The last glaciation in the headwater area of the Xiaokelanhe River, Chinese Altai: Evidence from 10Be exposure-ages

Quaternary Geochronology 56 (2020) 101054

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Research paper

The last glaciation in the headwater area of the Xiaokelanhe River, Chinese Altai: Evidence from 10Be exposure-ages Guocheng Dong a, b, c, d, *, Weijian Zhou a, c, d, Yunchong Fu a, c, d, Li Zhang a, c, d, Guoqing Zhao a, c, d, Ming Li a, c, d a

State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China State Key Laboratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Science, Lanzhou, 730000, China CAS Center for Excellence in Quaternary Science and Global Change, Xi’an, 710061, China d Shaanxi Key Laboratory of Accelerator Mass Spectrometry Technology and Application, Xi’an AMS Center of IEECAS & Xi’an Jiaotong University, Xi’an, 710061, China b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Last glaciation 10 Be surface exposure dating Xiaokelanhe river basin Altai mountains

The timing and extent of the last glaciation in the Altai Mountains are key to understanding climate change in this critical region. However, robust glacial chronologies are sparse across the Altai Mountains, especially in the Chinese Altai, impeding the correlation of glacial events and examination of the possible climate forcing mechanisms. Here, we report twenty new 10Be exposure-ages obtained from two moraines in the headwater area of the Xiaokelanhe River, Chinese Altai. The inner latero-frontal moraine yields exposure-ages ranging from 16.60 � 1.00 to 20.41 � 1.15 ka (n ¼ 5), reflecting a limited advance during the global Last Glacial Maximum (LGM). The morpho-stratigraphically older moraine remnants have exposure-ages of 14.36 � 0.94–38.98 � 2.23 ka (n ¼ 15). The tentatively determined moraine age of 34.10 � 4.99 ka suggests that the local LGM in the Xiaokelanhe River likely occurred during Marine Isotope Stage (MIS) 3 or earlier. From a compilation of the 20 new, and 79 previously published exposure-ages, we observe at least three distinct glacial events during the last glacial, with the local LGM occurring prior to MIS 2. A comparison between the timing of glacial activities and climate proxies suggests a potential combination of summer solar insolation, North Atlantic climate oscillations, and atmospheric CO2 levels, as triggers for glacial movements during the last glacial cycle. Precipitation delivered by the mid-latitude westerlies may have also contributed to glacial advances during MIS 3. These correlations remain tentative however, due to limited chronological control.

1. Introduction Mountain glaciers respond sensitively to climate change on regional scales (Oerlemans, 2005). As such, quantifying the timing and extent of former glaciers is a powerful means to understand past climate patterns in glaciated mountainous areas (e.g. Owen and Dortch, 2014 and ref­ erences therein), such as the Altai Mountains. The Altai Mountains are situated at the northern border of central Asia, where the climate is mainly controlled by two atmospheric circulation systems: the mid-latitude westerlies and the Siberian High pressure system (Benn and Owen, 1998). The investigation of past glacial activities in the Altai thus offers an opportunity to assess the interaction between two major climate systems.

In the past two decades, the timing and nature of Quaternary glaci­ ations in the Altai Mountains have attracted considerable attention (e.g. Lehmkuhl et al., 2004, 2007; Xu et al., 2009; Lehmkuhl et al., 2011; Jiang et al., 2012; Zhao et al., 2013; Zhang et al., 2015; Gribenski et al., €tsch, 2017; Yang et al., 2017; Blomdin et al., 2018; Gribenski 2016; Po et al., 2018; Jia et al., 2018). Previous studies have determined that the Altai region experienced a local last glacial maxima (LGM) that preceded the global LGM (ca. 26.5–19.0 ka; Clark et al., 2009) and Marine Isotope Stage (MIS) 2 (ca. 29–14 ka; Lisiecki and Raymo, 2005); as is the case for many other mid-latitude glaciers around the world (Hughes et al., 2013 and references therein). However, it is still an open question whether glacial advances pre-dating the global LGM were synchronous within the Altai (e.g. Lehmkuhl et al., 2011; Blomdin et al., 2018; Gribenski

* Corresponding author. State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences, Xi’an, 710061, China. E-mail address: [email protected] (G. Dong). https://doi.org/10.1016/j.quageo.2020.101054 Available online 5 February 2020 1871-1014/© 2020 Elsevier B.V. All rights reserved.

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et al., 2018). These issues reflect the paucity of well-resolved glacial sequences there. For example, in the Chinese Altai, numeric dating has been primarily limited to the Kanas Valley and its tributary valley (Xu et al., 2009; Jiang et al., 2012; Zhao et al., 2013; Zhang et al., 2015; Yang

et al., 2017; Gribenski et al., 2018). In particular, a lack of knowledge of the extent and timing of the last glaciation hinders our full under­ standing of the glacial histories in the Chinese Altai, and the forcing mechanisms behind glacial fluctuations remains a basic question,

Fig. 1. Map showing the study area. (A) Physiography of High Asia. The black star illustrates the location of the Guliya ice core (Thompson et al., 1997). (B) A shaded map indicating the location of the Xiaokelanhe River basin in the Altai Mountains. Also shown are the locations of previously published 10Be surface exposure ages (n ¼ 79) across the Altai. 2

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unanswered. In this study, we investigate moraine and moraine remnants in the Xiaokelanhe River headwater region, Chinese Altai, using cosmogenic 10 Be surface exposure dating. We also compile chronological data from published 10Be surface exposure dating studies throughout the range, to comprehend the regional glacial behaviors. We then compare the timing of glacial activities with multiple climate archives, with the aim of shedding new light on the forcing mechanisms behind Altai glacial fluctuations.

The mean annual temperature was 4.52 � C (Shen et al., 2007). The Xiaokelanhe River basin has an area of ~340 km2, and is currently free of glaciers. Nonetheless, glacial erosional and depositional features are identifiable there (Figs. 2 and 3). A total of eighteen welldeveloped glacial cirques can be found above ~2600 m asl; and two sets of morphologically distinct moraine remnants can be distinguished in one of the river headwaters, where six of the eighteen cirques comprise a sub-catchment (Figs. 2 and 3). A lateral moraine remnant (moraine M1) extends ~800 m from the sub-catchment mouth down to ~2390 m asl (Fig. 2). The moraine remnant can be traced downstream to terminal moraine remnants at ~2370 m asl (Fig. 2). The lateral moraine remnant is characterized by a sharp-crested surface, whereas those of the terminal moraine remnants are relatively flat (Fig. 3A–C). These moraine remnants stand ~5–15 m above the valley floor. A set of inner latero-frontal moraines (moraine M2) can be recognized ~500 m up­ stream of the terminal M1 moraine remnants (Fig. 2B). This moraine is situated at ~2380 m asl, and rises ~2–4 m above the present river floor (Figs. 2 and 3). Many sub-rounded to sub-angular granite and greywacke boulders, some exceeding 2 m in diameter, are embedded in, or rest on, the crests of the moraine remnants, which are mantled by a thin veneer of turf (Fig. 3). Some of these boulders are characterized by quartz vein or rock varnish (Fig. 3D and E).

2. Study area The Altai Mountains are a mountain range in central Asia that stretches ~2000 km along a NW-SE trend across Russia, Kazakhstan, China, and Mongolia (Fig. 1). The Chinese Altai follows the southwest slopes of the middle Altai Mountains, with the highest summit reaching 4374 m above sea level (asl) at Mount Nairamdal (Peak Youyi). The Xiaokelanhe River originates at an altitude of ~3198 m asl, on the southern slope of the Chinese Altai (Figs. 1B and 2) and flows ~35 km to the southwest, where it joins the Kelanhe River. From there it passes through Altai City and finally into the Irtysh River, the largest tributary of the Ob River. During the period 1971–2000, the mean annual precipitation was 191.3 mm at the Altai Weather Station, about 40 km south of the headwater area, at ~900 m asl (Shen et al., 2007).

Fig. 2. (A) Glacial geomorphology of the Xiaokelanhe River in the Chinese Altai. Location of the map is shown in Fig. 1B. Grey line illustrates the Xiaokelanhe River basin. (B) Mapped moraine remnants and 10Be samples with associated exposure-ages (ka), including the potential outlier(s) (in red). Location of the figure outline is delineated by the black dot-dashed line in Fig. 2A. 3

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Fig. 3. Selected field photographs showing glacial landforms and sampled glacial boulders in the head­ water area of the Xiaokelanhe River. (A) Looking westwards (downstream) from the lateral moraine remnant of moraine M1. (B) View towards north on the terminal moraine remnant of moraine M1. (C) Looking upstream (eastwards) on the lateral moraine remnant of moraine M1. Glacial cirques can be identified in the background. (D) View of granitic boulder with quartz vein. (E) A representative photograph showing sampled boulder that are char­ acterized by rock varnish on surface. (F) View of boulder sample numbered AET5.

3. Methods

protocol of Dortch et al. (2009), adapted from the procedures developed by Kohl and Nishiizumi (1992), and described in detail by Dong et al. (2014). The subsequent chemical preparation followed Zhang et al. (2016b). Sample 10Be/9Be ratios were calibrated against ICN-01-5-4 material, using a certified ratio of 2.851 � 10 12 (Nishiizumi et al., 2007), which corresponds to the 07KNSTD standardization. The measured isotopic ratios were corrected using background 10Be/9Be given by two procedural blanks (6.01 � 10 15 and 5.67 � 10 15), and converted to 10Be concentrations in quartz for exposure-age calculation. Quartz weights, 9Be carrier mass, and the procedural blanks are pre­ sented in Table 1. Zero-erosion exposure-ages were calculated using the online CREp program (Martin et al., 2017), based on a physically-based LSD model (Lifton et al., 2014) performing similar to previous empirical models (Borchers et al., 2016). Chosen parameters include the ERA40 atmo­ spheric model (Uppala et al., 2005) and the Lifton-VDM2016 geomag­ netic database (Lifton, 2016). We used a global production rate of 4.06 � 0.23 atoms g 1 yr 1 (Martin et al., 2017), which matches well with other recent compilations (Borchers et al., 2016; Heyman et al., 2016; Marrero et al., 2016). Rock density was set at 2.7 g cm 3. In the calcu­ lation, corrections for vegetation and snow cover were omitted as we have no estimates on vegetation and snow cover effects on cosmogenic production in our study area. As a result of these assumptions, the ages should be considered as minimum estimates.

3.1. Sampling Twenty glacial boulders were sampled during field work in 2016 and 2019. Our field sampling targeted quartz-bearing boulders embedded firmly in moraine crests. We generally avoided sampling boulders with heights no more than 50 cm above ground level with an exception of sample AET5 (Table 1; Fig. 3D–F). Also, we did not sample boulders that showed distinct signs of surface pitting, exfoliation, or fracturing. Rock samples were removed from the top center of boulders with near hori­ zontal or planar upper surfaces using hammer and chisel. The sampling locations (latitude, longitude, and altitude) were measured using a handheld global positioning system (GPS) instrument. At each sampling site we recorded boulder dimensions and lithology (Table 1) and pho­ tographed the sampled boulders (Fig. 3 and supplementary materials). Topographic cosmic-ray shielding was calculated using a Python tool developed by Li (2018) and the 30 m ASTER DEM within the ArcGIS environment. 3.2. Cosmogenic

10

Be surface exposure dating

Sample processing and isotopic measurements were both carried out at the Xi’an Accelerator Mass Spectrometry Center (Xi’an-AMS Center), Institute of Earth Environment, Chinese Academy of Sciences. Sample preparation and quartz separation were performed according to the 4

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Table 1 Cosmogenic10Be surface-exposure dating sample details and10Be exposure-ages from the headwater area of the Xiaokelanhe River, Chinese Altai. Moraine

Sample ID

Lithology

Latitude (� N)

Longitude (� E)

Elevation (m asl)

Boulder size, L/W/H (cm)

Sample thickness (cm)

Topographic shielding factor

Quartz weight (g)

Carrier added (g)a

10

Be/9Be (10 13)b

10

Be concentration (104 atoms g 1)c

Age (ka)d

Internal error (ka)

M2

AET1

Granite

48.084272

88.38392

2377

182/114/87

2.6

0.993040

30.0057

0.3265

53.15 � 1.39

Granite

48.084358

88.384076

2377

1.3

0.993167

30.0107

0.3263

AET3

Granite

48.084301

88.384285

2378

156/133/ 109 163/89/67

2.9

0.992608

30.0083

0.3266

AET4

greywacke

48.084129

88.383491

2376

72/64/46

3.7

0.993127

30.0288

0.3286

AET5

Granite

48.084291

88.383662

2376

158/121/52

1.7

0.993225

30.0228

0.3307

AET6

Granite

48.087718

88.387160

2391

72/68/55

0.9

0.998169

30.0068

0.3298

AET7

Granite

48.089491

88.392232

2399

193/134/89

5.5

0.994548

30.0057

0.3246

AET8

Granite

48.089460

88.391049

2398

83/76/53

2.4

0.996592

30.0027

0.3264

AET9

greywacke

48.088517

88.389067

2396

5.1

0.998214

30.0053

0.3268

AET10

Granite

48.089150

88.390022

2398

4.5

0.997719

23.1934

0.3269

19AET01

greywacke

48.088162

88.388630

2394

284/197/ 119 247/166/ 125 104/83/55

4.2

0.997825

28.7661

0.3282

19AET02

Granite

48.088130

88.388550

2393

248/155/83

3.9

0.997754

29.3738

0.3288

19AET03

greywacke

48.088050

88.388210

2393

112/131/76

1.2

0.996985

41.9516

0.3195

19AET04

Granite

48.087985

88.388167

2393

297/125/83

3.5

0.998026

29.8825

0.3249

19AET05

Granite

48.087763

88.387717

2392

116/94/65

3.7

0.997967

29.3371

0.3269

19AET06

Granite

48.087663

88.387468

2391

3.3

0.998154

29.7172

0.3276

19AET07

Granite

48.087653

88.387230

2390

346/270/ 161 168/130/56

4.1

0.998172

32.4848

0.3251

19AET08

Granite

48.088705

88.389540

2396

398/310/79

1.9

0.997823

30.4874

0.3267

19AET09

greywacke

48.089412

88.392965

2401

185/160/87

1.6

0.993568

39.8916

0.3185

19AET10

Granite

48.089323

88.394005

2407

159/118/54

3.7

0.992754

28.3337

0.3275

19.27 � 1.15 20.41 � 1.15 19.59 � 1.11 19.42 � 1.15 16.60 � 1.00 22.98 � 1.31 18.52 � 1.10 14.36 � 0.94 19.58 � 1.11 30.40 � 1.83 37.68 � 2.46 33.01 � 4.00 17.53 � 1.03 34.61 � 2.11 37.71 � 2.15 37.33 � 2.18 16.44 � 1.03 38.98 � 2.23 17.20 � 1.01 34.24 � 2.20

0.48

AET2

7.3703 � 0.1746 7.9068 � 0.1293 7.4731 � 0.1228 7.3158 � 0.1693 6.3136 � 0.1496 8.6674 � 0.1548 7.2491 � 0.1723 5.5750 � 0.2073 7.6274 � 0.1325 9.0548 � 0.1832 13.8110 � 0.4982 12.3649 � 1.3196 9.7921 � 0.2082 13.3821 � 0.3054 14.1895 � 0.2182 14.2349 � 0.2765 6.8242 � 0.2037 15.5288 � 0.3067 9.16059 � 0.1902 12.4919 � 0.3785 0.0601 � 0.0205 0.0567 � 0.0252

M1

5 b c d

0.3266

Blank1

0.3185

Carrier 9Be concentration is 1000 ppm for all samples. Ratios are not-corrected for background10Be detected in procedural blank. Reported10Be concentrations have been corrected using background10Be detected in procedural blank. Exposure-ages were calculated using the LSD scaling model (Lifton et al., 2014). The bold italic indicates samples that were identified as potential outliers.

53.91 � 1.06 53.06 � 1.36 46.03 � 1.20 63.22 � 1.31 51.97 � 1.36 40.09 � 1.57 55.08 � 1.12 84.72 � 1.93 104.88 � 3.95 92.08 � 9.92 49.55 � 1.18 96.82 � 2.43 105.24 � 1.95 104.46 � 2.30 45.27 � 1.45 110.80 � 2.47 48.58 � 1.13 96.06 � 3.08

0.38 0.37 0.48 0.42 0.46 0.47 0.55 0.38 0.69 1.38 3.56 0.41 0.84 0.68 0.81 0.52 0.83 0.39 1.08

Quaternary Geochronology 56 (2020) 101054

a

Blank

57.01 � 1.11

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3.3. Moraine age determination

fall in the last glacial periods. Five exposure-ages obtained from moraine M2 fall between 16.60 � 1.00 ka and 20.41 � 1.15 ka (Figs. 4A and 5A). The youngest exposureage differs distinctly from the other four exposure-ages that are tightly clustered at 19.50 ka (Fig. 4A). This exposure-age is also detected as a potential outlier by the Peirce’s criterion. Hence we reject this obviously young age as an outlier. The resulting outlier-free distribution yields a x2R value of 1.6 (p > 0.05), suggesting that the age scatter in the subset can be explained by measurement uncertainty alone and that the subset can be classified as class A. The apparently youngest boulder sample, AET5, exhibits a lower height, as compared with the other four (Table 1; Fig. 3F). Such a boulder might have gradually protruded from the moraine crest, following degradation, as argued by Heyman et al. (2016). Note however, that the apparently young exposure-age could also be caused by incomplete exposure resulting from other post-depositional modification processes, such as snow cover and/or boulder weathering, spallation, toppling, or tilting (e.g. Heyman et al., 2011 and references therein). The remaining four exposure-ages have an arithmetic mean of 19.67 � 0.51 ka, suggesting that moraine M2 likely marks the global LGM advance. Fifteen boulders sampled on moraine M1 present a bimodal age distribution (Fig. 4B), with a major age peak at ~38 ka and a minor age peak at ~17 ka. This bimodality supports the poor exposure-age statis­ tics (x2R ¼278.2 and p < 0.05), yet Peirce’s criterion does not identify any potential outliers. Morpho-stratigraphically, moraine M1 is older than moraine M2. Therefore, we regarded the six exposure-ages around 17 ka (Fig. 4B) as potential outliers (Table 1 and Fig. 5A). Removing these ages would result in a subset having exposure-ages of 22.98 � 1.31–38.98 � 2.23 ka (n ¼ 9). The nine remaining ages provide a mean of 34.10 � 4.99 ka and also exhibit poor statistics with a x2R value of 87.4 (p < 0.05), indicating that the subset should be labeled as class B. A plausible interpretation for the young age bias is that moraine degradation led to age underestimation, as some boulders may have toppled or tilted following boulder exhumation (Putkonen and Swanson, 2003; Heyman et al., 2011). The high effective moisture around 17 ka (Herzschuh, 2006, Fig. 6C) implies abundant rainfall, which might have contributed to moraine degradation. An assignment of the mean age to moraine M1 tentatively determines a glacial advance that is in line with MIS 3.

The spread in 10Be exposure-ages from moraines is commonly greater than would be expected because of age underestimation caused by incomplete exposure and/or age overestimation resulting from prior exposure (e.g. Hallet and Putkonen, 1994; Putkonen and Swanson, 2003; Applegate et al., 2010, 2012; Balco, 2011; Heyman et al., 2011, 2016). To explore age clusters, we plotted the probability density function of all exposure-ages using their internal uncertainties for each moraine. In order to statistically identify the potential outlier(s), we then applied Peirce’s criterion (Ross, 2003) following recent studies (e. g. Blomdin et al., 2016; Gribenski et al., 2016; Peng et al., 2019; Chevalier and Replumaz, 2019). After the removal of any potential outlier(s), we used the reduced Chi-square (x2R ) statistic to test whether the scatter in the subset could be explained solely by measurement un­ certainties (Rood et al., 2011; Li et al., 2014; Chen et al., 2015; Dong et al., 2017; Peng et al., 2019). If the test is not statistically significant (p > 0.05), the scatter can be interpreted as the result of measurement uncertainties alone; or else, the scatter likely results from geomorphic processes. Moraines of the latter sort are discussed case by case, based on their relative sequences. After eliminating the potential outlier(s), we determined moraine ages using the arithmetic mean and standard de­ viation (1σ) but divided age groups into quality classes A, B, and C following the criteria developed by Heyman (2014) and Blomdin et al., 2016. Class A defines age groups that are well-clustered (x2R �2). Class B represents moraines having moderately-clustered ages, which produce a x2R >2 as well as a standard deviation < 15% of the mean age. Class C defines poorly-clustered age groups having a x2R >2 and a standard de­ viation � 15% of the mean age. As proposed by Blomdin et al., 2016, 2018), only well and moderately clustered groups with samples �3 were considered when assigning glacial stages. 4. Results and discussion 4.1. Dating results and interpretation Twenty glacial boulders collected from the two moraines identified in the headwater area of the Xiaokelanhe River yield apparent exposureages of 14.36 � 0.94–38.98 � 2.23 ka (Table 1 and Figs. 4 and 5), which

Fig. 4. Probability density plot of 10Be exposure-ages for each moraine in the headwater area of the Xiaokelanhe River: (A) moraine M2; (B) moraine M1. Individual age is plotted as a probability density function (PDF) of a normal distribution using the exposure-age and internal uncertainty. The individual PDF is depicted by using the red thin curve. The cumulative PDF (black thick curve) is created by summing individual PDFs. 6

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Fig. 5. Comparison of 10Be exposure-ages in the Xiaokelanhe River basin (A) and those previously published across the Altai Mountains (B–I). These 10Be exposureages were calculated using CREp calculator and a global production rate of 4.06 � 0.23 atoms g 1 yr 1 (Martin et al., 2017), and reported using the LSD scaling model (Lifton et al., 2014). The open rectangles illustrate the potential outliers suggested in the original publications. The light grey-shaded boxes indicate Marine Isotope Stages (MIS) 4 and 2 (Lisiecki and Raymo, 2005). The oldest outlier identified in the Kanas Valley (Gribenski et al., 2018; Supplementary material) is not included in Fig. 5 because this age is beyond the last glacial periods.

4.2. The last glaciation in the Xiaokelanhe River basin

et al., 2018), even though this period is a well-defined mild interstadial (Voelker and workshop participants, 2002). Most of these studies have emphasized the impact of increased precipitation, induced by a strengthened Asian summer monsoon, on glacial advances (e.g. Owen et al., 2002; Shi and Yao, 2002). However, the Altai is less affected by the Asian summer monsoon. A MIS 3 glacial advance in this area may reflect strengthened mid-latitude westerlies at that time, recorded in a grain size analysis of the loess sequence in central Asia (Li et al., 2019). Despite making such an argument, we cannot fully exclude the possi­ bility of an earlier glacial advance in the Xiaokelanhe River, as proposed in the Kanas Valley (Gribenski et al., 2018), ~120 km northwest of the Xiaokelanhe River, due to the poor chronological control for moraine M1.

The dated moraines in the headwater area of the Xiaokelanhe River provide evidence of at least two glacial advances that occurred during the last glacial. The most recent moraine (M2) is dated to 19.67 � 0.51 ka, reflecting restricted glacial extent during the global LGM. An earlier glacial advance is more extensive; however, the correlated moraine (M1) cannot be accurately dated at present. The tentatively estimated age of 34.10 � 4.99 ka indicates a glacial advance before MIS 2 and possibly corresponding to MIS 3. Notwithstanding a relatively poorly dated glacial event prior to MIS 2, it seems that glaciers in the Xiaokelanhe River became sequentially less-extensive during the last glacial stages, which is at odds with the Northern Hemisphere ice sheets. This pattern of glacier variations in the Asian highlands has been ascribed to the relatively-arid climate during the global LGM, as compared with the early last glacial (e.g. Li et al., 2014; Zhang et al., 2016a; Blomdin et al., 2018). During the global LGM, a cold, dry climate dominated central Asia (Herzschuh, 2006). In spite of a decrease in temperature, global LGM precipitation dropped by 30–70% relative to the present (Shi et al., 1997, 2001), leading to an unfavorable climate for glacial expansion (Rupper et al., 2009, Fig. 6). In the Altai, aridity during the global LGM may have correlated with the Fennoscandian Ice Sheet, which reached its maximum during MIS 2 and likely served as an orographic barrier to reduce moisture advection through the mid-latitude westerlies (Krinner et al., 2011; Li et al., 2014; Chen et al., 2015; Zhang et al., 2016a). Furthermore, the Siberian High might also have aggravated the aridity by blocking the mid-latitude westerlies (Zech, 2012). Glacial advances have been frequently reported in High Asia throughout the MIS 3 interval (e.g. Phillips et al., 2000; Owen et al., 2002; Dortch et al., 2010; Chevalier et al., 2011; Wang et al., 2013; Dong

4.3. The last glaciation across the Altai Mountains We compiled the available published 10Be exposure-ages from the Altai Mountains (Figs. 1B and 5B–5I), aiming to develop a comprehen­ sive understanding of glacial histories across the Altai. For consistency, we recalculated all exposure-ages, using the online CREp program (Martin et al., 2017) with the LSD model (Lifton et al., 2014) (Fig. 5B–I and supplementary materials). We followed the original publications to reject potential outliers, and discussed each moraine case by case. Age group classifications and glacial stage assignments were then imple­ mented following the method stated in Section 3.3. In the Kanas Valley, Chinese Altai, two moraines near the valleymouth have been dated to the last glacial periods (Gribenski et al., 2018). Eight boulders from the inner moraine complex present a wide age-spread (Fig. 5F). Removing the youngest and the two oldest ages, as suggested by Gribenski et al. (2018), results in a subset that can be grouped into class B: six approximately concordant exposure-ages 7

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Fig. 6. The last glacial climatic archives relative to the timing of glacial activities during the last glacial across the Altai Mountains. (A) Northern Hemisphere summer insolation intensity at 60� N (Berger and Loutre, 1991). (B) δ18O records from the Guliya ice core, West Kunlun Shan (Thompson et al., 1997). (C) Mean effective moisture from central Asia (Herz­ schuh, 2006). (D) Composite of changes in CO2 con­ centration recorded in Antarctic ice cores (Monnin et al., 2001, 2004; Marcott et al., 2014; Bereiter et al., 2012, 2015; Schneider et al., 2013; Ahn and Brook, 2014). (E) Plots of the timing of glacial activities (colored rectangles and circles) using the mean and standard deviation of outlier-free age groups (n � 3) for different valleys in Figs. 1B and 5 following the same color scheme. The rectangles represent well-clustered age-groups marked as class A. The circles illustrate class B age-groups. The vertical light-grey bars highlight Heinrich Stadials (HS; Heinrich, 1988; Rashid et al., 2003; Rasmussen et al., 2003; Hemming, 2004).

2018) recognized one set of latero-frontal moraine (TUR2) and a series of inset moraine ridges (TUR1) near the valley-mouth. Using Peirce’s criterion and reduced Chi-square statistics, Blomdin et al. (2018) found one anomaly from the two moraines (Fig. 5B). This procedure returns two moderately-clustered age-groups of class B (supplementary mate­ rials), yielding mean ages of 22.53 � 3.31 and 45.24 � 1.92 ka. This suggests that moraine TUR1 and TUR2 document glacial advances that occurred in MIS 2 and MIS 3, respectively. In another valley (Boguty) in the Ikh Mountains, two sets of moraine complexes were also identified in the mountain front area (Blomdin et al., 2016, 2018). The two moraines, however, are sorted into class C according to the exposure-age statistics (supplementary materials),

(18.43 � 1.08–22.11 � 1.26 ka) yield a mean age of 20.21 � 1.54 ka and a x2R value of 13.7 (p < 0.05). This indicates that the moraine complex could have been deposited during the global LGM. An outer lateral moraine, morpho-stratigraphically older, has three exposure-ages of 16.97 � 1.07, 64.09 � 3.53, and 69.75 � 3.95 ka (Fig. 5F). The youngest age is not in morpho-stratigraphic order and was considered untenably young (Gribenski et al., 2018). Once this outlier is removed, the con­ sistency of the two remaining ages appears to imply a MIS 4 glacial advance. However, the small dataset (n ¼ 2) prevents us from making an unhesitating correlation at the present time. In the Turgen-Asgat Valley, Ikh Mountains, Blomdin et al., 2016, 8

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making it hard to further define the associated glacial events. Another example has been found from the interfluve between the Turgen Gol and Karkhiraa Gol valley, Mongolian Altai, where five widely scattered ages from a ground moraine (T1) is also categorized into group of class C €tsch, 2017; supplementary materials). (Po In the Turgen Gol and Karkhiraa Gol valley, moraine T2 and T4, situated at their valley-mouths, yield moderately-clustered exposureages between 19.25 � 1.33 and 24.78 � 1.61 ka (Fig. 5G and H), implying that the two moraines may have been formed during the global LGM. Five exposure-ages from an up-valley moraine (T3) in the Turgen Gol Valley are recalculated to 18.72 � 1.27, 18.91 � 1.25, 25.0 � 1.62, €tsch (2017) used the 34.06 � 2.25, and 79.62 � 5.07 ka (Fig. 5G). Po youngest one to represent the moraine depositional age based on morpho-stratigraphic considerations. The two youngest exposure-ages are indistinguishable. We thus hesitate to remove the exposure-age of 18.91 � 1.25 as an outlier, but refuse to correlate T3 with glacial event considering the small dataset. A roche moutonn�ee (R1), which lies ~9.4 km up-valley from moraine T3, produces two identical exposure-ages: 14.16 � 1.15 and 14.48 � 1.08 ka (Fig. 5G). A second roche moutonn�ee (R2) further upstream also yields two consistent ages: 12.47 � 0.87 and 13.02 � 0.88 ka (Fig. 5G). Roche moutonn� ee R1 and R2 may record glacial fluctuations in the late glacial; nevertheless the small datasets deny accurate correlations. In the Chagan Uzun valley, Russian Altai, five moraines gave indis­ tinguishable exposure-ages spanning the MIS 2 interval after rejecting three potential outliers identified by Gribenski et al. (2016; Fig. 5E and supplementary materials). These indistinguishable exposure-ages have been argued to result from surge-like events (Gribenski et al., 2016). In the Kuray valley, a moraine yields three highly concordant exposure-ages: 17.05 � 1.15, 17.76 � 1.40, and 17.30 � 1.33 ka (Fig. 5), manifesting an assignment of class A to this moraine (supplementary materials). The three ages overlap with the exposure-age of 16.50 � 1.17 ka, derived from the up-valley bedrock (Reuther, 2007, Fig. 5I). These glacial features reflect a last deglacial event that possibly responded to Heinrich Stadial 1 (HS1: Heinrich, 1988; Rashid et al., 2003; Rasmussen et al., 2003; Hemming, 2004). Two boulders from a down-valley moraine have exposure-ages of 44.34 � 2.91 and 103.03 � 9.01 ka (Fig. 5I). Although Reuther (2007) prefers taking the apparently old age, the dataset is too small to adequately determine moraine formational age. So at present, it is not unequivocal to conclude a MIS 5 glacial advance for this moraine. A similar scenario for moraines of group I in the Praviy Valley has been documented, with only two boulders that date to 10.87 � 0.74 and 22.63 � 1.53 ka (Ganyushkin et al., 2018, Fig. 5D). In summary, most of the compiled age-groups have been classified into classes B and C. The compilation indicates that at least three glacial events possibly occurred during the last glacial periods, though it re­ mains questionable whether the local LGM is synchronous across the Altai Mountains. Despite uncertainties in MIS 5 and MIS 4 glacial ad­ vances across the Altai, MIS 3 glacial advances have been found in the Turgen-Asgat Valley and the headwater area of the Xiaokelanhe River, verifying that the local LGM occurred before MIS 2 in the two valleys. Glacial advances during the global LGM appear to occur in most glacial valleys in the Altai Mountains. The following post-LGM glacial events likely correspond to HS 1 in the Kuray Valley.

2006); and atmospheric CO2 concentration in ice cores (Bereiter et al., 2015). We now consider the possible forcing mechanisms behind the spatio-temporal patterns of glacial fluctuations in the Altai Mountains. A synthesis of the new and compiled 10Be exposure-ages shows that glaciers advanced multiple times through the last glacial episodes, with the local LGM occurring prior to the global LGM across the Altai Mountains (Fig. 6E). Little evidence exists for glacial variations during MIS 5, with only occasional exposure-ages from individual moraines in the Turgen Gol and Kuray valley (Fig. 5G and I). By considering the small dataset and limited morpho-stratigraphic orders, we cannot unambiguously argue for a local LGM during MIS 5 in the two valleys. Similarly, a speculative MIS 4 glacial advance has not been firmly confirmed in the Kanas Valley (Fig. 5F). But we acknowledge that the low summertime solar insolation minima during MIS 5d, 5b, and 4 (Berger and Loutre, 1991, Fig. 6A) has the potential to cause cold con­ ditions allowing for glacier advances, as is the case in other central Asian areas (e.g. Amidon et al., 2013; Dortch et al., 2013; Zech, 2012; Zech et al., 2013; Blomdin et al., 2016; Rother et al., 2017). The local LGM in the Turgen Valley possibly occurred in mid-MIS 3, a relatively cold stage recorded in the Guliya ice core, the West Kunlun Shan (Thompson et al., 1997, Fig. 6B), mirroring the decisive role of high latitude summer insolation (Berger and Loutre, 1991, Fig. 6A) in causing glacial advance as well. In addition, glaciers advanced throughout most of the Altai Mountains during the global LGM (Fig. 6E). This signifies that sum­ mertime solar insolation is low enough to provide cold temperature permitting glacier advances, though multiple climatic proxies indicate a relatively dry climate (Herzschuh, 2006, Fig. 6C). The above arguments point to the significant impact of solar insolation as a trigger for glacial activities. However, climate in mid-MIS 3 is moderately wet in central Asia (Herzschuh, 2006, Fig. 6C), implying that a coupled temperature and precipitation control affect glacial activity. Another example has been found in the Xiaokelanhe River, where the local LGM apparently post-dates mid-MIS 3 and shows no consistent relationship with summer insolation (Fig. 6A and E). This also suggests that insolation changes alone cannot fully explain glacial fluctuations during the last glacial for the Altai Mountains. If assigning the mean of an outlier-free data set (n � 3) to represent moraine formation age, some glacial records in the Altai roughly coin­ cide with several Heinrich Stadials (Fig. 6E). A partial explanation is that glaciers in the Altai may have registered North Atlantic Ocean cooling signals, which are delivered to the Altai via the mid-latitude westerlies as suggested in the other regions of High Asia (Hong et al., 2003; Van­ denberghe et al., 2006; Han et al., 2008; An et al., 2012). Moreover, it is noteworthy that the last deglacial in the Kuray Valley, Russian Altai approximately parallel atmospheric CO2 levels (Monnin et al., 2001, 2004; Marcott et al., 2014; Bereiter et al., 2012; Schneider et al., 2013; Ahn and Brook, 2014, Fig. 6D and E). Increasing atmospheric CO2 concentration has been proposed as key to initializing the LGM termi­ nation in global mid-latitudes (Schaefer et al., 2006; Broecker, 2013). A potential relationship may also have existed between glaciers in the Altai and atmospheric CO2 concentrations. Strictly speaking, these hypothesized correlations are yet tentative, based on the following considerations: first and foremost, glacial chro­ nologies in the Altai are still poorly characterized, due to that most age groups are tagged as class B or class C (supplementary materials), but moraines with well-clustered ages (class A) are indispensably required when correlating glacial events with high-frequency record of climate changes, such as North Atlantic millennial-scale climate oscillations; second, there is no direct calibration of 10Be production rates across High Asia, which is necessary for accurate determination of moraine emplacement ages; and last, moraine initial ages vary with the choice of scaling models due to the lack of knowledge necessary to select an appropriate scaling model.

4.4. Possible driving mechanisms behind glacial fluctuations The primary controls of glacial fluctuations are variations in air temperature and precipitation (Rupper et al., 2009). Deciphering which one is the dominant is intricate because the forcing factors may have changed through time between regions (Benn and Owen, 1998). Tem­ perature and precipitation variability during the last glacial periods are reflected by the northern hemispheric summer insolation (Berger and Loutre, 1991) and a range of climatic proxies (Fig. 6), including ice core δ18O record (Thompson et al., 1997); lacustrine sediments (Herzschuh, 9

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5. Conclusions

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Twenty apparent 10Be exposure-ages from two sets of moraine remnants in the headwater area of the Xiaokelanhe River add to the database of glacial chronologies across the Altai Mountains. The inner latero-frontal moraine was dated to 19.67 � 0.51 ka, representing a restricted glacial advance during the global LGM. The tentatively determined age of 34.10 � 4.99 ka suggests that the outer moraine remnants possibly mark the local LGM in the Xiaokelanhe River. The compilation of new and previously published 10Be exposure-ages in the Altai Mountains suggests that glaciers advanced at least three times throughout the last glacial cycle, and that glaciers appear to reach their maximum extent before the global LGM. There is no clear correlation between a single climatic forcing factor and the glacial activities in the Altai. The Northern Hemisphere summer insolation, North Atlantic cli­ matic oscillations, CO2 concentration variations, and precipitation delivered by the mid-latitude westerlies may have combined to control glacier advance and retreat across the Altai Mountains. However, we acknowledge that these potential correlations remain tentative mainly because of the limited precisely-dated moraines. Declaration of competing interest We declare that we do not have any commercial or associative in­ terest that represents a conflict of interest in connection with the work submitted. Acknowledgement This study was supported by the National Natural Science Founda­ tion of China (Grant No. 41601010 and 41602195) and State Key Lab­ oratory of Cryospheric Science, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences (SKLCS-OP-2018-02). We appreciate Taibei Liu and Haiyan Zhao from Institute of Earth Envi­ ronment, Chinese Academy of Sciences for their help during the field work. We thank Pierre-Henri Blard, Arjen Stroeven, and one anonymous reviewer for their thorough and constructive reviews. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.quageo.2020.101054. References Ahn, J., Brook, E.J., 2014. Siple Dome ice reveals two modes of millennial CO2 change during the last ice age. Nat. Commun. 5, 3732. https://doi.org/10.1038/ ncomms4723. Amidon, W.H., Bookhagen, B., Avouac, J., Smith, T., Rood, D., 2013. Late Pleistocene glacial advances in the western Tibet interior. Earth Planet Sci. Lett. 381, 210–221. An, Z., Colman, S.M., Zhou, W., Li, X., Brown, E.T., Jull, A.J.T., Cai, Y., Huang, Y., Lu, X., Chang, H., Song, Y., Sun, Y., Xu, H., Liu, W., Jin, Z., Liu, X., Cheng, P., Liu, Y., Ai, L., Li, X., Liu, X., Yan, L., Shi, Z., Wang, X., Wu, F., Qiang, X., Dong, J., Lu, F., Xu, X., 2012. Interplay between the Westerlies and Asian monsoon recorded in lake Qinghai sediments since 32 ka. Sci. Rep. 2, 619. https://doi.org/10.1038/srep00619. Applegate, P.J., Urban, N.M., Laabs, B.J.C., Keller, K., Alley, R.B., 2010. Modeling the statistical distributions of cosmogenic exposure dates from moraines. Geosci. Model Dev. (GMD) 3, 293–307. Applegate, P.J., Urban, N.M., Keller, K., Lowell, T.V., Laabs, B.J.C., Kelly, M.A., Alley, R. B., 2012. Improved moraine age interpretations through explicit matching of geomorphic process models to cosmogenic nuclide measurements from single landforms. Quat. Res. 77, 293–304. Balco, G., 2011. Contributions and unrealized potential contributions of cosmogenicnuclide exposure dating to glacier chronology, 1990-2010. Quat. Sci. Rev. 30, 3–27. Benn, D.I., Owen, L.A., 1998. The role of the Indian summer monsoon and the midlatitude westerlies in Himalayan glaciation: review and speculative discussion. J. Geol. Soc. 155, 353–363. Bereiter, B., Eggleston, S., Schmitt, J., Nehrbass-Ahles, C., Stocker, T.F., Fischer, H., Kipfstuhl, S., Chappellaz, J., 2015. Revision of the EPICA Dome C CO2 record from 800 to 600 kyr before present. Geophys. Res. Lett. 42, 542–549.

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