10Be surface-exposure age dating of the Last Glacial Maximum in the northern Pamir (Tajikistan)

Quaternary Geochronology 34 (2016) 47e57

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Research paper

10

Be surface-exposure age dating of the Last Glacial Maximum in the northern Pamir (Tajikistan)

Elena Grin a, *, Todd A. Ehlers a, Mirjam Schaller a, Vasila Sulaymonova b, Lothar Ratschbacher b, Richard Gloaguen c a b c

University of Tübingen, Department of Geosciences, Wilhelmstrasse 56, 72074, Tübingen, Germany €t Bergakademie Freiberg, Geologie, Bernhard-von-Cotta-Strasse 2, 09599, Freiberg, Germany Technische Universita Helmholtz Institute Freiberg for Resource Technologies, Division of Exploration, Halsbrücker Strasse 34, 09599, Freiberg, Germany

a r t i c l e i n f o

a b s t r a c t

Article history: Received 2 November 2015 Received in revised form 23 March 2016 Accepted 25 March 2016 Available online 28 March 2016

Knowledge of the spatial and temporal variations in Alpine glaciations is essential for reconstructing the regional and global timing of ice ages. This study investigates glacial deposits at the mouth of the Muksu catchment in the northern Pamir using 10Be surface-exposure age dating. We sampled boulders from the furthest downstream recessional moraine (20 samples) and five lateral moraines (41 samples) near the former terminus of the Fedchenko Glacier, the longest (~72 km) present-day Alpine glacier of the Pamir. After the identification of outliers, the boulder population of the recessional moraine yielded a mean exposure age of 17.5 ± 1.9 ka. The maximum exposure age of the lateral moraines, collected ~5 km upvalley of the recessional moraine, is 18.2 ± 1.7 ka. The boulder ages reflect glacial deposition during the Last Glacial Maximum (Marine Isotope Stage 2) in the region; they are in accordance with published glacial deposition ages in the western Tian Shan. © 2016 Elsevier B.V. All rights reserved.

Keywords: Moraine Cosmogenic radionuclides Exposure-age dating Last glacial maximum Pamir Fedchenko Glacier

1. Introduction Glacial deposits, such as moraines, are archives that record advance and retreat cycles driven by climate change. Collecting spatial and temporal data about glaciations is essential for the reconstruction of both the regional and global timing of ice ages and climate change (e.g. Gillespie and Molnar, 1995; Thackray et al., 2008; Clark et al., 2009; Hughes et al., 2013). In this study, we establish the timing of the maximum extent of the Fedchenko Glacier in the Muksu catchment in the northern Pamir using cosmogenic 10Be surface-exposure age dating on moraine boulders (location C, Fig. 1). The Fedchenko Glacier is particularly important for understanding the glacial chronology in Central Asia, because it is the largest (~72 km) glacier outside the Polar Regions and the timing of its advance and retreat may be characteristic for the climate evolution of the Westerly-dominated Central Asia region. Previous studies in the Pamir and Tian Shan used 10Be bouldersurface dating to estimate the glacial history at various sites (Lisiecki and Raymo, 2005; Zech et al., 2005a, 2005b, 2013;

* Corresponding author. E-mail address: [email protected] (E. Grin). http://dx.doi.org/10.1016/j.quageo.2016.03.007 1871-1014/© 2016 Elsevier B.V. All rights reserved.

Abramowski et al., 2006; Narama et al., 2007, 2009a; Koppes € hringer et al., 2008; Seong et al., 2009b; Owen et al., 2012; Ro et al., 2012; Dortch et al., 2013; Xu et al., 2013; Lifton et al., 2014, Fig. 1). These studies identified glaciations during Marine Isotope Stage (MIS) 2 (14e29 ka), MIS 4 (57e71 ka), and MIS 5 (130e191 ka; Lisiecki and Raymo, 2005). The signals of these stages are found both in the semi-arid region of the Pamir Plateau and the semihumid Tian Shan. Furthermore, glacial landforms were investigated using OSL (optically stimulated luminescence), ESR (electron spin resonance) (Zhao et al., 2006, 2009, 2010, 2012; Wang et al., 2011), and 10Be exposure dating of landslides (Sanhueza-Pino et al., 2011). These observations show a wide range of glacialretreat signals spanning from the Mid- to Late Pleistocene (MIS 1e9; 14e337 ka) across the Tian Shan (Fig. 1). This study provides the timing of the maximum glacial extent in the northern Pamir constrained from exposure-age dating of boulders from lateral and recessional moraines. We focus on the most heavily glaciated catchment in the region and compare our results with published data from the Pamir and Tian Shan.

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Fig. 1. Location of the study area. The heavily glaciated Muksu catchment is located in the northern Pamir (see Fig. 2A). Overview of the regional glaciations, given as the MIS stages, is shown in white boxes (after Dortch et al., 2013). Letters refer to published data: A) Ghissar (Zech et al., 2013), B) Aksu (Abramowski et al., 2006), C) Muksu (this study), D) Koksu (Abramowski et al., 2006), E) Kitsh-Kurumdu (Zech, 2012), F) Ata Bash and G) Gulbel (Koppes et al., 2008), H) Ailuitek and Gurumdi (Abramowski et al., 2006), I) Yashikul and € hringer et al., 2012), J) Alichur and Uchkol (Abramowski et al., 2006), K) Muztaghata (Seong et al., 2009b), L) Bogoshir (Zech et al., 2005a, 2005b; Abramowski et al., 2006; Ro Kuzigun (Owen et al., 2012), M) Tashkurgan (Xu et al., 2013).

2. Study area

2.2. Climate in the Pamir

2.1. Geologic setting

2.2.1. Present-day climate Two climate zones straddle the Pamir (Abramowski et al., 2006; Fuchs et al., 2013): The eastern Pamir is a plateau with an arid climate and an average elevation of ~4000 m (Aizen, 2011). Moisture from strong Indian Summer Monsoons occasionally reaches the Pamir Plateau (Weiers, 1995, 1998; Pohl et al., 2015). The semihumid western and northern Pamir act as an orographic barrier, shielding the eastern part from the Mid-Latitude Westerlies' moisture supply. Today, the Pamir Plateau receives less than 300 mm/yr of precipitation. In contrast, the Ghissar and Alai Ranges of the western Tian Shan, and the northern Pamir receive >700 mm/yr precipitation from the Mid-Latitude Westerlies (Aizen et al., 2001, Fig. 3). In the Pamir, most of the precipitation occurs as snow during winter and spring, resulting in peak river discharge in spring (e.g. Fuchs et al., 2013). In contrast, the summers are warm and dry, and glacial melt water is the main contributor to river discharge (Chevallier et al., 2014).

The Pamir is a northward convex orocline at the northwestern margin of the IndiaeAsia collision (e.g. Burtman and Molnar, 1993; Schurr et al., 2014). It extends ~450 km from North to South and ~850 km from East to West, spanning elevations from ~300 m to ~7700 m. At present, the Pamir Thrust System d located south of the Alai Valley to the north of our study area (Fig. 1) d accommodates more than one third (~13e15 mm/yr) of the IndiaeAsia convergence (Mohadjer et al., 2010; Zubovich et al., 2010; Ischuk et al., 2013), and is seismically active (e.g. Strecker et al., 1995; Sippl et al., 2014; Schurr et al., 2014). The high shortening rates across the northern Pamir are also mirrored in a thick crust and the highest elevations of the Pamir (e.g. Mechie et al., 2012; Schneider et al., 2013). Consequently, the northern Pamir displays high relief with steeply incised valleys (Fuchs et al., 2013). Alluvial fans are prevalent across the area and typically obviate traces of glacial deposits. Our study area is located at the confluence of two rivers with Strahler Order 6 (Strahler, 1957), the Muksu and Kyzylsu (Fig. 2B); they continue as the Surhob River, which flows into the Vakhsh River, a tributary of the Amu Darya. The source of the Muksu River is the Fedchenko Glacier (Fig. 2A). The Kyzylsu drains the Alai Valley, which is filled with Quaternary fluvial and glacial deposits. The Muksu catchment stretches over more than 90 km and encompasses numerous side-valley glaciers before reaching the terminus of the present-day Fedchenko Glacier. The accumulation zones of these dendrite glaciers reach up to 6940 m a.s.l. (Aizen, 2011; Lambrecht et al., 2014). The present-day snow equilibriumline altitude (ELA) is between 4400 and 4800 m (Osipova and Dolgushin, 1989; Lambrecht et al., 2014).

2.2.2. Paleo-climate Previous studies (locations shown in Fig. 1) highlighted that glaciations in the western Tian Shan occurred during MIS 2, 4, and MIS 5 (Koppes et al., 2008; Narama et al., 2009a; Zech, 2012). In the Pamir, glaciation during MIS 1, 2, 4, 5, 7, and MIS 9 have been identified (Zech et al., 2005a, 2005b, 2013; Abramowski et al., €hringer et al., 2012). The tentative 2006; Seong et al., 2009b; Ro late MIS 3 e early MIS 4 glaciation signal at the Pamir Plateau, identified by Abramowski et al. (2006), was recalculated by Dortch et al. (2013) to MIS 4. The Mid Latitude Westerlies dominate the climate in the Tian Shan and the Pamir (Fig. 3). The Indian Summer Monsoon controls the Himalayan climate. Dortch et al. (2013) suggested that these

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history than in other parts of the Pamir (Seong et al., 2009b). The lack of older glaciations in the northern Pamir and western Tian Shan could be the result of a lack in age constraints on preserved moraines (e.g. Zech, 2012), or they may not have existed at this location due to differences in climate. The MIS 2 glacial extent was observed to be smaller in both the Tian Shan and the Pamir than older glaciations due to a southward shift in the Siberian atmospheric high-pressure cell (Siberian high; Weiers, 1995; Aizen et al., 2001). The strength of the Westerlies is mainly dependent on the position of the Siberian high (Aizen et al., 2001), because it can block humid western air masses and thus cause aridity in Central Asia. Svendsen et al. (2004) argued that during the last glaciation period, the Fennoscandian ice sheet could have functioned as an additional precipitation trap, which resulted in less moisture carried by the Westerlies. Overall, the Westerlies are thought to have been drier and colder during the LGM than €hringer et al., 2012). today (e.g. Ro In general, paleo-climate data are sparse in the Pamir. Soviet studies focused on the Holocene climate, which was investigated with radiocarbon dating, pollen analysis, lichenometry, and dendrochronology (Nikonov et al., 1981, 1989; Suslikov and Koshkina, 1989; Serebryanny and Solomina, 1996). Using palynologic and radiocarbon data, the mid-Holocene thermal optimum in the northern Pamir was constrained to 8000e4000 yrs BP (Nikonov et al., 1989), with increased humidity and warming at high and middle altitudes. This trend was not found on the Pamir Plateau (Nikonov et al., 1989). During 2900e2600 yrs BP a cooler and wetter climate signal was identified in the northern Pamir as well as in the southeastern Pamir Plateau (Suslikov and Koshkina, 1989). Traces of a Little Ice Age event have been suggested for the Pamir but were not studied in detail. The sparse and inconsistent data on previous glaciations in this region require additional studies; in particular, studies are needed that constrain the timing of glaciation in the northern Pamir. 3. Material and methods 3.1. Sampling and sample preparation

Fig. 2. A) Google-Earth image showing the glaciated Muksu catchment. B) Sampling areas comprising recessional and lateral moraines in the lower Muksu catchment. C) Interpretation of the geomorphologic features in the sampling area.

differences in prevailing winds and moisture transport have been a long-lasting phenomenon and resulted in asynchronous glaciations in the Himalaya, Tian Shan, and Pamir. For example, the monsoon triggered glaciations during MIS 3, 5, and MIS 6 (Dortch et al., 2013) but the MIS 3 or MIS 6 glaciations have been not reported from the Tian Shan or the Pamir. Koppes et al. (2008) argued that the MIS 5 glaciation in the eastern Tian Shan could have been sourced by moisture from the large intra-montane lakes in this region rather the Indian Summer Monsoon. A MIS 5 glaciation identified in the southern Pamir and eastern Tian Shan was interpreted by € hringer et al. (2012) and Dortch et al. (2013) as evidence that the Ro Indian Summer Monsoon moisture reached up to the south of the Pamir Plateau. The Muztaghata region (Fig. 3) of the eastern (Chinese) Pamir has been influenced partly by the Monsoon and partly by the Westerlies, which resulted in a more extensive glaciation

In the lowermost Muksu valley, we investigated a field of hummocky moraines at an altitude of ~2060 m and a suite of lateral moraines at ~2280 m (Figs. 2 and 4). The recessional moraine stretches over 2 km across the formerly glaciated valley, characterized by a hummocky moraine field. The lateral moraines are located ~5 km upstream from the recessional moraine on the southwestern valley side and can be traced for ~5.5 km. We sampled 20 boulders from the furthest downstream and best-preserved recessional moraine (Fig. 2B). This moraine could also represent the terminal moraine during the last glacial advance, but this was not clearly discernible in the field. We did not sample other moraine remnants, as land use or post-depositional fluvial reworking affected them. From the rounded crests of five lateral moraines, we sampled 41 boulders. The range of boulder heights from the recessional moraine is 5.0e0.5 m (mean 1.16 m without the largest two boulders) and 1.5e0.5 m (mean 0.93 m) for the lateral moraines (Table 1). We removed rock chips from the uppermost ~2.5 cm of the top surface of the boulders for analysis. Measurements for topographic shielding correction were taken every 60
. We collected additional samples to obtain a robust age determination for the smaller moraine crests. The sampled lateral moraines show some post-depositional degradation from erosion, possible periglacial overprint and/or anthropogenic (grazing) use; the exact amount of degradation is unknown. Rodent burrows locally indicate bioturbation of the moraine material underlying the boulders. We sampled a ~2 m deep depth profile on one lateral

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E. Grin et al. / Quaternary Geochronology 34 (2016) 47e57

Fig. 3. Mean annual precipitation data for the Tian Shan and Pamir from the TRMM (1998e2014) project (Huffman et al., 2007). The Mid-Latitude Westerlies are the main source of precipitation in the northern Pamir, the Ghissar Range, and the western Tian Shan. The Indian Summer Monsoon does not significantly influence the Pamir. The Pamir interior is also shielded from the Westerlies by the western Pamir acting as an orographic barrier. Sampling area is indicated by the black dot within the Muksu catchment.

Fig. 4. A) Recessional moraine on the hummocky moraine field. Boulder in the front is ~4 m high. B) Lateral moraines with A most far and E closest to the active river channel.

moraine crest to obtain an estimate of the magnitude of moraine degradation, but it yielded insufficient quartz for 10Be measurement. Out of the 20 samples collected from the recessional moraine, 16 yielded enough quartz to allow cosmogenic 10Be dating; 31 out of 41 samples of the lateral moraine set were amenable for dating (Table 1). Quartz preparation and Be separation with ion-exchange chromatography was based on the procedure described by von Blanckenburg (2005). 10Be measurements were performed at the AMS facility at the University of Cologne, Germany.

3.2. Age calculation The boulder-exposure ages were calculated with the CRONUSEarth online calculator (v. 2.2; Balco et al., 2008), using blankcorrected data and the beryllium-isotope standard for 10Be measurements defined by Nishiizumi et al. (2007) (07 KNSTD). We employed both the Dunai (2001) and Lal (1991) (as modified by Stone, 2000) scaling factors for the exposure-age calculations (Table 1). The ages calculated from the Dunai (2001) scaling are

slightly older than the ones calculated after Stone (2000) and Lal (1991). This is demonstrated by the Stone/Dunai factor in Table 1. For better comparison with the published data from the region, we used the exposure ages calculated after Stone (2000). We evaluated the impact of erosion on the ages by comparing no-erosion minimum exposure ages with ages calculated using a maximum erosion rate of 0.3 cm/kyr for the maximum exposure age (Abramowski et al., 2005). The effect of erosion on exposure ages was explored to provide an estimate of the uncertainty in the exposure ages, in the event moraine and boulder erosion occurred. The incorporation of erosion resulted in 4% higher ages. However, erosion is affecting different rock types differently, and individual € hringer et al., 2012). An boulders of the same lithology randomly (Ro additional factor that could influence the exposure ages is snow cover. For our study area (and many previous studies in the region), no reliable snow-cover data exist. This is due to the remote and mountainous nature of the study area and the lack of meteorological stations that measure snow depth. Given these limitations, we refrain from an estimation for snow cover, but point out, that boulders would be affected differently, depending on their height

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Table 1 Cosmogenic 10Be surface exposure ages for recessional moraine and lateral moraines were calculated with CHRONUS-Earth Online Calculator (v.2.2.; Balco et al., 2008) using a rock density of 2.4 g/cm3, 07KNSTD 10Be standard. No correction for snow shielding was applied. Altitudes were determined with a handheld GPS with an accuracy of ±5 m. Sample ID

Longitude

Latitude

Altitude in m

Boulder height in cm

Chip thickness in cm

Shielding factor at sample site

Boulder lithology

10Be conc in atoms/g

Lal (1991) and Stone (2000)

Lal (1991) and Stone (2000)

Minimum exposure agea in ka

Stone/ Dunaib

Maximum exposure agec in ka

No erosion/ erosiond

Recessional moraine ER001 39.28185 ER002 39.28128 ER003 39.28107 ER004 39.28098 ER006 39.28064 ER008 39.28021 ER009 39.28021 ER010 39.28001 ER011 39.28207 ER013 39.28186 ER014 39.28164 ER015 39.28188 ER016 39.28175 ER017 39.28307 ER019 39.28304 ER020 39.28411

71.42316 71.42244 71.42232 71.42111 71.41969 71.41484 71.41484 71.41446 71.42548 71.42601 71.42662 71.42793 71.42785 71.42754 71.42702 71.42448

2068 2072 2078 2068 2075 2059 2059 2062 2078 2078 2082 2090 2090 2069 2077 2060

180 70 110 140 50 220 140 500 90 100 150 400 100 100 100 100

3.0 2.5 2.0 2.5 2.0 2.5 2.5 2.5 2.0 2.0 2.5 2.0 2.5 2.5 2.5 1.5

0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99

Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Quartz vein Granodiorite Granodiorite Red Sandstone Conglomerat

5.20Eþ05 3.40Eþ05 2.31Eþ05 7.40Eþ05 2.28Eþ05 3.37Eþ05 3.38Eþ05 3.47Eþ05 2.79Eþ05 3.42Eþ05 3.98Eþ05 3.50Eþ05 2.27Eþ05 5.87Eþ05 4.25Eþ05 2.45Eþ05

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

1.77Eþ04 1.15Eþ04 9.21Eþ03 2.45Eþ04 7.63Eþ03 1.17Eþ04 1.16Eþ04 1.23Eþ04 9.46Eþ03 1.15Eþ04 1.30Eþ04 1.28Eþ04 7.55Eþ03 1.92Eþ04 1.53Eþ04 9.40Eþ03

25.6 17.0 11.6 35.6 11.5 17.0 17.1 17.4 14.0 17.0 19.7 17.3 11.3 28.7 21.0 12.4

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

2.3 1.6 1.1 3.3 1.1 1.6 1.6 1.6 1.3 1.6 1.8 1.6 1.0 2.6 1.9 1.2

0.97 0.95 0.91 0.98 0.91 0.94 0.94 0.95 0.93 0.95 0.95 0.95 0.91 0.97 0.96 0.92

27.3 17.7 12.0 38.6 11.9 17.7 17.8 18.2 14.5 17.6 20.7 18.0 11.6 30.8 22.1 12.7

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

2.7 1.7 1.2 3.9 1.1 1.7 1.7 1.8 1.4 1.7 2.0 1.7 1.1 3.0 2.2 1.2

0.94 0.96 0.97 0.91 0.97 0.96 0.96 0.96 0.97 0.96 0.95 0.96 0.97 0.93 0.95 0.98

Lateral moraines LMA002 39.23157 LMA003 39.23128 LMA004 39.23128 LMA005 39.23098 LMA008 39.23011 LMA009 39.22859 LMA010 39.22761

71.45891 71.45942 71.45942 71.46027 71.46222 71.46568 71.46804

2258 2258 2258 2264 2276 2315 2337

110 110 80 80 70 150 60

2.5 2.5 2.0 2.5 2.0 2.0 2.0

0.99 0.99 0.99 0.99 0.99 0.99 0.99

Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite Granodiorite

2.84Eþ05 3.24Eþ05 2.01Eþ05 3.38Eþ05 2.28Eþ05 2.40Eþ05 2.08Eþ05

± ± ± ± ± ± ±

1.09Eþ04 1.58Eþ04 7.03Eþ03 1.14Eþ04 7.63Eþ03 1.05Eþ04 8.28Eþ03

12.7 14.4 9.0 15.0 10.1 10.3 8.8

± ± ± ± ± ± ±

1.2 1.4 0.8 1.4 0.9 1.0 0.8

0.93 0.94 0.89 0.94 0.91 0.91 0.89

13.1 15.0 9.2 15.5 10.3 10.6 9.0

± ± ± ± ± ± ±

1.3 1.5 0.9 1.5 1.0 1.0 0.9

0.97 0.96 0.98 0.96 0.97 0.97 0.98

LMB001 LMB002

39.23146 39.22726

71.46003 71.47054

2262 2313

90 60

2.5 1.5

0.99 0.99

3.27Eþ05 ± 1.26Eþ04 1.84Eþ05 ± 1.54Eþ04

14.5 ± 1.4 7.9 ± 0.9

0.94 0.88

15.1 ± 1.5 8.0 ± 1.0

0.96 0.98

LMB004 LMB005 LMB006 LMB007 LMB008

39.22653 39.23091 39.22257 39.22294 39.22361

71.47211 71.46139 71.47916 71.47839 71.47729

2315 2266 2316 2316 2318

90 50 110 80 120

2.5 2.5 2.5 2.0 1.5

0.99 0.99 0.99 0.99 0.99

Granodiorite Red Sandstone Conglomerat Granodiorite Granodiorite Granodiorite Granodiorite Red Sandstone Conglomerat

LMC001 LMC002 LMC003 LMC004 LMC005 LMC006 LMC008

39.22558 39.22879 39.23003 39.23003 39.2309 39.23096 39.23105

71.4755 71.4679 71.46506 71.46506 71.46325 71.46301 71.46276

2303 2311 2293 2293 2285 2279 2278

100 80 120 120 70 110 90

2.5 2.5 2.5 2.5 2.5 3.0 2.0

0.99 0.99 0.99 0.99 0.99 0.99 0.99

LMC009 LMC010

39.23118 39.23162

71.46252 71.46124

2278 2271

50 110

2.5 1.5

0.99 0.99

LMD001 LMD002 LMD003

39.23211 39.23184 39.23448

71.46103 71.46165 71.45472

2251 2255 2241

130 90 50

2.0 3.0 2.5

LME003 LME005

39.22499 39.22637

71.47882 71.47633

2281 2288

130 110

LME007 LME010 LME012

39.2282 39.23125 39.23259

71.47279 71.46468 71.46098

2305 2285 2255

110 80 80

a b c d

2.79Eþ05 3.07Eþ05 3.80Eþ05 2.94Eþ05 2.61Eþ05

± ± ± ± ±

1.03Eþ04 1.07Eþ04 1.54Eþ04 1.34Eþ04 1.08Eþ04

12.0 13.6 16.2 12.6 11.1

± ± ± ± ±

1.1 1.3 1.5 1.2 1.1

0.92 0.93 0.95 0.93 0.92

12.4 14.1 16.9 13.0 11.4

± ± ± ± ±

1.2 1.3 1.7 1.3 1.1

0.97 0.97 0.96 0.97 0.98

Granodiorite Quartz vein Quartz vein Granodiorite Granodiorite Granodiorite Red Sandstone Conglomerat Granodiorite Red Sandstone Conglomerat

3.38Eþ05 3.54Eþ05 3.46Eþ05 3.97Eþ05 2.61Eþ05 3.87Eþ05 1.67Eþ05

± ± ± ± ± ± ±

1.95Eþ04 1.90Eþ04 1.33Eþ04 1.48Eþ04 1.01Eþ04 1.57Eþ04 6.89Eþ03

14.6 15.2 15.0 17.1 11.5 16.9 7.3

± ± ± ± ± ± ±

1.5 1.5 1.4 1.6 1.1 1.6 0.7

0.94 0.95 0.94 0.95 0.92 0.95 0.87

15.2 15.7 15.6 17.9 11.8 17.6 7.4

± ± ± ± ± ± ±

1.6 1.6 1.5 1.7 1.1 1.7 0.7

0.96 0.96 0.96 0.96 0.97 0.96 0.99

2.77Eþ05 ± 1.06Eþ04 2.92Eþ05 ± 1.19Eþ04

12.2 ± 1.1 12.9 ± 1.2

0.92 0.93

12.6 ± 1.2 13.2 ± 1.3

0.97 0.97

0.99 0.99 0.99

Granodiorite Granodiorite Granodiorite

3.24Eþ05 ± 1.10Eþ04 4.12Eþ05 ± 1.57Eþ04 3.44Eþ05 ± 1.40Eþ04

14.5 ± 1.3 18.2 ± 1.7 15.4 ± 1.5

0.94 0.96 0.94

15.0 ± 1.4 19.0 ± 1.9 16.0 ± 1.6

0.96 0.96 0.96

1.5 1.5

0.99 0.99

1.80Eþ05 ± 7.67Eþ03 3.49Eþ05 ± 1.34Eþ04

7.9 ± 0.7 15.1 ± 1.4

0.88 0.94

8.0 ± 0.8 15.6 ± 1.5

0.98 0.97

1.5 3.0 3.0

0.99 0.99 0.99

Quartz vein Red Sandstone Conglomerat Quartz vein Granodiorite Granodiorite

1.97Eþ05 ± 9.82Eþ03 2.84Eþ05 ± 9.22Eþ03 3.17Eþ05 ± 1.18Eþ04

8.5 ± 0.8 12.5 ± 1.1 14.2 ± 1.3

0.89 0.93 0.94

8.6 ± 0.9 12.9 ± 1.2 14.7 ± 1.4

0.98 0.97 0.97

Exposure age calculated with no erosion. Difference in exposure ages calculated with Stone (2000) and Dunai (2001) calibration sets. Maximum exposure age data was calculated with an estimated erosion of 0.3 cm/kyr. Difference in exposure ages calculated without erosion and with 0.3 cm/kyr erosion.

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and geometry. Generally, the exposure ages would be underestimated by at least ~10%, depending on snow thickness and the annual duration of snow cover (Gosse and Phillips, 2001). Therefore, all exposure ages are uncorrected for snow shielding and considered minimum ages. Other studies in similar regions of Central Asia also report ages without a snow-shielding correction for similar reasons as stated here (e.g. Kong et al., 2009; Lifton et al., 2014; Owen et al., 2012; Xu et al., 2013). 3.3. Data reduction To determine the exposure ages of moraines, different approaches have been used (Zech et al., 2005b; Owen, 2009; Dortch et al., 2013; Kelly et al., 2013). Herein, the exposure ages were calculated using Kernel probability density plots of the complete exposure-age sample population. We used the internal error to identify outliers in the age populations, employing the MatLab based program camelplot (G. Balco, http://depts.washington.edu/ cosmolab/pubs/gb_pubs/camelplot.m). For sample populations where a cluster of ages can be identified (as in our recessional moraine sample population discussed below), the outliers can be discarded based on the kernel plots and the mean of the remaining population can be interpreted as the moraine age (e.g. Dortch et al., 2013). If the age population shows a tendency towards too young ages, e.g. due to post-depositional shielding or erosion, as we infer for our lateral moraine data (see below), the oldest age of a population can be considered as the one closest to the moraineformation age (Putkonen and Swanson, 2003; Zech et al., 2005b; Applegate et al., 2010). Putkonen and Swanson (2003) showed that it is impossible to completely exclude boulders affected by post-depositional processes from an age calculation, but the calculation of the normalized age from a population of ages can assist in understanding the depositional age. The normalized boulder age range is calculated using the difference between the maximum and minimum ages from a single moraine population, divided by the maximum age; values may range from 0 to 1. Putkonen and Swanson (2003) suggested that a population of non-affected boulders yields a normalized age range of 0.12 (12%), which represents the random systematic errors of the calculated exposure ages. The conditions of having boulders unaffected by inheritance or post-depositional shielding are rarely met. Consequently, most of the sampled populations yield values above 0.12 (Putkonen and Swanson, 2003). In this study, we used the normalized age approach together with the kernel probability density plots to examine whether the mean or the age of the oldest population best represents the moraine data.

4. Results 4.1. Recessional moraine exposure ages The recessional moraine is the only one with a well-preserved arcuate ridge morphology (Figs. 4A and 5A); it extends over 2 km. We avoided sampling the few incised parts of the moraine and collected all samples from its crest. There are no better-preserved moraines further down valley from this location, so it is the best indicator of the maximum extent of the ice in the valley at the time of glaciation. The 16 10Be exposure ages range from 11.3 ± 1.0 to 35.6 ± 3.3 ka (Table 1, Fig. 6) with a normalized age range of 0.68. The kernel probability density analysis (Fig. 7A) of all exposure ages shows two peaks (11.7 ± 0.5 and 17.5 ± 1.5 ka) and identifies eight outliers in the boulder ages, which are defined as outside of the 2-s range of the mean (open symbols in Fig. 6). The outliers (11.3 ± 1.0, 11.5 ± 1.1, 11.6 ± 1.1, 12.4 ± 1.2, 14 ± 1.3, 25.6 ± 2.3, 28.7 ± 2.6, 35.6 ± 3.2 ka) comprise both too young and too old ages relative to the mean age

A B C D

E

45

Recessional Moraine 40

35

Exposure Age in ka

52

30

Exposure Ages too young moraine age too old formation age (1 σ)

Lateral Moraine Set

A

B

C

D

E

25

20

15

10

5

Fig. 6. 10Be exposure-age distributions in individual moraines. Boulder exposure ages are shown with one-sigma error bars. Outliers according to Kernel probability-density plots are indicated with empty symbols.

Fig. 5. Spatial distribution of the sampled boulder ages along the moraine crests for A) the recessional moraine and B) the lateral moraines. Age labels from the lateral moraine boulders are displayed from SSE to NNW along the individual moraine crests.

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14.5 ± 1.3 to 18.2 ± 1.7 ka (0.15), and lateral moraine E (5 samples) yielded ages from 7.9 ± 0.7 to 15.1 ± 1.4 ka (0.48; Fig. 8). We refrain from interpreting the overall mean age (12.8 ± 3.2 ka) as the formation age, because the randomly distributed younger ages (peaks <10 ka; Fig 8) on the crests are likely the result of moraine degradation (see Section 3.1 for possible mechanisms of degradation). As shown in Fig. 6, we interpret only the oldest ages of the individual moraine-age populations as the formation ages, and the younger peak in the ages as a possible post-depositional degradation event. Moraine A, located closest to the valley flank and highest up, yielded the youngest exposure age (15.0 ± 1.4 ka) within the moraine set. Moraines B (16.2 ± 1.5 ka), C (17.1 ± 1.6 ka), and D (18.2 ± 1.7 ka) progressively increase in age. The lowest, thus last deposited moraine E (15.1 ± 1.4 ka) yielded the same exposure age as the highest, thus earliest deposited moraine A. The analysis of the lateral moraine ages using the kernel probabilityedensity plots (Figs. 7B and 8) indicates several outliers that are too young but no outliers that are too old (Fig. 6, open symbols). 4.3. Comparison of exposure ages and boulder heights and lithology

Fig. 7. A) Kernel probability-density plots for the recessional moraine exposure-age distribution and B) for all lateral moraine exposure-age populations. Grey lines are Gaussian curves for individual ages and red lines are the kernel probability plots for the recessional and all lateral moraine age populations. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

of 17.5 ± 1.5 ka (Fig. 6). The too young outliers define a peak at 11.7 ± 0.5 ka, which will be discussed further in Section 5.1. Fig. 9 shows that this mean, calculated without the identified outliers, is in good agreement with the three largest boulders (samples ER 10: 17.4 ± 1.6 ka, ER 15: 17.3 ± 1.6 ka, and ER 8: 17.0 ± 1.6 ka); it yielded a normalized age range of 0.14. We consider the mean of 17.5 ± 1.5 ka the best minimum age estimate for the deposition of the recessional moraine.

Among others, factors that affect individual boulder exposure ages are: 1) Inheritance due to pre-depositional irradiation; 2) post-depositional shielding due to, for example, sediment cover after glacial retreat; 3) post-depositional reorientation of unstable boulders; 4) moraine degradation that results in a combination of items 2) and 3); 5) post-depositional shielding by snow, depending on the height and the geometry of the boulder; 6) boulder weathering and erosion that depends on climate and rock type. The granitoid rock type comprises ~75% of the boulder samples. Fig. 9 indicates that boulders smaller than ~1 m in height tend to underestimate the depositional age, regardless of their lithology. Boulders higher than ~1 m are prone to both underestimation and overestimation of the depositional age, although we observed overestimation only in our granitoid samples. In contrast to the sandstone, which may stem from clastic rocks throughout the Fedchenko catchment, two major granodiorite intrusions crop out in the upstream area (Vlasov et al., 1991). One large granodiorite occurs at the head of the Fedchenko Glacier, the other one in the catchments of two former tributary glaciers, ~30 km upstream of the investigated area. It is possible that the recessional moraine boulders stem from the glacier-head granitoid, thus experienced a different transport history than the one from the lateral moraines. A high altitude source, which results in significantly higher 10Be production rate, and a long travelling distance make the boulders prone to inheritance. To solve this issue, geochemical analyses of the boulder material and the possible sources would be needed, which is beyond the scope of this paper. Only boulders over 2 m height reliably reproduce the preferred depositional age (Fig. 9). The ages of the quartz veins and red sandstone are consistently younger than the exposure age of the lateral moraines (Fig. 9, Table 1). The lateral moraine boulders tend to underestimate the moraine formation age regardless of the boulder height and lithology. We interpret this observation to be primarily caused by lateral moraine degradation.

4.2. Lateral moraine exposure age 5. Discussion The five lateral moraines have distinct but well-rounded crests (Fig. 4B). We sampled all five moraines individually to assess the time interval over which this glaciation occurred. The overall age range is 7.3 ± 0.9 to 18.2 ± 1.7 ka (Table 1, Fig. 5). Lateral moraine A (7 samples) yielded ages from 8.8 ± 1.4 to 15.0 ± 1.4 ka, (normalized age range of 0.41). Ages from moraine B (7 samples) range from 7.9 ± 0.9 to 16.2 ± 1.5 ka (0.51), those of moraine C (9 samples) from 7.3 ± 0.9 to 17.1 ± 1.6 ka (0.57). Moraine D (3 samples) ages cover

In the following, we first discuss our new results and then compare them with the exposure-age results from other studies. 5.1. Synthesis of results Fig. 7A shows the kernel probabilityedensity plots of the ages of the recessional moraine, highlighting the two exposure-age peaks

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at 17.5 ± 1.5 and 11.7 ± 0.5 ka. We interpret the older peak as the time of glacial retreat from the LGM. We refrain from interpreting the younger peak as a second glacial stage, because it is only observed in a spatially random subset of the moraine samples and could be explained by post-depositional modification of the moraine. However, we cannot estimate the onset, duration, and the prevailing process of the moraine degradation (Section 3.1). Four of the lateral moraines, except moraine D, display d in addition to the LGM signal d also a younger degradation signal (A: 8.9 ± 0.8 ka, B: 7.9 ± 0.9 ka, C: 7.3 ± 0.7 ka, E: 8.2 ± 0.8 ka), which is overlapping within 2s-range with the observed recessional moraine degradation signal. It is unlikely that a younger glaciation would randomly affect boulders on all moraine crests without overprinting the LGM signal systematically or completely. We found no anomalously older peaks for the lateral moraines, suggesting that predepositional irradiation is not a problem for these samples. We interpret the oldest lateral moraine ages to be closest to the moraine-formation age. Two reasons may prohibit the observation of older moraine ages with closer proximity to the valley flank. Either their formation ages are indistinguishable with the chosen method and/or post-depositional shielding and moraine degradation eradicated the age trend. The latter often results in an underestimation of the moraine formation age. The variation in the oldest ages of the lateral moraines may indicate that some of these moraines are older than the recessional moraine. We suggest that all studied moraines formed at slightly different times (within the uncertainties of the method) and that subsequent moraine degradation obviated information on the individual lateral moraine ages. The lateral moraines likely formed between 15.0 ± 1.4 and 18.2 ± 1.7 ka, and the recessional moraine at 17.5 ± 1.5 ka. These dates suggest that the two moraine types are representing the same signal of glaciation, which correlates with the LGM in the Pamir and Tian Shan (Abramowski et al., 2006; Seong et al., 2009b; Zech, 2012; Dortch et al., 2013; Zech et al., 2013). 5.2. Comparison with previous studies In this study, the mean age of the recessional moraine (17.5 ± 1.5 ka) and the oldest age from the lateral moraines (18.2 ± 1.7 ka) yielded a maximum age of the most recent glaciation at 18.2 ± 1.7 ka (Table 1). This local glacial stage corresponds to the regional Semi-arid Western Himalayan-Tibet Stage (SWTHS) 2E of Dortch et al. (2013) (Fig. 10). This stage correlates with MIS 2 and the LGM. Thus, the predominant precipitation control for the glaciers in the northern Pamir was likely the Westerlies (Aizen, 2011; Dortch et al., 2013; Lambrecht et al., 2014). Comparison of our data with published ones from the Tian Shan and the Pamir suggests that the glaciation during MIS 2 prevailed longer in the Pamir region (SWTHS 2F e 2A) than in the Tian Shan, and the glaciation in the western Tian Shan started later than in the Pamir (SWTHS 2E e 2A; Fig. 10). In comparison with other studies, the Fedchenko Glacier system does not have a record of glacial stages other than MIS 2. The reasons could be: (1) previous glacial advances in this region did not extend to elevations as low as 2300 m a.s.l. or the youngest glaciation (17.5 ± 1.5 ka) removed evidence of them; (2) fluvial erosion by the Muksu River and mass movements removed evidence of previous glaciations; (3) previous glaciations never occurred in this

Fig. 8. Kernel probability-density plots for each lateral moraine (¼ LM) exposure-age population. Grey lines represent Gaussian curves of individual ages with uncertainties. Red lines represent the kernel probability-density plot for each lateral moraine age population. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

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Pamir. These moraines stem from a large glacier within the presentday Fedchenko Glacier catchment. Field observations and our new ages imply the following:

Fig. 9. Boulder-age dependence on boulder height and lithology. No correlation between boulder age and boulder height could be observed for the granitoid boulders. The quartz vein and the red sandstone samples tend to underestimate the likely exposure age. (For interpretation of the references to colour in this figure caption, the reader is referred to the web version of this article.)

Location Pamir Alay Tian 35 Range Shan 30

Glacial Stage MIS

SWHTS

3

2F

Time in ka

25 4 20

2E

2

Acknowledgements We thank the State Administration of Hydrometeorology Institute of Tajikistan, in particular Jamila Baidulloeva, and Boymahmat Baidulloev for fieldwork organization. The AMS Facility at the University of Cologne is thanked for prompt handling of the 10Be samples. This study was funded by the CAME project bundle TIPTIMON of the German Federal Ministry of Education and Research (support code 03G0809).

2D 15

(1) The last glacial retreat in the lower Muksu catchment occurred at 17.5 ± 1.5 ka (exposure age of the recessional moraine) and therefore can be correlated with the regional glacial stage SWHTS 2E (Dortch et al., 2013), which links to MIS 2 and the LGM. We derived the recessional moraine age from the mean exposure ages after discarding outlier ages that are too young due to moraine degradation and too old due to inheritance based on kernel probabilityedensity plots. (2) The lateral moraine ages were influenced by postdepositional moraine degradation, which resulted in a secondary age cluster at <10 ka. Consequently, we interpret the oldest ages as the minimum exposure ages. The differences in the formation ages of the individual lateral moraines are smaller than the estimated exposure-age uncertainty. Therefore, combined with the observed moraine degradation, it is difficult to constrain the formation ages of the individual lateral moraines with 10Be exposure age dating. (3) The lateral moraines and the recessional moraine likely formed during the same event and experienced degradation over approximately the same time. The tectonically and fluvially active setting of the Muksu River catchment likely destroyed remnants of older and/or younger glacial stages at the investigated sites. The combined results from the recessional and lateral moraines suggest a LGM age of the glaciation in the Muksu catchment. The LGM glaciation is also recorded on the Pamir Plateau and in the Tian Shan.

2C 2B 2A

10 References

5

0

1A-1E

A B

G

1

C I K M L H J

Fig. 10. Published cosmogenic 10Be moraine exposure-age data from the Ghissar-Alai Range, Tian Shan, and Pamir after Dortch et al. (2013) for the last 40 ka. Location numbers at the bottom of the Figure refer to Fig. 1, where the spatial distribution of the regional glacial stages (SWHTS) is shown.

catchment. Reason (3) is unlikely, given the Pamir and Tian Shan glaciation record.

6. Conclusion We present 10Be boulder exposure ages from recessional and lateral moraines preserved in the Muksu catchment of the northern

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