9 Chapter I:

Climatology of Andean glaciers: a framework to understand

glacier response to climate change

Manuscript published in the journal of Global and Planetary Change:

Sagredo E.A. and Lowell, T.V. (2012) “Climatology of Andean glaciers: a framework to understand glacier response to climate change.” Global and Planetary Change 86-87: 101-109.

Abstract

Recent glacial and climate models suggest that glaciers located in contrasting climates could respond with different magnitudes to identical climatic perturbations. This implies that to understand the response of glaciers to a particular climate perturbation or to compare glacial fluctuations between different regions, climate conditions that permit glaciers to exist must be taken into account. In this study we systematize, classify, and identify the spatial distribution of the climates that permit the occurrence of present-day glaciers in the climatically diverse Andes. A first approximation suggests that a sample of 234 Andean glaciers exist under three distinctive combinations of temperature and precipitation conditions: i) cold and dry, ii) intermediate, and iii) warm and wet conditions. Cluster analysis (CA) and Principal Component analysis (PCA) of temperature, precipitation, and humidity reveal seven climatic configurations that support present-day Andean glaciers and suggest that these configurations have a distinctive geographical distribution. The groups are: 1) inner tropics and Tierra del Fuego, 2) wetter outer tropics, 3) drier outer tropics, 4) subtropics, 5) central Chile-Argentina (semi-arid), 6) northern and central Patagonia, and 7) southern Patagonia. This classification provides a basis to examine the spatial variability of glacier sensitivity to climate change, to unravel the causes of past glacial fluctuations, to understand the climatic signals driving present- day glacier fluctuations, and perhaps to predict the response of glaciers to future climate changes.

10 Introduction

Valley glaciers are responsive systems to climate change (Dyurgerov and Meier, 2000; Lowell, 2000;

Oerlemans, 2005; Rupper et al., 2009). Records of former glacial fluctuations have been used extensively to reconstruct paleoclimate at different temporal and spatial scales (e.g., Rodbell, 1992; Oerlemans, 1994; Lowell et al., 1995; Denton et al., 1999; Klein et al., 1999; Porter, 2001). However, these reconstructions typically do not account for variations in regional climate, which likely controls the magnitude of glacier response to climatic perturbations (Rupper and Roe, 2008). Here, we present a climatic classification of Andean glaciers that can serve as the basis to improve the understanding of glacier response to climatic perturbations and thus to extract the climatic signals embedded in the glacial record.

Advances in glacial and climate modeling have improved our understanding of the climate-glacier relationship (e.g., Oerlemans and Fortuin, 1992; Seltzer, 1994; Braithwaite and Zhang, 2000; Kaser, 2001; Raper and Braithwaite, 2006; Fujita, 2008a). These studies suggest that glaciers subjected to different climatic regimes could respond with different magnitudes to similar climatic perturbations (Kaser, 2001; Favier et al., 2004; Fujita,

2008b; Rupper and Roe, 2008). For example, it has been suggested that glaciers in wetter settings are more sensitive to warming than those in drier areas (Meier, 1984; Oerlemans and Fortuin, 1992; Raper and Braithwaite,

2006; Rupper and Roe, 2008). Kaser (2001) and Kaser and Osmaston (2002) proposed that glaciers located above the mean annual 0°C isotherm (typical of arid regions) are very sensitive to changes in precipitation and insensitive to changes in temperature. Dropping temperatures will not change the rain/snow ratio of these glaciers, and hence, will not affect their net mass balance. In contrast, small changes in precipitation (i.e., accumulation) would directly increase or decrease the net mass balance (Klein et al., 1999; Rodbell et al., 2009). Recently, Fujita (2008a; 2008b), suggested that glaciers with summer accumulation are more sensitive to temperature than those with winter accumulation.

The above suggests that to compare glacial fluctuations in distant areas and to understand the climatic signal responsible for those fluctuations, a first step might be to identify and understand the spatial distribution of the climates that permit the presence of glaciers in those areas.

The Andes, along 9000 km of South America, span a broad range of both latitude and altitude. This dictates a large range of temperature and precipitation conditions, resulting in a mosaic of climates (Garreaud et al., 2009)

11 (Fig.1). In the northern portion of the Andes (12°N to 23.5°S), climate is influenced by tropical (Atlantic) circulation patterns. South of 23.5°S the tropical influence fades out and gives way to more Pacific influence. The area south of

31°S lies in the westerly circulation domain (see Rodbell et al., 2009 for a review of the climatic and glaciological conditions in each domain). This climatic diversity could be responsible for some of the variability in the response of

Andean glaciers to large-scale climatic perturbations.

In this paper, we systematize, classify, and identify the spatial distribution of the climates that permit the occurrence of present-day glaciers throughout the Andes.

We suggest that our classification provides a framework to understand the spatial variability of glacier sensitivity to climate change, and has important implications for the study of past glacial fluctuations, paleoclimatic reconstructions, the climatic signals driving present-day glacier fluctuations across the Andes, and perhaps the response of these glaciers to future climate changes.

Methods

Based on a visual inspection of Google Earth satellite imagery, we assembled a sample of 234 small

Andean glaciers (~< 2 km), with simple geometry, from 12°N to 55°S. Special attention was paid in gathering a spatially distributed sample of glaciers to represent all the climatic regimes existing in the region. Whereas the World

Glacier Inventory (National Snow and Ice Data Center, 1999, updated 2009) offers one of the most extensive collections of Andean glaciers in terms of sample size (N>7000), it does not cover all the glaciated regions. Cogley

(2009) presented an updated and more complete version of this inventory, offering an important improvement in terms of spatial coverage, particularly for the outer tropics and Patagonia. However, this dataset includes a group of glaciers between 18°30’ and 27°S, originally described by Garín (1987), that are likely to be snow patches (Ammann et al., 2001; Casassa et al., 2007). On the other hand, Casassa et al. (2007) presented an inventory with a more accurate representation of the present-day distribution of Andean glaciers. Our sample strategy produced a spatially representative subsample of the inventory developed by Casassa et al. (2007) (Fig 2).

For each glacier, the local climate (temperature, precipitation, and humidity) was derived from CRU CL 2.0, a 10’ latitude/longitude resolution dataset of mean monthly surface climate over global land areas (New et al., 2002).

12 Temperatures were extrapolated to the location and elevation of the glaciers’ midpoints, utilizing a lapse rate of

0.0065 °C m-1. The complexity of the processes controlling precipitation and humidity, as well as the limited information available, precludes the applicability of any method of vertical extrapolation of moisture to a large latitudinal range. Although this approach would not be sufficient to provide rigorous mass balance insights, these data provide a first approximation of climate at each glacier site.

A visual examination of the relationship between mean annual temperature and total annual precipitation from each glacier site suggests that our sample of glaciers falls into three groups (see below). This relationship prompted us to classify our sampled glaciers on a more formal basis using a more extensive collection of climatic variables (Table 1). The grouping was carried out using Cluster analysis (CA), and refined by a Principal Component analysis (PCA). Both statistical analyses were performed using PC-ORD statistical package (McCune and Mefford,

2011). Due to the different nature of the variables and their dissimilar ranges of variability, all data were scaled to lie between 0 and 1, using a percent-range transformation. This transformed dataset was the base for all subsequent analyses.

Cluster analysis is a statistical technique designed to classify samples into groups based on the degree of similarity among them with respect to a defined set of variables. CA has proven to be a useful technique to classify glaciers since the late 1980s (Lai and Huang, 1989). Here we used Euclidean distance as a measurement of similarity. Euclidean distance represents the linear distance (calculated by a multivariate extension of the

Pythagorean equation) between any pair of glaciers in a multidimensional Cartesian space, where the number of dimensions reflects the number of variables used. Clusters on the dendrogram produced by this analysis were defined by grouping the most similar samples (glaciers) or groups of samples, using the Unweighted Pair-Group

Method with Arithmetic Averaging (UPGMA).

The results of the Cluster analysis classification were compared and refined with a Principal Component

Analysis. PCA is a multivariate statistical technique that seeks to reduce the dimensionality of a dataset by diagnosing correlations among variables (in this case climatic variables) and essentially representing them as a smaller set of uncorrelated (orthogonal) variables called principal components. Thus, principal components are linear combinations of the initial (climatic) variables that geometrically capture directions of maximum variation in the data

13 (McCune and Mefford, 2011). Correlation coefficients between principal components and climatic variables were calculated by comparing sample (glaciers) scores for a given principal component against the transformed variable values for the samples. A high positive correlation for a given variable implies that the variable's highest values occur in samples that score highly on the principal component. A high negative correlation indicates that the variable's highest values occur in samples with low scores on the principal component.

Finally, we performed an analysis of similarities (ANOSIM) using the [R] statistical program (R Development

Core Team, 2008) to test the statistical significance of our classification. ANOSIM is a nonparametric method for testing whether there is a significant difference between two or more groups of elements (in this case glaciers) that have been previously defined (Seaby and Henderson, 2007).

Results

Temperature and precipitation vary greatly in the Andes (Fig.3). However, Figure 4 shows that our sample of glaciers occupies distinct and non-continuous sets of temperature and precipitation conditions. Specifically, three distinctive groups are present: 1) cold and dry conditions (mean annual temperature, (MAT) -7.0 to -1.8°C; total annual precipitation (TAP) 120 to 620 mm/yr); 2) intermediate conditions (MAT -1.5 to 3.6°C; TAP 520 to 1100 mm/yr); and 3) relatively warm and wet conditions (MAT -0.8 to 5.9°C; TAP 1400 to 2790 mm/yr). Figure 4 shows that whereas cold and dry glaciers are distinctive with regard to temperature, they exhibit some overlap with the intermediate group in terms of precipitation. On the other hand, warm and wet glaciers are unique with respect to precipitation; however, in terms of temperature, they have some overlap with the intermediate group. These three groups show distinct spatial distributions. Glaciers in the cold and dry group are located in the high elevations of the dry Andes, around the Atacama Desert. Warm and wet glaciers are under the permanent/semi-permanent influence of the westerly winds. This coincides with where the Andes are relatively low (~<2500 masl). Intermediate glaciers are located in the inner and outer (eastern Cordillera) tropics, central Chile and Argentina, and Tierra del Fuego (Fig.

4).

An analysis of additional climatic variables (Table 1) allowed subdivision of these groups. Figure 5 shows results of CA in the form of a dendrogram. A dendrogram is a tree diagram that graphically represents the

14 arrangement of elements (in this case glaciers) into clusters based on their degree of similarity (Raux et al., 2011).

The analysis classifies our 234 glaciers into six major groups, and these have a distinct spatial pattern (Fig. 5).

Groups 1, 2 (inner and wet outer tropical glaciers) are subgroups of the intermediate glaciers. Group 3 and most glaciers in group 4 (dry outer tropics and subtropics) correspond to the group classified as cold and dry. The southernmost glaciers of group 4 can be classified as intermediate glaciers. Finally, groups 5 and 6 (Patagonian glaciers) represent subgroups of the warm and wet group of our initial classification.

In order to refine this classification, we ran a PCA using the same dataset. Figure 6 shows the PCA results for the first three principal components. These three principal components explain 80.1 % of the total variance (PC1 explains 43.5%, PC2 19.9% and PC3 16.8%). PC1 exhibits high correlation with total precipitation (r= -0.879), elevation of the mean annual 0°C isotherm with respect to the minimum elevation of the glacier (r= -0.823), seasonality concentration index of the precipitation (r= 0.8015), mean temperature (r= -0.7619) and mean humidity

(r= -0.7057). PC2 correlates with the annual temperature range (r= 0.8173) and precipitation seasonality index (r=

0.7578). PC3 is highly correlated only with annual precipitation range(r= 0.7062) (Table 2). So it appears that PC1 tracks the climate at the glacier locations whereas PC2 and PC3 reflect the climate variability.

The PCA returned results similar to the CA. The one difference between the two analyses arises in the initial classification of group 4. The PCA approach suggests that group 4 may actually be subdivided into two independent groups. Even though these two groups can be identified in the CA, the values of similarity between them are too high to be considered as individual groups. When analyzed from a spatial perspective, results from PCA suggest that the splitting of group 4 occurs at 33°S. This is close to the latitude where the Pacific anticyclone permits occasional penetration of low-level frontal systems to the north during midwinter (Miller, 1976), marking the boundary between a wetter area (to the south) and a drier area (to the north). Lliboutry (1998) placed the divide between the Dry and Wet

Andes slightly to the south, at 35°S.

Although both Cluster and Principal Component analyses allow the classification of the samples into groups, they do not assure that the groups are statistically significant. ANOSIM is a nonparametric method for testing whether there is a significant difference between two or more groups of elements (glaciers) that have been previously defined.

The test statistic R scales from 1 to -1. A value of R=1 indicates that all of the most similar samples are within the

15 same groups; while R=0 occurs if the high and low similarities are perfectly mixed and bear no relationship to the group, and a R value of -1 indicates that the most similar samples are all outside of the groups (Seaby and

Henderson, 2007). The variable p is the estimated probability that the observed R could be generated by chance.

When we assessed our initial classification (CA) ANOSIM returned a statistic R=0.9675 (p=0.001). For the PCA classification we obtained a slightly higher R value of 0.9714 (p=0.001).

These ANOSIM results suggest that both classifications are robust, and that the climate factors affecting glaciers within any one group are significantly more similar than those affecting glaciers belonging to different groups.

However, considering that the R statistic is slightly higher for the PCA classification, and the climatic significance of the separation between groups 4 and 5 (group 4 in the CA classification), we consider that the PCA classification captures the climatic variability in the Andes better and hence this classification is used below.

Next, we describe each group as delineated by the PCA analysis (Fig.7 and Table 3). Herein all seasons referred to are austral seasons (summer= DJF; fall= MAM; winter= JJA; spring=SON).

The first group (group 1= g1) is located in the inner tropics. In this region, temperature varies little throughout the year, and the same is true of the elevation of the 0°C isotherm (Klein et al., 1999). Even though these glaciers receive precipitation all year round (total annual precipitation (TAP): 918 mm/yr), the accumulation pattern exhibits some seasonality. During the spring and fall months, when a convection zone (continental expression of the

Intertropical convergence zone, ITCZ) crosses the area in its annual migration cycle, these glaciers receive extra precipitation, defining two slightly “wetter seasons.” Nogami (1972) estimated the snowline to be between 4500 and

5000 m.a.s.l. in this area. It is interesting to notice that this group also includes a small set of sites in Cordillera

Darwin near the southern tip of the continent (Fig. 7). These glaciers have uniform precipitation throughout the entire year, with the total amount close to that observed in the inner tropics (TAP: 792 mm/yr). The main difference between these southern glaciers and their tropical counterparts is the temperature cycle. The annual temperature range in

Cordillera Darwin is ~7.4°C, whereas in the inner tropics it is less than 1.1°C.

The second group (g2) comprises glaciers of the Western Cordillera of Peru (north of 13°S), and Eastern

Cordillera of Peru and Bolivia (south of 13°S). These glaciers can be divided in two sub-groups. North of 13°S (g2.1) temperature is seasonally uniform (near 0°C), and average humidity is 71%. South of 13°S (g2.2), the temperatures

16 oscillate more than 4°C throughout the year, and the humidity is slightly lower (~59%).The accumulation in these outer tropical glaciers occurs during the summer months. Precipitation affecting g2.1 is mainly related to the southward shift of the convection zone (Garreaud et al., 2009). In the southern portion (g.2.2), on the other hand, the precipitation responds to a more complex circulation pattern, including southward displacement of the ITCZ, establishment of the Bolivian High and the formation of the Chaco Low (and the low-level jet). Some authors have described this circulation pattern as the South American summer monsoon (Zhou and Lau, 1998). The snowline in this area is located above ~5000 m.a.s.l. (Nogami, 1972).

The next group (g3) is represented by glaciers in the western Cordillera of Peru and Bolivia (south of 15°S), and the north of Chile-Argentina (north of 19°S). These glaciers are situated in the rain shadow of the eastern

Cordillera, and receive small amounts of tropical precipitation during the summer months. Glaciers within this group are subjected to extremely cold conditions. Every glacier in this group is located above the mean annual 0°C isotherm and persists despite very low humidity (~50%) values. The snowline for these glaciers has been estimated at ~5600 m.a.s.l. (Arnaud et al., 2001; Smith et al., 2008).

A fourth group of glaciers (g4), located between 27° and 31°S, is separated from the previous group by a distance of more than 900 km (between 18°30’ and 27°S). This gap, despite altitudes above 6000 m.a.s.l, is barren of glaciers due to its extreme aridity (Vuille and Ammann, 1997; Ammann et al., 2001; Casassa et al., 2007). G4 glaciers are subjected to even colder conditions than the previous group. During the summer, g4 is fed with moisture brought by low-level jets resulting from the southward channelization of easterly winds between the eastern slopes of the Andes and the Brazilian Plateau (Marengo et al., 2004). On the other hand, during the winter, the precipitation is linked to cut-offs of cold air masses from the Pacific (Vuille and Ammann, 1997). Hence, the small amount of precipitation received (~300 mm) is distributed throughout the entire year, with slightly more during the winter months. The snowline in the area varies between ~6000 and 5000 m.a.s.l, with lower values toward the south

(Nogami, 1972; Ammann et al., 2001).

Immediately to the south, and closely related with g4, g5 extends to ~38°S. This group is under the influence of westerly winds, which, during the winter months reach the area (Garreaud et al., 2009), producing a wet season ~four months long (December through March). These glaciers are subjected to mean monthly temperature

17 ranging from -5.3°C in the winter to 5.5°C in the summer. The snowline in this area decreases from 5000 m.a.s.l. in the north to ~2800 m.a.s.l. in the south (Nogami, 1972).

Our sixth group (g6) is composed of glaciers in northern and central Patagonia between 38° and 49°S, which is the same geographical area as the outlet glaciers from the Northern Patagonian Icefield. These glaciers extend well below the mean annual 0°C isotherm, and the area is under the permanent influence of the westerly winds. The snowline is generally below 2000 m.a.s.l. There is a strong north-south gradient in the seasonal concentration of precipitation. This gradient allows individualizing two subgroups: northern (~ north of 42°S) and central Patagonian glaciers (~ between 42 and 49°S), with the northernmost group receiving higher precipitation during the winter months.

To the south of 49°S, we identified a final group of glaciers (g7), which includes the southern Patagonian

Icefield outlet glaciers. The main distinction between this group and the previous one is that in this area glaciers are subjected to precipitation uniformly throughout the entire year.

Discussion

Glaciers currently exist in a variety of climates along the Andes. A visual examination of the mean annual temperature and total annual precipitation suggests that our sample of glaciers (N=234) can be divided into three groups: 1) cold and dry, 2) intermediate, and 3) relatively warm and wet. However, a more thorough statistical analysis, including a more extensive collection of climatic variables shows that Andean glaciers can be classified into seven climatic groups, each with a distinctive geographical distribution (Fig. 7). The groups are 1) inner tropics and

Tierra del Fuego, 2) wetter outer tropics, 3) drier outer tropics, 4) subtropics, 5) central Chile-Argentina (semi-arid), 6) northern and central Patagonia, and 7) southern Patagonia. Thus, our classification consists of groups and subgroups of glaciers organized in latitudinal bands between ~5 and 15° wide, affected by similar climatic conditions.

The only discrepancy is in the outer tropics where we found two distinct groups (g2.2 and g3) longitudinally juxtaposed, reflecting the climatic topographic control exerted by the Andes.

With the exception of the subgroup in Tierra del Fuego (g1.2), groups 1, 2 and 3 are under the

Atlantic/tropical influence; whereas groups 5, 6 and 7 form part of the Pacific/westerly domain. Group 4 represents a

18 transition zone, affected by both climatic domains. This explains the change in seasonality of precipitation between the northern and southern groups. It is interesting to note that glaciers in Tierra del Fuego are located to the south of the core of the westerlies, receiving less precipitation than neighboring Patagonian glaciers. This and the uniform distribution of the precipitation throughout the year place glaciers in the same climate group as the inner tropical glaciers, despite their distant location.

It has been suggested (e.g., Rupper and Roe, 2008), that glaciers in different climatic regimes could have different responses to identical climatic perturbations. If so, our classification of the Andean glaciers offers a framework to test this hypothesis. To illustrate, let us consider scenarios of climate change and the theoretical response of Andean glaciers. Suppose a uniform increase in the mean temperature of ~1°C affects South America.

A rise in temperature will increase net ablation by introducing more energy into the system and by prolonging the melting period. For those summer-accumulation type glaciers (glaciers that receive precipitation during summer months: i.e., g1, g2 and g3), this warming would also decrease snow accumulation (Fujita, 2008a). Hence, it is expected that summer-accumulation-type glaciers would undergo comparatively larger retreats (or rises in ELA) than winter-accumulation-type glaciers. For very dry and cold glaciers (g3 and g4), melting will start only after the temperature increases enough to displace the summer 0°C isotherm to the elevation of the glacier. Thus, in the

Andes, for a simple, uniform temperature rise, those glaciers in the inner tropics (g1, except Tierra del Fuego) and wet outer tropics (g2) would represent the most sensitive ones.

Conversely, dropping temperatures would have a larger impact on wetter environments (g6 and g7), where the increase of the snow/rain ratio will have a greater impact on the mass balance. Again, those glaciers in dry and cold settings (g3 and g4) will be the least responsive to this perturbation. Since these glaciers are already above the mean annual 0°C isotherm, a drop in temperature should not translate into any significant mass balance change.

Hence, Patagonian and inner tropic glaciers are the most sensitive to dropping temperatures. In the case of

Patagonian glaciers, as the mean temperature increases and precipitation begins to be more uniformly distributed throughout the year, their sensitivity increases southward (from g6.1 to g7).

Changes in precipitation would have a comparatively greater impact on glaciers in cold and dry environments (g3 and g4), where temperatures allow snow accumulation throughout the entire year. In these

19 environments where sublimation is the main ablation process, the energy required to ablate this extra snow is extremely high, being very difficult to balance with the extra accumulation. A decrease in precipitation in these dry environments will decrease the accumulation to very low values. Thus, it is expected that glaciers in the dry outer tropics (g3) and subtropics (g4) are the most responsive to changes in precipitation (e.g., Ammann et al., 2001;

Kaser, 2001; Smith et al., 2008).

Even though this classification provides a framework to understand the glacier response to independent changes in temperature and precipitation, it may not reflect the relative importance of these two variables (in relation to each other) as controlling factors of the ELA fluctuation. Our limited understanding of the relative importance of temperature and precipitation as controlling factors of glacial fluctuations, which is strongly bias toward mid-latitudes regions (e.g., Braithwaite and Zhang, 2000; Oerlemans, 2005), precludes the prediction of the glacier response to the combined effect of these two variables over a climatically diverse area as the Andes.

The regional variation of glacier sensitivity could have important implications for the interpretation of the geologic record. Consider the following scenario: the entire Andes undergoes three uniform major climatic perturbations: 1) a drastic drop in temperature followed by warming, 2) a drop in temperature followed by warming, and 3) an increase in precipitation followed by warming. Under these circumstances, glaciers in Patagonia (g6 and g7, wetter and warm environments) would theoretically undergo advances of decreasing magnitude associated with each perturbation. As a consequence, the geological record will likely preserve three moraine systems, each one smaller (closer to the present-day ice front) than the previous one. In contrast, in the arid Andes (g3 and g4) the increase in precipitation will probably trigger a more prominent glacier advance than the second drop in temperature.

The glacier fluctuation in response to the increase in precipitation will likely erode all surficial evidence of the previous two glacier advances, leaving evidence of only one moraine set. Thus, identical climatic changes can be recorded differently in different climatic regions.

Rodbell et al. (2009) presented a comprehensive review of Late Glacial and Holocene glacial fluctuations in the Andes. Rodbell et al.’s synthesis does not uncover any strong pattern in the glacial chronologies throughout the region, even at millennial time scales. Such a lack of pattern has been usually attributed to insufficiently precise chronologies (Grove, 2004) and unevenly distributed records (Denton and Broecker, 2008). However, the recent increase

20 in the number, quality and distribution of glacial chronologies in the Andean region has not unraveled any clear continental or even regional pattern. We suggest, as an alternative/complementary hypothesis, that the discrepancies of the glacial chronologies could be, at least partially, a consequence of regional glacier sensitivity reflecting the climate groupings outlined above.

Summary and final remarks

 Andean glaciers are subjected to a variety of climates. A statistical analysis of the climatic variables

(temperature, precipitation and humidity) permits the classification of Andean glaciers into seven distinctive

groups.

 The average climatic conditions for each group can be considered mean climate states that support the

presence of Andean glaciers.

 The distinct spatial distribution of these different climate groups provides a framework to test hypotheses on

spatial variability of glacier sensitivity to climate

 If glaciers in different climatic regimes respond with different magnitudes to identical climatic perturbations, as

has been suggested before, our classification of Andean glaciers could have important implications for the

understanding of the causes and mechanisms involved in past and present episodes of glacial fluctuations,

and perhaps for predicting the response of glaciers to future climate changes.

Acknowledgments

E.A. Sagredo acknowledges support from Fulbright-Conicyt Doctoral Fellowship and expresses his thanks to the Department of Geology at the University of Cincinnati for supporting this study as part of his doctoral research.

This work was supported by NSF Grant (EAR-1003072). We thank Colby A. Smith for valuable discussion and insightful comments on the manuscript. We also acknowledge Arnold I. Miller, Gary J. Motz and Anne J.

Lagomarcino for their helpful advice in statistical analyses. Special thanks to Roger J. Braithwaite and J. Graham

Cogley for the exhaustive review of the manuscript and their constructive comments.

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TABLE 1: Climatic variables used for the grouping of Andean glaciers.

Temperature Precipitation Humidity

Annual mean Total annual Annual mean

Annual range Annual range Annual range

Altitude of the Seasonality b 0oC isotherm a

a Value with respect to the elevation of the snout of the glacier. b Seasonality was assessed based on two indices (Fujita 2008a): Precipitation Seasonality Index (PSI) and Seasonality Concentration Index (SCI). PSI is defined as the number of months that separate the hottest month and the wettest month; SCI is defined as the standard deviation of the 12 monthly values of precipitation divided by their average.

TABLE 2: Correlation coefficients between the scores for a given principal component and the transformed variable values for the samples.

Principal Components Variables 1 2 3 Mean temperature -0.7619 -0.4534 0.2657 Annual temperature range -0.3635 0.8173 0.2019 Elevation annual mean 0°C isotherm -0.8233 -0.2695 0.1388 Annual precipitation -0.879 0.017 0.2673 Annual precipitation range -0.3737 0.0753 0.7062 Seasonality of precipitation (PSI) -0.534 0.7578 0.0035 Concentration of precipitation (SCI) 0.8015 -0.0268 0.4788 Mean Humidity -0.7057 -0.4405 -0.2917 Annual humidity range 0.4287 -0.2608 0.7025

TABLE 3: Average climatic conditions affecting each group of glaciers.

Glacier Groups

2 6

1 2.1 2.2 3 4 5 6.1 6.2 7 - - Mean annual temperature (°C) 1.2 1.6 1.6 0.0 1.4 2.8 3.7 4.0 4.5 Total annual precipitation (mm) 885 815 723 287 301 723 2119 1749 1996 Mean annual humidity (%) 84 71 59 50 51 52 72 74 67

FIGURE 1: Climatic domains, major wind patterns, and present-day location of the ITCZ across South America

(modified from Maslin et al., 2000).

FIGURE 2: Map of glacier distribution in South America. Crosses denote individual glaciers. A: Detailed inventory of glacier bodies from World Glacier Inventory (National Snow and Ice Data Center, 1999; updated 2009). Densely shaded areas are the product of superposition of crosses, indicating numerous glaciers in close proximity. This collection of more than 7000 glaciers does not cover some regions, particularly the outer Tropics and southernmost

South America. B: World Glacier Inventory - Extended Format, an updated and more complete version of the World

Glacier Inventory (N=~9000 glaciers) (Cogley, 2009). This dataset includes a group of glaciers between 18°30’ and

27°S, originally described by Garín (1987), that are believed to be snow patches (Ammann et al., 2001; Casassa et al., 2007). C: Distribution of glaciers in South America, modified from Casassa et al. (2007). Dark gray polygons represent major glaciers bodies. This inventory captures more accurately the present-day distribution of Andean glaciers. D: Inventory of 234 Andean glaciers used in this study. This sub-sample includes glaciers from all of the climatic regimes existing in the region.

FIGURE 3: Climatic conditions across the Andes. Smoothed mean annual temperature (A) and annual precipitation

(B) from the high-resolution data set of surface climate model CRU CL 2.0, 10’ lat/long resolution (New et al., 2002).

FIGURE 4: Mean annual temperature and total annual precipitation that support present-day Andean glaciers.

Climatic information for each glacier locality (N=234) was extracted using the surface climate model CRU CL 2.0, (10’ lat/long resolution, New et al. 2002). Values of temperature were adjusted to the elevation of the mid-point of the glacier using a lapse rate of 0.0065 °C m-1. This exercise reveals three major groups delineated by temperature and precipitation: i) warm and wet, ii) cold and dry, iii) intermediate glaciers. The spatial distribution of each group is given on the right side of the figure.

FIGURE 5: Cluster analysis of Andean glaciers. Left panel: Dendrogram (hierarchical tree) representing similarity between glaciers regarding the climatic variables at each location. Each leaf of the dendrogram (terminal in the left side of the dendrogram) represents a single glacier (N=234). Each vertical line links glaciers or groups of glaciers at certain levels of similarity. Right panel: Spatial distribution of groups of glaciers across the Andes.

FIGURE 6: Principal Component Analysis results in two dimensions (with PC1/PC2, PC1/PC3, PC2/PC3) and three dimensions (PC1/PC2/PC3). These three axes explain 80.11% of the total variance (PC1 explains 43.46%, PC2

19.88% and PC3 16.77%). The legend includes groups derived from Cluster analysis (CA) and Principal

Components analysis (PCA) for comparison. Group 4 in the CA classification was split into two independent groups

(4 and 5) in the PCA classification.

FIGURE 7: Distribution and mean climatic conditions for groups of Andean glaciers. Groups correspond to PCA classification. Color scheme is the same as in Figure 4. Plots represent mean climatic conditions within each group.

The horizontal axis represent the month of the years (from January throughout December). The bars represent monthly precipitation (mm), solid lines show mean monthly temperature (°C) and dashed lines represent mean monthly humidity (%). Axes plotted with same ranges to facilitate comparisons between groups.

35 Chapter II:

Sensitivities of the equilibrium line altitude to temperature and

precipitation changes along the Andes

Manuscript under review (2012) in the journal of Quaternary Research:

Sagredo E.A., Rupper, S. and Lowell, T.V. (in review) “Sensitivities of the equilibrium line altitude to temperature and precipitation changes along the Andes. Quaternary Research

Abstract

Surface energy and mass balance modeling in the South American Cordillera reveals strong spatial variability in the relative sensitivities of the equilibrium line altitudes to temperature and precipitation changes. Equilibrium line altitudes (ELA) of alpine glaciers are sensitive indicators of climate change that have been extensively used to reconstruct paleoclimates at different temporal and spatial scales. Model results showed that regional climate modulates

ELA sensitivity of individual glaciers to large-scale climate perturbations. We applied a full surface energy and mass balance model to quantify ELA sensitivity to temperature and precipitation changes across the climatically diverse Andes.

We find that ELA respond linearly with changes in temperature, with the magnitude of the response being prescribed by the local lapse rates. In contrast, ELA sensitivities to precipitation changes are nonlinear and are inversely correlated with the emissivity of the atmosphere. Hence, ELA is more sensitive to changes in precipitation in dry areas. Temperature sensitivities are greatest in the inner tropics; precipitation becomes more important in the subtropics and the northernmost mid-latitude regions. These modeling results provide a framework for understanding and comparing past episodes of glacial fluctuations throughout the Andes and ultimately for predicting the response of glaciers to future climate changes.

36 Introduction

Since the end of the nineteenth century, the Earth has been subjected to drastic and accelerated climate change (IPCC, 2007). As a consequence, glaciers and ice sheets have retreated and thinned worldwide, which has been particularly enhanced during recent decades (Casassa et al., 2007; Dyurgerov and Meier, 2000; Oerlemans,

2005). A thorough understanding of the climate-glacier dynamic interaction is critical for understanding the full impact of future climate changes and for reconstructing past climate changes recorded in the glacial record (e.g., Anderson and Mackintosh, 2006; Laabs et al., 2006).

Numerous studies have explored the climate-glacier relationship by applying numerical models to understand the glacier response to specific climatic perturbations, or the inverse approach, to identify the climatic signals responsible for specific glacier fluctuations (e.g., Braithwaite and Zhang, 2000; Favier et al., 2004; Fujita,

2008a; Fujita, 2008b; Kaser, 2001; Oerlemans and Fortuin, 1992; Raper and Braithwaite, 2006; Rupper and Roe,

2008; Seltzer, 1994). These studies suggest that local climate plays an active role through modulating the magnitude of glacier response to specific climate perturbations, leading to the idea of the existence of a regional variability of glacial sensitivity to climate change. For example, using a full surface energy and mass balance (SEMB) Rupper and

Roe (2008) and Rupper et al. (2009) showed that the relative ELA sensitivity to changes in temperature and precipitation is strongly tied to the dominant ablation process, which in turn is determined by the pattern in accumulation (Rupper and Roe, 2008). In areas with high precipitation, the ablation at the ELA is dominated by melting and surface runoff; and the ELA is more sensitive to changes in temperature. Conversely, in regions that receive little precipitation the ablation is dominated by sublimation, and the ELA is more sensitive to precipitation than ablation. These lessons likely apply to other regions of the world, but the method pioneered by these studies has not been applied elsewhere.

The Andes of South America is a relatively simple mountain chain that hosts a range of climates across a wide latitude range. Based on statistical analysis (i.e., cluster and principal component analysis) of the temperature, precipitation and humidity, Sagredo and Lowell (in press) classified the regions where present-day glaciers exist into seven distinct climatic groups (Fig. 1).

37 Here, we apply a full SEMB model, developed for Rupper and Roe (2008), to explore the response of the

Equilibrium Line Altitudes (ELA) to temperature and precipitation changes, throughout different climatic regimes across the Andes.

Methods

Surface Energy and Mass Balance model: general principles

A full surface energy and mass balance (SEMB) model was used to explore the variability of ELA response to climate perturbations across the range of climates that currently support glaciers throughout the Andes. Details of this SEMB model can be found in Rupper and Roe (2008). The model seeks the equilibrium line altitude, or the elevation at which total annual ablation equals the total annual accumulation (Rupper and Roe, 2008). This ELA is calculated based on climatological variables (“climatological ELA”), and dismisses any ice dynamics and topographic effects (such as shading and avalanching) controls on the ELA.

The model includes two algorithms: a surface energy balance and a mass balance. The surface energy balance model solves for the energy available for ablating, following

Q = S + L + Qs + Ql, where Q is energy available for melting snow/ice, S is the shortwave radiation flux absorbed at the surface, L is the net longwave radiation flux , Qs in the sensible heat flux, and Ql is the latent heat flux. Heat conduction at the glacier surface is neglected because it is likely small compared to the terms we considered (Kayastha et al., 1999). Melting

2 takes place when the surface temperature (Ts) equals 0°C and Q is greater than 0 Wm while evaporation occurs when Ts equals 0°C and the evaporation vapor pressure of the air (ea) is lower than the saturation vapor pressure at the surface (es). Sublimation occurs when Ts<0°C and es

38 The mass balance algorithm iteratively calculates the temperature (Tsoln) that results in a total annual ablation that is equal to the total annual accumulation, using the energy balance algorithm. This module assumes total annual accumulation at the ELA to be equal to the total annual precipitation. Once Tsoln is calculated, the model solves for the elevation at which that temperature is found, using a climatological lapse rate. Hence, by coupling algorithms for energy balance and mass balance, this model seeks the elevation at which a snow-covered surface is in mass and surface energy balance. By definition, the model solves for the ELA.

Application of the model

The SEMB model was applied to those areas in the Andes that presently host glaciers. By doing so, spurious results from areas unlikely to develop glaciers were avoided.

For model input, we used gridded climatological data from the surface climate model CRU CL 2.0 (New et al., 2002). CRU CL 2.0 is a 10’ latitude/longitude resolution dataset of mean monthly surface climate over global land areas, excluding Antarctica, interpolated from a dataset of station means for the period centered on 1961 to 1990.

Unfortunately, this high-resolution data set does not provide all necessary inputs for the SEMB model. The remaining required data were obtained from the NCEP-NCAR reanalysis output (Kalnay et al., 1996) (Table 1). NCEP-NCAR reanalysis uses an analysis/forecast system to perform data assimilation (2.5° resolution) using past data from 1948 to the present. To make the datasets compatible, the monthly reanalysis output for the period 1961 to 1990 was interpolated to a 10’ resolution, which is realistic for the free atmosphere but could be problematic if the ground topography is considered. Considering the resolution of the input variables, the SEMB results are not likely to represent the behavior of specific glaciers, but are more likely to capture regional patterns of ELA fluctuations.

In this paper, we discuss the results from only those CRU CL 2.0 grid cells (N=137) containing at least one of the 234 glaciers described by Sagredo and Lowell (Fig. 1, in press). The use of this sample of glaciers as a reference ensures that the grid cells fall into one of the seven groups with distinctive climate configurations, as described in Sagredo and Lowell (in press). It is important to note that the area between 18°30’ and 27°S is not included here because, despite altitudes above 6000 m.a.s.l, the area is barren of glaciers due to its extreme aridity

(Ammann et al., 2001; Casassa et al., 2007; Vuille and Ammann, 1997).

39 Model parameters and constants are the same as in Rupper and Roe (2008), except for albedo, which was set equal to 0.62, intermediate between firn and blue ice, to approximate the average conditions at the ELA (Cuffey and Paterson, 2010). As Rupper and Roe (2008) point out, albedo varies both in space and time, and it is unrealistic to know its value for all glacier surfaces across the large region of interest. Nevertheless, albedo is considered one of the most sensitive parameters of this model because it can significantly influence the ELA sensitivity to changes in precipitation. Based on a sensitivity analysis, Rupper and Roe (2008) concluded that modest changes in albedo result in small ELA sensitivity changes in melt-dominated regions and large ELA sensitivity changes in sublimation- dominated regions.

The model results were tested against independent data, namely, by comparing the modeled ELA with an estimation of present-day values. Then, we evaluated the response of ELAs under different climate change scenarios and across different climatic regimes. It is important to note here that the SEMB model is not internally coupled. For example, temperature changes do not affect relative humidity. Even though this assumption is not necessarily realistic, it allows exploring glacier sensitivity to specific climate elements.

Results

Present-day Equilibrium Line Altitudes: Testing the model results against independent data

Figure 2 shows the mean elevation of 234 glaciers included in Sagredo and Lowell (in press), used as a proxy of the present-day ELA (Porter, 2001), and two simulations of ELAs as modeled by the SEMB model. The two simulations were generated using: i) data from CRU CL2.0 climatic set model in combination with NCEP-NCAR reanalysis output (137 grid cells) and ii) exclusively the NCEP-NCAR reanalysis output (35 grid cells).

A comparison between the modeled ELAs and the present-day values (as prescribed by the mean elevation of the glacier) suggests that even though the simulations are vertically offset, the SEMB model is able to reproduce the general pattern of ELAs across the Andes (Fig. 2). Considering that the mean offset between the measured and modeled ELAs in the CRU/NCEP-NCAR simulation is smaller than with the NCEP-NCAR reanalysis alone (not shown), we refer exclusively to the CRU/NCEP-NCAR results for the remainder of the paper. Because the SEMB

40 model was designed to explore the spatial ELA sensitivity, rather than to reproduce the climatological mean ELA

(Rupper and Roe, 2008), we consider the similarity between modeled and estimated ELA to be adequate.

Discrepancies between the modeled and observed values can be attributed to i) resolution of the climate data, ii) uncertainty in model parameters, and iii) the fact that the modeled ELAs were calculated based on average climatic conditions for the period 1961 to 1990, whereas the mean elevations of the glaciers represent the transient condition in 2010.

ELA response to changes in temperature

In the SEMB model, the ELA is obtained by calculating the elevation of the temperature (Tsoln) that results in a total annual ablation that exactly equals the total annual accumulation. The temperature at the ELA (Tsoln) is an output of the model. When calculating the ELA after changes of temperature, the accumulation is held constant, and thus the energy (and the resulting temperature, Tsoln) necessary to balance that accumulation is the same as that required before the perturbation took place. The only unknown is the new elevation where the given temperature is found (the ELA). It follows that the relationship between ELA and temperature change is exclusively governed by the local lapse rate due to the model formulation. Higher lapse rates produce lower ELA sensitivities and vice versa.

We applied uniform temperature perturbations across the Andes ranging from -6°C to 6°C, in increments of

0.5°C. The ELA varies linearly with changes in temperature and, depending on the climatic zone, the ELA sensitivities range from 142 to 229 m/°C (Fig. 3A). The ELA in the least sensitive areas respond 38% less than in the most sensitive ones. For example, for a uniform drop of temperature equal to -6°C (as might be equal to the change during the Last Glacial Maximum, Porter, 2001), the model predicts an ELA drop between ~850 and 1350 m. This range of values is in good agreement with previous reconstructions of ELA fluctuations for the LGM in the Southern

Alps and the tropics (Porter, 1975; Porter, 2001).

This variability of ELA response to changes in temperature observed in the Andes exhibits a distinct spatial pattern (Fig. 3B and 3C). The least sensitive areas are in the subtropical regions, whereas the most sensitive are in the inner tropics.

41 ELA response to changes in precipitation

The diverse processes that control precipitation, as well as the broad range of precipitation received by

Andean glaciers dictate, that special considerations are needed when imposing changes in precipitation. Based on surface data from the CRU CL 2.0 climate model, Sagredo and Lowell (in press) showed that glaciers in the dry

Andes received on average 0.3 m/yr (with minima close to 0.1 mm), whereas glaciers in Patagonia received more than 1.9 m/yr. Here, two different scenarios of precipitation change were evaluated: i) uniform perturbation of precipitation throughout the study area, and ii) perturbation proportional to the total annual precipitation of each grid cell.

Our pilot experiments showed that the SEMB model does not behave properly in areas with very low accumulation (precipitation) values. The minimum accumulation needed for the model to generate reasonable results is not fixed, but rather depends on the interplay of all the climatic variables included in the model, and in general oscillates between 0.1 and 0.2 m. For this reason, and considering the low accumulation values exhibited for extensive areas of the Andes, the experiment of applying uniform drops in precipitation across the range does not yield realistic results everywhere. Therefore, we calculated ELA changes only for successive increases in total annual precipitation over a range of values from 0 to 1 m, in steps of 0.01 m (Fig. 4).

All grid cells experience a drop in ELA when they are subjected to increasingly higher amounts of precipitation (Fig. 4A). However, this relationship is not linear, and it varies between the different climatic settings.

Moreover, the relationships within individual grid cells may change depending on the magnitude of the perturbation. It is evident from Figure 4A (inset) that two out of the 137 grid cells show an atypical high sensitivity to a small increase in precipitation (0.1 m). Those two grid cells are located in the dry Andes and exhibit extremely low amounts of annual precipitation. These two grid cells were considered outliers and were excluded from further analysis. The remaining grid cells, as a whole, exhibit a semi-continuous range of ELA responses to changes in precipitation.

The complex response suggests that the ELA sensitivity to changes in precipitation depends on multiple factors. One of them, and perhaps the most important, is the emissivity of the atmosphere. Emissivity represents the ability of the atmosphere to emit energy by radiation (Cuffey and Paterson, 2010). It is governed by humidity, cloud cover, and the concentration of gases that absorb and emit energy in the thermal infrared. Areas with high (low)

42 emissivity exhibit low (high) ELA sensitivity to this climatic perturbation (Fig. 4B). It follows that grid cells located in the inner tropics are the least sensitive to uniform perturbation in precipitation. In contrast, grid cells in the dry outer tropics, subtropics and northernmost mid-latitude region are the most responsive. The remaining grid cells (outer tropics, Central and Southern Patagonia, and Tierra del Fuego) represent an intermediate response to this uniformly imposed increase in precipitation (Fig. 5A and 5B).

While these results provide insights on the regional pattern in ELA sensitivity throughout the Andes, a scenario of uniform precipitation change throughout the entire range is unlikely, and some of the perturbations proposed above are unrealistic (e.g., a change in precipitation of 1 m in the arid Andes). We therefore consider an alternative approach and calculate the ELA change after a perturbation proportional to the total annual precipitation.

Thus, the annual precipitation at each grid cell was increase by 25%, resulting in perturbations from 0.03 to 0.7 m

(Fig. 5C and 5D). The grid cells that underwent larger ELA changes to a proportional increase in precipitation were those located in Northern and Southern Patagonia, whereas the least responsive to this specific perturbation were the ones in the tropics (inner and outer) and subtropics (Fig. 5C and 5D). These spatial differences can be explained by a combination of the size of the precipitation change and the pattern in the ELA sensitivity to that perturbation.

Temperature versus Precipitation

Disentangling the relative influence of temperature and precipitation on former ELA fluctuations has been recognized as a challenging subject (e.g., Benn et al., 2005; Klein et al., 1999; Smith et al., 2008). In this section we compare the relative contribution of each factor to changes in ELA across the Andes.

Previous studies (Braithwaite and Zhang, 2000; Oerlemans, 2005) have suggested that the increase of precipitation required to compensate the mass loss due to a uniform warming of 1°C is around 25%. In this study we followed the same approach. Figure 6 shows that grid cells located to the south of 38°S (excluding Tierra del Fuego) required an average increase of 33±7% in annual precipitation to compensate for a warming of 1°C. Outside these latitudes the change in precipitation required is larger. For example, in the outer tropics this value oscillates between

84% and 146%. In the inner tropics (the extreme case), we find grid cells where the increment of precipitation required could be slightly greater than 290%.

43 The relative influence of temperature and precipitation is highly dependent on the values of albedo. An increase of the albedo will result in an increase of the relative influence of precipitation in the ELA fluctuation. Studies in Central Asia have shown that the impact of changes in albedo has greater impact in sublimation-dominated regions (Rupper and Roe, 2008). If true, the most sensitive areas in the Andes to changes in albedo would be represented by the western Cordillera of Peru and Bolivia (south of 15°S), and the north of Chile-Argentina (north of

19°S), and between 27°S and 31°S.

ELA response to present-day climatic variability

In addition to the spatial variability of mean annual values, the temperatures and precipitations exhibit an important temporal (interannual) variability throughout the Andes (Fig. 7A and 8A). It has been suggested that this intrinsic climatic variability could be responsible for glacier excursions of considerable magnitude (Roe and O'Neal,

2009). Next, we examine the ELA response to perturbations within the range of the observed present-day climatic variability throughout the Andes. To estimate the relative amount of ELA change we applied to each grid cell a perturbation equal to ±1 standard deviation (σ) of the mean annual temperature and ±1σ total annual precipitation calculated for the period 1961-1990.

Figures 7B and 7C show that, when subjected to the magnitude of present-day interannual temperature variability, the ELA exhibit a range of responses from 45 to 128 meters. The distribution of these responses can be explained by a combination of both the ELA sensitivity and the magnitude of the perturbation (interannual temperature variability at each grid cell). The region that underwent the largest response is northernmost Patagonia, with a ∆ELA of 112 m. By comparison, the region that showed the smallest response was Tierra del Fuego, with a

∆ELA equal to 54 m. Assuming an instantaneous response of glaciers to changes in ELA, these results suggest that based just on the magnitude of the interannual temperature variability we could expect theoretical ELA change of at least ~50 m throughout the entire Andes.

Now, when considering the present-day interannual precipitation variability, we estimated changes of the

ELAs ranging from 13 to 150 meters under a scenario of increasing precipitation (2 outliers, Fig. 8B and 8C). We note that both the most and least sensitive areas are located in the outer tropics (Fig. 8B and 8C). In the northern

44 outer tropics (north of 13°S) the ELA is expected to change ~25 m when subjected to a 1σ increase in precipitation, while glacier in the southern outer tropics the ELA can respond by as much as ~100 m to the same climatic perturbation. These differences can be explained by comparing the interannual precipitation variability between these areas. While in the northern outer tropics the precipitation values seem to be relatively constant over the last 30 years, the southern outer tropics exhibit larger interannual precipitation variability.

Discussion

As previously shown for Central Asia (Rupper and Roe, 2008), the SEMB model provides valuable insights into the response of the ELA to specific climate perturbations. Despite its limitations and assumptions, the SEMB model reproduces the general pattern of ELAs throughout the Andes, and allows us to estimate the spatial variability of ELA sensitivity to changes in temperature and precipitation across different climatic regimes.

Nonetheless, the results presented here must be considered an approximation of the pattern of ELA change as a response to climate change over large areas. Factors such as the resolution of the model inputs, the selection and application of constants throughout the entire area (e.g., albedo), and the omission of topographic (e.g., hypsometry and slope) and glacio-dynamic effects in the calculation of the ELA, make it unlikely that the results of the SEMB model can be taken to directly represent the behavior of specific glaciers.

Due to the constraints of the SEMB model, where temperature is an output term, the ELA varies linearly with changes in temperature. This relationship is prescribed by local lapse rate, where the pattern in ELA sensitivity to temperature changes is inversely correlated with the pattern in lapse rates. Temperature lapse rates tend to be steeper in drier regions than humid regions. Hence, ELA sensitivity to temperature will often be lower in drier regions than in humid regions. These results are in good agreement with previous studies, which have demonstrated that glaciers in the inner tropics are the most sensitive to changes in temperature, whereas glaciers in the dry subtropics are the least sensitive (Kaser, 2001).

Any change in moisture content of the atmosphere will impact local lapse rates, and, in turn, the ELA sensitivity. This idea should be taken into consideration when reconstructing paleo-temperature based on the glacial record.

45 On the other hand, when we studied the ELA response to increases in precipitation our results showed that this relationship is non-linear. We also suggested that this relationship could be partially controlled by the emissivity of the atmosphere. Areas with high emissivity can provide extra energy (latent heat) to balance the excess of accumulation associated with the increase in precipitation, resulting in a low ELA sensitivity to this climatic perturbation, and vice versa. Areas with high/low humidity and cloud cover (for the sake of this paper we dismiss the effect of non-water vapor greenhouse gases) have high/low emissivity, and in turn lower/higher ELA sensitivity to changes in precipitation. Thus, glaciers in the dry Andes are the most sensitive to this climatic perturbation, as previously shown by Kaser (2001) and Kull et al. (2008). According to our results, the north section of Northern

Patagonia should be also included in this group; currently, it is unknown what might be causing this distinctive response.

The relationship described above is especially important when considering a coupled climatic system, where changes in precipitation are likely accompanied by changes in cloud cover. For example, if the westerly wind belt migrates northward, the new areas under the direct influence of the wind belt would be exposed to an increase of total annual precipitation. Humidity and cloud cover fraction would also increase in the same areas. Hence, the same glaciers receiving extra precipitation would experience decrease ELA sensitivity to this perturbation. In this context the ELA behaves as a resilient system to changes in precipitation. Klein et al. (1999) found similar results for the

Central Andes of Peru, Bolivia and Northern Chile, where they demonstrated that as precipitation decreases the ELA sensitivity to this perturbation increases. This is just one example of the complex relationships that arise when considering coupled climatic systems.

Those areas most sensitive to temperature (i.e., inner tropics) are the least sensitive to uniform changes in precipitation, and vice versa. For this reason, some studies have related the ELA sensitivity to the total precipitation

(Keer and Sugden, 1994). However, our results showed that the ELA sensitivity can be explained better by the moisture content of the atmosphere. ELA sensitivity to changes in temperature depends directly on lapse rate and indirectly on the humidity content of the atmosphere, whereas its response to changes in precipitation is controlled by the emissivity of the atmosphere and indirectly controlled by the cloud cover and humidity.

46 The SEMB model is able to capture the variability of the ELA sensitivity to both temperature and precipitation between the eastern and western cordillera in the Central Andes of Peru and Bolivia. As earlier noted by

Klein et al. (1999), the ELA sensitivity to precipitation is higher in the western cordillera than in the eastern cordillera, whereas the sensitivity to temperature changes in the opposite direction. This spatial variability must be considered when comparing former glacial advances between these two areas.

The results above may not reflect the relative importance of temperature and precipitation as controlling factors of the ELA fluctuation. For this reason we calculated the increase of precipitation required to compensate the mass loss due to a uniform warming of 1°C. Our results may indicate previous calculations were too conservative.

Braithwaite and Zhang (2000) and Oerlemans (2005) estimated the extra precipitation required to balance a warming of 1°C to be ~25%. The area between 38° and 53°S of the Andes is the only region that exhibits a range of responses comparable to the previously published values, with an average increase of precipitation required of

~33%. All the other regions in the Andes required significantly higher values of precipitation (e.g., Tierra del Fuego required ~60%, outer tropics and subtropics ~100%; in some sectors of the inner tropics these values can be as high as ~290%). This suggests that previous studies overestimated the relative influence of precipitation in the ELA fluctuations. Having said that, the next step to understand the relative impact of temperature and precipitation changes on ELA fluctuation is to assess how likely these magnitudes of precipitation change are. For example, it is easy to see that a 300% increase in precipitation in the inner tropics is unlikely, suggesting that for this region temperature is the main factor controlling ELA changes. However, assigning a probability to different future precipitation change scenario is not possible at this time.

From our analysis we observe spatial variability in the ELA sensitivity to changes in temperature and precipitation across the different climatic regimes throughout the Andes. In general, glaciers within a single climatic group fall into grid cells with more similar ELA sensitivity than glaciers belonging to different groups. These results confirm previous studies that have suggested that regional climate plays an important role in modulating the magnitude of glacial responses to climate change (e.g., Favier et al., 2004; Fujita, 2008a; Kaser, 2001; Klein et al.,

1999; Rupper and Roe, 2008).

47 This spatial variability of ELA sensitivity has important implications for paleoclimatic reconstructions based on the record of former glacial fluctuations. A specific climatic perturbation could result in extensive glacial advances in sensitive areas, while it could lead to relative minor glacial fluctuations in less sensitive areas. As a consequence, identical climate changes can be recorded to a different extent across different climatic regions (Sagredo and Lowell, in press). On the other hand, similar geological records (e.g., sequence of moraines) in areas with different climate regimes (Fig. 1) could be the result of completely different climatic perturbations.

Summary and final remarks

• Our analysis reveals a spatial variability in the magnitude of response of the ELA to changes in temperature

and precipitation across the glaciated regions of South America. This variability has a general correspondence

with present climate conditions throughout the Andes.

• ELA varies linearly with changes in temperature. The estimated ELA sensitivity to temperature change varies

between 142 to 229 m per degree Celsius for the study area. This linear relationship is prescribed by the lapse

rate at each location. Because lapse rate depends on relative humidity, colder and drier areas have greater

ELA sensitivities to changes in temperature. The most sensitive areas are the inner tropics.

• The relationship between precipitation and ELA change is non-linear and asymmetrical. The change of ELA

after an increase in precipitation is inversely correlated to the emissivity of the atmosphere. Since emissivity is

governed, in part, by humidity and cloud cover, the ELA is more sensitive to changes in precipitation in dry

areas (e.g., subtropics and northernmost mid-latitudes). In contrast, regions with high cloud cover (i.e. inner

tropics) are the least sensitive to precipitation increase.

• In our experiments, precipitation increases ranging from ~20 to 290% are needed to compensate for the mass

loss due to a uniform warming of 1°C. We found that the values previously described (25%) by Braithwaite and

Zhang (2000) and Oerlemans (2005) are similar to the exhibited in the Patagonian region (33±7%). All the

other regions in the Andes require higher values of precipitation. Thus, we suggest that the previous

calculations overestimated the relative influence of precipitation on the ELA fluctuations.

48 • Our analysis provides a framework to interpret past changes in ELA, as recorded by moraines and other glacial

landforms. This spatial approach will also aid in predicting the response of Andean glaciers to future climate

changes.

Acknowledgments

This work was supported by NSF Grant EAR-1003072. We thank Patrick J. Applegate, Colby A. Smith and Michael

R. Kaplan for valuable discussion and insightful comments on the manuscript.

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TABLE 1: List of the model variables and units used.

FIGURE 1: Classification of Andean glaciers (modified from Sagredo and Lowell, submitted). Statistical analyses

(i.e., cluster and principal component analysis) of temperature, precipitation and humidity permit the classification of

Andean glaciers into seven distinctive groups. The sample includes 234 glaciers distributed throughout the Andes

(12°30’N - 55°S). Plots (in matching colors) represent mean climatic conditions within each group. The horizontal axis represent the month of the years (from January through December). The bars represent monthly precipitation

(mm), solid lines show mean monthly temperature (°C) and dashed lines represent mean monthly humidity (%). Axes were plotted with same ranges to facilitate comparisons between groups. All climatic information was extracted from

CRU CL 2.0 climate model, 10’ lat/long resolution (New et al., 2002).

FIGURE 2: Modeled versus estimated present-day ELA. Red dots: mean elevation of 234 glaciers included in

Sagredo and Lowell (submitted), used as a proxy of the present-day ELA. Elevation data were extracted from Google

Earth. Yellow dots: modeled ELA using data from CRU CL2.0 climatic set model in combination with NCEP-NCAR reanalysis output (137 grid cells). Green dots: modeled ELA using exclusively the NCEP-NCAR reanalysis output

(35 grid cells). The mean offset between the measured and modeled ELAs in the CRU/NCEP-NCAR simulation is smaller than with the NCEP-NCAR reanalysis alone; therefore we refer exclusively to the CRU/NCEP-NCAR results.

FIGURE 3: Sensitivity of the Equilibrium line altitude to changes in the mean temperature. A) modeled change in

ELA after uniform temperature perturbations, ranging from -6 to +6°C. Shaded area includes the linear response of the 137 grid cells analyzed. The slopes of the lines (m) represent the ELA change per degree Celsius, which range from 142 to 229 m/°C. B) spatial distribution of the ELA sensitivity to changes in the mean temperature throughout the Andes. Area of the circles is proportional to the ELA change. C) latitudinal distribution of ELA sensitivity to changes in the mean temperature. B) and C) share the vertical axis. Color scheme is the same as in Figure 1.

FIGURE 4: Sensitivity of the Equilibrium line altitude to changes in the mean annual precipitation. A) modeled change in ELA after uniform increases of precipitation, ranging from 0 to 1 m in steps of 0.1 m. Color scheme is the same as in Figure 1. Inset figure includes two outliers. B) plot showing the relation between ELA change after an increase of 1 meter of precipitation (Y axis) versus emissivity of the atmosphere. Areas with high (low) emissivity exhibit low (high) ELA sensitivity to increases in precipitation.

FIGURE 5: Distribution of the ELA sensitivity to changes in the mean annual precipitation. A) spatial distribution of the changes in the ELA after a 1 m increase of the mean annual precipitation. Area of the circles is proportional to the

ELA change. B) latitudinal distribution of the changes in the ELA after a 1 m increase of the mean annual precipitation. C) spatial distribution of the changes in the ELA after a 25% increase of the mean annual precipitation.

Area of the circles is proportional to the ELA change. D) latitudinal distribution of the changes in the ELA after a 25% increase of the mean annual precipitation. A) and B), and D) and C) share the vertical axis. Color scheme is the same as in Figure 1. Note different scales for A) and C).

FIGURE 6: Precipitation required for compensating the mass loss due to a uniform warming of 1°C, shown as A) absolute values. Each bar represents a grid cell. Colored segment of each bar shows the total annual precipitation at the grid cell; black segment represent the precipitation required for compensating the mass loss due to a uniform warming of 1°C. Bars were sorted by groups and then by latitude. B) spatial distribution of the percentage of the total annual precipitation. Area of the circles is proportional to the percentage of precipitation change. C) latitudinal distribution of the percentage of the total annual precipitation. B) and C) share the vertical axis. Color scheme is the same as in Figure 1.

FIGURE 7: ELA response to present-day temperature variability. A) Interannual variability of the mean annual temperature (1σ) throughout the Andes B) spatial distribution of the absolute change of the ELA after a temperature perturbation of 1σ of the mean annual value. Area of the circles is proportional to the ELA change. C) latitudinal distribution of the absolute change of the ELA after a temperature perturbation of 1σ of the mean annual value. B) and C) share the vertical axis. Color scheme is the same as in Figure 1.

FIGURE 8: ELA response to present-day precipitation variability. A) Interannual variability of the mean annual precipitation (1σ) throughout the Andes B) spatial distribution of the ELA changes in response to an increase in the annual precipitation of +1σ of the mean annual value. Area of the circles is proportional to the ELA change. C) latitudinal distribution of the ELA changes in response to an increase in the annual precipitation of +1σ of the mean annual value. B) and C) share the vertical axis. Color scheme is the same as in Figure 1.

Chapter III:

Little Ice Age equilibrium line altitudes along the Andes:

paleoclimatic implications

Manuscript in preparation:

Sagredo E.A., Lowell, T.V., Kelly, M.A. and Aravena, J.C. (in preparation). “Little Ice Age equilibrium line altitude along the Andes: paleoclimatic implications.”

Abstract

Understanding the mechanism underlying the Little Ice Age (LIA, sensu lato AD 1300-1850) requires documenting the spatial and temporal pattern of this glacial/climate event, and assessing those patterns against dynamical climate models able to capture the complex response of the climatic system to internal or external perturbations. In this paper, we estimate the change in the equilibrium line altitudes (ELAs) since the maximum glacial extent during the LIA to the present for three alpine glaciers located in different climatic regimes along the Andes, and reconstruct scenarios of the climatic conditions (temperature and precipitation anomalies) that accommodate these ELA fluctuations. The glaciers studied are: a tributary glacier at Cordillera Vilcanota (13°S), Cipreses glacier (34°S) and Tranquilo glacier (47°S). Our results suggest that there is not a single combination of temperature and precipitation changes that accommodate the ELA change recorded since the LIA maximum for the three sites. Assuming no changes in precipitation, the recorded ELA fluctuations could be explained by a cooling of at least: -0.7°C at Vilcanota, -0.5°C at Cipreses glacier and -1.3°C at Tranquilo glacier. On the other hand, if no changes in temperature are considered, the ELA fluctuations could be explained by increase in the precipitation greater than 0.51 m (63% of the annual precipitation) at C.

Vilcanota, 0.17 m (21%) at Cipreses glacier and 0.68 m (62%) at Tranquilo glacier. Our results can serve as targets to test predictions from models of global climate dynamics; hence, contributing to the understanding of the mechanism underlying the Little Ice Age.

Introduction

The Little Ice Age (LIA, sensu lato AD 1300-1850) represents a time of generalize (worldwide) glacier expansion occurred during the late Holocene (Porter and Denton, 1967; Grove, 2004), yet its full pattern, timing and cause remain elusive. Among predominant hypotheses are: i) reduction of solar irradiance (e.g., Denton and Karlén, 1973;

Eddy, 1976; Polissar et al., 2006; Wiles et al., 2008) , ii) volcanism (e.g., Schneider et al., 2009; Miller et al., 2012), and iii) reorganization of the ocean circulation systems (i.e. thermohaline, Broecker, 2000; Denton and Broecker, 2008).

Understanding spatial distribution of climatic conditions during the LIA is crucial to examine these hypotheses, since it will provide a target against which we can test the predictions of models of global climate dynamics for this period.

A recent multi-proxy global surface temperature reconstruction showed that the geography of the LIA climate exhibit an important spatial variability, resulting in a diverse mosaic of climatic conditions (Mann et al., 2009). Although this reconstruction is one of the most sophisticated and rigorous existing, it is mainly representative of the Northern Hemisphere and the tropics. The lack of abundant high resolution proxy records in the Southern Hemisphere (particularly in the extratropics) limits the reliability of this reconstruction for the southern third of the globe (Mann et al., 2009).

For example, records of former glacial fluctuations have been used extensively to reconstruct paleoclimate at different temporal and spatial scales (e.g., Rodbell, 1992; Oerlemans, 1994; Lowell et al., 1995; Denton et al., 1999; Klein et al., 1999; Porter, 2001). However, the existing records of LIA glacial fluctuation in the Southern Hemisphere are relatively scarce and spatially discrete (Grove, 2004; Jomelli et al., 2009; Masiokas et al., 2009). A network of well distributed records of former glacial fluctuation is a prerequisite for understanding large scale climate changes, since it has been demonstrated that glaciers located in different climatic regimes could respond with different magnitude to similar climatic perturbations

(e.g., Seltzer, 1994; Kaser, 2001; Favier et al., 2004; Fujita, 2008; Rupper and Roe, 2008; Sagredo et al., in preparation).

Here, we explore the timing, magnitude and climate scenarios (temperature and precipitation) for the maximum glacial advance that occurred during the LIA, at three alpine glaciers located in different climatic regimes along the Andes.

Study areas

The Andes, along 9000 km of South America, span a broad range of latitudes and altitudes, which in turn dictates a large range of temperature and precipitation conditions (Garreaud et al., 2009). Based on statistical analyses (i.e., cluster and principal component analysis) of the temperature, precipitation and humidity, Sagredo and

Lowell (2012) classified the regions where present-day glaciers exist into seven distinct climatic groups (Fig. 1). To cover a wide range of these climatic conditions, we selected three glaciers sites, each one located in different climatic regimes: Cordillera de Vilcanota (13°S) located in the outer tropics, Río Cipreses (34°S) in northernmost mid- latitudes, and Río Tranquilo (47°S) in Patagonia (Fig. 1).

Cordillera Vilcanota

This small mountain range, that extends east-west for almost 50 km, forms part of the eastern Cordillera of the southern Peruvian Andes (13°45’S). This region is characterized by a single marked wet season during the austral summer months (DJF), and a prolonged ablation season (Rodbell et al., 2009). Most of the ablation in this area occurs in the form of sublimation (Kaser, 2001). The range is composed of a combination of granitic, sedimentary and volcanic rocks (Audebaud, 1973). The equilibrium line altitude (ELA) in the north side of the range has been estimated in 5100 m (Mercer and Palacios, 1977). Our study focused on a small tributary glacier in the

Jasccara Valley (Fig. 2), in the southeast of the range (13°48’S; 70°59’W). With an area estimated of 0.74km 2, this steep alpine glacier is 1.3 km long, with elevations from ~5990 to 5075 m.

Río Cipreses valley, Sierra del Brujo

Sierra del Brujo (34°34’S) is a small range located in central Chile, in a transition zone between the arid

Andes and the extremely wet Patagonia. The region exhibits mild wet winters (~4 months) and dry summers. The summers are semi-permanently affected by the blocking of a high-pressure cell over the southeast Pacific Ocean, while during the winter the westerlies can reach this region and generate frontal and orographic precipitation (Bown et al., 2008). The closest meteorological station (Sewell: 34°05’S 70°24’W, 2.155 m), 40 km north of Sierra del Brujo, exhibits a mean annual temperature of 10.4°C, and a total annual precipitation of 891 mm (Dirección Meteorológica

de Chile, 2001). This range, composed of granodioritic rocks, represents one of the most glaciated mountains of the

Central Andes of Argentina and Chile. This study focuses on Cipreses glacier (34°34’S, 70°21’W; Fig. 2), which extends north-south for ~9 km through the middle of Sierra del Brujo. Considering the tributary valleys, Cipreses glacier comprises an area of ~21km2. The lower portion of the glacier tongue is debris-covered.

Río Tranquilo valley, Monte San Lorenzo

Monte San Lorenzo (47°35’S and 72°19’W) is an isolated granitic massif located ~95 km to the east of the southern limit of the Northern Patagonian Icefield, more than 165 km from the coast. This mountain is the third highest summit in the southern Andes with an elevation of 3,706 m.a.s.l.. The closest meteorological station

(Cochrane: 47°14’S 72°33’W, 182 m.a.s.l.) exhibits a mean annual temperature of 8.1°C, and total annual precipitation of 731 mm (Aravena and Luckman, in preparation). However, considering that Monte San Lorenzo is located ~50 km southeast of the station, we expect the climatic conditions in the site to be colder and drier. San

Lorenzo represents one of the most heavily glaciated mountains in the region (Caldenius, 1932). This study focuses on Río Tranquilo glacier (hereafter Tranquilo glacier, Fig. 2), a small valley glacier located on the northern flank of

Monte San Lorenzo. Tranquilo glacier, with a total length of ~2.2 km, occupies 2.7 km2 of the upper portion of the homonymic valley, from 1270 to 2200 m.a.s.l. No present-day ELA has been estimated at this site.

Methods

LIA-glacier reconstruction and chronology

Former glacier extents and paleo-glacier surfaces were reconstructed based on glacial geomorphic mapping of areas formerly occupied by the glaciers. We used aerial pictures (1:70 000), satellite imagery (ASTER, Quic kbird) and digital elevation model (ASTER GDEM Worldwide Elevation Data, 1.5 arc second resolution) analysis. All the information was ground-truthed in the field.

To constrain the age of the former glacial fluctuation, and thus to identify the landforms deposited during

LIA, direct ages of moraines were obtained using surface exposure measurements. We sampled boulders located on the tops of moraines for 10Be analysis. Samples of ~1 kg were removed from boulder surfaces (upper 5 cm) using a

hammer and chisel or a hammer drill and small explosives following Kelly (2003). Special attention was paid to select boulders exhibiting minimal evidence of erosion. These samples were processed at Dartmouth College at the Earth

Sciences Cosmogenic Nuclide Laboratory following the protocols modified from Stone (2001). 10Be/Be isotope ratios were measured at Lawrence Livermore National Laboratory.

Ages were calculated based on the methods and terms summarized in Balco et al. (2008) and Balco et al.

(2009), and are presented with both the recent production rate obtained by Putnam et al. (2010) and the cronus rate summarized in Balco et al. (2008). We used the methods incorporated in the CRONUS-Earth online exposure age calculator version 2.2, with version 2.2.1 of the constants file (Balco et al., , half life used is 1.36 Myr, http://hess.ess.washington.edu/math/docs/al_be_v22/ al_be_docs.htmlBalco et al., 2008). In this paper we use the

Southern Hemisphere mid-latitude 10Be production rate from Putnam et al. (2010), which was recently validated for use in southern South America (Kaplan et al., 2011). The Putnam et al. (2010) rate is significantly different than the

CRONUS production rate (Balco et al., 2008), which is based solely on Northern Hemisphere calibration sites studied prior to ~2008. However, the CRONUS production rate disagrees with subsequent calibration studies in the Northern

Hemisphere (Balco et al., 2009; Fenton et al., 2011). Ages are also presented with the scaling scheme of Lal

(1991)/Stone (2000), although the different scaling schemes agree within 5%. The use of different production rates and scaling schemes does not affect the main conclusions of this paper. All ages were calculated assuming zero erosion. Individual exposure ages are shown and discussed with analytical errors only.

Additionally, in Vilcanota site we collected radiocarbon samples from organic remains imbedded in a moraine ridge. AMS measurements were carried out at the National Ocean Science Accelerator Mass Spectrometry

Facility (NOSAMS). All radiocarbon dates were converted to calendar years BP (cal yr BP) using CALIB 6.01 (Stuiver and Reimer, 1993).

Once the landforms formed during the LIA were identified, we delineated the glacial margins based on moraines and other well preserved evidence of marginal positions left by the glaciers. Considering that the resolution of our dating technique precludes the distinction of different glacial advances that may have occurred during the LIA, for the sake of this study we focused on the most extensive glacial advance (outermost moraines) occurred during this period.

Hereafter, we refer to this event as the LIA maxima, recognizing they may not be totally in phase.

Modern and paleo (LIA) glacier surfaces were reconstructed by fitting a flat surface on both the present-day and reconstructed ice margins. The hypsometry of the glaciers was calculated using ReadArcGrid v.1.0.0 (Nash, 2007) and the

ASTER GDEM Worldwide Elevation Data (1.5 arc second resolution). This method does not capture the classic glacier surface topography (i.e. convex near the terminus, straight at mid-elevations and concave near the headwall). However, since the ultimate goal is to estimate changes in ELA, this method provides a close first approximation.

Modern and paleo ELAs

A variety of methods have been proposed for calculating modern and former ELAs. Each of these methods offer some advantages and disadvantages (for an exhaustive discussion of the available methods see Porter, 2001; Benn et al.,

2005; Osmaston, 2005)

Here, we adopted the Accumulation Area Ratio (AAR) method to calculate modern and past ELAs. Other methods were dismissed because their application was precluded or limited by particular characteristics of the glaciers, or of the method itself. Toe to Head Area Ratio (THAR) method, for example, is less appropriate for glaciers with complex geometry (e.g., Cipreses glacier) (Benn and Lehmkuhl, 2000; Benn et al., 2005). The application of the Maximum Elevation of the Lateral Moraine method (Meierding, 1982) to present day “transient” glaciers is precluded by the fact that moraine formation most likely occur under steady-state conditions. Finally, the Area-Altitude Balance Ratios (AABR) method requires as input the glacier hypsometry and the mass balance gradient. However, the latter is rarely available, thus limiting the application of this method.

The AAR method assumes that the accumulation area of a glacier occupies a fixed portion of a glacier’s total area. The ELA is calculated by applying an estimated AAR value to a hypsometric curve of the glacier. By doing this, we calculate the elevation at which the glacier can be divided in the proportion prescribed by the AAR. A caveat of this method is that the AAR varies from one region to another (Benn and Lehmkuhl, 2000; Benn et al., 2005). The steady-state AARs for mid and high-latitudes can typically fall between 0.5 and 0.8 (Meier and Post, 1962), with typical values between 0.55 and 0.65 (Porter, 1975). These values tend to be higher for the inner Tropics (AAA=~0.8, Kaser and Osmaston, 2002).

Considering the uncertainties associated with the selection of the AAR, we calculated the ELA using a broad range of ratios

(from 0.4 to 0.8).

Nevertheless, here we focused on those results calculated using the range of ratios more representative of each climatic region, as described by previous studies. No AAR values have been calculated specifically for the Vilcanota area.

Klein (1999) calculated an average AAR of 0.67 for 78 modern Bolivian glaciers. A similar value (0.66) fits an estimated late glacial ELA at Nevado Illimani in the Cordillera Real of Bolivia (Smith et al., 2011). However, all these values are on the low side for tropical glaciers (Kaser and Osmaston, 2002). Using a glacier-climate model, Kull (2008) calculated an average

AAR of 0.75 for 17 paleo-glaciers in Cordillera de Cochabamba, Bolivia. Finally, Seltzer (1992) suggests the use of an AAR of 0.77 for late glacial times for Cordillera Real. In this study we used a range of AAR from 0.7 to 0.8, considered more representative for tropical glaciers (Kaser and Osmaston, 2002).

At Tranquilo and Río Cipreses glaciers, we applied the range of AAR values most commonly used for mid-latitude glaciers of 0.55-0.65 (Porter, 1975; Bahr et al., 2009).

The AAR method was originally designed to calculate ELAs for glaciers under steady-state conditions. Thus, calculations of modern ELAs (for glaciers in “transient” conditions) based on this method will slightly offset from the actual value, and the estimation of changes in ELAs will be underestimated.

The change in ELA was calculated by subtracting the modern from the paleo-ELAs, using the mean, minimum and maximum AAR values for each zone. This method assumes no change in the AAR value for each glacier between the present and the LIA.

Paleoclimatic reconstruction

ELA fluctuations have been used extensively in paleoclimatic reconstructions. Here, we use a surface energy and mass balance model (SEMB model, Rupper and Roe, 2008) to reconstruct scenarios of climatic conditions (temperature and precipitation) for the maximum glacial advance occurred during the LIA. The model includes two algorithms: a surface energy balance and a mass balance. The surface energy balance model solves for the energy available for ablating, following

surface is neglected because it is likely small compared to the terms we considered (Kayastha et al., 1999). Melting

2 takes place when the surface temperature (T s) equals 0°C and Q is greater than 0 Wm while evaporation occurs when T s equals 0°C and the evaporation vapor pressure of the air (ea) is lower than the saturation vapor pressure at the surface (es). Sublimation occurs when T s<0°C and es

The SEMB model was initially validated for Central Asia (Rupper and Roe, 2008), where the model was shown to be capable of capturing the spatial variability of the ELA sensitivity across different climatic regimes.

Subsequently, Sagredo et al. (in preparation) applied this model to the Andes. Details of this SEMB model can be found in Rupper and Roe (2008).

Here, the model is perturbed with a range of temperature and precipitation values that permit an estimation of the change in ELA associated to these perturbations. Finally the combination of temperature and precipitation perturbations that accommodate the ELA change since the LIA maxima is identified.

For model input, the gridded climatological data from the surface climate model CRU CL 2.0 (New et al.,

2002) was used. CRU CL 2.0 is a 10’ latitude/longitude resolution dataset of mean monthly surface climate over global land areas, excluding Antarctica, interpolated from a dataset of station means for the period 1961 to 1990.

Unfortunately, this high-resolution data set does not provide all the necessary inputs for the SEMB model. The remaining required data was obtained from the NCEP-NCAR reanalysis output (Kalnay et al., 1996) (Table 1).

NCEP-NCAR reanalysis uses an analysis/forecast system to perform data assimilation (2.5° resolution) based on

data from 1948 to the present. To make the datasets compatible, the monthly reanalysis output for the period 1961 to

1990 was interpolated at a 10’ resolution, which is realistic for the free atmosphere but could be problematic if the ground topography is considered. Model parameters and constants are the same as in Sagredo et al.(in preparation).

Results

LIA-glacier reconstruction

The following is a description of the general glacial geomorphology and chronology of glacial fluctuation at the three study areas with a focus on the deposits inferred to have been deposited during the LIA times.

i) Cordillera Vilcanota

Geomorphic evidence suggest that during times of more severe climatic conditions the small glaciers that currently occupy the highest portion of the Jasccara basin expanded and deposited a series of moraine complexes on the hillslopes and at the bottom of the valley.

At least six moraine complexes can be identified in the area (Fig. 3a). The Jasccara I and Jasccara II complexes are associated with advances of the glaciers in the headwalls of the basin (9 and 7 km upvalley). The

Tributary I, Tributary II, and Tributary III complexes are the result of glacial fluctuations of the small tributary valleys on the northeast slope of the Jasccara valley. The Aljachaya complex, on the other hand, was deposited by a glacier coming from the homonymic valley. Four 10Be cosmogenic ages from the Tributary I moraines, extending over the period 680±30 to 150±10 yr BP (Tables 2 and 3, and Figure 4a), suggest that this complex was deposited during the

LIA. The age of this moraine system was confirmed by two maximum-limiting radiocarbon dates from organic remains imbedded in the outermost moraine of this complex. Samples from the top and bottom of this peat deposit yielded ages of 500-530 and 1010-1260 cal yr BP (Table 4), suggesting that the peat was growing upvalley from the

Tributary I moraines during this period when the ice was retracted. The peat was probably incorporated into the moraine by ~515 cal yr BP as the glacier overrode the bog. Five other 10Be cosmogenic ages suggest that Tributary II and Tributary III complexes are late glacial in age (not shown here).

Hereafter, when referring to Vilcanota site, the ELA analysis will reflect the glacier position of the Tributary I moraine complex.

ii) Cipreses

The upper section of the valley exhibits abundant evidence of former fluctuations of Cipreses glacier.

Terminal moraines permit the identification of at least four stages of moraine formation, labeled Medina II, Medina I,

Arriero and Alto Cipreses moraine complexes (Fig.4c). These systems are located approximately 5, 4.5, 2.7 and 2 km from the present-day ice margin, respectively.

The outermost complex, Medina II, includes two sets of moraines, and it is exclusively expressed in the flanks of the valley. All evidence for this glacial advance in the bottom of the valley has been completely eroded.

Approximately 1.5 km upvalley, Medina I complex is also formed by two sets of moraine, ~50 m apart. The Medina I inner moraines comprise at least 5 ridges, 3 to 5 m high. These ridges cross cut each other, forming a very irregular and interrupted system. The Medina I outer moraine corresponds to a single ridge, ~1.5 m high. Medina I moraines are dissected and partially buried by outwash. Arriero complex consist of two very well defined moraines and a series of discontinuous and subdued inner ridges (recessional?). Alto Cipreses moraine, on the other hand, corresponds to a single ridge, ~ 3 m high, located in the northern side of the river. It is characterized by the lack of fine matrix and an abundance of sizable boulders (>1.5m). The boulders atop all the moraine complexes lack lichens and do not exhibit evidence of post-depositional erosion.

There have been two previous attempts to establish a glacial chronology of these moraine complexes.

Röthlisberger (1987) radiocarbon dated two paleosoils underlying the lateral moraines in the upper portion of the valley (Fig. 4b). The samples yielded ages of 5270-6550 cal yr BP (5180±295 14C yr BP) and 300-800 cal yr BP

(625±155 14C yr BP), and represent maximum-limiting ages for the formation of these moraines. On the other hand,

Le Quesne (2009), based on historical records suggested that at least the three younger moraine complexes identified in this study were deposited after AD 1842. The chronology reported here suggests something different that either study. Samples for 10Be cosmogenic measurements were collected from the four moraine complexes (Table 2 and 3, and Fig. 4c). Seven out of ten boulder samples yielded ages between 990±60 and 530±40 yr BP. The other

three samples yielded considerably older ages (8050±230, 1980±80 and 1290±60 yr BP). Considering that all the younger ages, but one, came from the two outermost moraine complexes, it is suggested that the older samples are anomalous and present some inherency. This hypothesis is supported by the fact that the deposits associated with the four glacial margins do not present any evidence of erosion, and that the area inside those limits exhibits a bare surface and immature residual soil, all of them being diagnostic characteristic of moraines deposited over the last few centuries (Mercer, 1968). The discrepancy between these findings and previous chronologies can be attributed to: 1) in the case of Röthlisberger (1987)’s study, his moraines cannot be correlated with any marginal position, precluding any comparison. 2) Historical analyses are subjected to uncertainties associated with the original collection of the information (e.g., these studies no always record the glacier position), and to the later interpretation of the historical documents. It is suggested that the four moraine systems were deposited during the last millennia (likely sometime during the LIA).

iii) Río Tranquilo Glacier

There is abundant geomorphic evidence to suggest that during moments of major glacial expansion, several small glaciers that today occupy the headwalls of Río Tranquilo valley expanded and coalesced, forming the expanded version of Tranquilo glacier. This extended ice lobe flowed downvalley and deposited a series of moraine complexes on the flanks and the bottom of the Río Tranquilo valley.

Four groups of moraines can be identified: i) outer, ii) intermediate II, iii) intermediate I, and iv) inner moraines (Fig. 3d).

Annual tree-ring counting of living trees atop of the latter group suggests that these moraines stabilized sometime before AD 1632, during the LIA (Aravena and Luckman, in preparation). To confirm these results three samples for cosmogenic dating were collected from a single ridge inside this group. The samples yielded ages of

5260±150, 5270±150 and 5550±180 yr BP (Tables 2 and 3, Fig 4d), suggesting that the glacial advance responsible for the formation of these moraines occurred during the mid-Holocene. These dates are statistically indistinguishable from a maximum age of 5180 cal yr BP (4590±115 14C yr BP), obtained by Mercer (1968), for the advance of San

Lorenzo Este glacier (47°36'S 72°14'W) in the east side of the same massif.

The inner moraines consist of between 5 and 7 ridges, which enclose the valley ~3.5 km from the present ice margin. A more detailed examination of these features showed numerous crosscutting relations, accounting for a series of small glacial readvances.

Based on the fresh appearance of the forefield, the lack of vegetation and the poor soil development inside these moraines, our working hypothesis is that the LIA glacial advance reached a similar position, but slightly inside, than during the mid-Holocene advance. Indeed, a subtle ridge is plastered against the inner slope of the Inner moraine complex, which might represent the LIA moraine. Unfortunately, because this moraine is inset to the mid-Holocene moraines, it is constantly affected by rockfalls from the older system, precluding the dating of the boulders atop the younger system.

Modern and paleo-ELAs

Table 5 shows the ELAs corresponding to the different AAR values (from 0.4 to 0.8). Nevertheless, hereafter only those results estimated on AAR values representative of each area, as discussed in the method section will be considered.

Figure 5 illustrates the LIA ice-margin used to reconstruct the paleo-glacier surface and Figure 6 shows the estimations of modern and paleo-ELAs.

Tranquilo glacier exhibits the largest changes in ELA since the LIA maxima. Assuming that the subtle ridge located inside the mid-Holocene margin corresponds to the LIA, this change could have ranged from 249 to 299 m. These values are slightly smaller (~10 m) than for the mid-Holocene position.

On the other extreme, at Cipreses glacier, the ELA change since the LIA maxima is estimated to have ranged from 75 to 137 m. Given that the cosmogenic chronology is not conclusive at this site, the same experiment using the next two sets of moraines upvalley (Medina I and Arriero complexes) was conducted. The changes in ELA for these positions are, on average, 6 and 25 m smaller, respectively (not shown), which does not alter the general conclusions of this study.

Nevertheless, this interpretation is highly dependent upon the reconstruction of the LIA-ELA margin, which suggests that during this period two major tributaries coalesced with the main ice lobe, changing considerably the geometry and hypsometry of the basin.

Finally, at Vilcanota the ELA during the LIA was estimated to be at least between 128 and 148 m lower than the present.

It is important to note that the results reported in this study represent minimum values of ELA fluctuations.

Considering i) the warming trend observed worldwide since ~AD 1850 (IPCC, 2007) and ii) the lag time in the glacier response to a climatic perturbation (Johannesson et al., 1989), it is expected that the glaciers studied are currently out of balance. This means that our estimations of the present-day ELAs are lower than the actual values, resulting in an underestimation of the ELA change since the LIA. Rabatel et al. (2008) illustrated this problem by showing that the difference between AAR-derived ELAs and measured ELAs at Zongo glacier in the Bolivian Andes can differ by up to 150 m.

Climatic implications

Figure 7 shows the combination of temperature and precipitation anomalies required to change the ELA to its LIA conditions, for the four study areas.

The model results suggest that there is not a single combination of temperature and precipitation changes that can account for the ELA change since the LIA maxima to the present for all the sites (Fig. 6). Given that the calculations of ELA changes represent minimum values, it is important to consider the minimum climatic perturbations required to drop the ELA to its LIA position.

Assuming changes of only temperatures, at Cipreses site the ELA change could be explained by a cooling of at least 0.5°C. Considering a change in precipitation only, the LIA ELA can be explained by an increase of more than 0.17 m (corresponding to 21% of the present-day annual precipitation). On the other extreme, at Tranquilo site the ELA change can be explained by a temperature drop of at least 1.3°C (assuming no changes in precipitation), or by a precipitation increase of more than 0.68 m (62%, and no changes in temperature). In between these two extremes is Vilcanota, where the ELA change can be explained by a cooling of more than 0.7°C, or by precipitation increase of at least 0.51 m (63%).

Some of the changes in precipitation considered above could be considered improbable. For example, at

Vilcanota, the change of precipitation required to drop the ELA to its LIA position (without changing temperature), represents an increase in precipitation of 72%. For reference, Figure 7 (inset) shows the precipitation anomalies (in m water) that represent a 25%, 50%, 75% and 100% increase of annual values. Using those conditions, it is possible

to assess the combinations of temperature and precipitation changes that would allow the ELA to reach its LIA position.

For example, considering a maximum change of precipitation of 50% of the annual value, the minimum temperature change required to displace the ELA to the LIA position are i) Vilcanota : -0.3°C, ii) Cipreses: 0°C, and iii) Tranquilo: -

0.3°C.

In order to put these changes in the context of the current climate boundary conditions, in Figure 7 (inset) the present-day temperature and precipitation interannual variability (±1 standard deviation of the mean conditions for the period 1961-1990) were also identified.

Discussion

To date, there have been no large-scale studies attempting to solve the spatial variability of the ELA during the

LIA in South America. Several studies have focused on reconstructing the ELA for the Last Glacial Maximum (e.g.,

Hastenrath, 1971; Nogami, 1972; Fox, 1993; Klein et al., 1999; Porter, 2001; Condom et al., 2007); however, no regional efforts have been made for the LIA.

These results suggest an important variability of the ELA change from the LIA maxima to the present, across different climatic regimes along the Andes. In the wet Patagonian Andes (Río Tranquilo site), for example, the change in

ELA could have been as large as ~300 m, whereas at Cipreses glacier (in the transition zone between wet and dry Andes) this change could have been as little only 75 m. In between these two extremes is Vilcanota, where the ELA fluctuated between 128-148 m.

To assess these results, the calculations of ELA changes were compared with previous studies. At Vilcanota site, the ELA change since the LIA maxima is good agreement with the calculation from neighboring areas. At Cordillera Real,

Bolivia (16°S, Fig. 1), Rabatel et al. (2008) calculated the ELA change since the LIA maximum (~AD1650) to be ~150 m, with a total range of 70-190 m. This result is very similar to that obtained in Vilcanota (∆ELA 128-148 m). At Cordillera

Blanca (Fig. 1) Jomelli et al. (2008) proposed that the glacier reached similar maximum extents in AD 1350 and 1650. The

ELA change since these periods of maximum extent was estimated to be ~180 m (Jomelli et al., 2008), somewhat greater than the findings presented here.

At Cipreses glacier the change in ELA since the LIA maxima was estimated to be at least 75 to 137 m. Given that the geometry of the glacier could have undergone important changes between the LIA and today, this result is especially susceptible to uncertainties related to the reconstruction of the former ice margin. Nevertheless, the estimation in this study of ELA change overlaps with the 105 and 110±50 m calculated at El Peñón and El Azufre glacier, in Central Argentina

(35°S, Fig. 1) (Espizua, 2005). No other studies have reported LIA-ELAs for the same climatic region of Tranquilo glacier, precluding further comparisons.

The comparison presented above is highly dependent on the timing of the LIA glacial maxima. In many areas, it has been determined that younger LIA advances overrode the evidence of earlier events, resulting in a truncated surface record (Masiokas et al., 2009). Unfortunately, the ages obtained in this study are not conclusive in determining the exact timing of the LIA maxima. The interpretation of the ages from two of the three sites (Vilcanota and Cipreses) provides some suggestions that the LIA maxima along the Andes could have occurred around 500-

600 yr BP. However, more precise chronological control is needed to reach further conclusions.

Nevertheless, the ELA changes described above were used to reconstruct scenarios of climatic conditions during the LIA maxima. The SEMB model calculated the combination of temperature and precipitation anomalies that have accommodated the ELA changes since the LIA. This model was designed for application to large regions. Factors such as the resolution of the model inputs, the selection and application of constants to different areas, and the omission of topographic (e.g., hypsometry and slope) and glacio-dynamic effects in the calculation of the ELA increase the model uncertainties when applied to smaller areas (Rupper and Roe, 2008). However, if it is assumed that the ELA change at each glacier site is representative of its climatic region (an assumption that needs further testing), the

SEMB model provides a first approximation of the range of climatic conditions during the LIA across the study areas.

These results suggest that for each site there is a distinct set of temperature and precipitation changes that accommodate the ELA change from the LIA maxima. Assuming no changes in precipitation, the ELA fluctuation could be explained by a cooling of at least: -0.7°C at Vilcanota, -0.5°C at Cipreses and -1.3°C at Tranquilo glacier.

Except for Cipreses, all these perturbations are outside the range of the present-day interannual temperature variability. On the other hand, if changes only in the precipitation are considered, the ELA fluctuations could be explained by increases in the annual values greater than 0.51 m at Vilcanota, 0.17 m at Cipreses and 0.68 m at

Tranquilo glacier. These values represent an increase of 63%, 21%, and 62% of the annual precipitation, respectively. Assessing the likelihood of these changes in precipitation is not possible at this time. However, it can be said that they all are outside the range of the present-day interannual precipitation variability.

It is worth noting that the slope of the curves in Figure 7 illustrates the relative sensitivity of the ELA to temperature and precipitation. Steeper curves (e.g., Vilcanota) represent sites relatively more sensitive to temperature than to precipitation, while gentle slopes (e.g., Cipreses) correspond to the opposite situation.

The range of possible temperature and precipitation obtained can be further constrained based on proxy records from the corresponding regions. At Tranquilo glaciers, for example, a temperature reconstruction based on three tree-ring records (between 47° and 49°S), suggest that there were five 10 -year periods between AD1650 and the present where the temperature was, on average, 1.5°C colder than during the period 1930-1989. For 25 and 50-year periods the five coldest time-slices were, on average, 1.1°C and 0.9°C colder (Villalba et al., 2003). If it is assumed that the LIA maxima at Tranquilo glacier occurred in one of these five cold events, the precipitation increase required to drop the ELA to its LIA position would have been between ~ 0.24 m and 0 m (using the 50-year and 10-year period, respectively, and an AAR of 0.6). A precipitation reconstruction from varved sediments of Lago Plomo (47°S,

Elbert et al., 2012) suggested that winter precipitation anomalies (for 30-years periods, with respect to the period

1930-2002) since AD 1530 never exceeded 0.05 m, in agreement with this study.

Central Chile has been the focus of a series of tree-ring precipitation reconstructions, including the last 700 to 800 years (LaMarche, 1975; Boninsegna, 1988; Le Quesne et al., 2006; Le Quesne et al., 2009). The most recent of these studies reconstructed the precipitation of Santiago (33°26’S) based on a network of moisture tree-ring data, including samples from El Asiento (32°29’S) and Río Cipreses valley (Le Quesne et al., 2009). This study showed that the difference in annual precipitation between the wettest and driest 25-year period, from AD1300 to 2000, was

~0.25 m. The driest period of the record corresponds to the present. If it is assumed that the LIA maxima at Cipreses glacier coincides with one of the precipitation maxima identified in this study (AD 1500, 1650 or 1850), the temperature change required to displace the ELA to its LIA position is either very small (-0.1°C) or null, depending on the AAR value used to reconstruct the ELAs. This hypothesis is potentially incompatible with the high-resolution (5- year period) summer temperature reconstruction from Laguna Aculeo (33°50’) of von Gunten et al. (2009). This

study, based on pigments from lake sediment cores, suggested that summer temperatures were between 0.7 to

0.9°C colder between AD 1400 and 1750 than during the twentieth century, and certainly even colder than the present. Nevertheless, there are several reasons that could explain this discrepancy. For example: i) von Gunten et al (2009)’s record is a reconstruction of the summer temperatures, which is not necessarily representative of the mean annual temperatures, moreover, the ELA depression and the climatic anomalies estimated in this study correspond to minimum values. A greater ELA depression will translate to larger climatic anomalies; thus, the same changes in precipitation would require larger changes in temperature in order to drop the ELA to its LIA position.

Finally, uncertainties related to the reconstruction of the LIA ice margin, and in turn, of the estimation of the ELA fluctuations, could have similar implications as the last point discussed.

At Quelccaya (13°56’S, 70°50,W), less than 20 km to the east of Cordillera Vilcanota, an ice core from the summit of the ice cap provided information regarding the accumulation changes during the last 1500 years

(Thompson et al., 1995). This record shows that for a 25-year period, centered in AD 1392 (620 yr BP) the accumulation was ~0.1 meters of water equivalent (calculated based on ice density of 0.9167 g/cm 3) lower than for the last 25 years of the record (1960-1984). Assuming that accumulation is equal to the actual precipitation in the summit of the ice cap (which means neglecting sublimation), and that Vilcanota underwent similar precipitation anomalies, the temperature change required to compensate the precipitation decrease and to drop the ELA to its LIA position would have been approximately 0.9 to 1°C. Temperature reconstructions based on isotopic records from tropical ice cores (Thompson et al., 1995; Thompson et al., 1998; Ramirez et al., 2003) were not included here because recent studies have questioned if these records track changes in temperature (Hardy et al., 2003; Hoffmann et al., 2003; Vuille et al., 2003).

We suggest that these results can contribute to test hypotheses regarding causes and mechanisms involved in the generation of the LIA glacial and climatic event. The response of the climatic system to external perturbations is generally complex and involves sophisticated feedback processes. Because of this, numerical models have become crucial to understanding the causes underlying large scale climatic perturbation. However, the applicability of these models to events that have occurred in the past is often limited by the availability of information against which model predictions can be tested. This study has explored the timing, magnitude and climate of the LIA maxima across different climatic regimes

along the Andes and can provide targets against which the predictions of models of global climate dynamics for this period can be tested.

In addition, these results can also be used to study the changes in the climatic conditions observed since the LIA.

For example, the fact that the curves in Figure 7 do not intersect at a single point suggests that there is spatial variability in the climate change following the LIA. It is hoped that this type of study can become a first-order step to decipher both natural cycles and human impacts on climate change (Matthews and Briffa, 2005; Solomina et al., 2008).

Summary and final remarks

 Based on the calculation of ELA changes and the application of a full surface energy and mass balance model,

scenarios of the climatic conditions during the LIA at three alpine glaciers located in different climatic regimes along the

Andes have been reconstructed.

 The existing evidence is not conclusive enough to establish whether the LIA maxima occurred synchronically across

the three study areas. The maximum glacier advances of the LIA occurred in AD ~1400-1500 at Cordillera Vilcanota

and probably sometime between AD1000 and AD 1500 at Cipreses glacier. The exact timing of the LIA maximum at

Tranquilo glacier is still unknown.

 There are significant differences in the ELA change since the LIA maxima across the three study areas. Our results

suggest that Tranquilo glacier exhibits the largest ELA change, with an estimation ranging from 249 to 299 m. On the

other extreme, the ELA change at Cipreses glacier was only 75 to 137 m. Vilcanota exhibited intermediate values: 128

-148 m. However, given that present-day glaciers are in a transient condition (unbalanced), these results must be

taken with caution, because the ELA change calculation likely represent minimum-values.

 The SEMB model results suggest that there is not a single combination of temperature and precipitation changes that

accommodate the ELA change from the LIA maxima to the present for the three sites. Assuming no changes in

precipitation, the ELA fluctuation since the LIA could be explained by a cooling of at least: -0.7°C at Vilcanota, -0.5°C

at Cipreses and -1.3°C at Tranquilo glacier. Assuming no changes in temperature, on the other hand, the ELA

changes could be explained by an increase in the precipitation greater than 0.51 m (63%) at Vilcanota, 0.17 m (21%)

at Cipreses and 0.68 m (62%) at Tranquilo glacier.

 This approach can not only be used to reconstruct the climatic conditions during the LIA across different climatic

regimes but also to illustrate the changes in the climatic conditions observed since the LIA. In this sense, our results

suggest that there is spatial variability in the climate change recorded since the LIA across the different climatic

regimes existing along the Andes.

 Finally, these results can serve as targets to test predictions from models of global climate dynamics; hence,

contributing to the understanding of the mechanism underlying the Little Ice Age.

Acknowledgments

This work was funded by NSF Grants (EAR-1003072 and EAR-1003460) and Fondecyt (1080320). Our appreciation goes to Tomás Gómez, Jesús Soto and the Crispín family for providing field assistance; and Fundación Altiplano

MSV, Centro de Estudios Avanzados en Zonas Áridas (CEAZA) and Centro de Estudios del Cuaternario (CEQUA) for logistic support. We thank Colby A. Smith for valuable discussion and insightful comments on the manuscript.

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TABLE 1: List of the model variables and units used.

TABLE 2: Geographical and analytical data for the samples 10Be dated. Four separate batches of samples were processed, and with each batch one procedural blank was processed equally alongside the samples. The measured values of the blanks were used for background corrections of the samples from the corresponding batches. Ratios are normalized to the 07KNSTD standard. Shown are 1σ analytical AMS uncertainties. Density=2.6 gm/cm3. All rocks are Granitoids.

TABLE 3: 10Be ages (yr BP) with different scaling models and production rates, from the four study sites. Ages calculated based on methods incorporated in the CRONUS-Earth online exposure age calculator, version 2.2, with version 2.21 of the constants file (see text, Balco et al., 2008), which includes renormalization of Table 4 results relative to 07KNSTD (Nishiizumi et al., 2007). ‘Lm’ is the time dependent version of Stone/Lal scaling scheme (Lal,

1991; Stone, 2000). With asterisk uses the production rate of Putnam et al. (2010), which is similar to Balco et al.

(2009). All other columns show ages calculated with the cronus production rate originally discussed in Balco et al.

(2008). ‘Du’ is based on Dunai (2001), ‘Li’ on Pigati and Lifton (2004) and Lifton et al. (2008), and the ‘De’ scaling scheme on Desilets and Zreda (2003). We report external uncertainties that include scaling to the latitude and altitude of the sites. The discussion is based on the numbers given in the first age column (Lm*), as explained in the text. The different scaling schemes indicate that for either production rate, differences between models are less than

5%.

TABLE 4: AMS radiocarbon and calibrated ages from Vilcanota. All radiocarbon dates were converted to calendar years BP using CALIB 6.01 and the SHCal04 calibration curve (Stuiver and Reimer, 1993).

TABLE 5: Modern and LIA equilibrium line altitudes calculated based on the AAR method. ELAs and ELA changes corresponding to a broad range of AAR values are presented. Those values of ELA change corresponding to ratios more representative of each climatic region (as described by previous studies) are underlined.

FIGURE 1: Classification of Andean glaciers (modified from Sagredo and Lowell, 2012). Statistical analyses (i.e., cluster and principal component analysis) of temperature, precipitation and humidity permit the classification of

Andean glaciers into seven distinctive groups. The sample includes 234 glaciers distributed throughout the Andes

(12°30’N - 55°S). Each dot represents a single glacier. Plots (in matching colors) represent mean climatic conditions within each group. The horizontal axis represent the month of the years (from January through December). The bars represent monthly precipitation (mm), solid lines show mean monthly temperature (°C) and dashed lines represent mean monthly humidity (%). Axes were plotted with same ranges to facilitate comparisons between groups. All climatic information was extracted from CRU CL 2.0 climate model, 10’ lat/long resolution (New et al., 2002). Study sites are represented by the stars. Numbers represent sites used in the discussion (1: Cordillera Real, Bolivia; 2:

Cordillera Blanca, Peru; and 3: El Peñón and El Azufre glaciers, Argentina).

FIGURE 2: Study sites. a) Cordillera Vilcanota, b) Río Cipreses valley and c) Río Tranquilo valley. Stars show the specific location of the study areas.

FIGURE 3: Moraine complexes present in each study site. a) Cordillera Vilcanota, b) Río Cipreses valley and c) Río

Tranquilo valley. Each line represents an individual moraine ridge. In panel c) the dashed line represent a subtle ridge plastered against the inner slope of the Inner moraine complex, which we suspect corresponds to the LIA.

FIGURE 4: Dates constraining the age of the major glacial advances occurred during the LIA in each study site. a)

Cordillera Vilcanota, b) Río Cipreses valley and c) Río Tranquilo valley. Each line represents an individual moraine ridge. Yellow and white circles represent the location of the cosmogenic (ka) and radiocarbon ages (cal yr BP) in this study. Red dots show the location of radiocarbon ages (cal yr BP) from a previous study (Röthlisberger, 1987). In panel c) the red dashed line shows a subtle moraine ridge corresponding to the most likely candidate to represent the LIA maxima. Colors reflect legend shown in Figure 3.

FIGURE 5: Oblique Google earth image showing the present-day (yellow) and reconstructed LIA maxima (red) ice margin. Orientation of the images is shown by the circled arrows, which point to the North.

FIGURE 6: Hypsometry of the studied glacier. Solid lines represent modern hypsometry of the glaciers. Dashed lines represent reconstructed hypsometry of the glaciers during the LIA maxima. Vertical dotted lines intersect the X axis in the AAR value selected for each site. Horizontal dotted lines intersect the Y axis in the corresponding ELA.

FIGURE 7: Combination of temperature and precipitation anomalies (with respect to the present) that accommodate the ELA change from the LIA maxima to the present for the range of AAR used at each particular site (dashed lines correspond to the mean AAR value). Color scheme is the same as in Figure 1. Inset: values corresponding to the magnitude of perturbations equal to the present-day temperature and precipitation variability (1σ) and 25, 50, 75 and

100% of precipitation.

Final remarks

Valley glaciers are a very sensitive responder to climate change (Dyurgerov and Meier, 2000; Lowell, 2000;

Rupper and Roe, 2008).Consequently, an understanding of climate-glacier interaction across a glaciated region such as the Andes is a prerequisite to understand former episodes of climate change, as well as to predict the impact of future climate changes on glaciers. This study explored the magnitude of response of the equilibrium line altitude

(ELA) to different scenarios of climate change, along the climatically diverse Andes range, and its applicability to reconstruct paleoclimates.

It has been recognized that the Andes, in its 9,000 km along the western section of South America (from

~12°N to 55°S), intersects a broad range of climates (Garreaud et al., 2009). However, to date very little was known regarding the variability of the climatic conditions that host present-day Andean glaciers. In Chapter I of this dissertation, a statistical analysis of temperature, precipitation and humidity conditions at 234 glacier sites revealed that the climatic conditions that host present-day Andean glaciers can be classified into seven distinctive groups (Fig.

1). For instance, glaciers in the subtropics (i.e., group 4, Chapter I) are subjected to very cold and dry conditions

(mean annual temperature (MAT)= -4.5°C; total annual precipitation (TAP)= 0.3 m; mean annual humidity

(MAH)=51%); whereas in Patagonia (i.e., group 6, Chapter I) glaciers are subjected to warmer and wetter condition

(MAT=2.1°C; TAP= 2 m; MAH=73%). The remaining glaciers can be place somewhere in between these extreme sets of climatic conditions.

If there is variability in glacier sensitivity to climate change, as previously suggested (e.g., Kaser, 2001; Kull et al., 2008; Rupper and Roe, 2008), then it would be expected that glaciers corresponding to the different climatic

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groups will display differing magnitudes of response to an identical climatic perturbation. To explore this hypothesis, the response of the equilibrium line altitudes (ELA) to climate change across the different climatic was assessed. The

ELA is a climate sensitive parameter which marks the place where a glacier is in equilibrium and is less susceptible to localized effects than the glacier itself. A full surface energy and mass balance model (SEMB; Rupper and Roe,

2008) was applied to the glaciated regions along the Andes. This model uses as input climate data from NCEP-

NCAR reanalysis (Kalnay et al., 1996) and CRU CL 2.0 dataset (New et al., 2002), and solves for the climatological

ELAs. The model was forced with a range of temperature and precipitation perturbation, and the change in ELA for the different glacier sites (across different climate groups) was compared. The analysis reveals that there is a spatial variability in the magnitude of response of the equilibrium line altitudes (ELA) to uniform changes in temperature and precipitation. ELA varies linearly with changes in temperature, with values between 142 and 229 m per degree

Celsius, depending on the area (Fig. 2). On the other hand, the relationship between precipitation and ELA change is non-linear and asymmetrical. For example, for a precipitation increase of 0.1 m across the entire Andes, the ELA could change between 10 and100 m; whereas for a precipitation increase of 1 m the ELA may change between 110 to 460 m (Fig. 3). The spatial variability in ELA sensitivity has a general correspondence with present climate conditions throughout the Andes. In other words, the ELA response to a specific climatic perturbation (e.g., drop in temperature) within a single climatic group is more similar than the response between different groups. The most sensitive areas to changes in temperature are the inner tropics, whereas precipitation sensitivities are relatively greater in the subtropics and northernmost mid-latitudes.

This variability in the ELA sensitivity has implications for the reconstruction of former climates across large areas. For instance, an identical climatic perturbation (e.g., 1°C cooling) could result in dissimilar ELA changes across different climate regimes. Assuming everything else constant (hypsometry of the basin, glacier size, thermal regimes, etc.), areas more sensitive to changes in temperature would experience more extensive glacier advances than areas less sensitive. Hence, it is expected to observe differences in the resulting geologic record (e.g., sequence of moraines), even under similar climatic perturbations. As a result, the reconstruction of former climates,

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based on the glacial record, must consider the variability of ELA sensitivity to changes in temperature and precipitation, as well as the relative importance of both variables across different climatic regimes.

The later point was illustrated in Chapter III, where the climate conditions for the maximum glacial advance occurred during the Little Ice Age (LIA, sensu lato AD 1300-1850) were reconstructed along the Andes. To conduct this experiment, three glacial sites (located in different climatic regimes) were selected (Fig. 1): Cordillera Vilcanota

(13°S), Cipreses glacier (34°S) and Tranquilo glacier (47°S). First of all, the ELA changes since the LIA maxima to the present were estimated for the three study sites. Then, using the SEMB model, the different scenarios of the climatic conditions that can accommodate the observed ELA changes were calculated. The results consist of a set of combination of temperature and precipitation anomalies that can account for ELA changes from the maximum glacial advance that occurred during the LIA to the present for each site (Fig. 4). Assuming no changes in precipitation, the ELA fluctuation since the LIA could be explained by a cooling of at least: -0.7°C at Vilcanota, -0.5°C at Cipreses and -1.3°C at

Tranquilo glacier. Assuming no changes in temperature, on the other hand, the ELA changes could be explained by an increase in the precipitation greater than 0.51 m (63%) at Vilcanota, 0.17 m (21%) at Cipreses and 0.68 m (62%) at

Tranquilo glacier.

Because ELA are primarily controlled by both temperature and precipitation, the approach used here does not provide a unique answer, but the entire range of possible combinations of temperature and precipitation that could explain the ELA changes. Although this represents a significant contribution toward the understanding of former climates, the practical application of these results to specific problems could be limited for the broad range of solutions that this approach provides. It is suggested that the range of possible solutions could be further narrowed down by using independent climatic proxy records that allow to constrain the temperature and/or precipitation anomalies in the corresponding regions. This opens a new whole set of challenges that need to be addressed, and that have to do with the development of a network of high resolution climatic proxy records along the Andes (e.g., chironomids, diatoms , pollen, etc.), and the development of chronologies of glacial fluctuations with a resolution comparable to the proxy records.

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In addition, this study have shown that in order to decipher the causes and mechanisms underlying former glacial events occurred over large areas it is necessary to improve the coverage (spatial resolution) of the record of glacial fluctuation. To date, most of the studies of former glacial fluctuation in South America, including those corresponding to the last millennium, have focused on the tropical regions and Patagonia (Grove, 2004; Jomelli et al., 2009;

Masiokas et al., 2009). There is a considerably lack of information regarding the timing of glacial advances in the subtropical and northern mid-latitude areas. These areas correspond to the transitional zone between tropical

(Atlantic) climatic domain and the westerly (Pacific) climatic domain. Hence, these areas can easily track climate change that involved the migration of the major climatic belts. Consequently, it is suggested that the efforts of developing high-resolution chronologies and defining the magnitude of former glacial fluctuations along the Andes must include the areas between the ~17°S and 41°S. This certainly will result in a significant progress in the understanding of the spatial extent of glacial events and the spatial pattern of climate change.

By exploring the spatial variability in the response of the ELA altitude to climate change and its implication for the interpretation of the geological record and paleoclimatic reconstruction, this dissertation has contributed to the understanding of climate-glacier interaction across the glaciated region along the Andes. It is suggested that his research could provide basis for the study of former, present, and future glacial fluctuation. First of all, altogether, the three studies provide a new approach to interpret past changes in ELA, and to extract the climatic signals associated with former glacial fluctuations across large areas. This research also provides a spatial framework to assess the magnitude of present-day glacier fluctuation. Finally, the results of this study could be used to predict the response of

Andean glacier to future climate change.

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Andean glaciers into seven distinctive groups. The sample includes 234 glaciers distributed throughout the Andes

(12°30’N - 55°S). Each dot represents a single glacier. Plots (in matching colors) represent mean climatic conditions within each group. The horizontal axis represent the month of the years (from January through December). The bars represent monthly precipitation (mm), solid lines show mean monthly temperature (°C) and dashed lines represent mean monthly humidity (%). Axes were plotted with same ranges to facilitate comparisons between groups. All climatic information was extracted from CRU CL 2.0 climate model, 10’ lat/long resolution (New et al., 2002). Stars represent glacier sites studied in Chapter III.

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FIGURE 2: Sensitivity of the Equilibrium line altitude to changes in the mean temperature. A) modeled change in

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FIGURE 3: Sensitivity of the Equilibrium line altitude to changes in the mean annual precipitation. A) modeled change in ELA after uniform increases of precipitation, ranging from 0 to 1 m in steps of 0.1 m. Inset figure includes two outliers. B) spatial distribution of the changes in the ELA after a 1 m increase of the mean annual precipitation.

Area of the circles is proportional to the ELA change. C) latitudinal distribution of the changes in the ELA after a 1 m increase of the mean annual precipitation. Color scheme is the same as in Figure 1.

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FIGURE 4: Combination of temperature and precipitation anomalies (with respect to the present) that accommodate the ELA change from the LIA maxima to the present for the range of AAR used at each particular site (dashed lines correspond to the mean AAR value). Color scheme is the same as in Figure 1. Inset: values corresponding to the magnitude of perturbations equal to the present-day temperature and precipitation variability (1σ) and 25, 50, 75 and

100% of precipitation.

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