Peat brGDGTs-based Holocene temperature history of the Altai Mountains in arid Central Asia

Journal Pre-proof Peat brGDGTs-based Holocene temperature history of the Altai Mountains in arid Central Asia

Dandan Wu, Jiantao Cao, Guodong Jia, Haichun Guo, Fuxi Shi, Xinping Zhang, Zhiguo Rao PII:

S0031-0182(19)30724-2

DOI:

https://doi.org/10.1016/j.palaeo.2019.109464

Reference:

PALAEO 109464

To appear in:

Palaeogeography, Palaeoclimatology, Palaeoecology

Received date:

1 August 2019

Revised date:

12 November 2019

Accepted date:

12 November 2019

Please cite this article as: D. Wu, J. Cao, G. Jia, et al., Peat brGDGTs-based Holocene temperature history of the Altai Mountains in arid Central Asia, Palaeogeography, Palaeoclimatology, Palaeoecology (2019), https://doi.org/10.1016/j.palaeo.2019.109464

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© 2019 Published by Elsevier.

Journal Pre-proof

Peat brGDGTs-based Holocene temperature history of the Altai Mountains in arid Central Asia

Dandan Wu a, Jiantao Cao b, Guodong Jia b, Haichun Guo a, Fuxi Shi c, Xinping

a

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Zhang a, Zhiguo Rao a, *

College of Resources and Environmental Sciences, Hunan Normal University,

Jiangxi Provincial Key Laboratory of Silviculture, College of Forestry, Jiangxi

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c

State Key Laboratory of Marine Geology, Tongji University, Shanghai 200092, China

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b

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Changsha 410081, China

*

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Agricultural University, Nanchang, 330045, China

Corresponding author at: College of Resources and Environmental Sciences, Hunan

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Normal University, Changsha 410081, China. E-mail addresses: [email protected] (Z. Rao).

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Abstract: Due to their effects on snow/ice melting in mountains and evaporation in basins, temperature variations are an important influence on the hydrological cycle and water resources in arid Central Asia (ACA). We present independently-dated peat brGDGTs-based MBT'/MBT'5ME records from an alpine Sahara sand peatland (SSP)

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in the southern Altai Mountains, in ACA. During the past ~11 kyr, the SSP MBT' and MBT'5ME profiles exhibit consistently increasing trends and they are significantly

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positively correlated. Using a global peat-specific MBT'5ME-tempeature calibration,

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the long-term Holocene warming trend indicated by the quantitative SSP mean annual

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temperature (MATpeat) reconstruction is supported by the peat α-cellulose δ13C

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summer temperature record from the SSP, and by an independent peat α-cellulose

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δ18O winter temperature record from a nearby site (i.e. temporal validation). The increasing trends are also evident in Holocene peat MBT' records from the Hongyuan

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(HY) peatland in the NE Tibetan Plateau, and in the Shuizhuyang (SZY) peatland in SE China. Notably, both the MBT' data of the surface samples and the averaged MBT' data of the Holocene peat cores are consistent with the modern spatial temperature gradient at the SSP, HY and SZY (i.e. spatial validation). This evidence demonstrates the temperature significance of peat MBT'/MBT'5ME records. The long-term Holocene warming trend indicated by the SSP MBT'/MBT'5ME records is supported by recently-acquired Holocene temperature records from the Eurasian continent. This trend is the opposite to the traditional viewpoint of a long-term Holocene cooling trend, and it indicates that more effort is needed to obtain a reliable Holocene 2 / 47

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temperature history.

Keywords: alpine peatland; MBT'/MBT'5ME; quantitative reconstruction; Holocene warming; paleoclimate; organic geochemistry

1. Introduction

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The hydrological cycle and the status of water resources are extremely important

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for sustainable socio-economic and ecological development of arid inland regions,

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such as arid Central Asia (ACA). The Holocene moisture history of the Xinjiang

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region of northwestern China, which is an important part of the core zone of Westerlies-dominated ACA (Huang W. et al., 2015; Chen et al., 2016), has been

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intensively debated. One major viewpoint is that the moisture evolution of the

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Xinjiang region experienced a similar long-term drying trend during the Holocene as that in the Asian monsoon region (Rudaya et al., 2009; Li et al., 2011; Cheng et al.,

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2012); and the other major viewpoint is that the Xinjiang region experienced a long-term wetting trend during the Holocene, implying an anti-phased relationship with the moisture evolution of the Asian monsoon region (Wang and Feng, 2013; Hong et al., 2014; Chen et al., 2016). These differences in the interpretation of the Holocene moisture history of the Xinjiang region have recently been attributed to the different responses of high-altitude mountains and low-altitude basins to the Holocene temperature history (Rao et al., 2019a), since both snow/ice melting in the former and evaporation in the latter would be substantially affected by temperature changes. With regard to Holocene temperature history, a long-term cooling trend in both China (Shi et al., 1994) and worldwide (Marcott et al., 2013) has been proposed. 3 / 47

Journal Pre-proof However, more recently an increasing number of sediment-based reconstructions (Jiang et al., 2006; Marsicek et al., 2018) and climate simulation results (Jiang et al., 2012; Liu et al., 2014) have indicated a long-term warming trend during the Holocene. Similarly, in the Xinjiang region, although there are relatively few sediment-based Holocene temperature reconstructions, the results are still conflicting. For example, pollen-based results from alpine Swan Lake in the central Tianshan Mountains (Fig. 1) in the central Xinjiang region demonstrate a long-term warming trend during the past

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~8.5 kyr (1 kyr = 1,000 years) (Huang X.Z. et al., 2015). In addition, a long-term

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summer warming trend during the past ~11 kyr is indicated by a peat α-cellulose δ13C

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record from the Altai Mountains (Fig. 1) in the northern Xinjiang region (Rao et al.,

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2019b). However, an alkenone-based quantitative reconstruction from Lake Balikun in the eastern Xinjiang region indicated a long-term Holocene cooling trend in early

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summer temperature (Zhao et al., 2017). Therefore, more sediment-based Holocene

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temperature records are needed from the Xinjiang region, for the following reasons: i) Holocene temperature records for the Xinjiang region are currently rare; ii) the

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existing Holocene temperature records are in conflict; and iii) a robust Holocene temperature history for the region is important for improving our understanding of long-term moisture conditions and the status of water resources in the region. Branched glycerol dialkyl glycerol tetraether lipids (brGDGTs), with four to six methyls (I–III) and zero to two cyclopentyl moieties (a–c), are components of bacterial cell membranes. They have been proposed to be mainly sourced from anaerobic bacteria, with Acidobacteria as a possible source (Sinninghe Damsté et al., 2000; Weijers et al., 2006, 2009), although the actual source organism remains unknown. The methylation index (MBT) (Weijers et al., 2007) and the revised methylation index (MBT') (Peterse et al., 2012) of brGDGTs in surface soils have 4 / 47

Journal Pre-proof been proposed to be closely related to the environmental temperature, including the new MBT'5ME index that has been developed from the successful separation of 5-methyl and 6-methyl brGDGTs using improved chromatography (De Jonge et al., 2013, 2014). These findings demonstrate the potential of the MBT, MBT' and MBT'5ME indices of brGDGTs for paleotemperature reconstruction. Peat sediments are a widespread, often rapidly-accumulating terrestrial deposit; typically, they contain abundant terrestrial plant residues and therefore a reliable

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chronology of peat accumulation can usually be established based on AMS 14C dating.

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Notably, the peatland environment, with its low temperature, high humidity and

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anaerobic status, is favorable for the production of brGDGTs (Weijers et al., 2006).

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Consequently, abundant brGDGTs are widely reported in peat sediments (Sinninghe Damsté et al., 2000; Weijers et al., 2006, 2009, 2011; Huguet et al., 2010). Notably, a

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recent study of brGDGTs-derived MBT'5ME data based on 470 samples from 96

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globally-distributed peatlands found a significant positive correlation with corresponding environmental temperatures (Naafs et al., 2017a). These results

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highlight the potential and importance of peat sediments for brGDGTs-based paleotemperature reconstructions. Here we report Holocene peat MBT' and MBT'5ME records from the alpine Sahara sand peatland (SSP) in the Altai Mountains in ACA. The SSP was chosen as the study site for the following reasons: i) the previously reported peat brGDGTs results in China are mainly from the monsoon (Wang et al., 2017) and monsoon margin (Zheng et al., 2015) regions, while the SSP is located in the northern Xinjiang region of ACA (Fig. 1). ii) due to its specific location, human impacts at the SSP can be ignored, making it ideal for paleoclimatic studies (Rao et al., 2019b; Shi et al., 2019). iii) based on a modern process study, a peat α-cellulose δ13C record from the 5 / 47

Journal Pre-proof SSP has been determined to be a Holocene summer temperature record (Rao et al., 2019b), and therefore the resulting MBT' and MBT'5ME records can be directly compared with the α-cellulose δ13C summer temperature record from the same site. This enables the further validation of the temperature significance of peat MBT' and MBT'5ME data.

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2. Materials and methods

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2.1. Sahara sand peatland (SSP)

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The SSP (48°6′46.70″N, 88°21′46.78″E; ~2446 m a.s.l.; Fig. 1) is located in a

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small intermontane basin on the central southern slopes of the Altai Mountains,

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northern Xinjiang, in ACA. Due to its location in the centre of the Eurasian continent, the Altai region is far from the direct influence of the oceans and therefore it belongs

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to the mid-latitude temperate and continental climatic zone. The moisture supply for

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the region is mainly from the North Atlantic, with a minor contribution from the Arctic Ocean. According to instrumental climatic data (1981-2010) from Altai meteorological station (~35 km from SSP and at an altitude of 737 m a.s.l.), local mean annual precipitation (MAP) is 212 mm, with the maximum monthly precipitation occurring in July, November or December. The mean annual temperature (MAT) is 4.8 °C with large seasonal differences between summer (JJA, 20.7 °C) and winter (DJF, -13.5 °C). On the southern slope of the Altai Mountains, with a 100 m increase in altitude, the MAT typically decreases by 0.6 °C and precipitation amount shows

an

increasing

trend.

The

SSP

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is

snow-

and

ice-covered

from

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September-October to the following April-May, and therefore May-September is the growing season for the local plants which are overwhelmingly dominated by sedges (Carex pamirensis) (Rao et al., 2019a, 2019b; Shi et al., 2019).

2.2. Peat cores and samples

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During fieldwork in August 2017, we obtained two parallel peat cores from the center of the SSP. The two cores are designated ATM17A and ATM17B and they are

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150-200 m apart; their respective lengths are 600 and 652 cm. Both cores were split

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and sampled at a 1-cm interval in the field, transported as rapidly as possible to the

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laboratory, and refrigerated at -4 °C. The longer core, ATM17B, with the uppermost

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650 cm composed of almost pure plant residues and the lower 2 cm composed of

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steel-grey-colored clayey sediments with a minor amount of plant macrofossils (Fig. S1), was chosen for the study of brGDGTs. A total of 11 bulk peat samples at different

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depths of core ATM17B were sent to the Beta Analytic Radiocarbon Dating Laboratory (Miami, Florida, U.S.A.) for plant macrofossil separation and accelerator mass spectrometry (AMS)

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C dating. Information about the samples and the dating

results are given in Table 1.

2.3. Analytical methods 2.3.1. GDGT analysis A total of 131 samples at a 5-cm interval from core ATM17B were used for GDGT analysis. The samples were freeze-dried, ground to pass an 80-mesh sieve, and 7 / 47

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then accurately weighed. Then, 2000 ng of an internal standard consisting of synthesized C46 GDGT with a known concentration of 20 ng/µl was added (Huguet et al., 2006) and the samples were repeatedly ultrasonicated with dichloromethane (DCM)/MeOH (1:1, v/v) and centrifuged four times. All of the solutions were combined into a single sample which contained the total lipid extracts. The samples

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were then concentrated using a vacuum rotary evaporator, re-dissolved in DCM, dried in a gentle stream of N2, after which the final extract was dissolved in n-hexane. The

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nonpolar and polar components were then separated from each concentrated extract

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via silica gel column chromatography, using pure n-hexane and DCM/MeOH (1:1,

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v/v). Finally, the polar components, containing GDGTs, were dried in a gentle stream

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of N2, dissolved in n-hexane/EtOAc (84:16, v/v) and filtered through a 0.45 μm

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polytetrafluoroethylene filter. Samples with a constant volume of 200 μl were prepared for GDGT analysis.

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The GDGTs were analyzed using high-performance liquid chromatography-mass spectrometry (HPLC-MS; Agilent 1200 series 6460 QQQ), using two connected silica columns (150 mm × 2.1 mm, 1.9 μm, Thermo Finnigan; USA) to separate 5- and 6-methyl brGDGTs. The instrumental analysis procedures were similar to those described by Yang et al. (2015). Briefly, the columns were maintained at 40 °C, and the injection volumes were adjusted to the range of 5-20 μl according to the concentrations of the samples. GDGTs were eluted at 0.2 ml/min with 84% n-hexane (A) and 16% EtOAc (B) for 5 min. The ratio of EtOAc was then linearly increased to 18% from 5-65 min, and then linearly increased to 100% from 65-86 min, maintained 8 / 47

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at 100% for 4 min to wash the columns, and finally backed to 84/16 (A/B) and maintained for 30 min. The GDGTs were detected using a single ion monitoring (SIM) mode with selected m/z values of 1050, 1048, 1046, 1036, 1034, 1032, 1022, 1020, 1018 and 744. The final concentrations of the target GDGTs were quantitatively determined by comparing the retention times and peak areas with that of the C46

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GDGT internal standard (Huguet et al., 2006). Notably, due to the limited sample materials, two samples (from the depths of 196 and 201 cm) failed to provide reliable

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data. The results for the other 129 samples, including the absolute concentrations and

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relative abundances of different brGDGT homologues, are listed in Table S1 and S2

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respectively. All samples were analyzed at the State Key Laboratory of Marine

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Geology, Tongji University.

2.3.2. Calculation of MBT' and MBT'5ME proxies

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The Roman numerals that represent different GDGT homologues are explained in De Jonge et al. (2014). The MBT' index was calculated using Eq. (1) following the revised methylation index of brGDGTs in Peterse et al. (2012). The MBT'5ME index was calculated using Eq. (2), as in De Jonge et al. (2014). The peat MBT'5ME-based mean annual temperature (MATpeat) was calculated using Eq. (3), as in Naafs et al. (2017a). MBT' =

Ia + Ib + Ic

Ia + Ib + Ic + IIa + IIa'+IIb + IIb'+IIc + IIc'+IIIa + IIIa'

MBT'5ME =

Ia + Ib + Ic

Ia + Ib + Ic + IIa + IIb + IIc + IIIa  9 / 47

(1)

(2)

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3. Results 3.1. Chronology of core ATM17B

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All 11 AMS 14C dates for core ATM17B are in stratigraphic order (Table 1) and therefore they were all calibrated to calendar years using the INTCAL13 calibration

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curve (Reimer et al., 2013). The final results are expressed in years before present

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(B.P.) where “present” is defined as A.D. 1950 (Table 1). According to the calibrated

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dates, the age of the topmost sample at the depth of 2 cm is 8±8 cal yr B.P., indicating

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a modern organic carbon source for the topmost part of core ATM17B, as well as that

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of core ATM10-C7 (Fig. 2) which was previously obtained from the SSP (Rao et al., 2019b). The age of the lowest sample, at the depth of 652 cm, is 11,145±74 cal yr B.P.

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(Table 1), indicating that core ATM17B spans the entire Holocene. Notably, the oldest date for core ATM17B, 11,145±74 cal yr B.P., is very close to the previously reported age of 11,030 cal yr B.P. for the bottom of the peat sediments of core ATM10-C7 that are composed almost entirely of pure plant residues (Rao et al., 2019b). Based on the absolute calibrated ages, two methods, a cubic spline fit and linear interpolation/extrapolation, were adopted for the attempted reconstruction of chronology of core ATM17B. The deduced chronologies are generally consistent, with an average age offset for the same peat sample of < 10 years, with most offsets < 100 years, and with the greatest offset of ~200 years (Fig. 2). This indicates that the 10 / 47

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differences between the chronologies obtained using these two methods are insignificant. Therefore, the final age-depth model for core ATM17B was obtained using linear interpolation and extrapolation (Fig. 2). Notably, the dating results for cores ATM17B and ATM10-C7 and their age-depth models are mutually consistent and therefore are likely to be highly reliable and robust (Fig. 2). The minor

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differences, i.e. the slightly systematic older ages of core ATM17B (Fig. 2), may be related to the different depths and dating materials of the two cores. That is, core

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ATM17B has a basal depth of 652 cm and bulk plant macrofossils were used as the

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dating material, whereas core ATM10-C7 has a basal depth of 750 cm and the

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α-cellulose extracted from bulk plant macrofossils was used as the dating material

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(Rao et al., 2019b). For core ATM17B, there is an average age-control point every

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records is < 100 yr.

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1000 years, and the average time resolution of the brGDGTs-based MBT'/MBT'5ME

3.2. Molecular distribution of brGDGTs in core ATM17B For all 129 samples from core ATM17B, the absolute concentrations of the total extracted brGDGTs varied between ~30 and ~80 µg/g dw with an average value of ~44 µg/g dw (Table S1), with lower values in the early Holocene and higher values in the late Holocene. Therefore, there is an overall long-term increasing trend during the Holocene (Fig. S2). The molecular distribution characteristics of the extracted brGDGTs from core ATM17B are similar, indicating a relatively stable and largely unchanged bio-precursor(s) for the brGDGTs in the SSP (Figs. S3 and S4). The 11 / 47

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extracted brGDGTs are dominated by brGDGT II with an average relative abundance of 52.1%, very similar to the molecular distribution of brGDGTs in Hongyuan (HY) peatland (Fig. 1) in the northeastern Tibetan Plateau (Zheng et al., 2015). Correspondingly, the average relative abundances of brGDGT III and I are 31.6% and 16.3%, respectively (Fig. S4). The relative abundances of brGDGTs containing one or

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two cyclopentyl moieties are quite low, especially that of brGDGT Ic, IIc, IIc', IIIb, IIIb', IIIc, IIIc', which may be characteristic of brGDGTs produced in acidic

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environments (Weijers et al., 2007). Conversely, brGDGT IIa, containing no

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cyclopentyl moiety, has the highest relative abundance with an average of 32% (Fig.

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S4). Therefore, the average relative abundances decrease progressively from brGDGT

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IIa to IIIa, IIa', Ia, and finally to IIIa' (Fig. S4). The observation that the relative

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abundance of brGDGTs with one or two cyclopentyl moieties is substantially lower than that without cyclopentyl moiety was also reported in a study of the HY peatland

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(Zheng et al., 2015) and in a peatland in France (Huguet al., 2010, 2013). Along the time sequence, the variation of the relative abundance of different brGDGT homologues (Ia-Ic, IIa-IIc', IIIa-IIIc') can be roughly divided into four stages: 11.1-10.0, 10.0-5.5, 5.5-3.0, and 3.0-0 cal kyr B.P. (Figs. S3 and S4). Specifically, the relative abundance of brGDGT IIIa decreases over time (Fig. S3), with the average relative abundance of brGDGT IIIa changing from 26.2% (11.1-10.0 cal kyr B.P.), 24.2% (10.0-5.5 cal kyr B.P.), 18.0% (5.5-3.0 cal kyr B.P.), and to 12.3% (3.0-0 cal kyr B.P.). The average relative abundances of brGDGT Ia, IIa increase from 9.2% and 27.4% (11.1-10.0 cal kyr B.P.), 12.1% and 31.1% (10.0-5.5 cal kyr B.P.), 15.9% and 12 / 47

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32.0% (5.5-3.0 cal kyr B.P.), to 27.2% and 38.1% (3.0-0 cal kyr B.P.), respectively. The relative abundance of brGDGT IIa' within the range of 10-20% remains roughly constant, while the relative abundance of brGDGT IIIa' decreases from 11.3% (11.1-10.0 cal kyr B.P.), 10.6% (10.0-5.5 cal kyr B.P.), 8.5% (5.5-3.0 cal kyr B.P.), and to 2.5% (3.0-0 cal kyr B.P.) (Figs. S3 and S4). The relative abundances of

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different brGDGTs along the time sequence likely reflect the response of the

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molecular distribution of brGDGTs to environmental changes at the study site.

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3.3. Comparison of the MBT' and MBT'5ME values of core ATM17B

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The MBT' and MBT'5ME indices for peat core ATM17B both show a long-term

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increasing trend during the Holocene. MBT' ranges from 0.10-0.33, with an average

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of 0.16 and standard deviation of 0.05; and MBT'5ME ranges from 0.15-0.43 with an average of 0.23 and a standard deviation of 0.05 (Fig. 3). More specifically, both

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MBT' and MBT'5ME are lowest during 11.1-10.0 cal kyr B.P., in the early Holocene, with average values of 0.12 and 0.18, respectively. Subsequently, the MBT' and MBT'5ME values increase slightly during 10.0-5.5 cal kyr B.P., with average values of 0.14 and 0.2, respectively. During 5.5-3.0 cal kyr B.P., both MBT' and MBT'5ME exhibit rapidly increasing values, with maxima of 0.27 and 0.34, respectively. During 3.0-0.0 cal kyr B.P., in the late Holocene, MBT' and MBT'5ME reach their highest levels, with average values of 0.29 and 0.36, respectively (Fig. 3; Table S2). As a whole, the absolute values of MBT'5ME from core ATM17B are systematically ~0.07 higher than those of MBT' (Fig. 3). The results of linear 13 / 47

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regression analysis (Fig. S5) reveal a significant positive correlation between MBT' and MBT'5ME in core ATM17B (r2 = 0.92, p < 0.001, n = 129). The consistent co-variation of MBT' and MBT'5ME along the time sequence (Fig. 3), and their significant positive correlation (Fig. S5), show that either of the two indices can be

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used in the subsequent discussion.

4. Discussion

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4.1. Temporal validation of the SSP MBT'5ME-based quantitative temperature record

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Currently, most of the calibrations between the MBT index of brGDGTs and

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temperature are obtained from surface soils (Weijers et al., 2007; Peterse et al., 2012;

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De Jonge et al., 2014). It has been proposed that the MBT-temperature calibration for

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peat is different from that for soil (Naafs et al., 2017a). At present, there is only one peat-specific MBT'5ME-temperature calibration which has been recently reported,

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based on the brGDGTs data of 470 samples from 96 globally distributed peatlands (Naafs et al., 2017a). We use this calibration, i.e. Eq. (3), and the MBT'5ME data of core ATM17B from SSP, to reconstruct the quantitative temperature history of the SSP (Fig. 4a). The following observations need to be considered. First: i) the modern instrumental climatic data (1981-2010) from Altai station at an altitude of 737 m a.s.l. indicate a MAT of 4.8 °C and a standard deviation of ± 0.9 °C (1σ); ii) given the location of the SSP at an altitude of ~2446 m a.s.l. and the decrease in temperature with increasing altitude (~0.6 °C/100 m), the estimated modern MAT at SSP should 14 / 47

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be ~-5.4±0.9 °C. Using Eq. (3), the reconstructed MATpeat value for the topmost sample of core ATM17B is ~-6.1 °C, and the average reconstructed MATpeat value for the topmost two samples of core ATM17B is ~-5.7 °C (Fig. 4a). Thus, the reconstructed MATpeat value for the topmost two samples of core ATM17B is consistent with the estimated modern MAT at SSP.

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Second, using Eq. (3), the reconstructed MATpeat for the whole of core ATM17B varies from ~-13.9 °C (average of 11.1-10.0 cal kyr B.P.), ~-12.8 °C (average of

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10.0-5.5 cal kyr B.P.), ~-9.4 °C (average of 5.5-3.0 cal kyr B.P.), to ~-4.2 °C (average

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of 3.0-0 cal kyr B.P.), showing an overall warming trend during the Holocene with an

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overall change of ~10 °C (Fig. 4a). It is well known that the magnitude of temperature

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change should be greater at high latitudes and in high-altitude mountains (Pepin et al.,

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2015; Minder et al., 2018). Notably, the pollen-based MAT reconstruction from Lake Bayanchagan (at an altitude of 1355 m a.s.l.), in Inner Mongolia (Fig. 1),

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demonstrated an overall change of ~6-7 °C during the Holocene (Jiang et al., 2006). The pollen-based MAT reconstruction from Narenxia peatland (at an altitude of 1760 m a.s.l.), also in the southern Altai Mountains (Fig. 1), demonstrated an overall change of ~7-8 °C during the Holocene (Feng et al., 2017). The brGDGTs-based MAT reconstruction from Aweng Co (Co means lake, at an altitude of 4427 m a.s.l.) in the western Tibetan Plateau (Fig. 1), demonstrated an overall change of ~10 °C during the Holocene (Li et al., 2017). Comparing with these results and considering the differences in location, it seems the overall temperature change of ~10 °C of the brGDGTs-based SSP Holocene MATpeat record is reasonable. 15 / 47

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Third, based on the investigation of modern living peat plants in the SSP during the growing seasons of 2014 and 2017, and tree-ring samples for the period of 1954 to 2011, together with instrumental climatic data, the SSP peat α-cellulose δ13C data have been determined to be an indicator of summer temperature (Rao et al., 2019b). During the past ~11 kyr, the peat core α-cellulose δ13C record from the SSP indicates

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a long-term summer warming trend, with a warmer early (~8-6 cal kyr B.P.) and late (~4-0 cal kyr B.P.) Holocene and a colder middle (~6-4 cal kyr B.P.) Holocene (Fig.

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4b; Rao et al., 2019b). This pattern is supported by a nearby ice core δ18O

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warm-season (March-November) (Henderson et al., 2006) temperature record from

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the Western Belukha Plateau in the Siberian Altai Mountains (Fig. 1; Aizen et al.,

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2016). Interestingly, for the overlapping time intervals of ~10.4-9.5 and ~6.4-4.4 cal

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kyr B.P., the summer temperature variations indicated by the SSP α-cellulose δ13C record are further highly supported by the most recently reported independently-dated

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fossil beetle faunas results from the same peatland (Zhang and Elias, 2019). The monitoring of modern hydrological processes in the SSP, together with measurements of the δ18O values of various types of environmental sample during the growing seasons of 2014 and 2017, demonstrate that the water source of the sedge plants in the SSP (which is incorporated in α-cellulose), is mainly derived from the inflowing meltwater derived from the precipitation of the winter half-year (Shi et al., 2019). In the Altai region, winter precipitation δ18O has a significant positive relationship with winter temperature (Tian et al., 2007; Malygina et al., 2016). Consequently, the overall positive trend of the peat core α-cellulose δ18O record from the Big Black 16 / 47

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peatland (at an altitude of ~2168 m a.s.l.; Fig. 4c), also in the southern Altai Mountains and very close to the SSP (Fig. 1; Xu et al., 2019), should indicate a long-term winter warming trend during the Holocene. Moreover, this is supported by a stalagmite δ18O winter temperature record (Baker et al., 2017) from the southern Ural Mountains (Fig. 1) and by an ice wedge δ18O winter temperature record (Meyer

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et al., 2015) from the Lena River Delta in the Siberian Arctic (Fig. 1). Notably, the long-term Holocene warming trend is not only evident in the brGDGTs-based SSP

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MATpeat record, but also in the SSP peat α-cellulose δ13C summer temperature record

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and in the nearby peat δ18O winter temperature record. More importantly, the

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warming trend is much more significant after ~5.5 cal kyr B.P. in these three records

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(Fig. 4), highlighting the significance of the SSP peat MBT'/MBT'5ME data as a

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temperature indicator. Notably, the brGDGTs-based SSP MATpeat record is more similar to the nearby peat δ18O winter temperature record, but it shows differences to

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the SSP peat α-cellulose δ13C summer temperature record. For example, there is an obvious warm interval from ~8-6 cal kyr B.P. in the SSP peat α-cellulose δ13C summer temperature record, which is not clearly evident in the brGDGTs-based SSP MATpeat record and in the nearby peat δ18O winter temperature record. This may reflect the differences between mean annual temperature (the peat brGDGTs-based reconstructed temperature is mean annual temperature) and summer temperature, and it appears that the local mean annual temperature in the high-altitude Altai Mountains is dominated by winter temperatures. Compared with the peat α-cellulose δ13C/δ18O summer/winter temperature 17 / 47

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records, there are no substantial variations in the brGDGTs-based SSP MATpeat record during ~11-5.5 cal kyr B.P. (Fig. 4), which may reflect a possible “temperature threshold” effect on the molecular distribution of peat brGDGTs. Previous studies of surface soils in China (Zheng et al., 2016) and globally distributed surface soils (De Jonge et al., 2014; Naafs et al., 2017b) have indicated that the molecular distribution

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indices of brGDGTs were insensitive when the environmental temperature was either too high or too low. Previously reported short-term soil warming experiments

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demonstrated the adjustment of the soil bacteria community composition that

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responded directly/indirectly to the temperature changes (Xiong et al., 2014; Xiong et

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al., 2016). A similar biogeochemical response may also occur in brGDGTs-producing

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bacteria, as indicated by the recently reported results from geothermally heated soils

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along different temperature gradients in Iceland, which exhibit variations of MBT/MBT5ME indices of soil brGDGTs when temperature reaches a threshold value,

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as a result of the adjustment of the brGDGTs-producing bacteria community composition (De Jonge et al., 2019). In our results, the similar overall long-term increasing trends in the SSP peat MBT'/MBT'5ME records (Fig. 3), and the SSP record of absolute concentration of total brGDGTs (Fig. S2), partially demonstrate the response of the absolute concentration of total brGDGTs to environmental changes, especially temperature. It seems that both the bacteria community composition (Xiong et al., 2014; Xiong et al., 2016; De Jonge et al., 2019), the absolute concentration of total brGDGTs, and the MBT'/MBT'5ME data of brGDGTs (Weijers et al., 2007a; Naafs et al., 2017a), could respond to temperature changes, therefore contributing to 18 / 47

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the quantitative relationship between peat MBT'5ME data and temperature, i.e. Eq. (3), finally biasing the quantitatively-reconstructed temperature values. Notably, Eq. (3) is the only one currently existed quantitative peat-specific MBT'5ME-temperature calibration, which is based on the MAT range of -8 °C to 27 °C for 96 globally-distributed peatlands, with the MAT below 0 °C only occurring at six

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sites (Naafs et al., 2017a). Therefore, we conclude that the SSP MBT'/MBT'5ME data are indeed qualitative indicators of long-term temperature changes, and the

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quantitative estimation of temperature history of the SSP (Fig. 4a) is one type of

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meaningful attempt. We also emphasize that a region-specific peat MBT-temperature

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calibration is needed in order to provide more accurate peat brGDGTs-based

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quantitative paleotemperature reconstructions in the future.

4.2. Spatial validation of the SSP Holocene peat MBT' record

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Holocene peat MBT' records have been reported from the HY peatland in the northeastern Tibetan Plateau (Zheng et al., 2015) and from the SZY peatland in southeastern China (Fig. 1; Wang et al., 2017). Therefore, the Holocene peat MBT' records from the HY and SZY peatlands can be compared with the peat MBT' record from the SSP, in order to further validate the temperature significance of peat MBT' data. In this context, the following observations are significant: i) there is a significant positive correlation between the SSP MBT' and MBT'5ME data (Fig. S5) and they co-vary consistently along the time sequence (Fig. 3); and ii) there are significant environmental differences, especially in temperature, between the SSP, HY and SZY 19 / 47

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peatlands that are located in ACA, the monsoon margin and the monsoon region, respectively. Notably, the MBT' data from the HY and SZY peatlands were obtained using the traditional method using a single cyano column (Zheng et al., 2015; Wang et al., 2017); however, the MBT' data from the SSP were obtained using the new method

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with two connected silica columns. For 239 globally distributed surface soils, we recalculated the MBT' values based on the original data of the relative abundance of

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different brGDGT homologues reported by De Jonge et al. (2014) using the new

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method with four connected silica columns. Compared with previously reported MBT'

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values from the same samples using the traditional method with a single cyano

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column (Peterse et al., 2012), the recalculated MBT' values demonstrate that

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differences were generally less than 0.08, and mostly less than 0.01. By using the methods with two (Hopmans et al., 2016) and four (De Jonge et al., 2014) silica

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columns, the MBT'5ME data from 36 surface soils demonstrated that the differences did not exceed 0.01 (Hopmans et al., 2016). Based on these results, it appears that the influence of different methods using different columns (single cyano column, two connected silica columns, four connected silica columns) on the final MBT' data is insignificant. Therefore, we have directly compared the Holocene MBT' records from the SSP, HY (Zheng et al., 2015) and SZY (Wang et al., 2017), as discussed below, and the following observations can be made. First, as demonstrated by Figure 5, an increasing trend is clearly evident in the SSP and SZY MBT' records but is less clearly evident in the HY MBT' record. All 20 / 47

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three peat MBT' records show a warming trend during the Holocene and notably there is consistency between the SSP MBT' record and previously-proposed temperature records from the SSP and the nearby site (Figs. 2 and 3). Second and more importantly, the modern instrumental climatic data (average values of 1981-2010) from the nearest stations demonstrate the following: (i) at Altai

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meteorological station, at an altitude of 737 m a.s.l. and close to the SSP peatland at an altitude of 2446 m a.s.l., the MAT is 4.8 °C; (ii) at Hongyuan meteorological

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station, at an altitude of 3492 m a.s.l. and close to the HY peatland at an altitude of

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3507 m a.s.l., the MAT is 1.8 °C; and (iii) at Ningde meteorological station, at an

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altitude of 32 m a.s.l. and close to the SZY peatland at an altitude of 1007 m a.s.l., the

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MAT is 19.9°C. Taking the altitudinal differences into account, the in situ MAT for

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the SSP should be -5.4 °C, that for the HY peatland should be 1.7 °C, and that for the SZY peatland should be 14 °C; thus the records represent a large spatial temperature

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gradient. The MBT' values of the topmost peat samples are 0.3 for the SSP peatland, 0.4 for the HY peatland, and 0.8 for the SZY peatland (Fig. 5). Thus, the MBT' values of the topmost peat samples from the three peatlands are consistent with the modern spatial temperature gradient, with higher MAT values corresponding to higher peat MBT' values. Third, the spatiotemporal coherence in local and regional temperature changes has been recently debated; for example, no spatiotemporal coherence has been observed in the compiled temperature records worldwide over the preindustrial Common Era (Neukom et al., 2019); however, coherent variations in winter 21 / 47

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temperatures over North America and North Asia for the period of 1951 to 2015 have been reported (Yu and Lin, 2018). It appears that the coherence in temperature changes largely depends on the spatial and temporal scales. Considering the huge spatial gradient among the SSP, HY and SZY peatlands (Fig. 1) and the long timescale of the Holocene, the spatial temperature gradient among the three sites should be

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continuous over time, indicating that the MBT' values for the entire peat cores from the three sites can be further compared. During the past ~11 kyr, the peat MBT' values

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of the SSP peatland range from 0.10 to 0.33 with an average of 0.16 and a standard

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deviation of 0.05; those for the HY peatland range from 0.2 to 0.4 with an average of

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0.31 and a standard deviation of 0.04; and those for the SZY peatland range from 0.57

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to 0.80 with an average of 0.70 and a standard deviation of 0.06 (Fig. 5). Evidently,

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the MBT' values from the SZY peatland are substantially higher than those of the HY peatland (t = 56.82, p < 0.01), and the MBT' values from the HY peatland are

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substantially higher than those from the SSP peatland (t = 23.29, p < 0.01). It is obvious that the peat MBT' values - from the SZY in southeastern China, to the HY in northeastern Tibetan Plateau, and then to the SSP in central Asia - are not only consistent with the spatial temperature gradient, but they are also consistent with the positive correlation between the MBT index values of globally-distributed peat samples and the corresponding temperatures (Naafs et al., 2017a). In addition, apart from the long-term increasing trends and the absolute data ranges, the other minor differences among the peat MBT' records from the SSP, HY and SZY are not discussed in depth, due to the differences in data resolution and the 22 / 47

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quality of the chronologies (for example, the chronology quality and data resolution of the SZY record are lower than those of the other two records, Fig. 5).

4.3. Regional comparison of the proposed temperature records The temporal comparison (Fig. 4) between the SSP MBT'5ME-based MATpeat

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record and the proposed summer/winter temperature records from the SSP peatland (Rao et al., 2019b) and from the nearby Big Black peatland (Xu et al., 2019), together

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with the spatial comparison (Fig. 5) of peat MBT' records from the SSP, HY (Zheng et

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al., 2015) and SZY (Wang et al., 2017) peatlands, demonstrate the potential of the SSP

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Holocene peat MBT'/MBT'5ME indices as temperature indicators. Notably, these

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records reveal a long-term warming trend during the Holocene.

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An overall long-term Holocene cooling trend has long been thought to be evident (Shi et al., 1994; Marcott et al., 2013). However, recently reported results have

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suggested an overall Holocene warming trend, such as climate simulation results (Liu et al., 2014) and the reconstructed regional temperature history for Europe and North America based on pollen data from 642 sites (Marsicek et al., 2018). As mentioned above, the long-term warming trend during the Holocene indicated by the SSP MBT'/MBT'5ME records from the Altai Mountains in ACA is supported by a major set of results from the Eurasian continent, including but not limited to: the stalagmite δ18O winter temperature record (Fig. 6a) from Kinderlinskaya Cave in the southern Ural Mountains (Baker et al., 2017), the ice wedge δ18O winter temperature record (Fig. 6b) from the Lena River Delta in Siberian Arctic (Meyer et al., 2015), the peat 23 / 47

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α-cellulose δ13C summer temperature record from southern Altai Mountains (Rao et

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al., 2019b), the ice core δ18O warm-season temperature record (Fig. 6g) from the

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Western Belukha Plateau in Siberian Altai Mountains (Aizen et al., 2016), the

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pollen-based temperature record (Fig. 6h) from Tielishahan peat bog (also known as

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Narenxia peatland, Feng et al., 2017) in the southern Altai Mountains (Zhang et al.,

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2018), the pollen-based warmest month temperature record (Fig. 6i) from Lake Baikal (Tarasov et al., 2007), the pollen-based temperature record (Fig. 6j) from alpine Swan

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Lake in central Tianshan Mountains (Huang X.Z. et al., 2015), the alkenone-based summer temperature record (Fig. 6k) from Hurleg Lake in northern Tibetan Plateau (Zhao et al., 2013), the Guliya ice core δ18O record (Fig. 6l) from western Kunlun Mountains in the western Tibetan Plateau (Thompson et al., 1997), and the brGDGTs-based temperature record (Fig. 6m) from Aweng Co in western Tibetan Plateau (Li et al., 2017). The spatial distribution of these study sites is shown in Figure 1, location information is given in Table 2, and the corresponding proxy records are illustrated in Figure 6. The following important points can be made about these records. 24 / 47

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First, all of them show a long-term warming trend during the Holocene, with the sole exception of the Guliya ice core δ18O record; because it is located in the boundary area of the monsoon-dominated and westerlies-dominated regions, it may potentially be influenced by changes in moisture source (Rao et al., 2019a, 2019b). Notably, the chronology of the Guliya ice core δ18O record has been recently

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questioned (Hou et al., 2019). Given that most of the records were obtained in recent years and the adopted proxies have been determined to be indicators of temperature

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changes, we conclude that the records are significant not only at the local, but also at

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the regional level – as is also the case for the pollen-based regional Holocene

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temperature records for Europe and North America (Marsicek et al., 2018).

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Second, among these records (Fig. 6), several have been proposed as winter

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temperature records, such as stalagmite δ18O records from the southern Ural Mountains (Baker et al., 2017), the ice wedge δ18O record from the Lena River Delta

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(Meyer et al., 2015), and possibly the peat core α-cellulose δ18O record from the southern Altai Mountains (Xu et al., 2019), mentioned above. The long-term warming trend indicated by those records is more monotonic (Figs. 6a, b, c) than is the case in the other records and it is consistent with the brGDGTs-based MBT'/MBT'5ME records from the SSP in the southern Altai Mountains. Other records that have been proposed as indicators of warm-season temperature are the SSP α-cellulose δ13C record (Rao et al., 2019b) and the alkenone-based record from Hurleg Lake (Fig. 6k; Zhao et al., 2013) (summer temperature); ice core δ18O record from the Siberian Altai Mountains (Fig. 6g; Aizen et al., 2016) (warm-season temperature); and pollen-based records 25 / 47

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from Lake Bayanchagan (Fig. 6f; Jiang et al., 2006) and Lake Baikal (Fig. 6i; Tarasov et al., 2007) (temperature of the warmest month). All of these records show not only a long-term warming trend during the Holocene, but also generally a cold stage from ~6-7 to ~3-4 cal kyr B.P. (indicated by the vertical gray bars in Figure 6); thus, it appears that there is a potentially different pattern of evolution between summer and

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winter temperatures during the Holocene. That is, the basically more monotonic long-term warming trend is evident in the Holocene winter temperature record, and

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the general pattern of summer temperature is that of warmer early and late Holocene

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and a colder mid-Holocene (Fig. 6). In this context, it has been pointed out (Liu et al.,

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2014) that the seasonality of sedimentary proxy-based Holocene temperature records

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should be carefully considered. Notably, the SSP α-cellulose δ13C summer

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temperature record (Fig. 6; Rao et al., 2019b) that is supported by recently-reported SSP fossil beetle faunas results (Zhang and Elias, 2019), is also consistent with the

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recently reported pollen-based temperature record from the nearby Tielishahan peat bog located in the southern Altai Mountains (Fig. 6h; Zhang et al., 2018) (Fig. 1); thus the warmer intervals occurred in the early and late Holocene and a colder interval in the mid-Holocene. This is different to the SSP MBT'/MBT'5ME records, which show a relatively more monotonic long-term increasing trend (Figs. 3 and 6). The major growing season for the local terrestrial plants and beetles is summer, and the ice and snow cover in the SSP during the winter half-year is from September-October to the following April-May. This timing of snow/ice cover is potentially highly favorable for the growth of anaerobic bacteria, which are the most likely bio-precursor of brGDGTs 26 / 47

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(Weijers et al., 2006). Therefore, it is reasonable that that the SSP MBT'/MBT'5ME records are more similar to the proposed Holocene winter temperature records than to the proposed Holocene summer temperature records (Fig. 6). As pointed out previously, in the high-altitude Altai Mountains, the local mean annual temperature may be dominated by winter temperatures.

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Third, the occurrence of a long-term warming or cooling trend during the Holocene is an extremely important issue in Holocene paleoclimate studies, and it is

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related to major global or regional issues. For example, as has been pointed out

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previously (Rao et al., 2019b), the long-term cooling trend is consistent with the

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decreasing trend of North Hemisphere summer insolation during the Holocene;

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however, the long-term warming trend indicates that the dominant effect of increased

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winter insolation and greenhouse gas forcing may override the influence of decreasing summer insolation (Meyer et al., 2015; Baker et al., 2017). This emphasizes the

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significance of the long-term Holocene temperature trend for understanding the drivers of Holocene climatic evolution. From a regional perspective, under ongoing global warming, the accelerated ablation of glaciers, rising water levels of lakes that are fed by meltwater, and the intensification of local hydrological cycles, have been widely observed in the Tibetan Plateau (Gao et al., 2019) and in the cold regions of western China (Li et al., 2019a, 2019b, 2019c). As has been observed (Rao et al., 2019a), these phenomena could have been occurred over a long time-interval, even during the entire Holocene, against the background of long-term Holocene warming in these regions. This highlights the importance of reliably determining the long-term 27 / 47

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Holocene temperature trend for understanding and predicting regional hydrological cycles and water resources status in arid and cold regions. Our peat brGDGTs results from the SSP in the southern Altai Mountains support the reality of a long-term warming trend during the Holocene, which is supported by records from other regional sites, highlighting a likely regional long-term Holocene warming trend.

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However, the long-term Holocene warming trend is far from being determined to be a global phenomenon, due to the lack of records from some other regions, and therefore

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we emphasize the need to acquire additional Holocene temperature reconstructions.

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5. Conclusions

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We have reported the results of measurements of brGDGTs from an alpine

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peatland in the Altai Mountains, ACA. Based on an independent chronology, the MBT' and MBT'5ME data exhibit consistent warming trends through the Holocene, and

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they are significantly positive correlated. Comparison with proposed Holocene summer and winter temperature records from the same site and a nearby site, respectively, and with Holocene peat core MBT' records from the northeastern Tibetan Plateau and southeastern China, has enabled the temporal and spatial validation of the results. This leads us to conclude that the peat MBT' and MBT'5ME records from the Altai Mountains in ACA are reliable indicators of temperature changes. An increasing number of recent Holocene paleoclimatic studies from the Eurasian continent, including the results presented herein, have demonstrated an overall long-term warming trend during the Holocene. This trend is the opposite to the 28 / 47

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traditional concept of an overall long-term cooling trend during the Holocene. Given the importance of Holocene temperature history and its driving mechanisms, and its significance for understanding regional hydrological cycles, we appeal for additional studies of Holocene temperature history, especially based on quantitative records with

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unambiguous indicative significance.

Acknowledgments

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This work was supported by the Hunan Provincial Natural Science foundation of

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China (2018JJ1017), the National Natural Science Foundation of China (41772373,

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41372181), and the National Key R&D Program of China (2018YFA0606404).

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Constructive and valuable comments from Professor Philip A. Meyers and the other

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Captions for Figures and Tables: Fig. 1. Locations of the Sahara sand peatland (SSP, this study) and other cited study sites (HY, Hongyuan peatland in the northeastern Tibetan Plateau; SZY,

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Shuizhuyang peatland in southeastern China; a, Kinderlinskaya Cave in the southern Ural Mountains; b, Lena River Delta in the Siberian Arctic; c, Big

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Black Peatland in the southern Altai Mountains; d, the Caspian Sea in ACA; e,

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Kesang Cave in the western Tianshan Mountains; f, Lake Bayanchagan in

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Inner Mongolia; g, Western Belukha Plateau in the Siberian Altai Mountains; h,

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Tielishahan peat bog, also known as Narenxia peatland, in southern Altai

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Mountains; i, Lake Baikal in Siberia; j, Swan Lake in the central Tianshan Mountains; k, Hurleg Lake in the northern Tibetan Plateau; l, Guliya ice core

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in the western Tibetan Plateau; m, Aweng Co in western Tibetan Plateau). Fig. 2. The independent age-depth models of core ATM17B based on 11 AMS

14

C

dates of bulk plant macrofossils and two fitting methods (linear and cubic spline), and their comparison with the previously reported age-depth model of core ATM10-C7 based on 22 AMS

14

C dates of α-cellulose extracted from

bulk plant macrofossils (Rao et al., 2019b). Both cores are from the SSP. Detailed information on the 11 AMS

14

C dates for core ATM17B is given in

Table 1. Fig. 3. Times series of MBT' (a) and MBT'5ME (b) from core ATM17B. 40 / 47

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Fig. 4. Comparison of (a) the reconstructed MATpeat record from the SSP (black square with error bars indicate the average reconstructed MATpeat value for the topmost two samples of core ATM17B), (b) the peat α-cellulose δ13C summer temperature record from core ATM10-C7 from the SSP (Rao et al., 2019b), and (c) the peat α-cellulose δ18O record from the Big Black peatland which is

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very close to the SSP (Xu et al., 2019). Fig. 5. Comparison of the Holocene peat MBT' records from SZY in southeastern

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China (Wang et al., 2017), HY in the northeastern Tibetan Plateau (Zheng et

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al., 2015) and SSP in ACA (this study). The horizontal squares and error bars

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indicate the corresponding dating results from the study sites. The vertical

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squares and error bars on the right side represent the averaged MBT' values

study sites.

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and their standard deviations for the Holocene peat samples from the three

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Fig. 6. Comparison of relevant records from the Eurasian continent. Left-hand column: The MBT'5ME record from the SSP with absolute dating results is at the top. a. Stalagmite δ18O record from Kinderlinskaya Cave (Baker et al., 2017). b. Ice wedge δ18O record from the Lena River Delta (Meyer et al., 2015); the error bars show the age uncertainties. c. Peat α-cellulose δ18O record from the southern Altai Mountains (Xu et al., 2019). d. Pollen-based temperature record from the Caspian Sea (Leroy et al., 2014); the black curve is a three-point running mean. e. Stalagmite δ18O records from the western Tianshan Mountains; the gray curves are the data from stalagmite samples KS06 and 41 / 47

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KS06 (Cheng et al., 2012), and the black curves are the data from samples CNKS-7 and CNKS-9 (Cai et al., 2017). f. Pollen-based warmest month temperature record from Lake Bayanchagan in Inner Mongolia (Jiang et al., 2006). Right-hand column: Peat α-cellulose δ13C record from the SSP (Rao et al., 2019b) with absolute dating results is at the top. g. Ice core δ18O record

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from the Siberian Altai Mountains (Aizen et al., 2016). h. Pollen-based temperature record from the southern Altai Mountains (Zhang et al., 2018). i.

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Pollen-based warmest month temperature record from Lake Baikal (Tarasov et

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al., 2007). j. Pollen-based temperature record from the central Tianshan

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Mountains (Huang X.Z. et al., 2015). k. Alkenone-based summer temperature

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record from the northern Tibetan Plateau (Zhao et al., 2013); the gray bars

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indicate the temperature uncertainties of ~2.6-4.1 °C. l. Guliya ice core δ18O record from the western Tibetan Plateau (Thompson et al., 1997). m.

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brGDGTs-based temperature record from Aweng Co in the western Tibetan Plateau (Li et al., 2017); the gray bars indicate the temperature uncertainties of 1.2 °C. See Figure 1 and Table 2 for the locations and other information about these sites.

Table 1 AMS 14C dating results for the 11 samples from core ATM17B. Table 2 Information about the locations, adopted proxies and references of the study results from the SSP and the other sites that are cited herein. The site codes are the same as in Figure 1. 42 / 47

Journal Pre-proof Table 1 AMS 14C dating results for the 11 samples from core ATM17B. Depth

δ13C (‰,

Percent modern

Error

Conventional age

Error

Calibrated age

Error

code

(cm)

VPDB)

carbon (%)

(2σ)

(yr B.P.)

(2σ)

(cal yr B.P.)

(2σ)

ATM2

2

-23.8

97.30

0.36

220

30

8

8

ATM52

52

-23.4

72.80

0.27

2550

30

2547

47

ATM102

102

-24.4

63.72

0.24

3620

30

3916

73

ATM202

202

-23.8

54.95

0.21

4810

30

5511

37

ATM252

252

-23.4

51.83

0.19

5280

30

6058

72

ATM302

302

-27.2

48.04

0.18

5890

30

6720

65

ATM352

352

-24.5

44.91

0.17

6430

30

7356

68

ATM402

402

-23.2

41.52

0.16

7060

30

7897

58

ATM452

452

-24.0

37.96

0.14

7780

30

8559

53

ATM602

602

-24.9

33.02

0.12

8900

30

9994

81

ATM652

652

-24.1

29.93

0.15

9690

40

11145

74

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re

-p

ro

of

Sample

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Table 2 Information about the locations, adopted proxies and references of the study results from the SSP and the other sites that are cited herein. The site codes are the same as in Figure 1. Sit e co de SSP SSP HY SZY a b c d e f g

Location

Sahara sand peatland in southern Altai Mountains Sahara sand peatland in southern Altai Mountains Hongyuan peatland in northeastern Tibetan Plateau Shuizhuyang peatland in southeastern China Kinderlinskaya Cave in southern Ural Mountains Lena River Delta in Siberian Arctic Big Black Peatland in southern Altai Mountains Caspian Sea in ACA Kesang Cave in western Tianshan Mountains Lake Bayanchagan in Inner Mongolia Western Belukha Plateau in

Latitud e

Longitude

88°21′46.78″E 88°21′26.4″E

Sediment type

Altitude

48°6′46.70″ N 48°07′13.5″ N

Proxy

f o

o r p

Indicative significance

Reference

2446 m

peat

brGDGTs

temperature

this study

2450 m

peat

α-cellulose δ13C

summer temperature

Rao et al., 2019b

peat

brGDGTs

temperature

Zheng et al., 2015

r P

e

102°31′E

32°46′N

119°02′E

26°46′N

1007 m

peat

brGDGTs

temperature

Wang et al., 2017

56°54′E

52°12′N

stalagmite

δ18O

winter temperature

Baker et al., 2017

125°00′-127°1 5′E

72°00′-72°45 ′N

25 m

ice wedge

δ18O

winter temperature

Meyer et al., 2015

87°11′E

n r u

240 m

48°40′N

2168 m

peat

winter temperature

Xu et al., 2019

51°06′04″E

41°32′53″N

479 m

sea sediment

α-cellulose δ18O pollen

temperature

Leroy et al., 2014

81°45′E

42°52′N

2070 m

stalagmite

δ18O

winter temperature

Cai et al., 2017

115°12′E

41°39′N

1355 m

lake sediment

pollen

86°33′E

49°48′N

4115 m

ice core

δ18O

o J

l a

3507 m

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warmest month temperature warm-season

Jiang et al., 2006 Aizen et al., 2016

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h

i j k l m

Siberian Altai Mountains Tielishahan (Narenxia) peat bog in southern Altai Mountains Lake Baikal in Siberia Swan Lake in central Tianshan Mountains Hurleg Lake in northern Tibetan Plateau Guliya ice core in western Tibetan Plateau Aweng Co in western Tibetan Plateau

temperature temperature

Feng et al., 2017; Zhang et al., 2018

pollen

warmest month temperature

Tarasov et al., 2007

pollen

temperature

Huang X.Z. et al., 2015

86°55′E

48°48′N

1770 m

peat

pollen

106°09′E

52°31′N

455 m

lake sediment

84°22′55″E

43°02′45″N

2541 m

lake sediment

f o

96°54′E

37°17′N

2817 m

lake sediment

alkenone

81°29′E

35°17′N

6710 m

ice core

δ18O

81°38′-81°48′ E

32°42′-32°49 ′N

4421 m

lake sediment

brGDGTs

l a

e

r P

n r u

o J

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o r p

summer temperature Temperature/moistu re source temperature

Zhao et al., 2013 Thompson et al., 1997 Li et al., 2017

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Declaration of interests

f o

☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

e

o r p

r P

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

l a

n r u

o J

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Highlights: • The Holocene peat MBT'/MBT'5ME records from Altai Mountains in arid Central Asia • The peat MBT'/MBT'5ME records indicate the Holocene temperature history • The long-term Holocene warming trend is indicated by the peat MBT'/MBT'5ME records

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• The Holocene warming trend is supported by recent records from Eurasian continent

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e

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n r u

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6