Moisture variations in Lacustrine−eolian sequence from the Hunshandake sandy land associated with the East Asian Summer Monsoon changes since the late Pleistocene

Quaternary Science Reviews 233 (2020) 106210

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Moisture variations in Lacustrine
eolian sequence from the Hunshandake sandy land associated with the East Asian Summer Monsoon changes since the late Pleistocene Guodong Ming a, b, d, e, Weijian Zhou a, b, e, *, Hong Wang f, Peng Cheng a, b, c, Peixian Shu a, b, e, Feng Xian a, b, e, Yunchong Fu a, b, c a

The State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences (IEECAS), Xi’an, 710061, China Shaanxi Key Laboratory of Accelerator Mass Spectrometry Technology and Application, Xi’an AMS Center of IEECAS, China Xi’an Jiaotong University, Xi’an, 710049, China d University of Chinese Academy of Sciences, Beijing, 100049, China e CAS Center for Excellence in Quaternary Science and Global Change, Xi’an, 710061, China f Interdisciplinary Research Center of Earth Science Frontier, Beijing Normal University, Beijing, 100875, China b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 December 2019 Received in revised form 8 February 2020 Accepted 8 February 2020 Available online xxx

Paleoclimate records currently lack sufficient geographic detail to understand the spatiotemporal evolution of East Asian Summer Monsoon (EASM) rain belt since the late Pleistocene. In particular, there is no consensus on whether the EASM rain belt reached its modern northern limit by the early Holocene. Here, we present inferred moisture variations from a multi-parameter, absolute-dated lacustrine
eolian sequence from northern China that date back to the late Pleistocene (~14 ka BP or thousand years ago). We observed a sharp transition towards wet conditions at the onset of the Holocene Epoch. Maximum wet climate occurred here during ~11.3e8.5 ka. The climate remained predominantly wet until ~4.2 ka, then it became progressively drier. We observed alternating organic-rich, fine-grained lake deposits and organic-depleted, coarse-grained eolian sand layers during ~14
7 ka. These layers correspond to sedimentation associated with the Allerød, Younger Dryas (YD), post YD warming, pre-Boreal oscillation (PBO), early Holocene and 8.2 ka event. We interpreted the orbital scale moisture variation at our study site to the changes in insolation, assigning these abrupt and short-lived changes during the late Pleistocene
Holocene transition to a persistent teleconnection between the North Atlantic and east Asian climate zones. © 2020 Elsevier Ltd. All rights reserved.

Keywords: Paleo EASM variations Monsoon rainfall Climate instability Lacustrine
eolian sequence Desert
loess transition zone Northern China

1. Introduction The East Asian Summer Monsoon (EASM) is a unique subsystem of the Asian monsoon system. In monsoonal China, it features a poleward and equatorward migration of the monsoon rain belt. Generally, the annual poleward migration of the summer monsoon rain belt is quasi-stationary in mid-June at areas between the Yangtze and Yellow Rivers, while rain belt will migrate further north towards northern/northeastern China during July to August (Ding and Chan, 2005). The variable length and position of the monsoon front at different places can produce large spatiotemporal

* Corresponding author. Institute of Earth Environment, Chinese Academy of Sciences (IEECAS), No. 97 Yanxiang Road, Xi’an, 710061, Shaanxi, China. E-mail address: [email protected] (W. Zhou). https://doi.org/10.1016/j.quascirev.2020.106210 0277-3791/© 2020 Elsevier Ltd. All rights reserved.

differences in precipitation (Chiang et al., 2015; Huang et al., 2014; Tao and Wei, 2006; Zhang et al., 2019; Zhou et al., 2009). Understanding the dynamics and variability of the paleo EASM system is crucial to discuss spatiotemporal monsoon rainfall patterns (An et al., 2000; Chen et al., 2019; Wang et al., 2008; Zhou et al., 2016b) and future climate projections (Broecker and Putnam, 2013; Yang et al., 2015a). During the last few decades, these patterns and associated dynamics of the EASM strength have been intensely studied and debated. The early to middle Holocene has generally been considered to be a relatively wet period, and the late Holocene as relatively dry (An et al., 2015; Chen et al., 2019; Herzschuh, 2006; Wang and Feng, 2013; Wang et al., 2010; Wanner et al., 2008; Zhao et al., 2009). The main argument lies in the timing of Holocene optimum (HO) of the EASM. Stalagmite d18O records from monsoon China consistently

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indicate an early HO (Dong et al., 2010, 2015; Dykoski et al., 2005; Hu et al., 2008; Jiang et al., 2012; Wang et al., 2008; Yang et al., 2019; Zhang et al., 2019), suggesting synchronous change between Northern Hemisphere Solar Insolation (NHSI) and EASM rainfall in monsoonal China. On the other hand, lacustrine and eolian studies show a more disparate pattern. Some have argued that the HO occurred in the middle Holocene (Chen et al., 2015; Fan et al., 2017; Guo et al., 2018; Li et al., 2014; Liu et al., 2015; Lu et al., 2005, 2013; Mason et al., 2009; Wang et al., 2014; Wang and Feng, 2013; Wen et al., 2013; Xiao et al., 2004; Zhao et al., 2007; Zhou et al., 2008). Northern Hemisphere (NH) remnant ice, sea-level change, and related ocean-atmospheric circulation feedbacks have been proposed to address the ~3000 year time lag from the NHSI peak (Chen and Huang, 1998; Jin et al., 2012; Lambeck et al., 2014; Li et al., 2014; Liu, 1997a; Ruddiman, 2008; Wang, 1999). In contrast, along the north margin of the EASM (the semi-arid zone of northern China with mean annual precipitation ranging from 400 to 200 mm, Fig. 8), others have documented the onset of maximum moisture conditions from the early Holocene (An et al., 2012; Goldsmith et al., 2017; Li and Liu, 2018; Li and Sun, 2006; Sun and Feng, 2013; Sun et al., 2018; Tang et al., 2015; Wang et al., 2012; Yang et al., 2013; Zhou and An, 1991). The transition of monsoon rainfall from the late Pleistocene to the Holocene is critical to understanding past abrupt climate changes in East Asia, and the dynamic drivers behind them (Broecker and Putnam, 2013; Clark et al., 2012; Liu et al., 2013; Ma et al., 2012; Zhang et al., 2018; Zhou et al., 2001). The understanding of climate change in northern China during this period is hindered by chronologic uncertainties and/or climate proxies with insufficient sensitivity. Major climate events, from the Bølling-Allerød (BA) warming to YD cooling, and from post-YD warming to a series of short-lived late PleistoceneeHolocene climate oscillations, are muted or absent from EASM precipitation proxies in this region (Supplementary data; Tables S1 and S2) (An et al., 2012; Beck et al., 2018; Chen et al., 2015; Jin et al., 2007; Li and Liu, 2018; Lu et al., 2013; Tang et al., 2015). Hence, additional high-resolution records are needed. The desert
loess transition zone along the EASM margin is an ideal place to seek such records (Liu, 1997a; Zhou et al., 2001). Wang and Ding (2008) suggested that on orbital time scales, EASM intensity could be inferred from the northward extent of the paleo monsoon. Liu et al. (2014) and Liu et al. (2015) further imply that the precipitation pattern change in northern China is positively correlated to and probably the best indicator of EASM rainfall intensity. Here, we present a high-resolution, absolute-dated lacustrine
eolian record from the central Hunshandake sandy land (Hunshandake), on the northernmost margin of the EASM-affected region. We adopted the viewpoint that the HO can be identified as a period of maximum moisture conditions in northern China since ~11.7 ka (ka BP or thousand years before present, present mean CE1950). Sediment properties of this lacustrine
eolian section, with elemental parameters, total organic carbon content (TOC), and grain size, reflect detailed moisture variations and abrupt climate changes since ~14 ka within B-A NH warm climate period. We used 14 C dates obtained from bulk organic matter to establish a Bayesian age model. We compare results using both the OxCal 4.3 (“PSequence” model) (Bronk Ramsey, 2009) and the BACON (Blaauw and Christen, 2011; Blaauw et al., 2018) program. We also took advantage of the 14C-plateau at ~10.1e9.8 14C ka as a time marker. The goals of this paper are: 1) to resolve the pattern of moisture variations at our site since the late Pleistocene; 2) to present geological evidence for abrupt changes of moisture variations at Hunshandake during the late Pleistocene
Holocene transition; 3) to discuss the mechanisms controlling moisture changes in this region; and 4) to compare our results with published records from

the northern EASM margin, and lay out our understanding to highly debated research topics. 2. Regional setting Lake Bayan nuur (Fig. 1) is a closed saline lake located in the central Hunshandake (Fig. 1C). A potential spillover site lies to the northwest of the lake basin, at an elevation of 1080 m (Fig. 1D). The modern lake has a surface area of ~1 km2 in southern basin. The northern basin was occasionally covered with shallow water. It is covered by a 1e2 cm thick mirabilite layer during dried periods, which inhibit erosion. Hunshandake is situated along the northern EASM limit, with the Gobi desert to the north and semi-arid loess hills to the south (Fig. 1A and C). The mean annual precipitation, evaporation and temperature are ~280 mm, ~1831 mm and ~1.3e2.6  C, respectively. From the middle of November to early April, the ground is covered with ice and the lake is frozen for ~6 months each year. More than ~90% of the annual rainfall (~260 mm) and ~80% of the evaporation (~1441 mm) occur between May and October (Fig. 1B). 3. Material and methods 3.1. Sampling and stratigraphy A 5.24 m-long borehole (BN-2016) was retrieved from the center of the northern lake basin (Fig. 1D) using an LS250 acoustic percussion drill in November 2016. The lacustrine sequence is underlain by coarse eolian sands. The core/sequence was scanned and then subsampled at 1 cm intervals in the Institute of Earth Environment, Chinese Academy of Sciences. The stratigraphy of the core was divided into 4 primary units (Fig. 3). The four units include: I) 524
375 cm, grayish basal unit, consisting of coarse eolian sand with two distinct sublayers: plain clayey silt layer with interbedded coarse eolian sands from 486 to 464 cm; and a light brown silty sand layer at 406
386 cm; II) 375
289 cm, brownish lacustrine clayey silt (light gray after fully dried), with a sublayer at 300
289 cm with a purple hue; III) 289
88 cm, grayish sandy silt, with two 10
15 cm-thick silty sand sublayers between 265 and 225 cm. An interval with lower accumulation was identified based on 14C ages at ~225
205 cm (Fig. 2A) depth, which serve to subdivide Unit III into Unit III-I (289
205 cm) and III-II (205
88 cm) according to a distinct contrast in average TOC content (Fig. 3C1). Detailed facies analysis of this layer is given in Supplementary data section 3. IV) 88
0 cm, interbedded sand to sandy silt. 8e9 discernible grayish sandy silt sublayers deposited at 88
28 cm interval (Fig. 3). 3.2. AMS

14

C determinations and chronology

Forty-one bulk organic matter (OM) radiocarbon ages were determined at the Xi’an accelerator mass spectrometry (AMS) center (Zhou et al., 2006). The measurement protocol of Zhou et al. (2014) was employed, described in details in Supplementary data section 2. Radiocarbon dating results are given in Table 1. A Bayesian age model for the BN-2016 core was constructed with both the OxCal 4.3 (“P-Sequence”) (Bronk Ramsey, 2009) and BACON (Blaauw and Christen, 2011; Blaauw et al., 2018) programs, using the IntCal13 dataset (Reimer et al., 2013) (Fig. 2B). A Bayesian accumulation model allows for variable sedimentation rates and assumes only superposition of the samples in a coherent stratigraphic sequence (Blaauw, 2010; Blockley et al., 2007). Initially, all radiocarbon data were considered in the development of the age model. Four radiocarbon ages (XA19468, XA19466, XA18388 and XA18389) that fell outside the 95% uncertainty range of the age-

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Fig. 1. Geological setting of the study site (red solid star, Lake Bayan nuur, 43 070 N, 114 300 E, 1070 m). (A) Averaged atmospheric flow fields at the 850 hPa isobar in summer (JJA) from 1971 to 2010, data extracted from the NCEPeNCAR Reanalysis Project (Kalnay et al., 1996). Triangles indicate other records mentioned in the text. Blue triangles, Lake Dali (43 13e230 N, 116 29e450 E, 1220 m), Lake Gonghai (38 540 N, 112 140 E, 1860 m), Lake Qigai (39 300 N, 109 300 E), Lake Qinghai (36 320 e37 150 N, 99 360 e100 470 E, 3194 m); orange triangles, Mediwan (37 390 N, 108 370 E) and Dongxiang (35 330 N, 103 350 E) eolian sequences; purple triangle indicate Lianhua cave (38 100 N, 113 430 E, 1200 m) and Haozhu cave (30 410 N, 109 590 E, 1017 m). Green triangle indicates Dahu peat (24150 N, 115 20 E). The gray shaded area indicates the region where elevations exceed 3000 m (i.e. the Tibetan Plateau); (B) Meteorological observations from nearby Xilinhot station (1971e2000). Data collected from the National Meteorological Information Center of China; (C) Geological setting of Hunshandake. Blue triangles indicate other lacustrine records mentioned in the text. Lake Xiari (42 370 N, 115 280 E), Lake Ulan (41440 N, 115 050 E, elevation: 1246 m), Lake Bai (41380 N, 114 300 E, elevation: 1346 m), Lake Bayanchagan (41390 N, 115130 E), Lake Anguli (41180 N, 114 200 E), Lake Daihai (40 350 N, 112 410 E); (D) Satellite view of Lake Bayan nuur. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 2. Bayesian age-depth model for the BN-2016 core was calculated using both OxCal 4.3 (“P-Sequence”, B2) (Bronk Ramsey, 2009) and BACON (B1) (Blaauw and Christen, 2011; Blaauw et al., 2018) program with the IntCal13 dataset (Reimer et al., 2013). Similar result has been generated from these two methods and the model output from BACON program was adopted for the data analysis thereafter. Due to the large difference between accumulation rate and TOC content (A2 and Fig. 3C), the BN-2016 core was separated into two segments at 205 cm depth. We applied a local reservoir correction factor (R) of 360 years to the upper segment, as estimated from the intercept of the linear regression of all bulk OM AMS-14C data (A1). The effects of changing local R for the lower segment are discussed in the text (section 5.1). A R of 0 and 360 years was employed for Unit III-I and II, respectively. Plot A2 shows the mean accumulation rate of BN-2016 based on BACON age depth model. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

depth model were removed as outliers, from the final age model run. Due to the large difference between accumulation rate and TOC content (Figs. 2A2 and 3C1), the BN-2016 core was separated into

two segments at 205 cm depth. The upper segment, above 205 cm depth includes Units III-II and IV, and features relatively larger scatter of bulk OM 14C-ages. This is likely related to the low TOC content (<0.5%, Fig. 3C1) typical in this segment, making it more

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Fig. 3. Plots of stratigraphy and parameter measurements results of the BN-2016 sequence. From left to right, lithology photo; clay, silt and sand fraction; mean grain size (A); percentage of fine (1.45e76 mm) grain component (B); TOC (C1); TOC flux (C2); C/Norg ratios (C3) and Rb/Sr ratios (D). The right panel demonstrate XRF elemental properties: Ca, Mg, Cl and Br (E1 to E4), respectively. Dark/Red dash lines denote variation trend of TOC content, C/Norg ratios and elemental properties. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

vulnerable to exogenous OM inputs, and variable radiocarbon reservoir ages induced by frequent lake hydrological changes (Liu et al., 2017; Zhou et al., 2014). We applied a local reservoir age (R) of 360 years for the upper segment, as estimated from the intercept of the linear regression of all bulk OM AMS-14C data (Fig. 2A1). This value is close to the surface sample 14C measurements (Table 1). The basal eolian sand layer (Unit I) had no discernible reservoir age. The local R of the upper segment was not simply applied to the lacustrine unit II and III-I in the lower segment. Instead, we carried out a detailed sensitivity assessment to the chronology of the lower segment against the choice of a local R. The effects of changing local R are evaluated in the discussion, section 5.1. 3.3. Sediment analysis Elemental measurements (Ca, Mg, Cl, Br, Rb and Sr; denote Calcium, Magnesium, Chlorine, Bromine, Rubidium and Strontium, respectively) were determined at 0.5e1 cm intervals using an Avaatech X-ray fluorescence (XRF) scanner (Sun et al., 2016). TOC concentrations were measured at 1e2 cm intervals with a Vario EL III cube analyzer, with a measurement uncertainty of <0.1% (Zhou et al., 2016a). Total Nitrogen (TN) content was also measured simultaneously in order to calculate OM C/N ratios (C/Norg). Grain size analysis was made at 1 cm intervals with a Malvern Mastersizer 3000 laser-diffraction analyzer, which determines grain sizes of 0.01e3000 mm, with an analytical error within 2% (An et al., 2012; Sun et al., 2006). All results are shown in Fig. 3. 4. Results 4.1. Sediment parameter results The TOC weight percentage (Fig. 3C1) varies from 0.04 to 4.85%, with an average value of 0.74%. TOC in lake sediments is commonly regarded as an indicator of primary productivity and vegetation coverage of the lake basin (Beuning et al., 1997). In northern China,

TOC can also be taken as an indicator of monsoonal precipitation (An et al., 2012). During strong monsoon periods, simultaneous increases in temperature and precipitation cause biomass in the catchment to increase, so that terrestrial organic matter input and aquatic productivity were both enhanced, leads to increased TOC in the lake sediments (Xu et al., 2007). To address the uncertainty potentially caused by sedimentation rate and dry density variations in the TOC record (Chen et al., 2018), the mass accumulation rate of TOC (TOC flux, Fig. 3C2) was calculated as follows: TOC flux (g cm
2 yr
1) ¼ sediment accumulation rate (cm/yr)  sediment dry density (g/cm3)  TOC (%). Sediment dry density was measured at 3e5 cm intervals. The TOC and TOC flux records co-vary through the whole sequence (Fig. 3C, Pearson correlations, Ep ¼ 0.90; 2-tailed test of significance, P < 0.05; all significance correlations in text pass the 0.05 significance level), confirming that the sediment accumulation rate and dry density do not substantially affect TOC at this site. The mean grain size value (Fig. 3A) varies from 28.2 to 363 mm, with an average value of 179 mm. The silt (4e63 mm) and sand (>63 mm) fractions account for 33.5% and 64.5%, respectively. The grain size distribution (GSD) characteristics of the BN-2016 sequence are decomposed statistically and compared with typical modern sediments (Supplementary data; Figs. S1 and S2) to understand the process-grain size relationships in Lake Bayan nuur. The results show that the mean grain size and the fine-grained fraction (1.45e76 mm, Fig. 3B; Ep ¼ 0.95) can be regarded as an lake level/eolian activity indicator closely related to regional moisture conditions (Vandenberghe et al., 2018; Xiao et al., 2012, 2015). On a geological timescale, when moisture conditions are relatively enhanced, higher lake level prevents more coarsegrained components from reaching the core site. On the other hand, regional eolian activity and coarse eolian input are attenuated. Both processes lead to a higher percentage of fine grain component and lower mean grain size value. The Rb/Sr ratios (Fig. 3D) of lacustrine sediments nearby the Hunshandake have been interpreted as a measure of catchment weathering, which is mainly driven by climate (Brass, 1975; Jin

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Table 1 Radiocarbon ages from the BN-2016 sediments. Lab code

Depth

Dating material

cm XA22723 XA22724 XA18386 XA18385 XA18397 XA22722 XA22729 XA22728 XA22727 XA19465 XA22726 XA18398 XA22725 XA18399 XA19468 XA22731 XA22730 XA19466 XA18387 XA18388 XA19469 XA22739 XA18389 XA22738 XA22737 XA22736 XA22735 XA18400 XA18340 XA22734 XA22720 XA18392 XA18393 XA22719 XA18339 XA22718 XA22743 XA22742 XA18394 XA18395 XA18396

e e e e 28 32 61 73 78 84 90 100 105 124 133 143 150 158 160 169 170 188 203 233 241 264 278 295 308 321 331 336 351 361 371 374 388 392 466 466 478

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Calibrated age (Cal yr BP) (2d)

C age Error(1d) (yr)

Lower

Upper

Median

Modern (F C ¼ 1.0146 ± 0.0039) Modern (F14C ¼ 1.0176 ± 0.0028) 580 20 710 20 900 50 1480 25 1390 25 1930 30 1775 25 2360 30 2030 30 2930 30 2880 30 3220 30 3850 30 3305 30 3420 30 3420 30 4180 30 3910 30 4480 30 4580 35 4530 30 7270 35 7490 40 7730 40 7770 40 8860 40 9470 40 9805 40 9840 40 9920 40 10030 40 10100 40 10110 40 9890 40 10010 40 10010 50 10830 40 10620 40 10920 40

e e 540 570 710 1315 1280 1820 1610 2330 1900 2985 2890 3380 4155 3460 3590 3580 4620 4250 4980 5059 5050 8010 8200 8430 8440 9780 10585 11180 11200 11240 11330 11405 11410 11210 11310 11270 12680 12540 12700

650 690 925 1405 1335 1930 1810 2490 2110 3170 3110 3540 4410 3590 3820 3820 4835 4420 5290 5450 5310 8170 8380 8580 8610 10170 11060 11260 11310 11590 11720 11980 11990 11400 11710 11750 12780 12700 12850

610 670 820 1365 1300 1875 1680 2390 1985 3080 3010 3430 4270 3530 3670 3670 4725 4350 5170 5300 5150 8090 8320 8500 8550 10010 10710 11220 11240 11310 11520 11710 11730 11280 11500 11500 12720 12610 12770

(yr BP) Surface Surface Surface Surface TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC TOC

aquatic weeds reeds sample 1 TOC sample 2 TOC

14

All radiocarbon ages reported here were calibrated using the INTCAL13 dataset (Reimer et al., 2013). F14C denote fraction of modern atmospheric 14C value. Surface sample 1 and 2 were taken from different locations in the dry lake basin.

et al., 2006). The Ep between Rb/Sr and the fine-grained (1.45e76 mm, Fig. 3B) fraction in Lake Bayan nuur sediment is 0.88. Rb/Sr is also highly correlated with TOC parameter (Fig. 3C and D, Ep ¼ 0.91), suggesting that these three parameters all mirror similar climatic factors from different aspects. Moist conditions are coincident with increased dissolved Sr in the lake (lower Rb/Sr values), a higher fraction of fine-grained sediments (lower mean grain size) and TOC content, and vice versa. The geochemical signal (Ca, Mg, Cl and Br, Fig. 3E) of a saline lake provides a model of how water composition evolves as a consequence of precipitation of salts and mineral-brine interactions (Parrish, 1973). Specifically, it provides evidence for the onset of saline conditions (Liu, 1997b).

4.2. Unit I, II and III-I: Lacustrine
eolian succession during the late Pleistocene
Holocene transition The age model reveals continuous deposition during the late Pleistocene
Holocene transition (~14
7 ka). Illustrative sedimentary records from BN-2016 sequence are presented vs. depth (Fig. 4C), in order to avoid misinterpretations arising from 14C age interpolation and R estimation.

Between 485 and 465 cm depth (~13.2e12.8 ka, in the BACON age model), the fine-grained lacustrine deposits differ from the underlying eolian coarse sands. Two finer clayey silt layers are interbedded within this interval. The AMS 14C ages of these clayey silt layers are 10.62e10.83 ± 0.04 14C ka (12.55e12.78 ka) and 10.92 ± 0.04 14C ka (12.70e12.85 ka). Within this interval, the mean grain size (Fig. 4C1) and Rb/Sr (Fig. 4C2) sharply decrease from >240 mm to ~60 mm and 0.5 to ~0.2, respectively. TOC (Fig. 4C3) increases from <0.1% to ~0.5%, representing more humid conditions with enhanced vegetation coverage. We interpret the lacustrine unit to represent the Allerød chronozone with a couple of relatively dry fluctuations. Thus, the underlying coarse eolian sand layer may correlate with the Older Dryas event (Rasmussen et al., 2014). The following section is a thick layer of coarse eolian sand between 465 and 406 cm depth (~12.8e11.7 ka). The mean grain size and Rb/Sr value fluctuated around 280 mm and 0.5, respectively. TOC remains below 0.1%, reflecting a typical dry climate. This coarse eolian sand layer coincides with the YD chronozone (Rasmussen et al., 2014). The mean grain size and TOC parameters at this position further demonstrate relatively wet climate alternating with extremely dry conditions (Ma et al., 2012; Zhou et al., 2001), similar to d18O records from Greenland (Fig. 5A), with several small

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Fig. 4. Plots comparing the BN-2016 sequence and other regional paleoclimate records during ~14
8 ka BP. From left to right, (A) AMS-14C age and lithology of Dongxiang sequence (Zhou et al., 2001); (B) AMS-14C age, lithology and d13C of the Jingbian Mediwan sequence (Zhou et al., 2001); (C) Parameter results of the BN-2016 sequence (from left to right, lithology image; relative composition of clay, silt and sand; AMS-14C and calibrated age; mean grain size; Rb/Sr and TOC); (D) Paleo-lake level reconstruction records from lake Dali, eastern Hunshandake, squares and triangles denote age control points (Goldsmith et al., 2017). The cold (warm) color shaded area indicates the timing of dry (wet) climate intervals. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

amplitude, short-lived climatic YD fluctuations (Von Grafenstein et al., 1999). Between 406 and 375 cm depth (~11.7e11.3 ka), the lithology in Lake Bayan nuur transitions towards wetter conditions. Following the YD, TOC gradually increased to ~0.3%, and mean grain size slightly decreased to ~200 mm. After that, an abrupt return to dry climate conditions is observed at 10.01 ± 0.04 14C ka (11.31e11.75 ka)
9.89 ± 0.04 14C ka (11.21e11.40 ka) (Figs. 4C and 386
375 cm depth), with grain size, TOC and Rb/Sr sharply backed to YD levels. We interpret these important lithological changes to represent the post-YD warming and the PBO (Bjorck et al., 1997). The coarse eolian sand layer associated with the PBO reversal period consistently correlates with a cold interval during ~11.45e11.27 ka (Kobashi et al., 2008; Rasmussen et al., 2007), seen in the Greenland d18O record. This likely correlates with precipitous lake level fluctuations at Lake Dali in the eastern part of the Hunshandake during the very early Holocene (Fig. 4D). The PBO may vary subtly in response to abrupt climate change, and is occasionally observed beyond the North Atlantic region (Bos et al., 2007; Weldeab et al., 2007). The coarse eolian deposit in BN2016 during the YD and PBO events are broadly consistent with the rapid accumulation of eolian
peat deposits observed in the Mediwan (Fig. 4B) and loess-paleosol layers in the Dongxiang sequences (Fig. 4A) (Zhou et al., 2001), in the China Loess Plateau (CLP). These records suggest that the PBO has a significant impact on the EASM margin. Between 375 and 290 cm depth (~11.3e9.3 ka), we observed a sharp transition towards wet conditions at the onset of the Holocene epoch. TOC increased sharply from <0.1% to ~2e4%, while mean grain size decreased precipitously, from ~350 mm to ~80 mm, with the <76 mm fraction (Fig. 3B) accounting for ~80% of the total. Rb/Sr decreased from 0.4% to ~0.02%. Taken together, the multi-

proxy results from this interval indicate a high lake level with wet climatic conditions. The paleo-shoreline reconstruction of Lake Dali at the onset of the early Holocene indicates a precipitous increase in lake level of more than 40 m and remains high through the early Holocene (Fig. 4D). Lake Bayan nuur basin likely also shared the moist climate at that time. Above 290 cm depth (~9.3 ka), the sedimentary facies changed from fine-grained clayey silt to coarse-grained sandy silt, and the TOC content gradually decreased to below 1%. Two 10
15 cm-thick silty sand sublayers were deposited during 7.73 ± 0.04e6.96 ± 0.03 14 C ka (8.4e7.8 ka) at 265
225 cm interval. The mean grain size fluctuated around ~200 mm, and the proportion of the <76 mm fraction (Fig. 3B) fell to ~35% at the first silty sand sublayer and reached ~20% at the second. TOC and Rb/Sr also show an obvious signature at the second sublayer, which changed from 0.5% to 0.2% and 0.1 to ~0.25, respectively, indicating a dry climate with a double-peak structure (Ellison et al., 2006; Hillaire-Marcel et al., 2007; Jennings et al., 2015). We interpret this twin-layer of silty sand to correspond to the North Atlantic Bond 5 event, centred around 8.2 ka (Bond et al., 2001). 4.3. Unit III-II and IV: Progressive lacustrine to eolian transition during the middle to late Holocene Between 205 and 130 cm depth (~6.2e3 ka, Fig. 3) and especially after ~163 cm (~4.2 ka), TOC maintained a relatively low level (~0.5%) while Rb/Sr ratio remained between ~0.2 and 0.3, representing relatively low moisture conditions. Geochemical evidence supports this interpretation (Fig. 3E). During this stage, Ca deposition (Fig. 3E1) was reduced by ~50%, while Mg deposition (Fig. 3E2) increased ~40%. Halogen deposition (Cl and Br; Fig. 3E3 and E4, respectively), commenced at ~4.2 ka (Fig. 6D3),

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experienced a nearly 10-fold increase. This signal provides evidence for the onset of saline conditions (Liu, 1997b; Parrish, 1973), marking a gradual transformation from a fresh-water lake to a brackish lake. Mg and halogen deposition show peaks at ~130 cm depth (~3 ka). After ~3 ka, TOC fell below 0.5% and Rb/Sr increased from ~0.1 to 0.2 to ~0.4, with frequent oscillations. Ca, Mg and halogen deposition fell, indicating saline conditions and significant desiccation (Yang et al., 2015b). Several alternating relatively wet periods, indicated by 6e7 discernible grayish sandy silt sublayers at ~88
35 cm interval, occurred during the past few millennia. Since the ~35 cm depth (~0.75 ka), the hydrological conditions of the lake have stabilized at about the modern, nearly dry state. Overall, since ~4.2 ka, the environmental proxies and geochemical records demonstrate a gradual shrinking process, as the lake water evolved from a calcium carbonate, to a magnesium carbonate type, and then to a halide type (Liu, 1997b). 5. Discussion 5.1. Age uncertainty evaluation for variable local correction

Fig. 5. Mean grain size (Strati-graphical evidence, E and F) from the BN-2016 sequence were chosen to compare with other global records. (A) NGRIP d18O record (Rasmussen et al., 2014); (B) Ca/Sr ratios for core SL 170 in central Baffin Bay, North Atlantic. Elevated Ca/Sr ratios indicate increased detrital carbonate inputs in the YD and during Heinrich 0 (Jackson et al., 2017); (C) Detrital carbonate events recorded in MD99-2236, North Atlantic (Jennings et al., 2015). Carbonate peaks indicate enhanced freshwater forcing; (D) Panel D show the effect of local R on the timing of instability events in the BN-2016 sequence. Modelled ages for the onset and end of sand layers for the YD, PBO and 8.2 ka events, using different R values are shown with calculated uncertainties; (E and F) Mean grain size records of BN-2016 sequence. F show the original data. E show the time series after removing the millennial trend. In panel E, the upper/lower dark line represents an interval of ±0.5 standard deviation. A dry anomaly of moisture at a certain time was defined if the values lay above the lower dark line. In addition, the prominent sand layers of BN-2016 sequence provide best markers to corelated with the low NGRIP d18O values (A) and detrital carbonate events (B and C) of the North Atlantic/Polar region instabilities. In panel F, the dark triangle denotes 14C age control points of the BN-2016 sequence; (G) d18O record of Lianhua cave stalagmite, northern

14

C reservoir age

The chronology of the lower segment (>205 cm, Unit I, II and IIII) is based on 18 bulk-OM radiocarbon dates (Table 1 and Fig. 2). The uncertainties of our radiocarbon age model are a critical consideration, when comparing abrupt events observed in our record with remote climate records. The effect of local radiocarbon reservoir effect is of particular importance. Alkali lakes situated in a shallow depression in desert areas, like Lake Bayan nuur, usually have small radiocarbon reservoir ages, due to the gentle topography and high mean water residence time (Burr et al., 2019; Goldsmith et al., 2017; Jiang et al., 2006; Li and Liu, 2018; Liu et al., 2017; Xiao et al., 2004; Xu et al., 2018). The C/Norg in the BN-2016 lacustrine facies exhibit values between ~13 and 17 (Fig. 3C3), suggesting a predominantly terrestrial origin of the OM (Meyers and Lallier-Verges, 1999). Published records from surrounding regions have found R values from 0 years to ~600e800 years (Supplementary data; Table S1), e.g. 0 years for Anguli (Li and Liu, 2018), and less than 50 years for Xiari nuur (Tang et al., 2015; Xu et al., 2018). In addition, values of 360 years have been reported for Lake Daihai (Xiao et al., 2004), and 472 years for Lake Dali (Fan et al., 2017), 685 years for Lake Hulun (Xiao et al., 2009), 570 years for Lake Bayanchagan (Jiang et al., 2006), and 879 year for Lake Yanhaizi (Chen et al., 2003). However, R value can exhibit temporal variability due to climatic and hydrological variations (Zhou et al., 2014, 2016a). For the BN-2016 sequence, we lack datable material independent of the radiocarbon reservoir effect to compare to, such as terrestrial macrofossils to unambiguously assign reservoir ages for each section of the core. However, we expect that eolian sands should have a small or zero radiocarbon reservoir effect, and their ages could be used to constrain the radiocarbon reservoir age of adjacent units (Wang et al., 2008). Also, due to the high resolution China (Dong et al., 2015). The interpolated solid curve denotes a long-term trend (1 kyr low pass). During the late Pleistocene
Holocene transition, the BN-2016 mean grain size (~20 years resolution in average) was selected to corelated with d18O profile from NGRIP ice core and Lianhua cave stalagmite. All time-series were interpolated/ extrapolated to 20 years resolution (Crowley, 1999; Hu et al., 2017). The BN-2016 mean grain size shows significant Ep of
0.60, and
0.54, respectively, with these distal time series. All correlation here were passed the P < 0.05 significance test. The cold (warm) color shaded area indicates the timing of dry or cold (wet or warm) intervals for each record. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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of available 14C dates, the R ¼ 360 years for upper segment could be obtained with confidence and serve as a reference for R values estimation of the lower. We generated different age models with BACON, varying only the radiocarbon reservoir age from 0 to 800 years. We assessed potential variability in the timing of observed sand layers, and tested the impact of changing R values on the onset and end of those sand layers (Fig. 5D). The results show that the increasing R values has the expected effect of making these events younger. However, the onset and end of the YD and PBO sand layers remains within 95% confidence intervals of the final age model (Fig. 5D, red dot with uncertainty) when a >100 years R correction was adopted. The bulk 14C dates from BN-2016 sequence during late Pleistocene to Holocene transition follow a plateau at ~10.1e9.8 14C ka (Fig. 2A1 and 4), which correlate to the ~10.0e9.9 and ~9.6e9.5 14C ka atmospheric (INTCAL) 14C plateaux (Reimer et al., 2013). The former corresponds to the end of YD (Kromer and Becker, 1993). Thus, the onset of the BN-2016 14C dates plateau (Fig. 4, the eolian sand to silty sand transition at ~400 cm depth) provides a reference for the YD to Holocene transition. In addition, the earlier and longer bulk 14C age plateau from BN-2016 sequence imply an increase in local R at the very early Holocene to push the Holocene 14C dates forward. The uncalibrated age difference between the linear regression of the Unit II and the well-recognized YD end (~10.0 14C ka) is ~400 years (Fig. 2A1 and Supplementary data Fig. S3). This age difference is coincident with the mean R value of 360 years in the upper portion of our sequence. Thus, we adopt an R value of 360 years for Unit II. This correlates with the YD and PBO coarse eolian sands layers (Fig. 5, ~12.7e11.7 and ~11.5e11.3 ka, respectively). A R ¼ 360 years is insufficient to correct ages for Unit III-I. Since the silty sand layer indicates a dry climate, it likely had a relatively low reservoir age (Liu et al., 2017). We then tested the difference between applying R ¼ 0e400 years for radiocarbon ages in Unit III-I (Fig. 5D). This also has the effect of making the onset and termination of the silty sand layers younger, but new dates remain within the uncertainty (95% confident interval) of the final age model. Importantly, the sensitivity study indicates that the age model using the R < 360 years for Unit III-I is unlikely to upset the timing of the observed climatic instabilities, within age uncertainties (Fig. 5AeD). Thus, the variable R ¼ 0 for Unit III-I and R ¼ 360 for Unit II was chosen for the age model. Due to the existence of the YD coarse eolian sand layer (Fig. 4C), the early Holocene age of the overlying lacustrine Unit II, with maximum TOC and minimum grain size, is unequivocal. 5.2. Orbital-scale moisture variation in the desert area in and nearby the Hunshandake Multi-proxy data from the BN-2016 sequence (Fig. 6, B
D), including TOC, grain size and elemental parameters, show that the lake experienced several large climatic fluctuations at the end of the late Pleistocene. After that, the lake basin experienced a sharp transition towards wet conditions at the onset of the Holocene epoch. Maximum wet climate occurred during ~11.3e8.5 ka. Climate generally maintained wet until ~4.2 ka, and then became progressively drier in the late Holocene. The pollen data from Lake Xiari (Fig. 7D), also located in the central Hunshandake, support this view, with a similar marked increase in moisture during ~11.7e10.2 ka and maximum humidity during the early Holocene (Tang et al., 2015). A paleo-lake level reconstruction from Lake Dali (Fig. 7A) also shows that the lake reached high stands at ~11e5.5 ka (Goldsmith et al., 2017). Within 200 km, south of the Hunshandake, multi-proxy data from Lake Anguli sediments, including TOC (Fig. 7E) and mineralogy, indicate a wet interval at ~11.1e6.9 ka (Li and Liu, 2018). Sediment from Lake Bai, dozens of kilometers away

Fig. 6. Comparison between BN-2016 and other global records. The interpolated dark solid curve denotes a long-term trend (1 kyr low pass). (A) Northern Hemisphere Summer Insolation at 45 N (Laskar et al., 2011); (B) Mean grain size records of BN-2016 sequence; (C) TOC records of BN-2016 sequence; (D) Geochemical signal of BN-2016 sequence. D1 to D3 Ca, Mg and Cl deposition; (E) Western Pacific sea surface temperature (SST) variability records (Stott et al., 2004, 2007). The warm (cold) color shaded area indicates the timing of wet or warm (dry) intervals for each record. Light warm color shaded area indicates moderate wet periods in Lake Bayan nuur basin. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

from Lake Anguli, exhibits a maximum moisture condition at ~10.5e9.6 ka (Wang et al., 2012). The abrupt enhancement of moisture conditions in and nearby the Hunshandake at the onset of the Holocene (Fig. 7) is contemporary with rapid sea level rise during Melt Water Pulse 1B (Bard et al., 2010; Lambeck et al., 2014) and associated tropical ocean warming (Clark et al., 2004). An et al. (2012) proposed that these factors likely contributed to the strengthening of the summer

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monsoon through increased moisture supply. During the Holocene, TOC record of BN-2016 sequence shows a significant correlation with NHSI intensity (Fig. 6A, Ep ¼ 0.57) (Laskar et al., 2011) and West Tropical Pacific Sea Surface Temperature (SST; Fig. 6E, Ep ¼ 0.52) (Stott et al., 2004). The synchrony between these lacustrine data in and nearby the Hunshandake and orbitally induced changes in NHSI intensity supports the hypothesis that paleo-ASM intensity is principally controlled by NHSI variation at the orbital timescale (An et al., 2015; Kutzbach, 1981; Kutzbach et al., 2008; Ruddiman, 2008). During the early and middle Holocene, a warmer NH results in an enhanced thermal contrast between the ocean and Asian continent (COHMAP MEMBERS, 1988) and higher SST in the Western Tropical Pacific (Stott et al., 2007), both facilitating moisture flux transport and further northward migration of the Intertropical Convergence Zone (ITCZ) (Haug et al., 2001). This condition pushes the associated rain belt northward at least to the Hunshandake. After ~4.2 ka, with the attenuation of NHSI and West Tropical Pacific SST, the lacustrine humidity dropped and exhibit distinct fluctuations. 5.3. Disparate moisture variation pattern at places along the north EASM limit Along the northern EASM limit, records with similar moisture variation pattern have been observed (Fig. 7), e.g. a pollen-based Mean Annual Precipitation (MAP) record from Lake Qigai, in the Mu Us sandy land, exhibit HO from ~9.2 ka (Sun and Feng, 2013). An early HO can also be inferred from the Lake Qinghai Summer Monsoon Index (SMI, Fig. 7G) since ~11.5 ka (An et al., 2012). However, published paleo-records at places along the EASM margin do not always match well and lack consensus. Pollen-based MAP reconstructed in Lake Gonghai (Fig. 7F), ~450 km southwest of the Hunshandake, suggests that the peak in EASM precipitation during the Holocene occurred from ~7.8 to 5.3 ka, in the middle Holocene (Chen et al., 2015). A similar middle Holocene optimum was also implied by other pollen-based records from Lake Bayanchagan (Jiang et al., 2006, 2019), Lake Daihai (Xiao et al., 2004) and Lake Hulun (Wen et al., 2013). A pollen-based moisture index synthesized from six paleoclimatic sequences (Lake Qinghai, Sujiawan, Dadiwan, Lake Daihai, Lake Bayanchagan and Lake Hulun; Fig. 7H) along the EASM margin, indicates that the strongest summer monsoon precipitation occurred at ~8.5e4.5 ka (Wang and Feng, 2013). Eolian records show even larger disparities. In Hunshandake, an eolian sequence from the Lake Bayan nuur basin indicates paleosol formation started from ~10.2 ka, while another eolian sequence from the western portion of Hunshandake shows a few hundred Fig. 7. Comparison with other paleoclimate records in the northern EASM marginal zones. The interpolated dark solid curve denotes a long-term trend (1 kyr low pass). The green dash rectangles denote records show abrupt moisture enhancement at the onset of the Holocene epoch. (A) Dali lake level reconstruction (Goldsmith et al., 2017); (B and C) Proxies from BN-2016 sequence, this study: mean grain size, as lake level proxy (B) and TOC data (C); (D) Broadleaf tree content of Lake Xiari nuur (Tang et al., 2015); (E) TOC data from Lake Anguli (Li and Liu, 2018); (F) Pollen based MAP (mm) record of Lake Gonghai (Chen et al., 2015); (G) Summer Monsoon Index (Mainly TOC based) from lake Qinghai (An et al., 2012); (H) Pollen based moisture index synthesized from EASM margin over northern China (Wang and Feng, 2013). (I and J) Frequency distribution of paleosol (I) datas from Chinese loess plateau and eolian sand (J) datas from China’s four main sandy lands (Wang et al., 2014). (K and L) Number of records (frequency distribution) of active (K) and stable (L) dune datas from Hunshandake sandy lands (Guo et al., 2018). The warm (cold) color shaded area indicates the timing of wet (dry) intervals for each record. Light warm color shaded area indicates moderate wet periods in Lake Bayan nuur and Xiari nuur basin. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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Fig. 8. Map of northern China showing the EASM maximum through time based on paleoclimatic proxy data. Figure modified from An et al. (2000), with newly published lacustrine (blue triangles) and eolian (orange triangles) records added to the plot. The newly added records to Fig. 7 were supplemented with detail information and relevant references in supplementary data, Tables S1eS3. Red dashed circles denote climatic regions frequently uncover records with HO commenced from the early Holocene. Dark dashed circle denotes climatic region frequently uncover records with middle Holocene optimum. Synthesized records which originate from multiple independent researches were not included. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

years delay (Yang et al., 2013). Based on the study of 10 eolian sequences, Zhou et al. (2008) showed that paleosols in Hunshandake formed during ~8e2.7 ka. The synthesis of states of Holocene dune activity from Hunshandake (Fig. 7K and L) indicates that dune activity was reduced during ~9
4 ka (Guo et al., 2018). OSL data analysis of multiple eolian records from Hunshandake and Mu Us sandy land demonstrate significant variability in the onset of paleosol formation, that ranges from ~8.5 to 6 ka (Mason et al., 2009) to ~8/7e2.4 ka (Lu et al., 2005). In the Horqin and Hulunbuir sandy lands, northeast of the Hunshandake, eolian records indicate paleosol formation from ~7 to 2 ka (Zhao et al., 2007) and ~11.3e4.4 ka (Li and Sun, 2006), respectively. Frequency analysis, based on 267 paleosol dates from the CLP (Fig. 6I) and 89 OSL eolian sand dates from China’s four major sandy lands (Mu Us, Otindag, Horqin and Hulunbuir; Fig. 6J), demonstrate that the most frequent paleosols development and reduced eolian-sand activity occurred in the middle Holocene (~8.6e3.2 ka), indicating maximum EASM intensity during this period (Wang et al., 2014). However, also in the CLP, eolian
peat deposits from Mediwan and Dongxiang show the initiation of silty peat from the early Holocene, with peak moisture conditions during ~11e8.8 ka and ~5.5e3.8 ka (Fig. 4A and B). While loess-paleosol sequences from Luochuan and Baxie show paleosol formation from the early Holocene to ~5 ka, with a HO from ~10 to 8 ka (An et al., 2000; Zhou and An, 1991). The disagreement among these independent studies shows significant spatial variability in the position of the Holocene EASM rain belt at its modern northern margin. Fig. 8 shows the result of combining the aforementioned lacustrine and eolian records to a plot of monsoon maximum position through time along EASM margin, northern China (An et al., 2000). Clearly, there are large spatial differences in different climatic regions along the EASM

margin. However, moisture records show a generally similar pattern in and nearby the Hunshandake. Distant areas from the Hunshandake, especially in and around CLP, exhibit larger discrepancy with Lake Bayan nuur records. A well accepted viewpoint attributes such discrepancy to low temporal resolution, an absence of robust age control and distinct sensitivity of “understanding proxies” (Dong et al., 2015; Liu et al., 2015). This viewpoint could explain the discrepancies between records within the same region or the same site (Fan et al., 2017; Goldsmith et al., 2017; Jiang et al., 2006; Li and Liu, 2018; Tang et al., 2015; Xiao et al., 2004, 2015), e.g. the moisture variation discrepancy between Dali paleo-lake level reconstruction and sediment proxies (Fan et al., 2017; Goldsmith et al., 2017). For eolian records in and nearby the Hunshandake, the pedogenic maturity € vermann, 1985), site-specific topography, and local vegetation (Ho (Mason et al., 2009) can be critical. Soil formation rates depend on €vermann, 1985). vegetative cover and local geomorphology (Ho Yang et al. (2013) suggested soil formation can incur an offset of up to hundreds of years in certain regions in the Hunshandake. For records from different climatic regions, the interplay of monsoon and Westerlies were capable of producing geographically variable rainfall patterns along the EASM margin (An et al., 2012; Chen and Huang, 1998; Chiang et al., 2015; Liang and Wang, 1998; Liu, 1997a; Zhang et al., 2018; Zhang and Tao, 1998). Strong dry Westerlies in the early Holocene may have inhibited the monsoon rain belt and delayed moisture maximum at certain sites in the desert-loess transition zone (Zhao and Yu, 2012). Recent temperature reconstructions from the same latitude suggest a slightly higher temperature during the middle Holocene (Rao et al., 2019; Zheng et al., 2018). Chen et al. (2003) and Wang and Feng (2013) proposed that higher temperatures during the

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middle Holocene may exert considerable influence on the moisture evolution in the arid areas of the Mongolian Plateau. Recent studies show that vegetation in different parts of EASM region is controlled by both temperature and precipitation and exhibit strong regional variation (Zhou et al., 2016b). Meanwhile, climate-simulation results suggest nonlinear vegetation feedbacks also have significant impacts on regional climate, especially in arid/semi-arid regions (Chen et al., 2004; Dallmeyer et al., 2010; Zheng et al., 2004). Therefore, vegetation feedbacks may potentially amplify the regional climate response to orbital-induced monsoon change, producing regional variability on EASM rainfall (Zhao and Yu, 2012). 5.4. Major sub-orbital-scale climate instabilities during the late Pleistocene
Holocene transition The warming in the NH that triggered the last deglaciation was caused by changes in insolation (Ruddiman, 2008). This affected changes in ice sheets, greenhouse gas concentrations, and other amplifying feedbacks (Clark et al., 2012). The warming trend was punctuated by several large and rapid sea-level rise episodes (Bard et al., 2010; Lambeck et al., 2014) and three significant cold reversals: 1) the YD, 2) the PBO and 3) the 8.2 ka event (Rasmussen et al., 2007). During the late Pleistocene
Holocene transition, the BN-2016 record responses to these is preserved in detail (Fig. 5A
F). During 13
7 ka, there is significant correlation between BN-2016 mean grain size and NGRIP d18O records (Ep ¼
0.60). Wavelet transform coherence (WTC) analysis (Grinsted et al., 2004) shows persistent correlation between BN2016 mean grain size and the NGRIP d18O record at submillennial scale cycles (Supplementary data; Fig. S6), indicating persistent climate teleconnections between northern China and high-latitude North Atlantic regions. Although the connection between Chinese stalagmite d18O and monsoon precipitation is still unclear (Clemens et al., 2010; Goldsmith et al., 2017; Liu et al., 2014, 2015; Tan, 2016). Stalagmite d18O records in northern China have the virtue of precise age control, and are sensitive to abrupt climate change (Dong et al., 2015; Ma et al., 2012; Yang et al., 2019; Zhang et al., 2018, 2019). During 13
7 ka, the mean grain size curve anomalies (Fig. 5E) of the BN-2016 sequence generally corelate with short-termed high d18O intervals from Lianhua cave records, northern China, suggesting a co-response to abrupt reversal events. The cause of these high latitude cooling events has been hypothesized to have resulted from enhanced freshwater input from the NH ice sheet into the North Atlantic and Arctic Oceans (Barber et al., 1999; Clark et al., 2012; Fisher et al., 2002; Meissner, 2015; Renssen et al., 2015; Teller, 2012). As suggested from the 231 Pa/230Th record from the subtropical North Atlantic Ocean (McManus et al., 2004), and 14C anomaly in ocean and atmosphere reservoirs (Cheng et al., 2018; Kromer and Becker, 1993; Stein et al., 2006). Increased freshwater influx into the North Atlantic may impeded North Atlantic deep water formation (Clark et al., 2001), weakened thermohaline transport. Which leads to the reorganization of oceanic and atmospheric circulations and the mean latitudinal position of the ITCZ and associated rain belt (Chiang and Bitz, 2005; Wang et al., 2008). However, recently a trace element record of speleothem from Haozhu cave, central eastern China, indicates a wetter central eastern China during North Atlantic cooling episodes (Zhang et al., 2018). Dahu peat deposit, in southeastern China, do not show a clear YD signal or 8.2 ka event at all, suggesting that precipitation during the YD and other reversal periods were quite high in some low-latitude regions (Zhou et al., 2004). In the Mediwan and Dongxiang sequences, YD sedimentation shows a dry climate was punctuated by a brief period with enhanced summer monsoon

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precipitation (Fig. 4A and B). Independent studies from Kulishu Cave d18O records, northern China (Ma et al., 2012), verify this view. These phenomena indicate a similar “northern dry and southern wet” pattern which was widely observed in current weather system. In addition to thermohaline dynamic, a complex interplay of several factors modulates the behavior of monsoon rainfall distribution under the sub-orbital cooling background. The Westerlies can account for the rapid communication between the North Atlantic region and east Asia (Chen et al., 2019; Chiang et al., 2015; Zhang et al., 2018). Here, in addition to thermohaline dynamic, we further attributed monsoon rainfall patterns in monsoonal China to possible interplay between the East Asian Jet streams (EAJ) and the EASM circulation through oceanatmospheric coupling system. The North Atlantic Oscillation (NAO) modulates precipitation in the Mediterranean, northwest Europe, Greenland, and through teleconnections, in Asia (Hurrell, 1995; Qiao et al., 2017). Liang and Wang (1998) suggested that a poleward shift of the summer EAJ facilitate monsoonal precipitation over northern China (Fig. 9C). They also found that EAJ fluctuations are strongly coupled with ENSO variations, which corresponds to the role of atmosphereocean interaction in east Asia (Zhou et al., 2001). Huang et al. (2014) revealed that different EAJ configurations could influence the location of the summer monsoon rain belt in eastern China, producing spatial variation in summer rainfall (Fig. 9). If those modern short-term inter-annual fluctuations persisted during a past reversal phase, they could produce a long-term climate effect. Thus, during the abrupt cooling periods in the North Atlantic, a slow thermohaline circulation due to fresh water input, may have changed the state of the NAO (Wassenburg et al., 2016), which shifted the Polar Front Jet further south (Bjorck et al., 1997). This intensified interaction between cold air advection from high latitudes, and warm, moist air was transported across the tropical/ subtropical regions, varying with ENSO and tropical climate factors (Yan et al., 2011; Zhou et al., 2001). The net effect enhanced the positive EAJ anomaly (Fig. 9B) (Hurrell, 1995; Qiao et al., 2017). As a result, moisture transfer to higher latitudes was inhibited and geographically variable rainfall events occurred in northern and central/south eastern China (Fig. 9D). 6. Conclusions On the orbital timescale, sediment properties (TOC and grain size) with multi-elemental parameters demonstrate that moisture variability in Lake Bayan nuur basin was dominated by EASM rainfall intensity, ultimately controlled by Northern Hemisphere summer insolation. Moisture conditions became markedly wetter at ~11.7e11.5 ka, at the onset of Holocene Epoch. Maximum moisture conditions occurred at ~11.3e8.5 ka. Wet conditions generally prevailed for the first half of the Holocene, then became progressively drier since ~4.2 ka. Our findings support the view that the EASM rain belt reached its modern northern limit, at least to the Hunshandake, during the early Holocene. However, published paleo-records along the northern EASM margin do not always agree. The interplay between the monsoon and Westerlies, chronologic uncertainties and/or variable climate proxy sensitivities partly explain the differences. Regional factors, including landsurface and vegetation feedbacks, were also likely important. During the late Pleistocene
Holocene transition, we observed several distinct sub-orbital scale climatic events in the BN-2016 sequence. These include the YD and 8.2 ka climate reversals. Wetter climate (11.7e11.5 ka, 406
386 cm depth), followed by abrupt reversal (11.5e11.3 ka, 386
375 cm depth) during 11.7e11.3 ka, was observed. These events correlate well with post YD warming and the pre-Boreal oscillation in Greenland ice record.

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Fig. 9. Effects of different configurations of the East Asian Subtropical Jet (EASJ) and East Asian Polar front Jets (EAPJ) on the interdecadal summer (JJA) precipitation anomalies of eastern China. Data from the daily NCEP-NCAR reanalysis dataset (1960e2010). Fig A and B, 200 hPa summer zonal wind speed anomalies (m/s; shaded area and black contour line) between 10 Negative (EASJ is strengthened and northward) and 9 Positive (EAPJ is strengthened and southward) year mean, and 50-year mean. Fig C and D, 200 hPa summer zonal wind speed (m/s; black counter line), and summer surface rainfall anomalies (mm/d; shaded area) between 10 Negative and 9 Positive year mean and 50-year mean. Figure modified from Huang et al. (2014). The position of East Asian Jet stream (EAJ) modulates precipitation in east Asia is linked to the North Atlantic Oscillation (NAO). Negative (positive) winter NAO indices correspond with negative (positive) EAJ configurations over central East Asia during the following summer, when the EAJ tends northward (southward) (Hurrell, 1995; Qiao et al., 2017). This influence contributes to enhanced (reduced) rainfall over northern China and rainfall anomalies over central China (C and D) (Huang et al., 2014; Liang and Wang, 1998). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

The response to these high latitude short-term reversals observed in proxy records from monsoonal China is widespread, but with disparate patterns. We discuss a possible explanation for these abrupt oscillations in monsoonal China, based on oceanatmosphere coupling. During cooling periods of the North Atlantic, a slow-down of the thermohaline circulation may have changed the state of the North Atlantic Oscillation, pushing the Polar Front southward. An intensified tropical-polar ocean-atmosphere interaction then enhanced the East Asian Jet stream. This inhibited moisture transfer to higher latitudes produced geographically variable rainfall events in eastern China.

CRediT authorship contribution statement Guodong Ming: Conceptualization, Methodology, Software, Validation, Formal analysis, Investigation, Writing - original draft, Writing - review & editing. Weijian Zhou: Conceptualization, Methodology, Resources, Writing - review & editing, Supervision, Funding acquisition. Hong Wang: Conceptualization, Writing review & editing. Peng Cheng: Methodology, Validation, Writing review & editing. Peixian Shu: Investigation, Writing - review & editing. Feng Xian: Writing - review & editing. Yunchong Fu: Validation, Writing - review & editing.

Declaration of competing interest Acknowledgments The authors declare no conflict of interest. This research was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDB40010100),

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the international partnership program of Chinese Academy of Sciences (grant no. 132B61KYSB20170005), and the Open-end Fund Program of State Key Laboratory of Loess and Quaternary Geology, Institute of Earth Environment, Chinese Academy of Sciences (grant no. SKLLQG1715). We acknowledge Prof. George Burr for linguistic assistance. We also acknowledge Prof. Youbin Sun and Dr. Fei Guo for help in core scanning. We thank Dr. Ling Tang, Dr. Gang Xue, Dr. Yifei Hao and other colleagues for comments on the earlier manuscript, and specially thank Dr. Yubing Wu for art designing of the Fig. 9. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.quascirev.2020.106210. Author statement We declare that this paper has not been published and is not being considered for publication elsewhere. All authors have made substantial contributions to this submission. The detail of individual contributions of each co-author to the article has listed in Author contributions segment, and we confirm that all authors have approved the final version of the manuscript. References An, Z.S., Porter, S.C., Kutzbach, J.E., Wu, X.H., Wang, S.M., Liu, X.D., Li, X.Q., Zhou, W.J., 2000. Asynchronous Holocene optimum of the east Asian monsoon. Quat. Sci. Rev. 19, 743e762. An, Z.S., Colman, S.M., Zhou, W.J., Li, X.Q., Brown, E.T., Jull, A.J., Cai, Y.J., Huang, Y.S., Lu, X.F., Chang, H., Song, Y.G., Sun, Y.B., Xu, H., Liu, W.G., Jin, Z.D., Liu, X.D., Cheng, P., Liu, Y., Ai, L., Li, X.Z., Liu, X.J., Yan, L.B., Shi, Z.G., Wang, X.L., Wu, F., Qiang, X.K., Dong, J.B., Lu, F.Y., Xu, X.W., 2012. Interplay between the westerlies and Asian monsoon recorded in lake Qinghai sediments since 32 ka. Sci. Rep. 2, 619. An, Z.S., Wu, G.X., Li, J.P., Sun, Y.B., Liu, Y.M., Zhou, W.J., Cai, Y.J., Duan, A.M., Li, L., Cheng, H., Shi, Z.G., Tan, L.C., Yan, H., Ao, H., Chang, H., Feng, J., 2015. Global monsoon dynamics and climate change. J. Earth Environ. 6, 341e381 (in Chinese with English abstract). Barber, D.C., Dyke, A., Hillaire-Marcel, C., Jennings, A.E., Andrews, J.T., Kerwin, M.W., Bilodeau, G., McNeely, R., Southon, J., Morehead, M.D., Gagnon, J.M., 1999. Forcing of the cold event of 8,200 years ago by catastrophic drainage of Laurentide lakes. Nature 400, 344e348. Bard, E., Hamelin, B., Delanghe-Sabatier, D., 2010. Deglacial meltwater pulse 1B and Younger Dryas sea levels revisited with boreholes at Tahiti. Science 327, 1235e1237. Beck, J.W., Zhou, W.J., Li, C., Wu, Z.K., White, L., Xian, F., Kong, X.H., An, Z.S., 2018. A 550,000-year record of East Asian monsoon rainfall from 10Be in loess. Science 360, 877e881. Beuning, K.R.M., Talbot, M.R., Kelts, K., 1997. A revised 30,000-year paleoclimatic and paleohydrologic history of Lake Albert, East Africa. Palaeogeogr. Palaeocl. 136, 259e279. Bjorck, S., Rundgren, M., Ingolfsson, O., Funder, S., 1997. The Preboreal oscillation around the Nordic Seas: terrestrial and lacustrine responses. J. Quat. Sci. 12, 455e465. Blaauw, M., 2010. Methods and code for ‘classical’ age-modelling of radiocarbon sequences. Quat. Geochronol. 5, 512e518. Blaauw, M., Christen, J.A., 2011. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analy 6, 457e474. Blaauw, M., Christen, J.A., Bennett, K.D., Reimer, P.J., 2018. Double the dates and go for Bayes d impacts of model choice, dating density and quality on chronologies. Quat. Sci. Rev. 188, 58e66. Blockley, S.P.E., Blaauw, M., Bronk Ramsey, C., van der Plicht, J., 2007. Building and testing age models for radiocarbon dates in Lateglacial and Early Holocene sediments. Quat. Sci. Rev. 26, 1915e1926. Bond, G., Kromer, B., Beer, J., Muscheler, R., Evans, M.N., Showers, W., Hoffmann, S., Lotti-Bond, R., Hajdas, I., Bonani, G., 2001. Persistent solar influence on North Atlantic climate during the Holocene. Science 294, 2130e2136. Bos, J.A.A., van Geel, B., van der Plicht, J., Bohncke, S.J.P., 2007. Preboreal climate oscillations in Europe: wiggle-match dating and synthesis of Dutch highresolution multi-proxy records. Quat. Sci. Rev. 26, 1927e1950. Brass, G.W., 1975. The effect of weathering on the distribution of strontium isotopes in weathering profiles. Geochem. Cosmochim. Acta 39, 1647e1653. Broecker, W.S., Putnam, A.E., 2013. Hydrologic impacts of past shifts of Earth’s thermal equator offer insight into those to be produced by fossil fuel CO2. Proc.

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