Tropical snowline changes at the last glacial maximum: A global assessment

ARTICLE IN PRESS Quaternary International 138–139 (2005) 168–201 Tropical snowline changes at the last glacial maximum: A global assessment B.G. Mar...

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ARTICLE IN PRESS

Quaternary International 138–139 (2005) 168–201

Tropical snowline changes at the last glacial maximum: A global assessment B.G. Marka,b,c,, S.P. Harrisona,d, A. Spessaa, M. Newe, D.J.A. Evansb,f, K.F. Helmensg a

Max Planck Institute for Biogeochemistry, P.O. Box 100164, D-07701 Jena, Germany Department of Geography & Geomatics, University of Glasgow, Glasgow G12 8QQ, UK c Department of Geography, The Ohio State University, 1136 Derby Hall, 154 N. Oval Mall, Columbus, OH 43210, USA d School of Geographical Sciences, University of Bristol, University Road, Bristol BS8 1SS, UK e School of Geography and the Environment, University of Oxford, Mansfield Road, Oxford OX1 3TB, UK f Geography Department, University of Durham, South Road, Durham DH1 3LE, UK g Arctic Centre, University of Lapland, P.O. Box 122, 96101 Rovaniemi, Finland b

Available online 28 April 2005

Abstract Snowline reconstructions from the tropics and subtropics (331S–331N) at the last glacial maximum (LGM) have been extracted from a new database which provides information on glacier equilibrium-line altitudes (ELAs) for over 350 glacier-valley localities during the Late Quaternary. About 60% of the intra-regional variability observed change in ELA (DELA) between the LGM and today is related to headwall altitude, reﬂecting the inﬂuence of basin morphometry (e.g. catchment size, glacier slope) on the response to climate change. Glacier-valley aspect, which inﬂuences the local patterns of radiation and precipitation, also causes intra-regional variability in DELA although this inﬂuence varies greatly between regions. Overall, reconstructed DELAs are smallest in the Himalayas, relatively small in the southern central Andes and East Africa, and largest in the northern Andes, Mexico and Papua New Guinea. Estimates of the temperature change implied by the range of DELA within any one region, calculated using the CRU 100 modern climate database, are consistent with high-altitude temperature changes projected from reconstructions of temperature changes at lower altitude sites based in vegetation and geochemical data in the 21 ka TROPICS data set [Farrera et al., 1999. Climate Dynamics 15, 823–856]. r 2005 Elsevier Ltd and INQUA. All rights reserved.

1. Introduction Energy transfer from the tropics to the poles is fundamental to the maintenance of atmospheric and oceanic circulation regimes. There is disagreement about the extent to which homeostatic mechanisms can buffer the tropics against changes in temperature (Stocker et al., 2001). The need to resolve this issue has provided a motivation for investigations of tropical climate changes during the late Quaternary, and particularly investigations of the magnitude of tropical cooling at the Corresponding author. Department of Geography, The Ohio State University, 1136 Derby Hall, 154 N. Oval Mall, Columbus OH 43210, USA. Tel.: +1 614 247 6180. E-mail address: [email protected] (B.G. Mark).

last glacial maximum (LGM, conventionally deﬁned here as 21,00072000 calendar years B.P., approximately equivalent to ca. 18,00072000 14C years B.P). The earliest reconstructions of sea-surface temperatures (SSTs) indicated that the mean LGM cooling in the equatorial zone was 41 1C (CLIMAP, 1976, 1981). However, it was rapidly pointed out that such small changes in SST were inconsistent with evidence from tropical Australasia for a major lowering of snowlines at the LGM, and the 5–8 1C cooling inferred from these snowline changes (Webster and Streten, 1978). Rind and Peteet (1985) showed that climate models could not allow such small changes in tropical SSTs and simultaneously produce a 5–8 1C cooling at high altitudes. Several potential causes for this mismatch have been put forward (see the summary in Pinot et al., 1999),

1040-6182/$ - see front matter r 2005 Elsevier Ltd and INQUA. All rights reserved. doi:10.1016/j.quaint.2005.02.012

ARTICLE IN PRESS B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

including errors in the SST reconstructions (see e.g. Anderson and Webb, 1994; Webb et al., 1997), errors in the snowline data, limitations in the representativeness of the snowline data (Rind and Peteet considered only four sites), errors in the use of SST data for model forcing (Broccoli and Marciniak, 1996), or problems in the parameterisation of tropical processes with the models themselves (see Kagayama et al., this volume). Recent estimates of tropical SSTs by various methods have converged on an estimated mean zonal cooling of 2–3 1C (Rosell-Mele´ et al., 1998, 2004; Sonzogni et al., 1998; Bard et al., 1998a; Mix et al., 1999) However, while new SST estimates are lower than those shown by CLIMAP, they do not support a generalised cooling of the tropical oceans by 45 1C. Furthermore, there are geographic patterns in the SST anomalies with some tropical regions showing larger cooling than others. Reconstructions based primarily on pollen evidence from tropical lowlands (Farrera et al., 1999) are consistent with marine estimates for regional cooling (Folland et al., 2001; Harrison, 2001). Farrera et al. (1999) indicated that land temperatures at low elevations in the tropics were on average 2.5–3 1C lower than today at the LGM, with large changes (5–6 1C) in the Neotropics (Central and northern South America) and relatively small changes (o2 1C) in the western Paciﬁc Rim. These spatial differences in lowland temperatures were accompanied by differences in the lapse rate as shown by differential vertical migration of forest belts. Lapse rates estimated by differential migration were steeper by ca. 2 1C/km in the western Paciﬁc Rim and by only 1 1C/km in the circum-Indian Ocean region. There was no clear evidence for a change in lapse rate in Central America or northern South America, although the data from these regions showed considerable scatter. Farrera et al. (1999) suggested that such large changes in lapse rates as those estimated for the western Paciﬁc Rim and the circum-Indian Ocean region, extrapolated to still higher elevations, might be sufﬁcient to explain the temperature changes inferred from snowline shifts. A new synthesis of snowline data was required to test this hypothesis. This initiative was timely for several reasons. First, there has been a major increase in the number of published snowline studies. Rind and Peteet (1985) based their assessment on only four sites, and more recent analyses (e.g. Broecker and Denton, 1990; Ono and Naruse, 1997; Hostetler and Clark, 2000; Porter, 2001) have included only a subset of the sites available today. Second, there have been signiﬁcant advances in dating techniques, in particular with the advent of cosmogenic-isotope dating (Nishizumi et al., 1993), resulting in better chronological control on reconstructed snowline changes. Chronologies based on cosmogenic dating have substantially altered estimates of LGM glacial extent in many regions (e.g. Shanahan and Zreda, 2000; Barrows et al., 2002;

169

Brigham-Grette et al., 2003; Finkel et al., 2003) and glaciologists have shown that it is potentially misleading to date glacial deposits on the assumption that glacial advances are synchronous from region to region, or on the assumption that the largest advances necessarily occurred at the LGM (e.g. Mercer, 1984; Lowell et al., 1995; Clapperton, 2000; Owen et al., 2000, 2002c). Finally, there have been important developments in our understanding of the climatic controls on glacial extent. In particular, there is now widespread recognition that snowline changes may be caused by precipitation changes as well as by temperature changes (e.g. Kuhn, 1989; Ohmura et al., 1992; Seltzer, 1994c; Hostetler and Clark, 2000). The aim of this paper is to present a new synthesis of tropical snowline changes between the LGM and the present-day based on valley-speciﬁc equilibrium-line altitudes (ELA, or snowline) estimates, and to explore the causes of these changes and their implication for our understanding of tropical climate change. The data are derived from a new database, which documents valleyspeciﬁc changes in ELA during the Late Quaternary. We describe the Snowline Database, including the methods and assumptions used in the data compilation, in Section 2. We describe the methods employed to select and analyse LGM snowline data in Section 3. We present results of analyses of the causes of intra-regional differences in ELA changes between the LGM and today in Section 4. We analyse inter-regional differences in ELA changes as a prelude to reconstructing the regional temperature changes implied by these ELA changes in Section 5. We also compare these reconstructions with estimates based on data from the 21 ka TROPICS data set of Farrera et al. (1999). We discuss our results and the implications for LGM climate reconstructions based on snowline changes in Section 6.

2. The snowline database The snowline database contains information about palaeosnowlines from tropical and subtropical regions (331S–331N) during the last glacial period and the Holocene, extracted from the published literature or provided by glaciologists directly. The database contains snowline reconstructions for individual glacier advances, as well as metadata on location, site type and aspect, chronology and the nature of the geomorphic deposits used to reconstruct snowline position. The basic unit of the snowline database is the palaeo equilibrium-line altitude (palaeo-ELA) of a valleyspeciﬁc locality. The ELA (which is sometimes referred to as the snowline) is conventionally deﬁned as marking the position on the glacier at which accumulation is balanced by ablation: above the ELA accumulation4 ablation, while below the ELA ablation4accumulation.

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The palaeo-ELA is not always available from the literature. However, the database includes information on the geographic dimensions of the palaeo-glacier, which can be used to derive a valley-speciﬁc palaeoELA. These parameters include the altitude of the headwall (i.e. the upper limit of the cirque or the glacial accumulation zone) and terminus (or toe) of the palaeoglacier, the maximum altitude of lateral moraines associated with the palaeo-glacier, and the average altitude of the palaeo-glacier catchment. Information on the altitude of the mountain summit and the highest summit altitude in the region are also included. Where ELA data is not published but terminus altitude is available, palaeo-ELA is computed as a ﬁxed proportion of the vertical distance between the lowest and highest glacier limits using a user-speciﬁed geographic elevation index (GEI). The default GEI is the ratio of the vertical span of the palaeo-glacier according to the formulation known as the toe-to-headwall ratio (THAR, Meierding, 1982). The ratio can also be speciﬁed by the user; the default scheme is a simple mid-point altitude (THAR ¼ 0.5). Other GEIs can be computed which make use of the available metadata to approximate the upper glacier limit, including the highest summit in the catchment, or the average catchment area elevation (Benn and Lehmkuhl, 2000). Thus, when the headwall altitude (HW) is not included for a locality, the vertical span of the palaeo-glacier is computed as the terminus altitude subtracted from the altitude of the ﬁrst available of the following variables: summit altitude, average catchment altitude, or regional summit altitude. There are instances in the literature where the data for individual glaciers have been aggregated and the published ELA reconstructions are only available as an average for valleys with similar aspect in a given region (e.g. Kilimanjaro: Osmaston, 1989a; Kaser and Osmaston, 2002) or even for all valleys within a region (e.g. Zanskar: Damm, 1997; Taylor and Mitchell, 2000; Owen et al., 2002a, b). These reconstructions are included in the database for completeness. To facilitate comparisons between individual palaeo-glacier reconstructions and such pre-aggregated regional averages, each of the individual palaeo-glacier reconstructions is allocated to a larger spatial region. This enables individual reconstructions to be aggregated regionally, or by aspect within a region. Nevertheless, the spatial scale of these regions varies, given the inherent variability in published data. In some regions, many localities have been identiﬁed in close proximity, justifying a local name (e.g. Iztaccı´ huatl, or Bogota´), while in other regions, localities are more dispersed, requiring a more general name (e.g. Central Andes or East Africa). Therefore, to facilitate the identiﬁcation and comparison of localities, the country of each locality is also retained in the database.

The database includes estimates of modern ELA for each locality, so that we can estimate the change in ELA compared to present (DELA). The derivation of this parameter can be problematic. The majority of tropical valleys that once contained glaciers have no modern glaciers. Where modern glaciers exist, they are not necessarily in mass balance equilibrium (Meier et al., 2003) and thus the published information on the modern ELA represents a ‘‘snapshot’’ estimate for a particular time rather than representing a long-term average in the same way as the palaeo-ELA estimates. Furthermore, published estimates of the modern ELA are derived from a variety of sources including estimates based on recent or historic moraine positions; observations of ﬁrm line position from aerial photographs, and mass balance measurements on individual glaciers or ice masses. There are difﬁculties associated with the interpretation of each of these sources. Modern ELA data based on mass balance measurements are very rare (Kaser and Osmaston, 2002), and speciﬁc to the date of observation. Modern ELA estimates based on glacier geometries are generally idealised estimates, which may underestimate the altitude of true equilibrium for the prevailing climatic conditions (Benn et al., this volume). Even when modern ELA estimates are published, it may not be possible to determine the methods used to derive the ELA. To accommodate these problems, the database contains additional information which can be used to estimate modern ELA, including a cross-reference search of the modern World Glacier Inventory (National Snow and Ice Data Center, 1999). Modern ELA is assigned according to a successive priority search, such that the ﬁeld is ﬁlled with the ﬁrst available of: 1. published ELA for LGM valley locality; 2. midpoint altitude of nearest glacier to LGM valley locality in the World Glacier Inventory. The size of the search window (in decimal degrees of latitude and longitude) can be deﬁned by the user; 3. ELA calculated from: a. midpoint of modern glacier (THAR ¼ 0.5); b. midpoint of highest summit and modern terminus; 4. highest summit (ﬂagged as a minimum topographic estimate); 5. regional modern ELA. Although there are uncertainties attached to the estimates of modern ELA, the consequences of these uncertainties for estimating DELA are likely to be small compared to the uncertainties generated by other problems (such as chronological uncertainties). Chronological details are retained for each palaeoELA locality in the Snowline Database, including information on the location, context, laboratory identiﬁcation and treatment method for each dated sample and information on the geomorphic feature dated.

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Where radiocarbon dating is applied, individual dates are given in both radiocarbon ages and calibrated years before present (B.P.). We used the online calibration program CALIB version 4.3 (http://depts.washington.edu/qil/calib; Stuiver et al., 1998; Bard et al., 1998b) to calibrate radiocarbon dates without published calibrations. The database includes various measures for ranking reconstructions on the basis of both the dating method used (dating method control, DMC) and the quality of chronology (dating control, DC). DMC is classiﬁed using a 5-point scale, thus: DMC ¼ 1: Chronology based on radiometric dating of terminus position; DMC ¼ 2: Chronology based on geomorphologic correlation of terminus position with radiometrically dated feature within the glacier valley; DMC ¼ 3: Chronology based on geomorphic correlation with radiometrically dated feature within a region, where the region may be an individual mountain or mountain range. In the case of isolated mountains, the dated feature must be within 50 km; in case of a mountain range, the feature must be within 200 km and have the same aspect; DMC ¼ 4: Chronology based on correlation with a radiometrically dated regional sequence; DMC ¼ 5: Chronology based on correlation with a generalised or global glaciation scheme. Thus, when the timing of a glacier advance is established by a cosmogenic date on a boulder from a terminal moraine or a radiocarbon date on organic material from within the terminal moraine, the glacier advance would be assigned a DMC of 1. When the advance is dated by radiometric dating of glacial outwash deposits or of lacustrine sediments formed after the retreat of the glacier, the advance would be assigned a DMC of 2. When the advance is dated by correlation with radiometrically dated deposits from a speciﬁc glacier valley in close proximity to the study site, the advance is assigned a DMC of 3. DMC 4 is assigned when the advance in a speciﬁc valley is dated by comparison with a regional dating scheme, where such a scheme was devised by pooling dates from several localities. An assignment of DMC 5 implies that there is no radiometric dating. The 7-point dating control (DC) scheme was modiﬁed from the COHMAP scheme, as described by Yu and Harrison (1995). In the COHMAP scheme, a fundamental distinction is made between records with continuous sedimentation (where bracketing radiocarbon dates provide a high level of conﬁdence on the identiﬁcation of a particular interval even though the actual dates may be quite far apart) and records based on dating isolated deposits (where the level of conﬁdence

171

is strictly dependent on how close a single date is to the target date). Glacial records are based almost exclusively on isolated deposits. The discontinuous DC scheme used by COHMAP is rather stringent and not well-suited to glacial deposits. We therefore modiﬁed the scheme as follows: DC ¼ 1: Date is less than 500 years from the target interval; DC ¼ 2: Date is less than 1000 years from the target interval; DC ¼ 3: Date is less than 2000 years from the target interval; DC ¼ 4: Date is less than 3000 years from the target interval; DC ¼ 5: Date is less than 4000 years from the target interval; DC ¼ 6: Date is less than 5000 years from the target interval; DC ¼ 7: Date is greater than 5000 years from the target interval. Sites with a DMC equal to 5 are, by deﬁnition, not associated with a radiometric date. The DC for any locality with DMC ¼ 5 is therefore given as 8.

3. LGM snowlines: analytical methods Data on the change in ELA between the LGM and present (DELA) was extracted from the Snowline Database. The LGM is deﬁned here to be consistent with the time of maximum global ice volume during the last glacial cycle (Marine Isotope Stage 2: MIS 2). It does not necessarily correspond to the time when mountain glaciers in any particular region of the tropics reached their maximum extent. We deﬁne the LGM as 21,00072000 calendar years B.P., following the convention used by the EPILOG project (Mix et al., 2001) and by Farrera et al. (1999). For those sites for which the chronology was based on radiocarbon dating, we assume that the LGM corresponds to 18,00072000 14C years B.P. The Snowline Database contains sufﬁcient information to compute the DELA for 359 valley-speciﬁc localities (Table 1, Fig. 1). In cases where it was not possible to obtain valley-speciﬁc palaeo-ELA estimates, we have made use of regionally averaged estimates. Information on the average snowline elevation for an entire mountain range/massif is available for 63 localities, and information on average elevation for sites with the same aspect within a given range/massif for a further 39 localities (Table 1, Fig. 1). Thus, there are 461 records in total. In 85% of the localities, all of the necessary information to compute DELA was available but in 73 of the localities one or more of the required

172

Table 1 Sites providing estimates of changes in ELA between the LGM and present (DELA) LGM snowline locality

Region

Lat (deg)

Valley-speciﬁc Media Naranja

Colombia

Bogota´

4.21

Laguna de Colorado

Colombia

Bogota´

Rio Chicasa

Colombia

Boca Grande

Lon (deg)

Highest summit altitude (m)

Head-wall TerLGM altitude minus ELA (m) altitude (m) (m)

LGM ELA method

Modern ELA (m)

Modern ELA Method

DELA

DMC DC

References

74.25

NW

3990

3925

3275

3755

AABR (BR ¼ 1)

4700

945

3

3

4.38

74.23

W

3829

3850

3350

3671

AABR (BR ¼ 1)

4700

1029

3

3

Bogota´

4.28

74.22

N

3990

3975

3450

3765

AABR (BR ¼ 1)

4700

935

3

3

Colombia

Bogota´

4.30

74.14

NNW

3900

3850

3400

3711

AABR (BR ¼ 1)

4700

989

2

3

Q. Seca

Colombia

Bogota´

4.33

74.13

NW

3900

3850

3400

3713

AABR (BR ¼ 1)

4700

987

3

3

Q. Blanca

Colombia

Bogota´

4.32

74.13

NW

3900

3850

3325

3699

AABR (BR ¼ 1)

4700

1001

3

3

Q. Los Salitres

Colombia

Bogota´

4.33

74.12

NW

3900

3825

3475

3703

AABR (BR ¼ 1)

4700

997

3

3

Q. Piedragorda

Colombia

Bogota´

4.35

74.11

NW

3900

3825

3225

3562

AABR (BR ¼ 1)

4700

1138

3

3

Carrasposo

Colombia

Bogota´

5.07

74.10

SW

3750

3725

3375

3596

AABR (BR ¼ 1)

4700

1104

3

3

Alto Cerca de Piedra

Colombia

Bogota´

4.73

73.88

NW

3650

3600

3225

3479

AA

4700

1221

3

3

Laguna de America

Colombia

Bogota´

4.77

73.85

WNW

3775

3750

3300

3554

AABR (BR ¼ 1)

4700

1146

3

3

Rio Siecha

Colombia

Bogota´

4.78

73.85

NW

3775

3750

3275

3589

AABR (BR ¼ 1)

4700

1111

3

3

Pena los Picachos

Colombia

Bogota´

4.80

73.84

WNW

3650

3600

3350

3519

AA

4700

1181

3

3

Alto de la Tabla de Cacao Q. Salitre

Colombia

Bogota´

4.81

73.84

NW

3650

3600

3275

3465

AA

4700

1235

3

3

Colombia

Bogota´

4.78

73.83

NNE

3700

3700

3175

3494

AABR (BR ¼ 1)

4700

1206

3

3

Rio Chipata

Colombia

Bogota´

4.81

73.83

NNE

3500

3500

3050

3310

AABR (BR ¼ 1.5)

4700

1390

3

3

Rio Tunjo

Colombia

Bogota´

4.78

73.82

NE

3725

3700

3000

3388

AABR (BR ¼ 1)

4700

1312

3

3

Cuchilla de Bocachica Colombia

Bogota´

4.83

73.82

NW

3500

3450

3025

3297

AABR (BR ¼ 1.5)

4700

1403

3

3

Alto del Paramo

Colombia

Bogota´

4.84

73.82

NW

3450

3425

3150

3320

AABR (BR ¼ 1.5)

4700

1380

3

3

Q. el Chuscal

Colombia

Bogota´

4.84

73.81

NW

3500

3450

3075

3341

AABR (BR ¼ 1.5)

4700

1359

3

3

Alto de Pena Negra

Colombia

Bogota´

4.87

73.80

NW

3500

3450

3150

3359

AABR (BR ¼ 1.5)

4700

1341

3

3

Alto los Hoyos

Colombia

Bogota´

4.86

73.80

NNW

3500

3450

3150

3344

AABR (BR ¼ 1.5)

4700

1356

3

3

Q. de los Organos

Colombia

Bogota´

4.85

73.80

NW

3500

3450

3150

3370

AABR (BR ¼ 1.5)

4700

Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation ﬁeld observation

1330

3

3

Cerro de la Muerte Morrenas Valley

C.Rica C.Rica

Caribb Caribb

9.56 9.47

83.75 83.49

NW N

3491 3819

3480 3700

3250 3310

3400 3583

CF 50007187 THAR ¼ AAR ¼ 0.7 50007187

01 isotherm 01 isotherm

1500 1317

3 2

7 7

Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com. Herd (1974), Helmens (1988), Helmens (pers com.) Herd (1974), Helmens (1988), Helmens (pers com.) Lachniet and Seltzer (2002) Lachniet and Seltzer (2002), Lachniet and Vasquez-Selem (this volume)

ARTICLE IN PRESS

Aspect

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Country

Caribb

9.50

83.49

SSW

3819

3700

3300

3580

THAR ¼ AAR ¼ 0.7 50007187

01 isotherm

1320

3

7

Talari Glacier Morrenas Glacier

C.Rica C.Rica

Caribb Caribb

9.47 9.50

83.49 83.49

SW N

3819 3819

3700 3700

3140 3040

3489 3464

AABR ¼ 0.4 AABR ¼ 0.4

5000 5000

01 isotherm 01 isotherm

1511 1536

3 2

7 7

Mamancanaca Glacier Colombia

Caribb

10.80

73.60

SW

4800

4500

3400

3963

AABR ¼ 0.4

5100

map estimate

1137

4

4

Pico Ojeda Glacier

Colombia

Caribb

10.80

73.60

NW

5490

5000

2900

3967

AABR ¼ 0.4

5100

map estimate

1133

4

4

Guardian Glacier

Colombia

Caribb

10.80

73.60

S

5235

5000

3400

4382

AABR ¼ 0.4

5100

map estimate

718

4

4

Mucubaji Glacier

Venezuela

Caribb

8.88

70.81

N

4600

4525

3500

3760

AABR ¼ 4.0

4700

THAR ¼ 0.4

940

2

3

Laguna Negra Glacier Venezuela

Caribb

8.88

70.81

N

4600

4525

3100

3604

AABR ¼ 4.0

4700

THAR ¼ 0.4

1096

3

3

La Canoa Glacier Cordillera Real1 Cordillera Real3 Cordillera Real2 Cordillera Real4 Rio Palcoco Cordillera Real6 Milluni-Zongo

Caribb CenAndes CenAndes CenAndes CenAndes CenAndes CenAndes CenAndes

8.93 16.02 16.06 16.05 16.08 16.17 16.30 16.28

70.70 68.41 68.39 68.38 68.38 68.33 68.12 68.12

E SW SW SW SW SW SW NE

3800 6000 6000 6000 6000

3800 5440 5420 5500 5600 5460 5730

2850 4720 4920 4720 4720 4260 4880 3600

3364 4660 4680 4680 4780 4780 4800 4200

AABR ¼ 4.0 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 MELM

4700 4980 5100 5000 5040 5100 5200 5050

THAR ¼ 0.4 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 THAR ¼ 0.37 map estimate

1336 320 420 320 260 320 400 850

2 4 4 4 4 2 4 4

7 6 6 6 6 7 6 7

Laguna Kollpa Kkota Bolivia

CenAndes

17.43

67.88

W

4560

4560

4400

4460

THAR ¼ 0.37

5100

AAR ¼ 0.77

640

2

1

Quimsa Cruz

Bolivia

CenAndes

17.06

67.24

W

4650

AAR ¼ 0.77

5050

map estimate

400

4

7

C.Oriental W(m)10–12 C.Oriental W(m)9 C.Oriental W(l)43 C.Oriental W(l)42 C.Oriental W(m)8 C.Oriental W(m)13–15 C.Oriental W(m)1–7 C.Oriental W(l)49–52 C.Oriental W(m)16 C.Oriental W(l)46–48 C.Oriental W(m)17–19 C.Oriental W(m)27–30 C.Oriental W(m)20–24 C.Oriental W(l)53–58 C.Oriental W(l)59–63 C.Oriental E(m)32–38 C.Oriental E(m)41 N.Solimana N3

Peru

CenAndes

7.63

77.56

WSW

4200

3960

3300

3495

THAR ¼ 0.4, 0.2

4630

GT

1135

3

6

Lachniet and Seltzer (2002), Lachniet and Vasquez-Selem (this volume) Lachniet and Seltzer (2002) Orvis and Horn (2000), Horn (1990), Lachniet and Seltzer (2002) Wood (1970), Bartels (1984), van der Hammen (1984) Wood (1970), Bartels (1984), van der Hammen (1984) Wood (1970), Bartels (1984), van der Hammen (1984) Schubert (1970), Schubert and Rinaldi (1987) Schubert (1970), Schubert and Rinaldi (1987) Mahaney et al. (2000) Seltzer (1992) Seltzer (1992) Seltzer (1992) Seltzer (1992) Seltzer (1992) Seltzer (1992) Seltzer et al. (1995), Wagnon et al. (1999) Seltzer (1994a, b), Seltzer et al. (1995) Seltzer (1994a, b), Seltzer et al. (1995) Rodbell (1991, 1992)

Peru Peru Peru Peru Peru

CenAndes CenAndes CenAndes CenAndes CenAndes

7.54 7.72 7.71 7.59 7.65

77.56 77.55 77.55 77.55 77.55

W SW SW SW WSW

4100 4250 4200 4150 4200

3800 4075 4075 3950 3933

3375 3900 3750 3500 3350

3503 3985 3848 3635 3527

THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4,

0.2 0.2 0.2 0.2 0.2

4630 4700 4700 4660 4620

GT GT GT GT GT

1128 715 853 1025 1094

3 3 3 3 3

6 6 6 6 6

Rodbell Rodbell Rodbell Rodbell Rodbell

(1991, (1991, (1991, (1991, (1991,

1992) 1992) 1992) 1992) 1992)

Peru Peru Peru Peru Peru

CenAndes CenAndes CenAndes CenAndes CenAndes

7.60 7.80 7.66 7.78 7.67

77.55 77.54 77.54 77.53 77.53

W WNW SW SW SW

4400 4525 4150 4200 4300

3840 4095 3950 4025 3870

3500 3725 3600 3725 3450

3605 3837 3705 3725 3575

THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4,

0.2 0.2 0.2 0.2 0.2

4660 4650 4615 4600 4630

GT GT GT GT GT

1055 814 910 875 1055

3 3 3 3 3

6 6 6 6 6

Rodbell Rodbell Rodbell Rodbell Rodbell

(1991, (1991, (1991, (1991, (1991,

1992) 1992) 1992) 1992) 1992)

Peru

CenAndes

7.73

77.52

W

4350

4050

3500

3675

THAR ¼ 0.4, 0.2

4640

GT

965

3

6

Rodbell (1991, 1992)

Peru

CenAndes

7.69

77.51

W

4300

3850

3500

3605

THAR ¼ 0.4, 0.2

4640

GT

1035

3

6

Rodbell (1991, 1992)

Peru Peru Peru Peru Peru

CenAndes CenAndes CenAndes CenAndes CenAndes

7.83 7.83 7.63 7.66 15.40

77.50 77.48 77.48 77.46 72.90

SSW SW E E N

4250 4240 4300 4300 6093

4120 4075 3890 3630

3680 3700 3000 3000 4850

3780 3738 3153 3188 5400

4640 4640 4555 4560 5588

GT GT GT GT inentory (MID)

860 903 1403 1373 188

3 3 3 3 5

6 6 6 6 8

N.Solimana N2

Peru

CenAndes

15.40

72.90

N

6093

4750

5250

5588

inentory (MID) 338

5

8

N.Solimana N1

Peru

CenAndes

15.40

72.90

N

6093

4600

5000

5588

inentory (MID) 588

5

8

N.Solimana SE

Peru

CenAndes

15.40

72.90

SE

6093

4500

4850

5432

inentory (MID) 582

5

8

N.Solimana SW1

Peru

CenAndes

15.40

72.90

SW

6093

4400

4725

5413

inentory (MID) 688

5

8

N.Solimana SW2

Peru

CenAndes

15.40

72.90

SW

6093

THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67

5413

inentory (MID) 563

5

8

Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Dornbusch (2002), Smith this volume Dornbusch (2002), Smith this volume Dornbusch (2002), Smith volume Dornbusch (2002), Smith volume Dornbusch (2002), Smith volume Dornbusch (2002), Smith volume

Venezuela Bolivia Bolivia Bolivia Bolivia Bolivia Bolivia Bolivia

6000 6088

et al., et al., this et al., this et al., this et al., this

173

4850

et al.,

ARTICLE IN PRESS

C.Rica

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Talari Valley

174

Table 1 (continued ) Country

Region

Lat (deg)

N.Coropuna SW

Peru

CenAndes

15.59

Cerros Jollpa/ Yanahuara S2 Cerros Jollpa/ Yanahuara S1 Cerros Jollpa/ Yanahuara SW Cerros Jollpa/ Yanahuara E Upismayo Valley Huancane Valley N.Sara Sara NE

Peru

CenAndes

Peru

Lon (deg)

LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

Highest summit altitude (m)

72.39

SW

6377

4450

4750

15.59

72.39

S

5270

4850

5000

CenAndes

15.59

72.39

S

5270

4375

4700

Peru

CenAndes

15.59

72.39

SW

5270

3850

4450

Peru

CenAndes

15.59

72.39

E

5270

Peru Peru Peru

CenAndes CenAndes CenAndes

13.76 13.97 15.33

71.25 70.88 70.44

NW W NE

6384 5645 5505

N.Sara Sara SW

Peru

CenAndes

15.33

70.44

SW

Sara Sara I NE

Peru

CenAndes

15.33

70.44

Sara Sara II NE

Peru

CenAndes

15.33

Sara Sara I SW

Peru

CenAndes

Sara Sara II SW

Peru

Quebrada Llaca Quebrada Cojup Quebrada Churup Quebrada Shallap Quebrada Cashan Quebrada Pongos Quebrada Pariac Quebrada Quilcayhuanca Quebrada Rurec Rio Negro Quebrada Gueshque Quebrada Queroccocha Quebrada Cotush Quebrada Huantsan Quebrada Shancompampa Rio Pumapampa Quebrada Carhuascancha Quebrada Pamparaju Quebrada Huamish Quebrada Tambillo Quebrada Tayash Bwahit 2 Bwahit 1 Mesarerya 1 Silki 3 Abba Yared 2 Abba Yared 2L Abba Yared 1

DMC DC

References

inentory (MID) 808

5

8

5600

inentory (MID) 600

5

8

5600

inentory (MID) 900

5

8

5600

inentory (MID) 1150

5

8

5600

inentory (MID) 900

5

8

5100 5300 5350

AAR ¼ 0.5 170 AAR ¼ 0.5 230 inentory (MID) 700

2 2 5

4 7 8

5112

inentory (MID) 612

5

8

5350

inentory (MID) 700

5

8

5350

inentory (MID) 550

5

8

5112

inentory (MID) 612

5

8

5112

inentory (MID) 412

5

8

4975 5200 5000 4950 5075 5125 5175 4950

THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5

715 1058 895 805 690 988 783 873

3 3 3 3 3 3 3 3

6 6 6 6 6 6 6 6

Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002) Smith et al., this volume Dornbusch (2002), Smith et al., this volume Mark et al., 2002 Mark et al., 2002 Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Dornbusch (2002), Smith et al., this volume Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992)

LGM ELA method

Modern ELA (m)

Modern ELA Method

5558

DELA

4300 4745 4300

4930 5070 4650

5505

4050

4500

NE

5505

4300

4650

70.44

NE

5505

15.33

70.44

SW

5505

CenAndes

15.33

70.44

SW

5505

Peru Peru Peru Peru Peru Peru Peru Peru

CoBlanca CoBlanca CoBlanca CoBlanca CoBlanca CoBlanca CoBlanca CoBlanca

9.47 9.47 9.50 9.51 9.58 9.76 9.54 9.50

77.47 77.43 77.43 77.42 77.42 77.40 77.38 77.38

SW SW SW SW SW NNE SW SW

6160 6180 5500 6370 5720 5580 6370 6222

5450 5525 5050 5425 5400 5100 5425 5250

3750 3550 3700 3950 3950 3725 3950 3950

4260 4143 4105 4145 4385 4138 4393 4078

MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 THAR ¼ 0.45 THAR ¼ 0.45 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 MID, MELM, AAR ¼ 0.67 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2

Peru Peru Peru Peru

CoBlanca CoBlanca CoBlanca CoBlanca

9.60 9.65 9.80 9.70

77.36 77.30 77.30 77.30

WSW WSW WSW WSW

5700 5720 5630 5237

5075 5070 4950 4900

3850 3900 4250 3850

4218 4251 4460 4165

THAR ¼ 0.4, 0.2 THAR ¼ 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2

4925 4875 4975 4775

THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5

708 624 515 610

3 2 3 3

6 6 6 6

Rodbell Rodbell Rodbell Rodbell

Peru Peru Peru

CoBlanca CoBlanca CoBlanca

9.75 9.55 9.60

77.30 77.26 77.26

WSW E NE

5360 5425 5100

4825 4900 4900

3850 4050 4050

4143 4305 4305

THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2 THAR ¼ 0.4, 0.2

5000 5030 5030

THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5

858 725 725

3 3 3

6 6 6

Rodbell (1991, 1992) Rodbell (1991, 1992) Rodbell (1991, 1992)

Peru Peru

CoBlanca CoBlanca

9.90 9.47

77.25 77.24

W ENE

5682 5870

5050 5300

4025 3850

4333 4285

THAR ¼ 0.2 THAR ¼ 0.4, 0.2

4950 4850

THAR ¼ 0.5 THAR ¼ 0.5

618 565

3 3

6 6

Rodbell (1991, 1992) Rodbell (1991, 1992)

Peru Peru Peru Peru Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia

CoBlanca CoBlanca CoBlanca CoBlanca EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica

9.53 9.65 9.67 9.78 13.24 13.25 13.21 13.35 13.34 13.35 13.35

77.24 77.23 77.21 77.13 38.19 38.20 38.21 38.26 38.28 38.28 38.30

SSE E NE N W N NW N NW W NE

5100 5240 5200 5208 4430 4430 4353 4420 4409 4300 4300

5000 4900 4900 5200 4350 4375 4325 4350 4325 4200 4225

4000 3700 3900 4350 4200 3950 4075 3875 3775 3800 3900

4300 4060 4200 4605 4280 4200 4190 4113 4075 4000 4080

THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, THAR ¼ 0.4, MID MID MID MID MID MID MID

5030 5030 5100 5075 4800 4800 4800 4800 4800 4800 4800

THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 THAR ¼ 0.5 inentory (MID) inentory (MID) inentory (MID) 01 isotherm 01 isotherm 01 isotherm 01 isotherm

730 970 900 470 520 600 610 688 725 800 720

3 3 3 3 3 3 3 3 3 3 3

6 6 6 6 7 7 7 7 7 7 7

Rodbell (1991, Rodbell (1991, Rodbell (1991, Rodbell (1991, Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989)

4700 5700 5470

4800 4050

4500 4700

0.2 0.2 0.2 0.2

(1991, (1991, (1991, (1991,

1992) 1992) 1992) 1992)

1992) 1992) 1992) 1992)

ARTICLE IN PRESS

Aspect

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

LGM snowline locality

13.32 13.31 13.31 13.30 13.21 13.23 13.20 13.23 13.26 13.21 13.23 13.28 13.27 7.89

38.31 38.32 38.33 38.34 38.34 38.35 38.35 38.36 38.36 38.37 38.38 38.38 38.44 39.38

N N NE NE W NW S NW NW NE NE NW NW NW

4400 4453 4450 4453 4543 4543 4543 4473 4449 4543 4473 4449 4465 4150

4300 4440 4350 4425 4500 4460 4500 4375 4400 4450 4475 4225 4450 4150

3950 4100 4000 3960 4075 3975 4300 3975 3975 4200 4325 3910 3880 3800

4125 4270 4175 4200 4300 4210 4400 4175 4180 4325 4400 4090 4165 3975

MID MID MID MID MID MID MID MID MID MID MID MID MID MID

4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800 4800

01 01 01 01 01 01 01 01 01 01 01 01 01 01

isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm isotherm

675 530 625 600 500 590 400 625 620 475 400 710 635 825

3 3 3 3 3 3 3 3 3 3 3 3 3 3

7 7 7 7 7 7 7 7 7 7 7 7 7 7

Badda 1

Ethiopia

EAfrica

7.81

39.39

SW

4070

4070

3700

3885

MID

4800

01 isotherm

915

3

7

Badda 6c

Ethiopia

EAfrica

7.90

39.39

NW

4210

4210

3920

4065

MID

4800

01 isotherm

735

3

7

Badda 6a

Ethiopia

EAfrica

7.89

39.39

W

4190

4190

3620

3905

MID

4800

01 isotherm

895

3

7

Badda 2

Ethiopia

EAfrica

7.84

39.39

W

4070

4070

3720

3895

MID

4800

01 isotherm

905

3

7

Badda 5

Ethiopia

EAfrica

7.88

39.40

W

4150

4150

3740

3945

MID

4800

01 isotherm

855

3

7

Badda 4

Ethiopia

EAfrica

7.87

39.40

W

4130

4130

3740

3935

MID

4800

01 isotherm

865

3

7

Badda 3

Ethiopia

EAfrica

7.85

39.40

W

4090

4090

3730

3910

MID

4800

01 isotherm

890

3

7

Badda 7

Ethiopia

EAfrica

7.90

39.41

NW

4170

4170

3560

3865

MID

4800

01 isotherm

935

3

7

Badda 8

Ethiopia

EAfrica

7.92

39.41

NW

4100

4100

3840

3970

MID

4800

01 isotherm

830

3

7

Badda 15

Ethiopia

EAfrica

7.82

39.42

E

4070

4070

3500

3785

MID

4800

01 isotherm

1015

3

7

Badda 9

Ethiopia

EAfrica

7.95

39.42

N

4110

4110

3620

3865

MID

4800

01 isotherm

935

3

7

Badda 13

Ethiopia

EAfrica

7.88

39.44

E

4180

4180

3320

3750

MID

4800

01 isotherm

1050

3

7

Badda 12

Ethiopia

EAfrica

7.91

39.44

E

4100

4100

3280

3690

MID

4800

01 isotherm

1110

3

7

Badda 11a

Ethiopia

EAfrica

7.95

39.44

NE

4090

4090

3360

3725

MID

4800

01 isotherm

1075

3

7

Badda 14

Ethiopia

EAfrica

7.87

39.45

E

4100

4100

3240

3670

MID

4800

01 isotherm

1130

3

7

Badda 10

Ethiopia

EAfrica

7.99

39.45

N

4030

4030

3720

3875

MID

4800

01 isotherm

925

3

7

Badda 11b

Ethiopia

EAfrica

7.93

39.46

E

4050

4050

3520

3785

MID

4800

01 isotherm

1015

3

7

Burguret

Kenya

EAfrica

0.16

37.24

W

5199

3200

4300

THAR ¼ 0.5

4760

inentory (MID) 460

3

7

Teleki valley

Kenya

EAfrica

0.19

37.24

W

5199

3200

4300

THAR ¼ 0.5

4781

inentory (MID) 481

3

7

Hausberg valley

Kenya

EAfrica

0.14

37.24

WNW

5199

3100

4300

THAR ¼ 0.5

4700

inentory (MID) 400

3

7

Mackinder valley

Kenya

EAfrica

0.08

37.27

NW

5199

3800

4500

THAR ¼ 0.5

4795

inentory (MID) 295

3

7

Liki North

Kenya

EAfrica

0.06

37.28

NW

5199

3800

4500

THAR ¼ 0.5

4760

inentory (MID) 260

3

7

Hohnel valley

Kenya

EAfrica

0.04

37.30

W

5199

3300

4300

THAR ¼ 0.5

4760

inentory (MID) 460

3

7

Sirimon valley

Kenya

EAfrica

0.01

37.34

N

5199

3400

4500

THAR ¼ 0.5

4760

inentory (MID) 260

3

7

Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Hurni (1989) Potter (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Potter, (1976), Street (1979), Hurni (1989) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002)

175

EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica EAfrica

ARTICLE IN PRESS

Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia Ethiopia

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Kidis Yared 2 Kidis Yared 3 Kidis Yared 4 Kidis Yared 5 Ras Dejen 1 Ras Dejen 2 Ras Dejen 14 Analu 3 Tefew Leser 4 Ras Dejen 13 Analu 12 Tefew Leser 5 Weynobar 8 Badda 6b

176

Table 1 (continued )

inentory (MID) 560

3

7

4760

inentory (MID) 510

3

7

THAR ¼ 0.5

4760

inentory (MID) 510

3

7

3200

THAR ¼ 0.4

3900

THAR ¼ 0.4

700

5

8

5500

3500

THAR ¼ 0.4

4300

THAR ¼ 0.4

800

5

8

SW

5500

3300

THAR ¼ 0.4

4050

THAR ¼ 0.4

750

5

8

77.10 77.18 77.21 77.25 77.27 77.31 77.36 77.37 77.37 77.39 77.43 77.44 77.46 77.48 77.48 77.50 77.52 77.53 77.54 77.57 77.62 77.64 77.67 86.80

SE SSW SSW NNW SSW NW SSW E SW N SSW NNE S ESE SSW NNE E ENE NW NE SSW E NE W

5928

1044 1380 1320 906 1194 658 1260 604 794 104 1070 406 146 484 1112 820 204 798 604 830 1150 340 370 1080

3 5 5 3 5 3 5 3 3 3 5 3 3 3 5 5 3 5 3 5 5 3 2 3

6 8 8 6 8 6 8 6 6 6 8 6 6 6 8 8 6 8 6 8 8 6 6 2

Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) Hastenrath (1984), Kaser and Osmaston (2002) DeTerra and Paterson (1939), Holmes and Street-Perrott (1989) DeTerra and Paterson (1939), Holmes and Street-Perrott (1989) DeTerra and Paterson (1939), Holmes and Street-Perrott (1989) Taylor and Mitchell (2000) Burbank and Fort (1985) Burbank and Fort (1985) Taylor and Mitchell (2000) Burbank and Fort (1985) Taylor and Mitchell (2000) Burbank and Fort (1985) Taylor and Mitchell (2000) Taylor and Mitchell (2000) Taylor and Mitchell (2000) Burbank and Fort (1985) Taylor and Mitchell (2000) Taylor and Mitchell (2000) Taylor and Mitchell (2000) Burbank and Fort (1985) Burbank and Fort (1985) Taylor and Mitchell (2000) Burbank and Fort (1985) Taylor and Mitchell (2000) Burbank and Fort (1985) Burbank and Fort (1985) Taylor and Mitchell (2000) Taylor and Mitchell (2000) Burbank and Fort (1985)

27.90

86.83

660

3

2

Burbank and Fort (1985)

Himalaya

28.07

210

1

2

Nepal

Himalaya

650

Pakistan

Himalaya

Rongbuk

Tibet

Xopana´ valley Nahualac valley Milpulco valley El Marrano valley Hueyatlaco valley Yautepemes valley Vaquerı´ as valley

Mexico Mexico Mexico Mexico Mexico Mexico Mexico

Richards et al. (2000b), Owen and Benn (this volume) Asahi and Watanabe (2000), Tsukamoto et al. (2002) Owen et al. (2002a), Owen and Benn (this volume) Williams (1983), Burbank and Kang (1991), Mann et al. (1996) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data)

Modern ELA Method

4200

THAR ¼ 0.5

4760

3300

4250

THAR ¼ 0.5

3300

4250

4500

W

75.34

32.56 34.40 34.37 32.94 34.35 32.57 34.34 33.77 32.44 32.25 34.31 32.72 32.50 32.89 34.30 34.00 32.41 33.96 33.70 33.91 34.24 33.78 32.69 27.85

Himalaya

Nepal

Kanchenjunga Batura

Region

Lat (deg)

Hobley valley

Kenya

EAfrica

0.19

Gorges valley

Kenya

EAfrica

Hinde valley

Kenya

Ningle valley

Lon (deg)

LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

Aspect

Highest summit altitude (m)

37.37

SE

5199

3200

0.15

37.39

ESE

5199

EAfrica

0.10

37.41

E

5199

India

Himalaya

34.05

74.30

NE

Sind valley

India

Himalaya

34.30

75.25

Liddar valley

India

Himalaya

34.04

Beas (Solang Nala) Temesgam Saspui Kurgiakh Likir Milang Basgo Lungtung W Kulti Chhatiru Nimu Lungtung N Chandra Tal Lingti Tharu Stok N. Dakka W. Mattoo Lungtung E E. Mattoo Leh Nimaling Kenlung Pangboche E

India India India India India India India India India India India India India India India India India India India India India India India Nepal

Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya Himalaya

Nangazon

Nepal

Khumbu

DELA

6401 6111 4949

5640 5550 5650 6100 5500 5940 5500 5940 5790 6100 5450 5940 6100 5790 5300 5550 5940 5500 5940 5450 5400 6100 5940 4949

2500 3200 3200 4140 3510 3610 3400 4500 3150 3760 3750 4530 4190 4500 3780 4100 4200 4170 4500 4150 3650 4200 4590 4160

3756 4120 4180 4294 4306 4542 4240 4896 4206 4696 4430 5094 4954 5016 4388 4680 4896 4702 4896 4670 4350 4960 5130 4520

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 MELM, CF

4800 5500 5500 5200 5500 5200 5500 5500 5000 4800 5500 5500 5100 5500 5500 5500 5100 5500 5500 5500 5500 5300 5500 5600

S

5500

5500

4500

4940

MELM, CF

5600

86.87

SW

8848

6800

4160

5440

MELM

5650750

27.61

87.88

SW

8586

8400

2520

4850

THAR

5500

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 Field observation Field observation Field observation Map, MELM

1

1

36.55

74.62

E

7785

2480

5000

AAR

5100

Map estimate

100

1

1

Himalaya

28.14

86.84

N

5100

5600

AAR ¼ 0.6 (70.05)

5850

ﬁeld observation 250

3

2

Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl

19.17 19.20 19.14 19.22 19.16 19.20 19.20

98.70 98.69 98.69 98.69 98.69 98.69 98.69

3300 3420 3240 3540 3300 3540 3670

4020 4030 3900 4070 4020 4080 4150

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4

4970790 4970790 4970790 4970790 4970790 4970790 4970790

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4

3 1 1 3 1 1 3

3 3 3 3 3 3 3

W W W WNW W WNW NW

6248 6401 6000 5640 6401 6221 6096 6517 5899

6264 6096

5286 5286 5000 5000 5150 5000 5000

5100 4950 4900 4860 5100 4880 4860

950 940 1070 900 950 890 820

ARTICLE IN PRESS

References

Modern ELA (m)

Country

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

DMC DC

LGM ELA method

LGM snowline locality

19.23

98.68

NW

5000

4700

3650

4070

THAR ¼ 0.4

4970790

THAR ¼ 0.4

900

1

3

Vasquez-Selem (unpublished data)Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data)

Alcalican valley Tlacopaso valley Apatlaco valley Apipilulco valley Texcalco valley Tlalqueco´tchcotl valley Hueytecoxco valley Apol valley San Diego valley Tlatzala valley Hueyatitla valley Alseseca valley Kurobegoro

Mexico Mexico Mexico Mexico Mexico Mexico

Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl

19.13 19.26 19.10 19.11 19.24 19.13

98.67 98.65 98.61 98.61 98.60 98.59

WSW NW S SE NE ESE

4700 4660 4700 4700 4660 4700

4640 4620 4600 4600 4600 4420

3440 3400 3500 3460 3180 3260

3920 3890 3940 3920 3750 3720

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4

4970790 4970790 4970790 4970790 4970790 4970790

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4

1050 1080 1030 1050 1220 1250

2 1 1 3 3 3

3 3 3 3 3 3

Mexico Mexico Mexico Mexico Mexico Mexico Japan

Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Iztaccı´ huatl Japan

19.23 19.12 19.16 19.19 19.18 19.14 36.38

98.59 98.59 98.58 98.58 98.57 98.57 137.54

NE SE E ENE E E E

4660 4700 5150 5286 5286 5150 2840

4540 4600 5100 5100 5100 5000 2840

3200 3200 3420 3200 3200 3040 2000

3740 3760 4090 3960 3960 3820 2520

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 AAR ¼ 0.6

4970790 4970790 4970790 4970790 4970790 4970790 2970

THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 THAR ¼ 0.4 Climate model

1230 1210 880 1010 1010 1150 450

3 3 3 3 3 3 1

3 3 3 3 3 3 7

Suisho-B,C,D

Japan

Japan

36.42

137.60

NE

2986

2980

2340

2710

AAR ¼ 0.6

2970

Climate model

260

1

7

Murodo

Japan

Japan

36.57

137.62

W

3015

3000

2290

2600

AAR ¼ 0.6

2970

Climate model

370

5

8

Gozendani

Japan

Japan

36.57

137.62

E

3015

3000

1990

2630

AAR ¼ 0.6

2970

Climate model

340

5

8

Oyamatan

Japan

Japan

36.57

137.62

SE

2992

2950

1800

2490

AAR ¼ 0.6

2970

Climate model

480

5

8

Chojirodan

Japan

Japan

36.62

137.62

E

2998

2910

2100

2640

AAR ¼ 0.6

2970

Climate model

330

5

8

Gozendani North

Japan

Japan

36.57

137.62

E

2970

2900

2300

2660

AAR ¼ 0.6

2970

climate model

310

5

8

Masagozawa Kuranosuke Tsurugisawa Betsuzanzawa Masagozawa North Nogaike Senjojiki Hosozawa Kitazawa Tottabetsu AB Proshiri East BCD Esaoman Tottabetsu BNM25338_66279

Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Indonesia

Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Japan Mt Jaya

36.57 36.57 36.62 36.57 36.57 35.79 35.78 35.64 35.65 42.75 42.72 42.68 3.94

137.62 137.62 137.62 137.62 137.62 137.82 137.82 138.24 138.24 142.70 142.70 142.78 136.99

E E N NE E NE SE E NE NE E NW N

2880 2999 2998 2880 2880 2870 2931 3190 3130 1960 2052 1902 3820

2850 2850 2850 2800 2720 2850 2880 3160 3120 1710 1850 1630 Na

2500 2420 1800 2080 2260 2640 2220 2490 2390 1175 1320 1100 3455

2770 2550 2570 2550 2560 2680 2690 2850 2785 1500 1575 1450 3710

AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR ¼ 0.6 AAR, maxQ AAR, maxQ AAR, maxQ AABR ¼ 3

2970 2970 2970 2970 2970 3900 3900 4130 4130 2750 2680 2700 4800

200 420 400 420 410 1220 1210 1280 1345 1250 1105 1250 1090

5 5 5 5 5 1 1 5 5 2 5 1 3

8 8 8 8 8 3 7 8 8 1 8 7 4

BNM26258_65586

Indonesia

Mt Jaya

3.93

137.01

N

3965

Na

3330

3590

AABR ¼ 3

4800

1210

3

4

Prentice et al. (this volume)

BNM27196_64798

Indonesia

Mt Jaya

3.93

137.03

N

3935

Na

3330

3725

AABR ¼ 3

4800

1075

3

4

Prentice et al. (this volume)

BNM28659_63904

Indonesia

Mt Jaya

3.94

137.05

NNE

4300

3750

3220

3810

AABR ¼ 3

4800

990

3

4

Prentice et al. (this volume)

BNM33945_60020

Indonesia

Mt Jaya

3.93

137.07

NNE

4400

4200

3450

3800

MELM

4800

1000

3

4

Prentice et al. (this volume)

BNM34855_59923

Indonesia

Mt Jaya

3.94

137.10

NNE

4500

4300

3150

3900

MELM

4800

900

3

4

Prentice et al. (this volume)

Bakopa

Indonesia

Mt Jaya

3.96

137.13

NE

4500

4330

3350

3950

MELM

4800

850

3

4

Prentice et al. (this volume)

Dani

Indonesia

Mt Jaya

3.98

137.16

NE

4705

4495

3600

3975

AABR ¼ 3

4800

825

3

4

Prentice et al. (this volume)

Dugundugu

Indonesia

Mt Jaya

4.00

137.18

NE

4860

4495

3500

3935

AABR ¼ 3

4800

865

3

4

Prentice et al. (this volume)

Discovery

Indonesia

Mt Jaya

4.04

137.22

NNE

4955

4460

3650

3950

AABR ¼ 3

4800

Climate model Climate model Climate model Climate model Climate model n/a n/a n/a n/a n/a n/a n/a Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Ono (1992, pers. comm., 2002), Ono et al. (2003) Ono (1992, pers. comm., 2002), Ono et al. (2003) Ono (1992, pers. comm., 2002), Ono et al. (2003) Ono (1992, pers. comm., 2002), Ono et al. (2003) Ono (1992, pers. comm., 2002), Ono et al. (2003) Ono (1992, pers. comm., 2002), Ono et al. (2003) Yanagimachi (1983), Aoki (2000a, b, Ono et al. (2003) Ono (pers. comm., 2002) Ono (1992, pers. comm., 2002) Ono (1992, pers. comm., 2002) Ono (1992, pers. comm., 2002) Ono (1992, pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Ono (pers. comm., 2002) Prentice et al. (this volume)

850

3

4

Prentice et al. (this volume)

177

Iztaccı´ huatl

ARTICLE IN PRESS

Mexico

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Llano Grande valley

178

Table 1 (continued ) LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

LGM ELA method

Modern ELA (m)

Modern ELA Method

DELA

DMC DC

References

137.22

N

4400

4175

3615

3890

AABR ¼ 3

4800

910

3

4

Prentice et al. (this volume)

4.03

137.31

N

4400

4175

3615

3885

MELM

4800

915

3

4

Prentice et al. (this volume)

NZ-Aus

36.47

148.26

SW

2228

2228

2020

2100

CF

2500

400

1

4

Lo¨efﬂer (1972)

Australia

NZ-Aus

36.41

148.31

SE

2150

2150

1840

1950

CF

2500

550

1

3

Lo¨efﬂer (1972)

Kumbivera

PNG

PNG

5.75

143.23

E

3750

3340

3550

MID

4600

1050

3

5

Lo¨efﬂer (1972)

Gil_Yamboro

PNG

PNG

6.06

143.82

SW

4368

3800

3200

3552

MID

4600

1048

2

4

Lo¨efﬂer (1972)

Gil_S

PNG

PNG

6.07

143.83

S

4368

3800

3200

3552

MID

4600

1048

2

4

Gil_T2

PNG

PNG

6.03

143.83

WNW

4368

3900

3000

3452

MID

4600

1148

2

4

Gil_T1

PNG

PNG

6.04

143.83

NW

4368

3800

3100

3502

MID

4600

1098

2

4

Gil_1

PNG

PNG

6.02

143.84

NW

4368

3900

3150

3527

MID

4600

1073

2

4

Gil_2

PNG

PNG

6.01

143.85

NW

4368

3900

3175

3540

MID

4600

1061

2

4

Gil_3

PNG

PNG

6.01

143.85

NW

4368

3900

3050

3477

MID

4600

1123

2

4

Gil_SW

PNG

PNG

6.08

143.85

SW

4368

3800

3300

3602

MID

4600

998

2

4

Gil_4

PNG

PNG

6.01

143.86

NW

4368

4000

3300

3602

MID

4600

998

2

4

Gil_5

PNG

PNG

6.00

143.87

NW

4368

4000

3100

3502

MID

4600

1098

2

4

Gil_7

PNG

PNG

5.99

143.87

NW

4368

4000

3000

3452

MID

4600

1148

2

4

Gil_8

PNG

PNG

5.99

143.87

NW

4368

3800

3000

3452

MID

4600

1148

2

4

Gil_6

PNG

PNG

6.01

143.87

NW

4368

4000

3400

3652

MID

4600

948

2

4

Gil_Akura

PNG

PNG

6.09

143.88

S

4368

4000

2750

3327

MID

4600

Field observation Field observation rough estimate, given mod MAT 3.4 at 2000 m rough estimate, given mod MAT 3.4 at 2000 m Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

1273

2

4

Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1971), Lo¨efﬂer (1972)

Region

Lat (deg)

Nortsing

Indonesia

Mt Jaya

4.02

Hogayaku

Indonesia

Mt Jaya

Lake Cootapatamba

Australia

Blue Lake valley

Lon (deg)

Gil_9

PNG

PNG

5.99

143.88

NW

4368

3800

3400

3652

MID

4600

Gil_19

PNG

PNG

6.08

143.88

S

4368

3700

3000

3452

MID

4600

Gil_10

PNG

PNG

5.98

143.89

NNW

4368

3700

3000

3452

MID

4600

Gil_11

PNG

PNG

5.97

143.89

NNW

4368

3700

3100

3502

MID

4600

Gil_18

PNG

PNG

6.08

143.89

S

4368

3700

2950

3427

MID

4600

Gil_17

PNG

PNG

6.08

143.90

S

4368

3800

3000

3452

MID

4600

Gil_Iaro2

PNG

PNG

6.08

143.91

S

4368

3900

2975

3440

MID

4600

948

2

4

1148

2

4

1148

2

4

1098

2

4

1173

2

4

1148

2

4

1161

2

4

ARTICLE IN PRESS

Highest summit altitude (m)

Country

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Aspect

LGM snowline locality

Gil_12

PNG

PNG

5.97

143.92

N

4368

3700

3400

3652

MID

4600

Gil_Iaro1

PNG

PNG

6.07

143.92

SE

4368

4000

3100

3502

MID

4600

Gil_16

PNG

PNG

6.06

143.92

SE

4368

4000

3300

3602

MID

4600

Gil_A4

PNG

PNG

6.03

143.93

E

4368

3800

3500

3702

MID

4600

Gil_13

PNG

PNG

5.98

143.93

NE

4368

3700

3300

3602

MID

4600

Gil_15

PNG

PNG

6.05

143.93

SE

4368

4000

3200

3552

MID

4600

Gil_Gogon

PNG

PNG

5.99

143.93

NE

4368

3700

2975

3440

MID

4600

Gil_14

PNG

PNG

6.04

143.93

E

4368

4000

3350

3627

MID

4600

PNG

6.03

143.93

E

4368

3800

3175

3540

MID

4600

PNG

6.02

143.93

E

4368

3800

3150

3527

MID

4600

Gil_Tamal

PNG

PNG

6.00

143.93

NE

4368

3800

3000

3452

MID

4600

Gil_A1

PNG

PNG

6.02

143.93

E

4368

3800

3200

3552

MID

4600

Gil_A5

PNG

PNG

6.04

143.93

E

4368

4000

3150

3527

MID

4600

Hagen1

PNG

PNG

5.77

144.01

SW

3777

3777

3370

3574

MID

4600

Hagen2

PNG

PNG

5.77

144.02

S

3777

3777

3450

3614

MID

4600

Hagen3

PNG

PNG

5.77

144.04

S

3777

3777

3380

3579

MID

4600

Ban_Sankwep

PNG

PNG

6.29

147.07

SW

4121

4000

3570

3724

MID

4600

Ban_Kwama

PNG

PNG

6.28

147.09

NNE

4121

3900

3300

3589

MID

4600

Ban_Mongi

PNG

PNG

6.30

147.10

E

4121

3900

3550

3714

MID

4600

Alb_Ed4

PNG

PNG

8.37

147.33

NW

3990

3800

3350

3600

MID

4600

Alb_Ed3

PNG

PNG

8.36

147.34

SW

3990

3800

3350

3600

MID

4600

Alb_Ed2

PNG

PNG

8.35

147.34

NW

3990

3700

3350

3600

MID

4600

Alb_Ed1

PNG

PNG

8.34

147.35

NW

3990

3700

3350

3600

MID

4600

Alb_Ed6

PNG

PNG

8.44

147.36

S

3990

3688

3450

3650

MID

4600

Alb_Ed5

PNG

PNG

8.40

147.36

N

3990

3688

3300

3575

MID

4600

Alb_Ed7

PNG

PNG

8.44

147.36

S

3990

3688

3525

3688

MID

4600

Alb_Ed12

PNG

PNG

8.36

147.37

N

3990

3650

3350

3600

MID

4600

Alb_Ed8

PNG

PNG

8.44

147.38

SE

3990

3688

3450

3650

MID

4600

Alb_Ed11

PNG

PNG

8.37

147.38

E

3990

3650

3350

3600

MID

4600

Alb_Ed10

PNG

PNG

8.38

147.39

N

3990

3700

3400

3625

MID

4600

Alb_Ed9

PNG

PNG

8.42

147.41

SW

3990

3750

3575

3713

MID

4600

2

4

1061

2

4

1073

2

4

1148

2

4

1048

2

4

1073

2

4

1027

3

5

Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Blake and Lo¨efﬂer (1972) Lo¨efﬂer (1972)

1098

2

4

998

2

4

898

2

4

998

2

4

1048

2

4

1161

2

4

973

2

4

(1971), Lo¨efﬂer

987

3

5

Lo¨efﬂer (1972)

1022

3

5

Lo¨efﬂer (1972)

877

3

5

Lo¨efﬂer (1972), Porter (2001)

1012

3

5

Lo¨fﬂer (1972), Porter (2001)

887

3

5

Lo¨fﬂer (1972), Porter (2001)

1000

2

6

Lo¨fﬂer (1972)

1000

2

6

Lo¨fﬂer (1972)

1000

2

6

Lo¨fﬂer (1972)

1000

2

6

Lo¨fﬂer (1972)

950

2

6

Lo¨fﬂer (1972)

1025

2

6

Lo¨fﬂer (1972)

913

2

6

Lo¨fﬂer (1972)

1000

2

6

Lo¨fﬂer (1972)

950

2

6

Lo¨fﬂer (1972)

1000

2

6

Lo¨fﬂer (1972)

975

2

6

Lo¨fﬂer (1972)

888

2

6

Lo¨fﬂer (1972)

(1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer (1971), Lo¨efﬂer

ARTICLE IN PRESS

PNG PNG

948

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Gil_A3 Gil_A2

179

Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

180

Table 1 (continued ) LGM snowline locality

Country

Region

Victoria1

PNG

PNG

R51

Uganda

R16c

Lat (deg)

Lon (deg)

LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

LGM ELA method

Modern ELA (m)

Modern ELA Method

DELA

DMC DC

References

Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

983

3

5

Lo¨fﬂer (1972)

433

3

4

960

3

4

990

3

4

686

3

4

838

3

4

686

3

4

625

3

4

990

3

4

838

3

4

960

3

4

899

3

4

503

3

4

868

3

4

716

3

4

777

3

4

808

3

4

929

3

4

686

3

4

716

3

4

990

3

4

Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002)

8.88

147.54

NE

4035

3675

3200

3618

MID

4600

Rwenzori

0.43

28.87

W

4785

4785

3261

4267

AABR

4800

Uganda

Rwenzori

0.36

29.80

NE

4633

4633

2072

3840

AABR

4800

R47

Uganda

Rwenzori

0.39

29.80

N

4267

4267

3352

3810

AABR

4800

R49

Uganda

Rwenzori

0.40

29.80

NNW

5090

5090

3048

4114

AABR

4800

R48

Uganda

Rwenzori

0.38

29.81

N

4419

4419

3352

3962

AABR

4800

R46

Uganda

Rwenzori

0.35

29.82

W

5059

5059

2987

4114

AABR

4800

R45

Uganda

Rwenzori

0.33

29.82

W

4876

4876

2804

4175

AABR

4800

R38

Uganda

Rwenzori

0.29

29.83

W

4419

4419

3139

3810

AABR

4800

R37

Uganda

Rwenzori

0.28

29.83

W

4297

4297

3352

3962

AABR

4800

R39

Uganda

Rwenzori

0.30

29.83

W

4419

4419

3017

3840

AABR

4800

R36

Uganda

Rwenzori

0.28

29.83

W

3962

3962

3627

3901

AABR

4800

R50

Uganda

Rwenzori

0.43

29.83

NW

5090

5090

2987

4297

AABR

4800

R35

Uganda

Rwenzori

0.27

29.83

WSW

4236

4236

3413

3932

AABR

4800

R40

Uganda

Rwenzori

0.31

29.84

NW

4572

4572

3413

4084

AABR

4800

R44

Uganda

Rwenzori

0.33

29.84

NW

4267

4267

3749

4023

AABR

4800

R34

Uganda

Rwenzori

0.26

29.84

W

4114

4114

3779

3992

AABR

4800

R33

Uganda

Rwenzori

0.24

29.84

SW

4053

4053

3505

3871

AABR

4800

R42

Uganda

Rwenzori

0.32

29.84

W

4267

4267

3749

4114

AABR

4800

R52a

Uganda

Rwenzori

0.48

29.85

N

4633

4633

3444

4084

AABR

4800

R32

Uganda

Rwenzori

0.23

29.86

SW

4023

4023

3444

3810

AABR

4800

R74

Uganda

Rwenzori

0.24

29.86

S

4145

4145

3292

3627

AABR

4800

R31

Uganda

Rwenzori

0.22

29.87

SW

4236

4236

3048

3779

AABR

4800

R75

Uganda

Rwenzori

0.24

29.87

S

4023

4023

3352

3718

AABR

4800

R53

Uganda

Rwenzori

0.49

29.88

NNW

4785

4785

3048

4053

AABR

4800

R54

Uganda

Rwenzori

0.48

29.89

NW

4267

4267

3505

3962

AABR

4800

R30

Uganda

Rwenzori

0.22

29.89

S

4602

4602

2408

3992

AABR

4800

1173

3

4

1021

3

4

1082

3

4

747

3

4

838

3

4

808

3

4

ARTICLE IN PRESS

Highest summit altitude (m)

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Aspect

R16a

Uganda

Rwenzori

0.40

29.90

SE

5090

5090

2072

4023

AABR

4800

R16b

Uganda

Rwenzori

0.37

29.90

E

4846

4846

2072

3901

AABR

4800

R22

Uganda

Rwenzori

0.40

29.90

E

3657

3657

3109

3474

AABR

4800

R29

Uganda

Rwenzori

0.23

29.91

SW

3992

3992

3109

3505

AABR

4800

R28

Uganda

Rwenzori

0.23

29.91

S

3992

3992

3200

3413

AABR

4800

R27

Uganda

Rwenzori

0.22

29.92

S

3992

3992

3231

3657

AABR

4800

R26

Uganda

Rwenzori

0.23

29.93

S

3992

3992

2834

3444

AABR

4800

R55

Uganda

Rwenzori

0.49

29.94

NE

4785

4785

3048

4114

AABR

4800

Rwenzori

0.25

29.94

SE

4267

4267

2347

3657

AABR

4800

Rwenzori

0.49

29.94

N

4693

4693

3048

4023

AABR

4800

R72

Uganda

Rwenzori

0.28

29.94

SSE

3657

3657

3352

3535

AABR

4800

R71

Uganda

Rwenzori

0.28

29.94

SSE

3566

3566

3383

3535

AABR

4800

R73

Uganda

Rwenzori

0.29

29.94

SE

4175

4175

3322

3688

AABR

4800

R57

Uganda

Rwenzori

0.47

29.95

NNE

4053

4053

3566

3962

AABR

4800

R20

Uganda

Rwenzori

0.37

29.95

E

3779

3779

3535

3566

AABR

4800

R19

Uganda

Rwenzori

0.37

29.95

E

3810

3810

3292

3566

AABR

4800

R21

Uganda

Rwenzori

0.36

29.95

SE

3749

3749

3139

3474

AABR

4800

R18

Uganda

Rwenzori

0.38

29.95

E

3810

3810

3352

3627

AABR

4800

R23

Uganda

Rwenzori

0.35

29.95

SE

3840

3840

3048

3596

AABR

4800

R17

Uganda

Rwenzori

0.38

29.95

E

3840

3840

3596

3749

AABR

4800

R24

Uganda

Rwenzori

0.27

29.95

SSE

4572

4572

2255

3749

AABR

4800

R58

Uganda

Rwenzori

0.46

29.96

N

4053

4053

3657

3840

AABR

4800

R59

Uganda

Rwenzori

0.47

29.96

NW

3932

3932

3535

3810

AABR

4800

R60

Uganda

Rwenzori

0.48

29.96

W

3932

3932

3627

3810

AABR

4800

R61

Uganda

Rwenzori

0.49

29.96

WNW

3901

3901

3535

3779

AABR

4800

R62

Uganda

Rwenzori

0.50

29.97

N

4724

4724

2895

3901

AABR

4800

R15

Uganda

Rwenzori

0.38

29.98

SE

4358

4358

2743

3627

AABR

4800

R63

Uganda

Rwenzori

0.48

29.98

NW

3871

3871

3352

3688

AABR

4800

R14

Uganda

Rwenzori

0.39

29.98

S

4206

4206

2743

3596

AABR

4800

R64

Uganda

Rwenzori

0.49

29.98

NW

3810

3810

3474

3657

AABR

4800

R65

Uganda

Rwenzori

0.50

29.99

NW

3810

3810

3444

3627

AABR

4800

3

4

599

3

4

1326

3

4

1295

3

4

1387

3

4

1143

3

4

1356

3

4

686

3

4

1143

3

4

777

3

4

1265

3

4

1265

3

4

1112

3

4

838

3

4

1234

3

4

1234

3

4

1326

3

4

1173

3

4

1204

3

4

1051

3

4

1051

3

4

960

3

4

990

3

4

990

3

4

1021

3

4

899

3

4

1173

3

4

1112

3

4

1204

3

4

1143

3

4

1173

3

4

181

Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002) Osmaston (1989b), Kaser and Osmaston (2002)

ARTICLE IN PRESS

Uganda Uganda

637

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

R25 R56

Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

182

Table 1 (continued ) LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

LGM ELA method

Modern ELA (m)

Modern ELA Method

DELA

DMC DC

References

29.99

NW

3718

3718

3413

3596

AABR

4800

1204

3

4

0.50

30.00

NW

3688

3688

3444

3627

AABR

4800

1173

3

4

Rwenzori

0.50

30.00

WNW

3810

3810

3352

3566

AABR

4800

1234

3

4

Uganda

Rwenzori

0.51

30.01

WNW

3840

3840

3352

3627

AABR

4800

1173

3

4

R13

Uganda

Rwenzori

0.40

30.02

SE

4389

4389

2377

3749

AABR

4800

1051

3

4

R70

Uganda

Rwenzori

0.52

30.03

N

3840

3840

3261

3566

AABR

4800

1234

3

4

R12

Uganda

Rwenzori

0.41

30.03

SE

4389

4389

2438

3627

AABR

4800

1173

3

4

R8

Uganda

Rwenzori

0.45

30.04

E

3688

3688

3383

3505

AABR

4800

1295

3

4

R9

Uganda

Rwenzori

0.44

30.04

SE

3688

3688

3352

3566

AABR

4800

1234

3

4

R11

Uganda

Rwenzori

0.42

30.05

SSE

4114

4114

2560

3566

AABR

4800

1234

3

4

R6

Uganda

Rwenzori

0.47

30.05

E

3810

3810

3200

3535

AABR

4800

1265

3

4

R7

Uganda

Rwenzori

0.46

30.05

E

3810

3810

3048

3566

AABR

4800

1234

3

4

R5

Uganda

Rwenzori

0.47

30.05

SE

3749

3749

3170

3505

AABR

4800

1295

3

4

R10

Uganda

Rwenzori

0.43

30.05

E-NE

4023

4023

2987

3566

AABR

4800

1234

3

4

R4

Uganda

Rwenzori

0.49

30.05

SE

3810

3810

3261

3535

AABR

4800

1265

3

4

R3

Uganda

Rwenzori

0.49

30.07

S

3840

3840

3078

3505

AABR

4800

1295

3

4

R1

Uganda

Rwenzori

0.50

30.07

SE

3840

3840

3292

3566

AABR

4800

1234

3

4

R2

Uganda

Rwenzori

0.50

30.07

SE

3840

3840

2987

3535

AABR

4800

Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation Field observation

1265

3

4

Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston Osmaston

Aspect-averaged P/J-West Chimborazo-West Chimborazo-East Cari-West P/J-East Cari-East Ilinizas-West Ilinizas-East El Altar-West C/P-West Antisana-West C/P-East Antisana-East Cayambe-West Cayambe-East Cerros Cuchpanga Cordillera CallejonWest

Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Ecuador Peru Peru

Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib Carrib CenAndes CenAndes

0.86 1.47 1.47 1.40 0.92 1.41 0.66 0.66 1.67 0.27 0.49 0.37 0.49 0.03 0.03 10.71 11.00

78.87 78.84 78.79 78.78 78.74 78.74 78.72 78.70 78.43 78.20 78.17 78.13 78.11 78.03 77.95 76.67 76.60

W W E W E E W E W W W E E W E E W

4200 5800 5800 4900 4200 4900 5100 5100 5200 4400 5600 4400 5600 5600 5600

4000 3800 3600 3900 3800 3600 3700 3500 3600 3600 3600 3000 3150 3300 3000 4900 4500

4040 4200 3860 4100 3880 3860 3980 3820 3920 3760 4000 3280 3640 3760 3520 4500 4600

THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 CF CF

4900 5160 4920 4700 4900 4620 4860 4860 4720 4890 4960 4620 4640 4820 4600 4900 4900

860 960 1060 600 1020 760 880 1040 800 1130 960 1340 1000 1060 1080 400 300

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3

7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 4 4

Region

Lat (deg)

R66

Uganda

Rwenzori

0.50

R67

Uganda

Rwenzori

R68

Uganda

R69

Lon (deg)

5800 5800 5800 5800

5200 4400 5753 4400 5753 5600 5600 5300 5200

THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 THAR ¼ 0.2 air photo ID air photo ID

(1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002) (1989b), (2002)

Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Clapperton (1987) Wright (1984) Wright (1983)

Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and Kaser and

ARTICLE IN PRESS

Highest summit altitude (m)

Country

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Aspect

LGM snowline locality

CenAndes

11.20

76.50

E

5200

brx erosion

4900

air photo ID

400

2

4

Wright (1984)

Peru Ethiopia

CenAndes EAfrica

10.69 13.21

76.08 38.39

W S

4543

4200 4300

4300 4400

CF THAR ¼ 0.5

4900 4800

air photo ID n/a

500 400

3 3

4 7

4543

3760

4100

THAR ¼ 0.5

4800

n/a

700

2

7

NE

5199

3500

4450

THAR ¼ 0.5

4760

THAR ¼ 0.5

310

3

7

37.40

NE

5199

3500

4450

THAR ¼ 0.5

4760

THAR ¼ 0.5

310

3

7

3.06

37.34

S, W

5882

5882

3232

4575

AABR (BR ¼ 1)

5366

AABR (BR ¼ 1) 791

3

2

Wright, 1985 Hurni (1989), Messerli and Winiger (1992) Hurni (1989), Messerli and Winiger (1992) Hastenrath (1984), Mahaney (1990), Kaser and Osmaston (2002) Hastenrath (1984), Mahaney (1990), Kaser and Osmaston (2002) Osmaston (1989a)

Simen N

Ethiopia

EAfrica

13.21

38.39

N

Kazita West

Kenya

EAfrica

0.05

37.39

Kazita East

Kenya

EAfrica

0.08

Kibo S&WKilimanjaro Kibo NWKilimanjaro Kibo E&NEKilimanjaro Mawenzi NKilimanjaro Mawenzi SKilimanjaro Mawenzi EKilimanjaro Elgon S

Tanzania

EAfrica

Tanzania Tanzania

EAfrica

3.06

37.36

NW

5882

5882

3872

4540

AABR (BR ¼ 1)

5457

AABR (BR ¼ 1) 917

3

2

Osmaston (1989a)

EAfrica

3.08

37.39

E, NE

5882

5882

4421

5180

AABR (BR ¼ 1)

5701

AABR (BR ¼ 1) 521

3

2

Osmaston (1989a)

Tanzania

EAfrica

3.07

37.44

N

5150

5150

3354

4300

AABR (BR ¼ 1)

5122

HAO estimate

822

3

2

Osmaston (1989a)

Tanzania

EAfrica

3.12

37.45

S

5150

5150

3232

4240

AABR (BR ¼ 1)

5122

HAO estimate

882

3

2

Osmaston (1989a)

Tanzania

EAfrica

3.10

37.46

E

5150

4358

3354

3930

AABR (BR ¼ 1)

5122

HAO estimate

1192

3

2

Osmaston (1989a)

Uganda

EAfrica

1.12

34.53

S

4320

3660

3900

THAR ¼ 0.5

4660

regional correlation

760

3

7

Elgon N

Uganda

EAfrica

1.12

34.53

N

4210

3400

3700

THAR ¼ 0.5

4460

regional correlation

760

2

7

Mauna Kea NW Mauna Kea SW Mauna Kea NE Mauna Kea SE Kanchenjunga

USA USA USA USA Nepal

Hawaii Hawaii Hawaii Hawaii Himalaya

19.84 19.81 19.84 19.81 27.61

155.49 155.49 155.46 155.46 87.88

NW SW NE SE SW

4206 4206 4206 4206 8586

3615 3510 3555 3465 2520

3850 3805 3765 3720 4850

AAR ¼ 0.6570.05 AAR ¼ 0.6570.05 AAR ¼ 0.6570.05 AAR ¼ 0.6570.05 THAR

47157190 47157190 47157190 47157190 55007500

0isotherm 01 isotherm 01 isotherm 01 isotherm n/a

865 910 950 995 1050

1 2 1 3 1

1 1 1 1 1

Swat Himalaya Tasman River-Lake Pukai Qinghai Nan Shan

Pakistan N.Zealand

Himalaya NZ-Aus

35.72 43.75

72.65 170.14

SW S

5920 3745

2430 550

3200 955

AAR ¼ 0.6(70.1) AAR ¼ 0.670.05

4120 1830

observation observation

920 875

1 4

7 5

Hamilton and Perrott (1978, 1979), Osmaston and Harrison (this volume) Hamilton and Perrott (1978, 1979), Osmaston and Harrison (this volume) Porter, 1979, Dorn et al., 1991 Porter (1979), Dorn et al. (1991) Porter (1979), Dorn et al. (1991) Porter (1979), Dorn et al. (1991) Asahi and Watanabe (2000), Tsukamoto et al. (2002) Porter (1970), Richards et al. (2002) Porter (1975)

China

Tibet

36.50

102.00

N

4550

3950

MID

600

5

8

Porter (unpublished data)

Carrib Carrib

3.24 4.45

75.92 74.09

5250 3990

3250 3275

4100 3488

MID MID

Colombia

Carrib

10.80

73.60

5775

3400

3800

Colombia

Carrib

6.45

72.20

5493

3300

4395

MID

5000

3200 3470

3260 3600

CF

4900 4900

MID

4700

Thouret et al. (1996, 1997) Herd (1974), Helmens (1988, in press) Herd (1974), Schubert and Clapperton (1990) Van der Hammen et al. (1980/1981), Helmens et al. (1997) Lachniet and Seltzer (2002) Hastenrath (1974), Lachniet and Vasquez-Selem (this volume) Schubert (1974, 1984), Schubert and Clapperton (1990) Osmaston and Harrison (this volume) Potter (1976), Street (1979), Osmaston and Harrison (this volume) Messerli et al. (1977), Osmaston and Harrison (this volume)

Range/massifaveraged Cordillera Central Colombia High plain of Bogota Colombia

8400

5100 4700

01 isotherm 1000 ﬁeld observation 1200

2 2

1 3

4700

ﬁeld observation 1300

4

4

2

4

3 5

7 8

1

3

Sierra Nevada de Santa Marta Sierra Nevada del Cocuy Cerro Kamuk Los Cuchamatanes Range Cordillera de Merida

Costa Rica Guatemala

Carrib Carrib

9.27 15.50

83.03 91.53

3549 3837

Venezuela

Carrib

8.54

71.04

5002

3250

3800

Mt Kecha

Ethiopia

EAfrica

7.38

39.08

4174

3050

3600

4100

500

5

8

Mt Bada

Ethiopia

EAfrica

7.95

39.43

4170

3650

3800

4200

400

2

7

Bale

Ethiopia

EAfrica

6.93

39.74

4377

4000

4100

4300

200

2

6

3480

605 1640 regional 1300 correlation ﬁeld observation 900

183

4500

ARTICLE IN PRESS

Peru

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Cordillera CallejonEast Junin-East Simen S

184

Table 1 (continued ) Lat (deg)

Lon (deg)

Aspect

Highest summit altitude (m)

LGM ELA method

Modern ELA (m)

Modern ELA Method

DELA

DMC DC

References

3600

THAR ¼ 0.5

4800

01 isotherm

350

2

7

3200

4300

THAR ¼ 0.5

4800

500

1

7

LGM Head-wall Terminus ELA altitude altitude (m) (m) (m)

Region

Aberdare

Kenya

EAfrica

0.32

36.62

4001

3200

Mt Kenya

Kenya

EAfrica

0.15

37.32

5198

Ladakh

India

Himalaya

34.00

77.00

Zanskar

India

Himalaya

33.00

77.00

Garhwal Himalaya

India

Himalaya

31.00

79.00

Langtang

Nepal

Himalaya

28.00

Nanga Parbat, MidIndus Hunza Valley

Pakistan

Himalaya

Pakistan

Tancı´ taro Cofre de Perote Djurdjura (Kabylien) Toubkal

6100

—

3600

4500

AAR, THAR

5300

AAR, THAR

800

1

7

6400

—

4140

4700

AAR, THAR

5700

AAR, THAR

1000

1

4

N,S,W

7050

5200–6400 2300

4300

AAR, THAR

4950

AAR, THAR

650

1

7

85.00

N,S

7239

—

2600

5920

AAR ¼ 0.6

5320

100

5

8

35.00

74.00

N,S,E

8126

—

1500

3350

THAR ¼ 0.4

4500

AAR ¼ 0.4, MELM THAR ¼ 0.4

1120

1

6

Himalaya

36.00

74.00

E

7885

—

2450

3700

—

4800

—

1100

1

1

Mexico Mexico Algeria Morocco

Mexico Mexico NAfrica NAfrica

19.43 19.50 36.47 31.07

102.30 97.15 4.15 7.92

3840 4200 2308 4165

3740 4050

3150 3390

3390 3660 2000 3450

THAR ¼ 0.4 THAR ¼ 0.4

4960 5050 4500

1570 1390 1400 1050

2 4 5 5

5 3 8 8

Ayachi

Morocco

NAfrica

32.48

4.93

3751

3250

4500

1250

5

8

Tidiguin (Tidirhin)

Morocco

NAfrica

34.84

4.51

2453

2500

3700

1200

5

8

Dj. Naceur, Bou Jblane Mt Trikora Star Mts Mt Giluwe

Morocco

NAfrica

33.59

3.88

3340

2950

3650

700

5

8

Indonesia PNG PNG

PNG PNG PNG

4.35 5.00 6.04

138.65 142.50 143.88

4214 4368

3625 3460 3525

CF MID, CF

4800 4800 4600

1175 1340 1075

3 2 2

5 6 4

Mt Wilhelm Saruwaged range Mt Albert Edward

PNG PNG PNG

PNG PNG PNG

6.00 6.20 8.50

145.11 146.80 147.50

4509 4121 3996

3550 3675 3625

MID, CF MID, CF MID, CF

4600 4600 4800

1050 925 975

2 3 2

5 5 6

Hastenrath (1984), Hurni (1989), Osmaston and Harrison (this volume) Mahaney (1990), Shanahan and Zreda (2000), Osmaston and Harrison (this volume) Fort (1983), Burbank and Fort (1985), Bovard (2001) Damm (1997), Taylor and Mitchell (2000), Owen et al. (2002d) Sharma and Owen (1996), Barnard et al. (2004) Shiraiwa and Watanabe (1991), Shiraiwa (1993), Fort (1995) Scott (1992), Richards et al. (2000a, 2001), Phillips et al. (2000) Li et al. (1984), Owen et al. (2002a), Spencer and Owen (2004) Vasquez-Selem (unpublished data) Vasquez-Selem (unpublished data) Osmaston, pers. comm. (2002) Mensching (1955), Osmaston and Harrison (this volume) Mensching (1955), Osmaston and Harrison (this volume) Mensching (1955), Osmaston and Harrison (this volume) Messerli (1967), Osmaston and Harrison (this volume) Hope (2001) Lofﬂer (1972), Porter (2001) Blake and Lofﬂer (1971), Lofﬂer (1972), Porter (2001) Lofﬂer (1972), Porter (2001) Lofﬂer (1972), Porter (2001) Lofﬂer (1972), Porter (2001)

Mt Victoria Ojos de Salado Ojo de las Losas Laguna Blanca Nevados de Compuel Sierra Zuriara Q.Filo Pishca Cruz Q.Nevado de Chuscha Q.El Pabellon

PNG Argentina Argentina Argentina Argentina Argentina Argentina Argentina Argentina

PNG SAndes SAndes SAndes SAndes SAndes SAndes SAndes SAndes

8.90 27.10 27.03 26.37 25.94 26.25 26.17 26.15 16.15

147.53 68.53 68.30 67.05 66.58 66.52 66.22 66.18 66.13

4036 6885 6620 6200 5472 5266 5200 5468 5000

3675 5500 5500 5000 4800 5000 4800 4800 4800

MID, CF

4600 5700 5700 5300 5200 5200 5100 5100 5090

925 200 200 300 400 200 300 300 290

3 5 5 5 5 5 5 5 5

6 8 8 8 8 8 8 8 8

Lofﬂer (1972), Porter (2001) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002)

A.Nevados del Candado Nevados del Cerillo A.Cerro Negro

Argentina

SAndes

27.20

66.12

S, W, E

5350

4300

5000

obs 700

5

8

Haselton et al. (2002)

Argentina Argentina

SAndes SAndes

27.13 27.12

66.06 66.05

W

5550 5000

4400 4600

5100 5066

5 5

8 8

Haselton et al. (2002) Haselton et al. (2002)

A.Cerro Laguna Verde

Argentina

SAndes

27.10

66.00

E

5100

4450

5200

photo, ﬁeld obs 700 regional 466 correlation photo, ﬁeld obs 750

5

8

Haselton et al. (2002)

S

2650

2875

regional correlation photo, ﬁeld photo, ﬁeld photo, ﬁeld photo, ﬁeld photo, ﬁeld photo, ﬁeld photo, ﬁeld regional correlation photo, ﬁeld

obs obs obs obs obs obs obs

ARTICLE IN PRESS

Country

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

LGM snowline locality

SAndes

27.02

65.93

E

4600

4400

5030

A.Morro del Zarzo

Argentina

SAndes

26.98

65.92

NW

5064

4800

5026

A.Alto de Munoz Argentina C.C. Cerro El Negrito Argentina

SAndes SAndes

26.88 26.68

65.83 65.72

E S

4437 4660

4200 4400

CF

5000 4960

C.C. Alto de la Mina Argentina

SAndes

26.62

65.70

SE

4762

4650

CF

4960

C.C. Alto de la Nieve Argentina

SAndes

26.72

65.70

SE

4634

4300

CF

4960

C.C. Qda. Del Matadero Volcan Bonete Nevado Tres Cruces Cerro Incahuasi Mt Kinabulu Taiwan Shan Nyainqentanglha Tanggula Shan Kunlun Shan

Argentina

SAndes

26.63

65.70

E

4250

4250

CF

4960

Chile Chile Chile Malaysia Taiwan China China China

SAndes SAndes SAndes SEAsia SEAsia Tibet Tibet Tibet

27.43 27.05 26.50 6.08 23.46 30.00 33.00 35.00

69.00 68.47 66.75 116.60 120.94 90.00 91.00 94.00

6850 6330 5167 4101 3997 7162 6525 6860

3800 — — —

3230 3300 4300 5100 —

5500 5500 4800 3665 3400 5350 4950 4500

MID CF, GT TSAM TSAM TSAM

5800 5800 5100 4570 3800 5950 5250 5050

China

Tibet

34.00

99.00

6282

—

3900

4900

TSAM

5369

—

4050

4850

E

N,S N,S

630

5

8

Haselton et al. (2002)

226

5

8

Haselton et al. (2002)

obs 800 560

5 5

8 8

Haselton et al. (2002) Haselton et al. (2002)

310

5

8

Haselton et al. (2002)

660

5

8

Haselton et al. (2002)

710

5

8

Haselton et al. (2002)

TSAM TSAM TSAM

obs 300 obs 300 obs 300 905 400 600 400 550

5 5 5 2 5 5 1 5

8 8 8 7 8 8 8 8

5200

TSAM

300

1

7

TSAM

5100

TSAM

250

1

4

regional correlation TSAM

650

1

7

Haselton et al. (2002) Haselton et al. (2002) Haselton et al. (2002) Hope (2001, 2005) Ono (1988), Porter (2001) Lehmkuhl et al. (2002) Lehmkuhl (1995, 1997) Lehmkuhl (1995, 1997), Derbyshire (1996) Lehmkuhl (1995, 1998), Owen et al. (2003b) Lehmkuhl and Lui (1994), Owen et al. (2003b) Owen et al. (2003a)

800

1

7

Anyemaqen Mountains Nainboyeze Mountains La Ji Mountains

China

Tibet

33.00

101.00

China

Tibet

36.00

101.00

NE

4469

—

3850

4350

AAR ¼ 0.5–0.8

5000

Qilian Shan

China

Tibet

37.00

101.00

N,S

5650

—

3200

4150

TSAM

4950

AABR

Area-altitude balance ratio Accumulation area ratio Cirque-ﬂoor altitude Glacier threshold Maximum elevation of lateral moraine Mid-line altitude Toe-to-headwall altitude ratio Arithmetical average of altitudes of highest peak and terminal moraine

AAR CF GT MELM

MID THAR TSAM

regional correlation regional correlation photo, ﬁeld regional correlation regional correlation regional correlation regional correlation photo, ﬁeld photo, ﬁeld photo, ﬁeld

Lehmkuhl (1995), Zhou et al. (2002), Owen et al. (2003c)

The localities are presented in 3 type-groupings depicting the scale of snowline data: valley-speciﬁc, averaged by aspect, and averaged by entire massif/range. With in each group, localities are arranged in ascending order of latitude (i.e. west to east), and the associated metadata in columns include: country, region, latitude and longitude in digital degrees (where north and east are conventionally positive and south and west are negative), aspect of paleoglacier valley, highest summit altitude (m), headwall altitude (m), terminus altitude (m), modern and LGM snowlines (m) with methods used for reconstructing each, and ﬁnally the dating control (DC) and dating-method control (DMC), based on the schemes described in Section 2. The following abbreviations are used for reconstruction method: AAR ¼ accumulation area ratio, AABR ¼ accumulation area balance ratio, CF ¼ cirque ﬂoor altitude, GT ¼ glaciation threshold, MELM ¼ maximum elevation of lateral moraines, MID ¼ median altitude, THAR ¼ toe-to-headwall altitude ratio, TSAM ¼ method of Louis (1955) using arithmetic average of altitudes of highest peak and terminal moraine.

ARTICLE IN PRESS

Argentina

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

A.Abra del Toro

185

ARTICLE IN PRESS 186

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Fig. 1. Maps of all localities (n ¼ 477) with LGM ELA, sorted by type. Top panel: 369 localities provide valley-speciﬁc snowline information; Bottom panel: 39 localities have snowline data averaged by aspect (open circles), and a further 69 represent averaged snowlines for entire mountain ranges/massifs (closed circles).

parameters were estimated using algorithms in the database as described in Section 2. Only data from valley-speciﬁc sites were used in the initial analyses presented here. We excluded sites with poor dating control (DC46, DMC43). Thus, the chronology of all the selected sites is based on radiometric dating, and all sites have at least one radiometric date within 5000 years of the LGM. Applying more stringent selection criteria would have made it difﬁcult to obtain statistically signiﬁcant results and resulted in too few localities to explore spatial patterns (Table 2). Only 253 of the 359 valley-speciﬁc LGM sites pass these chronological quality-control criteria. To include data points from the subtropics in a manner consistent with the 21 ka TROPICS data set of Farrera et al. (1999), we restrict our analyses geographically to include sites between 331S and 331N. There is a considerable range in the DELA within any one region. This variation cannot be caused by largescale regional climate changes and it must therefore reﬂect the inﬂuence of local factors in modulating the

response of individual glaciers to regional climate changes. Investigations of the relationship between a range of local factors and DELA, using simple linear regression, showed that most of the intra-regional difference could be explained by differences in glacier HW and aspect. To investigate these relationships further, we identiﬁed 8 different key regions (Iztaccı´ huatl, Bogota´, Central Andes, Cordillera Blanca, Rwenzori, Himalaya, Papua New Guinea and Mt. Jaya: Table 3) with multiple (n410) valley-speciﬁc ELA reconstructions, each of which passed the initial screening criteria for dating method control and dating control and also had adequate data on headwall and aspect. For the analyses of the data from the subtropical Himalayas, 4 sites lacked headwall data, and were discounted. We also omitted two sites from Kanchenjunga and Khumbu, which appear to have extremely high HWs compared to other glaciers in the region (Ono, pers. comm., 2002). The topography is known to be extremely steep in these regions, and glaciers are often disconnected from steep upper faces, so that the reported

ARTICLE IN PRESS B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

187

Table 2 The number of valley-speciﬁc sites meeting successively better quality control of type and dating control in different regions Region

Database abbreviation

Total no. of localities

DC p6

DC p5

DC p4

DC p3

DC p2

DC p1

Bogota´ Circum-Caribbean Central Andes Cordillera Blanca East Africa Himalayas Iztaccı´ huatl (i.e. Mexico) Japan Mt Jaya massif, Indonesia New Zealand & Australia Papua New Guinea Rwenzori range Total

Bogota´ Caribb CenAndes CoBlanca EAfrica Himalaya Iztaccı´ huatl Japan Mt Jaya NZ-Aus PNG Rwenzori

23 11 45 21 48 32 20 19 12 2 51 75 359

23 5 23 21 0 19 20 2 12 2 51 75 253

23 5 2 0 0 6 20 2 12 2 39 75 186

23 5 2 0 0 6 20 2 12 2 34 75 181

23 2 1 0 0 6 20 2 0 1 0 0 55

0 0 1 0 0 6 0 1 0 0 0 0 8

0 0 1 0 0 2 0 1 0 0 0 0 4

The abbreviated name for each region used in the database as provided in Table 1 is provided. The ﬁrst numeric column gives the total number of qualifying localities in each region. The remaining columns show the number of localities remaining as the level of chronological precision, as shown by the value of dating control (DC), increases.

Table 3 Summary of reconstructed changes in ELA (DELA) and temperature (DT) for the key regions used in the statistical analyses Key region

No. of localities

Min DELA (m)

Max DELA (m)

Mean DELA (m)

Range DELA (m)

Min DT (1C)

Max DT (1C)

Mean DT (1C)

Bogota´ Central Andes Cordillera Blanca Himalaya Iztaccı´ huatl Mt Jaya PNG Rwenzori

23 44 21

920 260 470

1426 1403 1058

1217 804 753

456 1143 588

6.5 2.2 4.2

9.0 12.4 6.7

7.9 7.2 6.1

0.83 2.76 0.73

13 20 12 51 73

104 820 800 877 503

1044 1250 1210 1273 1387

513 1030 945 1040 1043

940 430 410 397 884

7.3 6.0 9.4 11.2 3.1

12.1 8.2 11.2 14.6 7.9

7.5 7.1 10.0 12.8 5.9

1.99 0.67 0.53 1.13 1.17

Total

236

104

1426

978

1322

2.6

14.6

8.37

Stdv DT (1C)

3.04

The Central Andes region includes the localities of the Cordillera Blanca, which also comprise a separate key region used in the analyses. The 21 localities of the Cordillera Blanca are not counted twice towards the total, and likewise are italicized in the table.

headwall values may not be reliable (Benn and Lehmkuhl, 2000). For each key region, we performed analysis of covariance (ANCOVA; Wildt and Ahtola, 1978; Stevens, 1992) with DELA as the response variable, glacier HW as the covariate and aspect group (AG) as a nominal variable comprising two or more levels, following the procedure of Crawley (2000). Since our dataset has limited numbers of localities, and since the topographic controls on DELA are multifactorial, grouping similar localities into nominal categories increased the statistical power of the analysis. AG represents an a priori classiﬁcation of localities according to aspect based on differences in meso-scale climate factors (particularly precipitation source) identiﬁed either by the original authors or by us. The rationale

for the number of levels used, and the range of aspect included in each class, for each key region is given in Table 4. For key each region, we ﬁtted the maximal model ﬁrst (that is, DELA ¼ B0+B1 HW+B2 AGi+B3 HW AGi). Non-signiﬁcant terms were progressively discarded with back-checking to test whether discarded terms were signiﬁcant in the reduced models. We report only the minimal model that explains the most variation in DELA using the least number of terms. To assess the amount of variation in DELA explained by this minimal model, we employ the ratio of the sums of squares of the ﬁtted model to the total sum of squares. Where applicable, we assessed the relative amount of variation explained by the main effects of HW and AG by changing the ﬁt order of these factors in the model, and then assessing their respective sums of

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Table 4 Basis for aspect group (AG) variable used in statistical analysis of DELA variance in each key region Key region

No. of AG and explanation

Bogota´

Geological constraints mean glaciers conﬁned to north and northwest aspects, but 2 groups distinguishing exposure to precipitation bearing winds

A ¼ facing moisture-bearing winds, B ¼ facing away from moisture-bearing winds

Helmens, 1988, pers. com., Lachniet and Vasquez-Selem (this volume)

Central Andes (44 localities)

Two groups reﬂecting precipitation regimes and tectonic control

A ¼ N, NE, E, B ¼ S, SW, W

Rodbell, 1993, Seltzer, 1993

Cordillera Blanca (21 localities)

Two groups reﬂecting precipitation regimes and tectonic control

A ¼ N, NE, E, B ¼ S, SW, W

Rodbell, 1993, Seltzer, 1993

Himalaya

Two groups based on predominant precipitation sources (from Westerlies or from southwest monsoons)

A ¼ NW, N, NE, B ¼ SE, S, SW

Owen and Benn (this volume)

Iztaccı´ huatl

Two groups reﬂecting precipitation regimes from Paciﬁc and Gulf of Mexico

A ¼ east, B ¼ west

Lachniet and Vasquez-Selem (this Volume)

Mt Jaya

Two groups based on separate massifs along east–west transect

A ¼ Carstensz (west), B ¼ Bakopa (east)

Prentice et al. (this volume)

PNG

No evidence for physiological or precipitation gradients impacting ELA, divided into 3 aspect groups of equal span

A ¼ NW, N, B ¼ NE, E, SE, C ¼ S, SW

Lo¨fﬂer, 1972

Rwenzori

Four groups reﬂecting precipitation gradient across range

A ¼ north, B ¼ east, C ¼ south, D ¼ west

Osmaston, 1989b, Kaser and Osmaston, 2002

squares against total variation. For statistically signiﬁcant AG terms comprising42 levels, post hoc comparisons were performed using Tukey’s method of honestly signiﬁcant differences (Crawley, 2000). All tests of statistical signiﬁcance were assessed at the 5% level of Type I error (alpha ¼ 0.05). Inter-regional differences in DELA reﬂect the largescale climatic control on glacier behaviour. Both temperature and precipitation changes can inﬂuence ELA (Ohmura et al., 1992; Seltzer, 1994c). Here, for comparison with reconstructions based on other tropical palaeoenvironmental indicators, we estimate the apparent temperature change implied by the changes in regional snowline between LGM and present on the assumption that precipitation did not change. We assume that the ELA position corresponds to a mean summer (June–September in northern hemisphere; December–March in southern hemisphere) temperature of 0 1C. Mean summer temperature has been shown to provide a good estimate for ablation on mid-latitude glaciers (Paterson, 1994) and Greene et al. (2002) have shown that glacier ELA is highly correlated with summer freezing height in the tropics. Furthermore, mean summer temperature varies little from annual mean temperature in the tropical regions (Kaser and Osmaston, 2002; Benn et al., this volume). We derived mean summer temperature at the exact location of every LGM ELA, using thin-plate spline interpolation from the CRU (Climate Research Unit, University of East

Relevant publications

Anglia) high-resolution (100 latitude/longitude) data set, which contains mean monthly temperature (New et al., 2002) for the period from 1961 to 1990. The spline interpolation (Hutchinson, 1999) is a three-dimensional (i.e. altitude-sensitive) interpolation, and was based on 5151 meteorological stations from the tropics and subtropics (331S–331N). The change in temperature (DT) from LGM to present was calculated as the difference between the modern temperature at the LGM ELA site and 0 1C.

4. Analytical results 4.1. Intra-regional variability There is considerable intra-regional variability in reconstructed DELA (Fig. 2, Table 3). Within the Central Andes region, for example, the minimum change in ELA is 260 m and the maximum change is 1403 m, a range of 41100 m. A similarly large range of DELA is found in the Himalaya region. The smallest range of DELA is found in Papua New Guinea (ca. 400 m). Most of the intra-regional variation in DELA (ca. 60%; Fig. 3) can be explained by differences in the altitude of the headwall (HW). Results of a simple linear regression of DELA against HW for all sites with headwall data (n ¼ 238) show that DELA is signiﬁcantly

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1800 1600 1400 ∆ELA (m)

1200 1000 800 600 400 200 0 -180 -150

-120

-90

-60

-30

0 30 Latitude

60

90

120

150

180

Fig. 2. DELA plotted by latitude illustrates the large intra-regional variance by showing individual localities as long linear clusters. Valley speciﬁc localities are plotted in red, aspect-averaged estimates in green, and regional averages in blue.

1600 1400

Bogotá

Central Andes

Cordillera Blanca

Himalaya

Iztaccíhuatl

Mt Jaya

PNG

Rwenzori

∆ELA (m)

1200 1000 800 600 400

n=238 y = -0.29x + 2237 R2 = 0.59

200 0 3000

3500

4000

4500 5000 HW (m)

5500

6000

6500

Fig. 3. Global relationship between headwall altitude and DELA, showing all localities having headwall data with DCp6, colour-coded by region.

negatively correlated to HW (Fig. 3): as the altitude of the headwall becomes lower then the change in ELA from LGM to present increases. The form of the relationship differs between regions, but is usually negative. Intra-regional variability in DELA is also related to the aspect of the palaeo-glacier valley (Fig. 4). In the Rwenzori, for example, glaciers on south- and eastfacing slopes show larger DELAs than glaciers facing northeast and west. Similarly, in the Himalayas, glaciers facing west and southwest show larger DELAs than glaciers facing northeast and east. The most extreme differences with aspect occur in the highlands near Bogota´, in the Northern Andes, where LGM glacier advances were large on northwest-facing slopes and there is no evidence of advances on south- and eastfacing slopes.

The interaction between altitude and aspect within speciﬁc regions was further investigated using ANCOVA (Table 5). These analyses conﬁrm that there is a strong negative relationship between DELA and HW altitude in most regions even when differences in aspect are taken into account (Fig. 5). In the Bogota´ and Himalayan regions, both the mean DELA at a given altitude and the ELA gradient with altitude (i.e. the slope of the relationship) appears to be unaffected by aspect. In the Rwenzori, Iztaccı´ huatl and Mt Jaya regions, however, the magnitude of DELA differs with aspect although the slope of the relationship between DELA and HW altitude is the same (Table 5). Speciﬁcally, mean DELA for north- and west-facing glaciers (aspect group A and D) are signiﬁcantly less than those for east- and south-facing glaciers (aspect group B and C) in the Rwenzori; east-facing glaciers

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190

N

N Central Andes

Bogotá

1500 NW

NE

1000

NW

500 E

W

SE

SW

N

N NW

NE

1000

SE

S

Iztacchíhuatl

N

NE

NW

E

NE

1000

0

E

SE S

S

N

W

SW

SE

SW

PNG

Mt Jaya

500

0

S

N

SE

1500

1000

W

E

SW

500

E

SW

Rwenzori

1500

1500

NW

NE

1000 500

W

0

1500

0

NW

W

SE

Himalaya

NE

1000

S

500 W

E

SW

1500

Cordillera Blanca

500

0

S

NW

NW

NE

1000 500

0

W

N 1500

1500

NE

1000 500

0

E

SW

W

SE

0

E

SW

S

SE S

Fig. 4. Radar plots of the range of DELA (m) by aspect for the regions with enough data. The Cordillera Blanca is included in the Central Andes, and the data for Mt. Jaya are combined with PNG. Grey areas are bounded by the maximum and minimum DELA for each of 8 aspect categories.

(aspect group A) had signiﬁcantly larger mean DELA than west-facing glaciers (aspect group B) in Iztaccı´ huatl; and glaciers in the Bakopa massif (aspect group B) had signiﬁcantly larger mean DELA than glaciers in the Carstenz (aspect group A) in Mt Jaya. In the Central Andes, both the magnitude and the slope of the relationship vary with aspect, although the relationship between DELA and HW altitude is always negative. In the Cordillera Blanca, north- and east-facing glaciers (aspect group A) show a negative relationship but southand west-facing glaciers (aspect group B) show a positive relationship with HW altitude. There is no apparent relationship between either HW or aspect and DELA in Papua New Guinea.

5. Inter-regional variability and tropical climate The considerable intra-regional variability in DELA does not obscure the fact that there are major differences

in DELA among regions. These differences are apparent in both the minimum and mean estimates of DELA (Fig. 6), and in the range of DELA from the region (Table 3). The minimum changes are smallest (ca. 100 m) in the Himalaya region, relatively small in the southern central Andes (260 m) and Rwenzori (503 m), and larger in the northern Andes, Papua New Guinea and Mexico (800–920 m). A similar grouping is seen in the mean DELA: smallest changes are seen in the Himalaya region (513 m) and the largest changes in the northern Andes, Papua New Guinea and Mexico (945–1217 m). However, the ordering of the regions is not identical: the Rwenzori would be grouped with regions showing relatively small changes according to minimum DELA but with regions showing largest changes according to mean DELA. Interregional differences in the maximum DELA are smaller than inter-regional differences in either minimum or mean DELA and the ordering of regions appears to bear little relationship to the ordering shown using the other parameters.

Table 5 Summary statistics from ANCOVA analyses for each key region Statistical relationship between DELA and HW. Bold type is minimal model from ANCOVA

Variation in DELA explained by HW

Variation in DELA explained by AG

Multiple comparisons of mean DELA b/w AG categories

Statistical relationship between DELA and AG

Variation in DELA explained by HW AG interaction

Total variation in DELA explained by minimal ACOVA model (%)

Bogota´

A:18 B: 5 AS: 23

A: DELA ¼ 1769–0.21 HW B: DELA ¼ 1992–0.24 HW AS: DELA ¼ 4079–0.78 HW

A: 89.3% (**) B: 66.5% (NS) AS: 84.9% (**)

AS: 2.8% (NS)

Not applicable

Not applicable

AS: 0.3% (NS)

84.90

Central Andes

A: 9

A: DELA ¼ 3268–0.50 HW

A: 79.4% (**)

AS: 4.1% (NS)

Not applicable

Not applicable

AS: 3.9% (NS)

61.8

B: 35 AS: 44

B: DELA ¼ 2033–0.27 HW AS: DELA ¼ 2211–0.3 HW

B: 52.7% (**) AS: 53.1% (**)

A: 7

A: DELA ¼ 4294–0.7 HW

A: 35.8% (NS)

AS: 0.2% (NS)

Not applicable

Not applicable

AS: 26.0% (*)

27.2

B: 14 AS: 21

B: DELA ¼ 624+0.27 HW AS: DELA ¼ 458+0.06 HW

B: 19.2% (NS) AS: 0.7% (NS)

Himalaya

A: 10 B: 5 AS: 13

A: DELA ¼ 1214–0.13 HW B: DELA ¼ 1022–0.07 HW AS: DELA ¼ 7677–1.21 HW

A: 0.3% (NS) B: 4.1% (NS) AS: 34% (*)

AS: 8.5% (NS)

Not applicable

Not applicable

AS: 16.5% (NS) 34

Iztaccı´ huatl

A: 10 B: 10 AS: 20

A: DELA ¼ 2461–0.29 HW B: DELA ¼ 2339–0.29 HW AS: DELA ¼ 2601–0.40 HW

A: 52.2% (**) B: 10.6% (NS) C: 36.3% (**)

AS: 34.9% (**)

B (955)oA (1104)

A: DELA ¼ 1104 B: DELA ¼ 955

AS: 1.6% (NS)

Mt Jaya

A: 6

A: DELA ¼ 1858–0.23 HW

A: 76.3% (**)

AS: 59.4% (**)

A (869.2)oB (1044.2)

A: DELA ¼ 869.2

AS: 0.05% (NS) 71.0

B: 6 AS: 12

B: DELA ¼ 1951–0.23 HW AS: DELA ¼ 2477–0.37 HW

B: 23.0% (NS) AS: 63.0% (**)

PNG

A: 21 B: 17 C: 13 AS: 51

A: DELA ¼ 537–0.13 HW B: DELA ¼ 970–0.02 HW C: DELA ¼ 118–0.24 HW AS: DELA ¼ 643–0.104 HW AS: DELA ¼ 1041

A: 5.1% (NS) B: 0.06% (NS) C: 4.4% (NS) AS: 2.0% (NS)

AS: 1.4% (NS)

Not applicable

Not applicable

Rwenzori

A: 19

A: DELA ¼ 2624–0.40 HW

A: 84.7% (**)

AS: 30.3% (**)

A or D: DELA ¼ 919.5 0.4% (NS)

B: 13

B: DELA ¼ 2715–0.40 HW

B: 69.0% (**)

D (919.2) ¼ A (919.8) o B (1109.9) ¼ C (1180.6).

C: 25 D: 18 AS: 75

C: DELA ¼ 2799–0.40 HW D: DELA ¼ 2601–0.40 HW AS: DELA ¼ 2601–0.40 HW

C: 67.4% (**) D: 58.7% (**) AS: 65.2% (**)

Cordillera Blanca

58.6

B: DELA ¼ 1044.2 AS: 1.2% (NS)

0

79.2

B or C: DELA ¼ 1156.4

191

‘AG’ is aspect group, ‘AS’ is all sites, and A/B/C/D refer to particular AG categories within regions. **: signiﬁcant at po0.01, *: signiﬁcant at 0.01opo0.05, NS: not signiﬁcant (p40.05). In the multiple comparison tests, ‘o’ denotes ‘signiﬁcantly less than’, ‘ ¼ ’ denotes not signiﬁcant, and the number in parentheses is the mean DELA for that AG. ‘Not applicable’ on account of AG factor deemed non-signiﬁcant.

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Sample size

B.G. Mark et al. / Quaternary International 138– 139 (2005) 168–201

Key region

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500

1500

∆ELA (m)

∆ELA (m)

1000

0 3000

3500 4000 HW (m)

Cordillera Blanca

500

5000 5500 HW (m)

0 4000

4500 5000 HW (m)

PNG

1000

500

0 3500

4000

4500

1000

500

6000 HW (m)

6500

Mt Jaya

1000

500

0 3500

5500

4000 4500 HW (m)

5000

Rwenzori

1500

∆ELA (m)

∆ELA (m)

1500

Himalaya

1500

∆ELA (m)

∆ELA (m)

500

500

0 5500

6000

1000

1000

1500

Iztaccíhuatl

1500

Central Andes

0 3500 4000 4500 5000 5500 6000 HW (m)

4500

1000

0 4500

1500

Bogotá

∆ELA (m)

∆ELA (m)

1500

1000

500

0 3000 3500 4000 4500 5000 5500

HW (m)

HW (m) group A

group B

group C

group D

Fig. 5. Relationship between headwall altitude and DELA according to different aspect groups in those regions for which there are sufﬁcient data.

We have estimated the apparent change in regional temperature (DT) using minimum, maximum and mean DELA for each region (Fig. 7, Table 3). The choice of minimum, mean or maximum regional DELA is crucial in determining both the magnitude of reconstructed temperature changes and the discrimination of interregional patterns. Our analyses of the relationship

between HW and DELA suggest there are theoretical grounds for considering minimum DELA to be the best reﬂection of changes in temperature. One way of evaluating this assumption is to compare the temperature changes reconstructed from all of the DELA estimates with independent evidence for temperature changes in each region.

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193

Fig. 6. Global map showing the minimum and mean DELA for each region as a double circle, scaled by diameter (500 m scale bar included in lower left). The inner solid circle represents the minimum DELA and the dashed outer circle the mean DELA.

-180 -150 -120

90

-90

-60

-30

0

30

60

90

120

150

180

60 30 0 -30 -60 16.0 14.0 12.0 ∆T (˚C)

10.0 8.0 6.0 4.0

M

tJ

ay

G PN

a al im

en w

ay

zo H

lle C

or

di

R

ra

lA tra en

ri

ca Bl an

es nd

tá go Bo C

Iz

ta

cc

íh

ua

tl

0.0

a

2.0

Fig. 7. Top: Global map showing the mean and minimum temperature change from LGM to present (DT) for each region as a double circle, scaled by diameter (5 1C scale bar included in lower left). The inner solid circle represents the minimum DT and the dashed outer circle the mean DT, calculated from the range of LGM ELA for all localities in the respective regions. Bottom: range of DT plotted for each region as a vertical bar connecting max and min values range through the mean (black square).

5.1. Comparison with the 21 ka tropics data synthesis The most comprehensive reconstruction of changes in temperature over continental regions at the LGM is based on the 21 ka TROPICS database (Farrera et al.,

1999). Farrera et al. (1999) identiﬁed three regions, which differed in terms of the magnitude of cooling at low elevation and in the degree to which the lapse rate was apparently steeper at the LGM. The Neotropics showed large cooling at both sea level and high

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194

elevation, and therefore no discernible change in lapse rate, although the scatter in the data is large at all elevations. The circum-Indian Ocean region showed moderate cooling at sea level and somewhat larger changes at high elevation, implying a steeper-thenpresent lapse rate. The Paciﬁc Ocean region had the smallest cooling at sea level but the greatest change at high elevation, implying a maximum steepening of the lapse rate. To compare these estimates of temperature changes with the reconstructions based on the snowline data, we have overlaid the range of apparent temperature changes associated with regional ranges of DELA on plots of the data from 21 ka TROPICS (Fig. 8). Farrera et al. (1999) presented these results as changes in the mean temperature of the coldest month (MTCO) because this is a key climatic parameter determining the limits of tropical and montane forests. However, Farrera et al. (1999) point out that this variable is close to mean annual temperature in the tropics. We have reconstructed mean summer temperature as the variable most closely controlling the position of the snowline; but 0

0

Indian Ocean Rwenzori

Neotropics Andes

-1

again, we assume that this value is close to mean annual temperature. It is therefore reasonable to compare the two sets of reconstructions. The Farrera et al. (1999) data set includes estimates of the mean change in MTCO for individual localities and maximum–minimum estimates of the change in MTCO for regions; we have used both sets of estimates. They estimated separate regressions for the individual localities and based on all the data but the relationships are very similar. We compare these data with minimum, mean and maximum estimates of DT based on the LGM DELA for each region. In the Neotropical region (i.e. Mexico, Central America and the Northern Andes), the reconstructed range of DT falls within the same range as estimated by Farrera et al. (1999) and also shows a very wide scatter. The Indian Ocean region in 21 ka TROPICS overlaps with E Africa, and the projected regression line is consistent with DT based on the Rwenzori DELA. In the Paciﬁc Region, the projected regression lines intercept the lower end of our DT from DELA on Mt Jaya. These comparisons are consistent with the hypothesis of

-1

mean

mean

0

Pacific Ocean PNG-Jaya mean

-2

-2 -2 -3

-4 -3

MTCO change (K)

-4 -5

-6

-4

-6 -8

-5 -7 -6

-8 -9

-10

-7 -12

-10 -8 -11 -14

-9

-12

-16

-10

-13 0

2000 4000 6000

0

1500 3000 4500 Altitude (m)

0

1500 3000 4500

Fig. 8. Comparison of DT reconstructed from snowline shifts with DT estimates from the 21 ka TROPICS data set (Farrera et al., 1999). The graphs are divided by the regions deﬁned in Farrera et al. (1999), and show cooling as a function of site altitude using the same symbols. Solid lines represent the regressions based on point estimates of MTCO change in K from 21 ka TROPICS (solid diamonds), and dashed lines are regressions for all points, including the minimum estimates (smallest cooling, shown as +), and maximum estimates (greatest cooling, shown as ). Note that the regression for the circum-Indian Ocean region is slightly different from the regression given by Farrera et al. (1999) because of an error in their calculations with respect to one site (Bosumtwi). The range of reconstructed temperature estimates from the snowline data are presented as thicker dappled lines with endpoints for each region.

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greater temperature changes at higher altitudes at the LGM in this region. They also support our suggestion that the minimum values of DELA are best suited for interpreting ELA shifts in terms of DT.

6. Discussion and conclusions This paper documents a new database, containing records of snowline changes at valley-speciﬁc localities within the tropics (331S–331N) during the last glacial period and the post-glacial. Information has been extracted from this database in order to investigate the nature and potential causes of snowline changes between the LGM and today. Although the analyses are preliminary in nature, they are based on the most extensive data set for LGM tropical snowlines currently available. There is a signiﬁcant negative relationship between DELA and HW elevation HW) globally: glaciers, which originate at higher elevations tend to show a smaller change in ELA than glaciers which originate at lower elevations. Differences in DELA within any one region are also negatively related to HW elevation, with the exception of Papua New Guinea and the Cordillera Blanca. The range of headwall elevations found in these two regions is very small (o300 m in PNG and o600 m in the Cordillera Blanca) which suggests that part of the reason for not ﬁnding a relationship between DELA and HW is because we have an inadequate sample of the topography. The strong relationship between HW and DELA found in other regions and overall across the tropics suggests that it reﬂects an underlying physical mechanism related to some aspect of basin or glacier morphometry. One possibility is that HW altitude is correlated with glacier slope, such that glaciers originating at higher elevations tend to have steeper overall slopes. There is some indication that the sensitivity of glaciers to recent climate changes is inﬂuenced by glacier slope (H. Oerlemanns, pers. comm., 2004). An alternative explanation is that the HW altitude is a surrogate for the size of the glacier catchment. Except in the case of high-elevation plateaux, surface area decreases with altitude in mountainous topography and there is a concomitant decrease in the effective catchment area of a glacier. If we assume that HW altitude can be considered as a surrogate for the size of the glacier catchment, our analyses suggest that the response of speciﬁc glaciers experiencing broadly similar local climate changes is determined by the size of the glacier catchment: the larger the contributing catchment, the larger the response to a given regional and/or local climate change. It is not possible to evaluate the relationship between glacier slope and/or catchment area and DELA directly because the snowline database does not include inde-

195

pendent estimates of these parameters for individual glaciers. However, while HW is not the best surrogate for either of these parameters, the fact that similar (though less strong) relationships are found between DELA and other parameters related to altitude (e.g. the altitudes of modern ELA, LGM ELA, and LGM moraines) suggests that it does appear to provide a reasonable measure of the catchment control on ELA changes. The idea that basin morphometry modulates the response of individual glaciers to climate change is not new (see e.g. Furbish and Andrews, 1984; Kerr, 1993) but has implications for the use of snowline data to reconstruct past climates. Speciﬁcally, the use of isolated reconstructions of snowline changes to estimate climate changes may provide an unrealistic estimate of regional climate changes. Averages based on reconstructions of snowline changes from multiple sites within a massif/ range provide a better way of taking morphometric differences into account. However, comparison of the temperature changes reconstructed from snowline data within speciﬁc regions with independent estimates based on pollen data suggests that the minimum DELA is likely to provide the best estimate of regional climate changes within the tropics. Our analyses show that valley aspect exerts a strong control of glacier response in certain regions. Regional climate changes are modulated by aspect, which determines e.g. the gradients in precipitation between rainward and leeward slopes, and surface radiation budgets between shaded and non-shaded slopes (see e.g. Hastenrath and Kruss, 1992; Wagnon et al., 1999; Porter, 2001; Kaser and Osmaston, 2002; Mo¨lg et al., 2003; Kaser et al., 2004). This modulation results in differences in the response of glaciers to large-scale climate changes according to aspect. It may be possible to exploit such differences to provide insights into the nature of regional climate changes, and particularly the stability or otherwise of precipitation-bearing winds (see e.g. Osmaston and Harrison, this volume). At the very least, the existence of different responses within the same massif/range as a function of valley aspect emphasises the need for caution in the construction of average snowline changes within a region. Despite the difﬁculties in interpreting individual snowline records, it is possible to draw some conclusions about the nature of high-elevation climate changes within the tropics because of the availability of multiple records from speciﬁc regions. Our reconstructions show that the glacier response to LGM climate changes is not zonally uniform across the tropics. Both minimum and mean DELA show substantial differences from region to region, with relatively small changes occurring in the Himalayas (ca. 100 m), the southern Andes (ca. 260 m) and East Africa (ca. 500 m) and larger changes occurring in the northern Andes, Central America and Papua New

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Guinea (800–920 m). The existence of spatial patterning in tropical cooling at the LGM has already been shown by Farrera et al. (1999) and is consistent with modelling results (Kagayama et al., this volume). Nevertheless, it is worth stressing this point because of the tendency to assume that records from individual regions can be used as a measure of the tropical response to climate forcing (e.g. Guilderson et al., 1994) and because the homogeneity of tropical cooling produced by at least mixedlayer ocean–atmosphere simulations of the LGM (Pinot et al., 1999; Folland et al., 2001; Harrison, 2001; Kagayama et al., this volume) and possibly fully coupled ocean-atmosphere simulations (e.g. Bush and Philander, 1999; Hewitt et al., 2001; Kitoh et al., 2001; Shin et al., 2003) varies from model to model. Our estimates of regional temperature changes at high elevation based on snowline changes are broadly consistent with the changes in high-altitude temperature and regional lapse rates as reconstructed by Farrera et al. (1999) from pollen data. The snowline data indicate a cooling of up to 10 1C over Papua New Guinea, of ca. 3–4 1C in East Africa, and of ca. 4–8 1C in the Neotropics. These reconstructions, combined with the estimates of temperature changes at lower elevations based on pollen data (Farrera et al., 1999), provide concrete targets for the evaluation of climate model simulations. There are clearly a number of possible limitations of our analyses. In order to obtain enough valley-speciﬁc sites for analysis, we were obliged to use fairly relaxed quality-control criteria for site selection. We used a broad deﬁnition of the LGM (21,00072000 years B.P.) and included all sites with a DCp6 and a DMCp3. These criteria restrict consideration to radiometrically dated localities with at least one date within 5000 years of the target interval. Even these extremely tolerant screening criteria resulted in a reduction of ca 30% in the number of valley-speciﬁc sites available for analysis. The mean DELA across the tropics does not change very much as more stringent quality control criteria are applied until DC ¼ 2 (i.e. until all sites are required to have a date within 1000 years of the target interval). However, by this stage, the number of sites available for analysis is too small to allow regional patterns to be reconstructed (Table 2). For example, there are no sites from Central America or the northern Andes, from East Africa, or from Papua New Guinea that have a DCp2. Thus, although our results appear to be robust as the selection criteria become more stringent, there is no doubt that better chronologies would enhance our conﬁdence in the reconstructed regional patterns of ELA change. The number of tropical localities with precisely dated LGM moraines is quite small and, as indicated by the regional summaries presented in this volume, reﬁning glacial chronology should be a primary focus of future research.

Our analyses were based primarily on published snowline reconstructions (Table 1); we have not attempted to construct independent estimates of ELA using e.g. methods that are considered most appropriate for tropical glaciers (see Benn et al., this volume). This was partly for practical reasons: publications rarely contain sufﬁcient information on basin hypsometry to justify independently estimating ELA using alternative GEIs. Although we believe that the main conclusions presented here are robust, it is possible that they are inﬂuenced by the use of different reconstruction methods used in different regions. The adoption of standard and improved methods in the ﬁeld, as recommended by Benn et al. (this volume) and the publication of more extensive information on each glacier, would make it possible to test the impact of reconstruction techniques on snowline reconstructions at some future date by repeating our analyses. The fact that glacier mass balance is controlled by a number of different climate parameters (temperature, precipitation, surface radiation receipts) means that it is unrealistic to use reconstructed snowline changes to infer regional temperature changes directly. The broad similarity between the reconstructed temperatures based on snowlines (on the assumption that other factors were unchanged) and independent estimates of regional cooling suggests that precipitation changes had a minor inﬂuence on LGM snowlines in most regions, an inﬂuence that is less than the noise engendered by uncertainties in the reconstructions of DELA itself and the age models used to select LGM sites. As reconstruction methods and chronologies improve, it will be necessary to take the inﬂuence of changes in precipitation regimes on ELA into account. It may be possible to use independent data to constrain one of the controlling variables, e.g. pollen data to constrain changes in temperature, and thus derive estimates of the other climate parameters. However, our preliminary comparisons of the snowline data and independent temperature estimates from Farrera et al. (1999) suggest that considerably greater precision in the snowline reconstructions is required before such an approach is workable. The use of explicit models of glacier mass balance may provide a better approach to determining the nature of the climate changes which lead to changes in tropical ELA at the LGM (e.g. Klok and Oerlemans, 2003; Plummer and Phillips, 2003). The framework of the snowline database has been designed to be extremely ﬂexible. This will facilitate the extension of both the spatial and the temporal coverage of the database in order to address problems other than LGM tropical cooling. It will be possible to extend the spatial coverage to include extratropical mountain regions, to determine e.g. how far the inﬂuence of catchment size on glacier sensitivity is a generic relation-

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ship. It will also be possible to extend the database to include earlier glaciations, phases within the last glacial period (e.g. MIS 4), the Younger Dryas, or neoglacial intervals in the Holocene. This would allow us reexamine the question of tropical homeostasis by determining how typical the patterns of tropical cooling reconstructed for the LGM are. The LGM snowline (and temperature) reconstructions presented here should provide a useful data set for evaluations of climate model simulations within the ongoing framework of the Palaeoclimate Modeling Intercomparison project (PMIP: Joussaume and Taylor, 2000). PMIP is currently conducting simulations of the response to LGM climate forcing using coupled ocean–atmosphere and ocean–atmosphere-vegetation general circulation models (Harrison et al., 2002). The data presented here, in conjunction with previous data syntheses (e.g. Farrera et al., 1999), provide a series of challenges for the models. Speciﬁcally, the simulations must reproduce regional differences in the magnitude of tropical cooling at low elevation between e.g. the western Paciﬁc (as represented by records from Papua New Guinea) and the eastern Paciﬁc (as represented by records from Central America and the northern Andes) and reproduce regional changes in lapse rates sufﬁcient to explain the large observed differences between low-elevation (o1500 m a.s.l.) and high-elevation (ca 4000–6000 m a.s.l.) changes in regions such as Papua New Guinea and East Africa. Key targets are:

changes of ca. 1–2 1C at low elevation and 8–10 1C at high elevation over Papua New Guinea; changes of 2–3 1C at low elevation and ca 3–4 1C at high elevation over East Africa; changes of ca. 2–4 1C at high-elevation sites in the central Andes; changes of ca. 6–8 1C at both low and high elevation sites in Central America and the northern Andes; changes of ca. 7–8 1C at high elevations in the Himalayas, coupled with a marked reduction in precipitation.

Acknowledgements The construction of the Snowline Database was motivated by ongoing discussions within the Palaeoclimate Modelling Intercomparison Project, and we thank our colleagues in PMIP for encouraging us to complete the re-evaluation of tropical climates begun by Farrera et al. (1999). We thank Gerhard Bo¨nisch for his assistance with database construction, Silvana Schott for compiling maps, and Colin Prentice, Ju¨rgen Ehlers, Steve Hostetler and Joel Guiot for their comments on an earlier version of this manuscript.

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Tropical snowline changes at the last glacial maximum: A global assessment

Tropical snowline changes at the last glacial maximum: A global assessment

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