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  <front>
    <journal-meta><journal-id journal-id-type="publisher">CP</journal-id><journal-title-group>
    <journal-title>Climate of the Past</journal-title>
    <abbrev-journal-title abbrev-type="publisher">CP</abbrev-journal-title><abbrev-journal-title abbrev-type="nlm-ta">Clim. Past</abbrev-journal-title>
  </journal-title-group><issn pub-type="epub">1814-9332</issn><publisher>
    <publisher-name>Copernicus Publications</publisher-name>
    <publisher-loc>Göttingen, Germany</publisher-loc>
  </publisher></journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.5194/cp-18-2381-2022</article-id><title-group><article-title>Palynological evidence reveals an arid early Holocene for <?xmltex \hack{\break}?> the northeast Tibetan Plateau</article-title><alt-title>An arid early Holocene on the northeast Tibetan Plateau</alt-title>
      </title-group><?xmltex \runningtitle{An arid early Holocene on the northeast Tibetan Plateau}?><?xmltex \runningauthor{N. Wang et al.}?>
      <contrib-group>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Wang</surname><given-names>Nannan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1 aff2">
          <name><surname>Liu</surname><given-names>Lina</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Hou</surname><given-names>Xiaohuan</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff1">
          <name><surname>Zhang</surname><given-names>Yanrong</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="no" rid="aff3">
          <name><surname>Wei</surname><given-names>Haicheng</given-names></name>
          
        </contrib>
        <contrib contrib-type="author" corresp="yes" rid="aff1">
          <name><surname>Cao</surname><given-names>Xianyong</given-names></name>
          <email>xcao@itpcas.ac.cn</email>
        <ext-link>https://orcid.org/0000-0001-5633-2256</ext-link></contrib>
        <aff id="aff1"><label>1</label><institution>Alpine Paleoecology and Human Adaptation Group (ALPHA), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research (ITPCAS), Chinese Academy of Sciences (CAS), Beijing 100101, China</institution>
        </aff>
        <aff id="aff2"><label>2</label><institution>University of the Chinese Academy of Sciences, Beijing 100049, China</institution>
        </aff>
        <aff id="aff3"><label>3</label><institution>Qinghai Provincial Key Laboratory of Geology and Environment of Salt Lakes, Xining 810008, China</institution>
        </aff>
      </contrib-group>
      <author-notes><corresp id="corr1">Xianyong Cao (xcao@itpcas.ac.cn)</corresp></author-notes><pub-date><day>25</day><month>October</month><year>2022</year></pub-date>
      
      <volume>18</volume>
      <issue>10</issue>
      <fpage>2381</fpage><lpage>2399</lpage>
      <history>
        <date date-type="received"><day>21</day><month>May</month><year>2022</year></date>
           <date date-type="rev-request"><day>13</day><month>June</month><year>2022</year></date>
           <date date-type="rev-recd"><day>1</day><month>September</month><year>2022</year></date>
           <date date-type="accepted"><day>13</day><month>September</month><year>2022</year></date>
      </history>
      <permissions>
        <copyright-statement>Copyright: © 2022 Nannan Wang et al.</copyright-statement>
        <copyright-year>2022</copyright-year>
      <license license-type="open-access"><license-p>This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</ext-link></license-p></license></permissions><self-uri xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022.html">This article is available from https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022.html</self-uri><self-uri xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022.pdf">The full text article is available as a PDF file from https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022.pdf</self-uri>
      <abstract><title>Abstract</title>

      <p id="d1e142">Situated within the triangle of the East Asian monsoon, the Indian monsoon, and the westerlies, the Holocene patterns of climate and vegetation changes on the northeast Tibetan Plateau are still unclear or even contradictory. By investigating the distribution of modern pollen taxa on the east Tibetan Plateau, we infer the past vegetation and climate since 14.2 ka BP (1000 years before present) from a fossil pollen record extracted from Gahai Lake (102.3133<inline-formula><mml:math id="M1" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E, 34.2398<inline-formula><mml:math id="M2" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N; 3444 m a.s.l.) together with multiple proxies (grain size, contents of total organic carbon and total nitrogen) on the northeast Tibetan Plateau. Results indicate that the Gahai Basin was covered by arid alpine steppe or even desert between 14.2 and 7.4 ka BP with dry climatic conditions, and high percentages of arboreal pollen are thought to be long-distance wind- transported grains. Montane forest (dominated by <italic>Abies</italic>, <italic>Picea</italic>, and <italic>Pinus</italic>) migrated into the Gahai Basin between 7.4 and 3.8 ka BP driven by wet and warm climatic conditions (the climate optimum within the Holocene) but reverted to alpine steppe between 3.8 and 2.3 ka BP, indicating a drying climate trend. After 2.3 ka BP, vegetation shifted to alpine meadow represented by increasing abundances of Cyperaceae, which may reflect a cooling climate. The strange pollen spectra with high abundances of Cyperaceae and high total pollen concentrations after ca. 0.24 ka BP (1710 CE) could be an indication of disturbance by human activities to some extent, but needs more direct evidence to be confirmed. Our study confirms the occurrence of a climate optimum in the mid-Holocene on the northeast Tibetan Plateau, which is consistent with climate records from the fringe areas of the East Asian summer monsoon, and provides new insights into the fluctuations in the intensity and extent of the Asian monsoon system.</p>
  </abstract>
    </article-meta>
  </front>
<body>
      

<sec id="Ch1.S1" sec-type="intro">
  <label>1</label><title>Introduction</title>
      <p id="d1e181">Vegetation, as an essential component in the terrestrial ecosystem, responds to and is a good representation of environmental and climatic changes. Investigating the patterns and mechanisms of past vegetation changes provides a reliable analogue for predicting future climate and vegetation changes (Mykleby et al., 2017; Zhao et al., 2017). Since the sharp climate warming during the last deglaciation (after ca. 15 ka BP in the Northern Hemisphere; Wang et al., 2001; Andersen et al., 2004; Dykoski et al., 2005; Xu et al., 2013), the response of vegetation to climate warming could be a valuable palaeo-analogue for understanding current vegetation changes under global warming and for predicting future vegetation trends (Birks, 2019).</p>
      <p id="d1e184">The northeast Tibetan Plateau lies in the transition between the East Asian
summer monsoon, the Indian summer monsoon, and the westerlies, is sensitive
to climate change, and is an ideal region to study past vegetation and
climate variation (Bryson, 1986; An et al., 2012; Chen et al., 2016).
Nevertheless, the climate records from different lacustrine sediments on the
northeast Tibetan Plateau show a lack of consistency, for example,
regarding the climatic conditions during the early Holocene. Some records
reveal that the climate was relatively dry on the northeast Tibetan Plateau
and controlled by the East Asian monsoon during the early Holocene (Shen et
al., 2005; Herzschuh et al., 2006; Cheng et al., 2013), while other records
such as those from Hala Lake and Genggahai Lake show that there was maximum
water depth and hence a climatic optimum in the early Holocene (Qiang et
al., 2013; Yan and Wünnemann, 2014; Wang et al., 2021). Therefore, more
studies are needed to clarify the early Holocene climatic conditions, which
are necessary to resolve the environmental evolution of the northeast
Tibetan Plateau.</p>
      <p id="d1e187">Pollen plays an important role in reconstructing the past vegetation and
climate owing to its preservation in various sediment types (Chevalier et
al., 2020). However, pollen-based vegetation and climate reconstructions on
the Tibetan Plateau are also confronted with challenges for instance, the
current quantitative reconstructions of vegetation and climate are based on
pollen percentages, which can be biased when there is much exogenous
arboreal pollen, especially in strata with extremely low pollen
concentrations because the exogenous arboreal pollen will form a larger
proportion of the pollen sample (Herzschuh, 2007; Ma et al., 2017, 2019).
Exogenous arboreal pollen can be recognized in areas far away from forested
regions, mainly because no trees grow around the lake or its adjacent areas
nowadays, such as Luanhaizi Lake (Herzschuh et al., 2010), Donggi Cona Lake
(Wang et al., 2014), and Kuhai Lake (Wischnewski et al., 2011). Arboreal
pollen can then be excluded in subsequent analysis to ensure the correct
interpretation of vegetation and environment. However, it is somewhat
difficult to recognize the contribution of exogenous pollen from areas near
the forest on the eastern part of the Tibetan Plateau, which could seriously
impact the results of vegetation reconstructions, such as from Naleng Lake
(Kramer et al., 2010) and Qinghai Lake (Shen et al., 2005). Solving this
issue of clarifying the influence of exogenous pollen is an important
prerequisite for a better understanding of the early Holocene climate
shifts. Understanding the spatial distribution characteristics of modern
pollen and their relationships may be an effective way to identify such
arboreal pollen properties.</p>
      <p id="d1e190">In this study, we integrate multiproxy records, e.g. pollen, grain size, total organic carbon (TOC) and total nitrogen (TN) of Gahai Lake to reconstruct the
climate and vegetation evolution since the last deglaciation. We assess the
dispersal ability and biotopes of the main pollen taxa in the pollen record
by investigating the distribution of modern pollen and their relationship
with the climate. We attempt to recognize exogenous pollen and evaluate the
influence on reconstruction results to determine whether the early Holocene
of the northeast Tibetan Plateau was dry or wet.</p>
</sec>
<sec id="Ch1.S2">
  <label>2</label><title>Study area</title>
      <p id="d1e201">Gahai Lake (102.3133<inline-formula><mml:math id="M3" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E, 34.2398<inline-formula><mml:math id="M4" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N; 3444 m a.s.l.) is
situated in the upper reaches of the Yellow River on the northeast Tibetan
Plateau, a transitional zone between the Tibetan Plateau, the mountainous
area of Longnan, and the Loess Plateau (Fig. 1). Gahai Lake is a typical
plateau interior freshwater lake, with a total area of 15 km<inline-formula><mml:math id="M5" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msup></mml:math></inline-formula> and a
mean water depth ranging from 2 to 2.5 m. The water supply of the lake is
mainly from precipitation, groundwater recharge, and surface runoff from
surrounding mountains to the south and south-east including Qiongmuqiequ,
Wenniqu, and Geqiongkuhe rivers, and there is a single outflow stream at the
north-western end of the basin (Duan et al., 2016; Fig. 1). Gahai Lake
currently belongs to the alpine humid climate zone, which is influenced by
the West Pacific Subtropical High in summer and controlled by westerlies in
winter. Climate characteristics are rain in the warm season, and large
seasonal and diurnal temperature differences (Liang, 2006). Mean annual
temperature of this region is 1.2 <inline-formula><mml:math id="M6" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C and mean annual precipitation is 782 mm, with about 80 % of precipitation falling in the rainy season (from June to September), and mean annual evaporation is 1150 mm (Duan et al., 2016).</p>
      <p id="d1e240">Vegetation cover in the Gahai Basin exceeds 90 %. There are abundant species
in the grassland community, which is at the intersection of various flora,
and perennial herbs predominate. The dominant plant species include <italic>Poa annua</italic>, <italic>Carex</italic>, <italic>Clintoniaudensis</italic>, <italic>Polygonum</italic>, <italic>Ranunculus japonicus</italic>, <italic>Potentilla fruticosa</italic>, <italic>Neyraudia reynaudiana</italic>, and <italic>Elymus nutans</italic>. Forest is found in the eastern low mountains with a mosaic distribution of meadow and shrub, dominated by <italic>Abies</italic>, <italic>Picea</italic>, <italic>Betula</italic>, and Cupressaceae. <italic>Picea</italic> is found in damp areas at the foot of mountains and replaced by <italic>Betula</italic> as a transitional community after being cut down; <italic>Abies</italic> occurs on shady and semi-shady
slopes between 3200 and 3400 m a.s.l.; Cupressaceae is distributed mostly on sunny and semi-sunny slopes of more than 35<inline-formula><mml:math id="M7" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>. This region belongs to a typical stockbreeding district, and the grazing activity focuses on the
grassland. In addition, there is small-scale agriculture along the river
valley at low elevations (Liang et al., 2006; Duan et al., 2016).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F1" specific-use="star"><?xmltex \currentcnt{1}?><?xmltex \def\figurename{Figure}?><label>Figure 1</label><caption><p id="d1e298"><bold>(a)</bold> The locations of the related lakes and modern surface samples (Du, 2019). Lakes referred to in the text: 1, ZB08-C1; 2, ZB10-C14; 3, Hongyuan peatland; 4, Ximencuo Lake; 5, Dalianhai Lake; 6, Luanhaizi Lake;
7, Qinghai Lake; 8, Genggahai Lake; 9, Kuhai Lake; 10, Donggi Cona Lake; 11,
Koucha Lake; 12, Hala Lake; 13, Gahai Lake (Qaidam basin). <bold>(b)</bold> Catchment map and coring site of Gahai Lake. <bold>(c)</bold> Distribution of modern pollen samples in the vicinity of Gahai Lake.</p></caption>
        <?xmltex \igopts{width=469.470472pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f01.png"/>

      </fig>

</sec>
<sec id="Ch1.S3">
  <label>3</label><title>Material and methods</title>
<sec id="Ch1.S3.SS1">
  <label>3.1</label><title>Modern pollen data and their climate data</title>
      <p id="d1e330">Our modern pollen dataset (<inline-formula><mml:math id="M8" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">731</mml:mn></mml:mrow></mml:math></inline-formula>) is derived from the east Tibetan Plateau ranging from 94.07 to 103.02<inline-formula><mml:math id="M9" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> E and from 29.13 to 38.48<inline-formula><mml:math id="M10" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula> N, with elevations from 2515 to 5008 m a.s.l. These modern pollen data are mainly from the modern pollen database of China and Mongolia (Cao et al., 2014) and recently published pollen data for the east Tibetan Plateau (Cao et al., 2021; Wang et al., 2022). The pollen sites are generally evenly distributed across the east Tibetan Plateau, covering subalpine forest, alpine meadow, alpine steppe, and alpine desert (Fig. 1). Pollen sample types include topsoil, lake surface sediments, and moss polsters mainly.</p>
      <p id="d1e363">We selected four important climate variables including mean annual precipitation (<inline-formula><mml:math id="M11" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>), mean temperature of the warmest month
(Mt<inline-formula><mml:math id="M12" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula>), mean temperature of the coldest month (Mt<inline-formula><mml:math id="M13" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula>), and mean annual temperature (<inline-formula><mml:math id="M14" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>), together with elevation (Elev) to investigate the relationship between pollen assemblages and the environment because these are important factors influencing the pollen distribution on the Tibetan Plateau (Lu et al., 2011; Cao et al., 2021; Wang et al., 2022). Modern climatic data were obtained from the Chinese Meteorological Forcing Dataset (CMFD; gridded near-surface meteorological dataset), and each sample is assigned to the nearest pixel of the CMFD using the <italic>fields</italic> package version 12.3 (Nychka et al., 2021) of R (version 4.0.3; R Core Team, 2021). The detailed processes of obtaining climatic data are presented in Fig. A1 (in the appendix).</p>
</sec>
<sec id="Ch1.S3.SS2">
  <label>3.2</label><title>Sediment sampling and radiocarbon dating</title>
      <p id="d1e417">A 329 cm long sediment core (named GAH) was obtained using a UWITEC platform
from the deepest part of Gahai Lake (ca. 2 m) in January 2019 (Fig. 1), and
then transported to the Institute of the Tibetan Plateau Research for
preservation. GAH was sub-sampled at 1 cm intervals, and all sub-samples
were freeze-dried.</p>
      <p id="d1e420">The age-depth model for GAH was established by <inline-formula><mml:math id="M15" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb, <inline-formula><mml:math id="M16" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs, and
accelerator mass spectrometry (AMS) radiocarbon dating. The top 20 cm of the
sediment was measured at 1 cm intervals for <inline-formula><mml:math id="M17" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb and <inline-formula><mml:math id="M18" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs at the
School of Geographical Science, Nantong University.
The constant rate of supply (CRS) model was selected to calculate the <inline-formula><mml:math id="M19" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb dates and the results revealed that the <inline-formula><mml:math id="M20" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb
date of 1963 CE was mainly consistent with the <inline-formula><mml:math id="M21" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs peak (1963 CE), indicating this model was suitable and obtained a good effect (Appleby, 2001).
Finally, an age-depth model based on a <inline-formula><mml:math id="M22" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb-CRS model corrected by the <inline-formula><mml:math id="M23" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs peak was generated (Fig. 4a). Overall, 13 bulk organic sediment samples of 1 cm thickness were sent for AMS <inline-formula><mml:math id="M24" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">14</mml:mn></mml:msup></mml:math></inline-formula>C dating by Beta Analytic Inc., USA, owing to a lack of macrofossils (Table 2). The age-depth model was
established using the Bayesian age-depth modeling in the <italic>rbacon</italic> package (version 2.5.7; Blaauw and Christen 2011; Blaauw et al., 2021) in R (R Core Team, 2021) and the IntCal20 radiocarbon calibration curve (Reimer et al., 2020).</p>
</sec>
<sec id="Ch1.S3.SS3">
  <label>3.3</label><title>Laboratory analysis</title>
      <p id="d1e525">The pollen samples (0.6–22 g; <inline-formula><mml:math id="M25" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">111</mml:mn></mml:mrow></mml:math></inline-formula>; at 1–2 cm intervals) were treated with hydrofluoric acid sieving analysis (Fægri and Iversen, 1989). <italic>Lycopodium</italic> spores (ca. 27 560 grains) were added to the samples to calculate the pollen concentration, then samples were processed with 10 % HCl, 10 % KOH, and 36 % HF, and sieved through a 7 <inline-formula><mml:math id="M26" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> nylon mesh, followed by acetolysis (<inline-formula><mml:math id="M27" display="inline"><mml:mrow><mml:mn mathvariant="normal">9</mml:mn><mml:mo>:</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:math></inline-formula> mixture of acetic anhydride and sulfuric acid) treatment. Finally, glycerin was added to preserve the samples. The pollen taxa were identified and counted with a <inline-formula><mml:math id="M28" display="inline"><mml:mrow><mml:mn mathvariant="normal">400</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math></inline-formula> LEICA DM 2500 optical microscope, with
the aid of modern pollen reference slides collected from the eastern and
central Tibetan Plateau (including 401 common species of alpine meadows; Cao
et al., 2020) and published atlases for pollen and spores (Wang et al., 1995; Tang et al., 2017). At least 100 terrestrial pollen grains were
counted for most samples, except for 10 samples owing to extremely low
pollen concentration and more than 3000 <italic>Lycopodium</italic> spores were counted for each sample which could reflect the palaeo-vegetation at that time. Because of the low pollen concentrations below the depth of 176 cm, only pollen data for the upper part of the core are presented and discussed.</p>
      <p id="d1e579">For the grain size analysis, freeze-dried samples (1 g; <inline-formula><mml:math id="M29" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">176</mml:mn></mml:mrow></mml:math></inline-formula>) were treated with 30 % H<inline-formula><mml:math id="M30" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula>O<inline-formula><mml:math id="M31" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2</mml:mn></mml:msub></mml:math></inline-formula> to remove organic matter and 10 % HCl to remove carbonate, cleaned with deionized water and kept stable for 24  h, before adding 0.5 N sodium hexametaphosphate (10 mL) and
undergoing ultrasonic cleaning for 10 min. A laser diffraction particle
size analyser MASTERSIZER 3000 (Chen et al., 2013) was used, with each
sample being tested 3 times and their average value used in the final
grain size data.</p>
      <p id="d1e612">A total of 176 samples were analysed to obtain organic matter change since
the last deglaciation, including TN and TOC. Catalysts were added to
freeze-dried samples and reacted quickly. The TN was measured with an Elementar
element analyser (CNS analyser, Vario MAX Cube), which
has a measurement accuracy of 0.001. The TOC was measured with a Vario MAX C
analyser, and has the same accuracy as TN. All samples were ground to ensure
sufficient reaction before testing. The <inline-formula><mml:math id="M32" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratio was calculated by dividing TOC by TN.</p><?xmltex \hack{\newpage}?>
</sec>
<sec id="Ch1.S3.SS4">
  <label>3.4</label><title>Numerical analyses</title>
      <p id="d1e636">Ordination analyses were employed to investigate the modern relationship
between pollen taxa and climatic variables for the eastern Tibetan Plateau.
Pollen taxa (with <inline-formula><mml:math id="M33" display="inline"><mml:mrow><mml:mo>≥</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula> % maximum and <inline-formula><mml:math id="M34" display="inline"><mml:mrow><mml:mo>≥</mml:mo><mml:mn mathvariant="normal">30</mml:mn></mml:mrow></mml:math></inline-formula> occurrences) from the
731 modern pollen assemblages were used for detrended correspondence
analysis (DCA; Hill and Gauch, 1980). The length of the first axis of the
pollen data was 3.29 SD (standard deviation units), indicating that a linear
response model is suitable for the modern pollen dataset (ter Braak and
Verdonschot, 1995). Hence, we performed redundancy analysis (RDA) to
visualise the distribution of pollen species and sampling sites along the
climatic gradients. We used the variance inflation factor (VIF) to determine
high collinearities within the model, and stopped adding variables to ensure
all VIF values are lower than 20 (ter Braak and Prentice, 1988; Table 1).
All ordination analyses were run using the <italic>rda</italic> function in the <italic>rioja</italic> package version 0.9–26 (Juggins, 2020) in R, using square-root transformed modern pollen percentages to optimize the signal-to-noise ratio (Prentice, 1980).</p>
      <p id="d1e665">For the fossil pollen dataset obtained from GAH, 22 pollen taxa (those
present in at least 3 samples and with a <inline-formula><mml:math id="M35" display="inline"><mml:mrow><mml:mo>≥</mml:mo><mml:mn mathvariant="normal">3</mml:mn></mml:mrow></mml:math></inline-formula> % maximum) with
square-root transformed percentages were selected for ordination analyses.
The length of the first axis was 1.67 SD, indicating that a principal component
analysis (PCA) is suitable to investigate the relationship between the
pollen taxa. The PCA was run using the <italic>rda</italic> function in the <italic>vegan</italic> package (version 2.5-4; Oksanen et al., 2019) in R.</p>
      <p id="d1e684">In addition, weighted-averaging partial least squares (WA-PLS) was employed to establish a pollen-climate transfer function using the modern pollen dataset, and to quantitatively reconstruct past climate for the GAH pollen
record. More details of the reconstruction are presented in the Supplement.</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T1" specific-use="star"><?xmltex \currentcnt{1}?><label>Table 1</label><caption><p id="d1e691">Summary statistics for redundancy analysis (RDA) with 19 pollen taxa and 4 climate variables. VIF: variance inflation factor; <inline-formula><mml:math id="M36" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>: mean annual precipitation (mm); Mt<inline-formula><mml:math id="M37" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula>: mean temperature of the coldest month (<inline-formula><mml:math id="M38" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C); Mt<inline-formula><mml:math id="M39" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula>: mean temperature of the warmest month (<inline-formula><mml:math id="M40" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C); <inline-formula><mml:math id="M41" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>: mean annual temperature (<inline-formula><mml:math id="M42" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C); and Elev: elevation (m a.s.l).</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="6">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="center"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="center"/>
     <oasis:colspec colnum="5" colname="col5" align="center"/>
     <oasis:colspec colnum="6" colname="col6" align="center"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Climate</oasis:entry>
         <oasis:entry colname="col2">VIF</oasis:entry>
         <oasis:entry colname="col3">VIF</oasis:entry>
         <oasis:entry colname="col4">Climate variables</oasis:entry>
         <oasis:entry namest="col5" nameend="col6">Marginal contribution based </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">variables</oasis:entry>
         <oasis:entry colname="col2">(without <inline-formula><mml:math id="M43" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col3">(add <inline-formula><mml:math id="M44" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>)</oasis:entry>
         <oasis:entry colname="col4">as sole predictor</oasis:entry>
         <oasis:entry rowsep="1" namest="col5" nameend="col6">on climate variables </oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">Explained variance</oasis:entry>
         <oasis:entry colname="col5">Explained variance</oasis:entry>
         <oasis:entry colname="col6"><inline-formula><mml:math id="M45" display="inline"><mml:mi>p</mml:mi></mml:math></inline-formula>-value</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2"/>
         <oasis:entry colname="col3"/>
         <oasis:entry colname="col4">(%)</oasis:entry>
         <oasis:entry colname="col5">(%)</oasis:entry>
         <oasis:entry colname="col6"/>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M46" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">3.0</oasis:entry>
         <oasis:entry colname="col3">3.1</oasis:entry>
         <oasis:entry colname="col4">5.2</oasis:entry>
         <oasis:entry colname="col5">7.1</oasis:entry>
         <oasis:entry colname="col6">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Mt<inline-formula><mml:math id="M47" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">4.5</oasis:entry>
         <oasis:entry colname="col3">133.6</oasis:entry>
         <oasis:entry colname="col4">4.9</oasis:entry>
         <oasis:entry colname="col5">0.3</oasis:entry>
         <oasis:entry colname="col6">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Mt<inline-formula><mml:math id="M48" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">6.5</oasis:entry>
         <oasis:entry colname="col3">111.7</oasis:entry>
         <oasis:entry colname="col4">3.7</oasis:entry>
         <oasis:entry colname="col5">5.7</oasis:entry>
         <oasis:entry colname="col6">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Elev</oasis:entry>
         <oasis:entry colname="col2">2.5</oasis:entry>
         <oasis:entry colname="col3">3.0</oasis:entry>
         <oasis:entry colname="col4">4.5</oasis:entry>
         <oasis:entry colname="col5">0.2</oasis:entry>
         <oasis:entry colname="col6">0.001</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1"><inline-formula><mml:math id="M49" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col2">–</oasis:entry>
         <oasis:entry colname="col3">403.9</oasis:entry>
         <oasis:entry colname="col4">–</oasis:entry>
         <oasis:entry colname="col5">–</oasis:entry>
         <oasis:entry colname="col6">–</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
</sec>
<sec id="Ch1.S4">
  <label>4</label><title>Results</title>
<sec id="Ch1.S4.SS1">
  <label>4.1</label><title>Relationships of pollen taxa to climatic variables and elevation</title>
      <p id="d1e1047">The modern pollen dataset for the east Tibetan Plateau contains 107 pollen
taxa and covers a long <inline-formula><mml:math id="M50" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> gradient (161–963 mm) and broad Mt<inline-formula><mml:math id="M51" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> gradient (1.8–18.5 <inline-formula><mml:math id="M52" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) (Figs. 2; A1). High abundances of arboreal pollen taxa including <italic>Abies</italic>, <italic>Quercus</italic> (evergreen, E), <italic>Corylus</italic>, and <italic>Carpinus</italic> are mainly  distributed in regions with <inline-formula><mml:math id="M53" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> higher than 450 mm and Mt<inline-formula><mml:math id="M54" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula> higher than <inline-formula><mml:math id="M55" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">15</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M56" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C (Figs. 2; A1). <italic>Pinus</italic> (up to 2.3 %, mean 0.3 %), <italic>Picea</italic> (up to 25.7 %, mean 0.5 %), and <italic>Betula</italic> (up to 5.7 %, mean 0.4 %) are also widely distributed and appear in extremely dry and cold sampling sites where <inline-formula><mml:math id="M57" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> is lower than 450 mm and Mt<inline-formula><mml:math id="M58" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula> lower than <inline-formula><mml:math id="M59" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">15</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M60" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C, although their high abundances are restricted to warm and wet areas (Figs. 2; A1). Drought-tolerant taxa such as Amaranthaceae and <italic>Ephedra</italic> are restricted to regions with low <inline-formula><mml:math id="M61" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and high Mt<inline-formula><mml:math id="M62" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula>, and they have quite low abundances in wet areas (Fig. 2). In addition, elevation is also an important factor influencing the pollen distribution on the eastern Tibetan Plateau. Arboreal pollen taxa including <italic>Pinus</italic>, <italic>Picea</italic>, <italic>Abies</italic>, <italic>Betula</italic>, <italic>Quercus</italic> (deciduous, D), and <italic>Corylus</italic> are mainly distributed in areas below 3900 m a.s.l., while <italic>Quercus</italic> (E) is concentrated in areas above 3700 m a.s.l. The high percentages of Cyperaceae, <italic>Artemisia</italic>, and Amaranthaceae are mainly concentrated in the lower elevations (below 3200 m a.s.l.).</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F2" specific-use="star"><?xmltex \currentcnt{2}?><?xmltex \def\figurename{Figure}?><label>Figure 2</label><caption><p id="d1e1231">Pollen assemblages of surface sediment samples with annual
precipitation (<inline-formula><mml:math id="M63" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>) from the eastern Tibetan Plateau.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f02.png"/>

        </fig>

      <p id="d1e1251">Redundancy analysis shows that the first two axes explain 28 % of the
pollen data (axis 1: 15.5 %; axis 2: 12.5 %; Fig. 3). Arboreal pollen
taxa are located in the left of the biplot and are positively correlated
with <inline-formula><mml:math id="M64" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and Mt<inline-formula><mml:math id="M65" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula>. Asteraceae, Poaceae, <italic>Thalictrum</italic>, Ranunculaceae, Caryophyllaceae, and Cyperaceae show a negative relationship with Mt<inline-formula><mml:math id="M66" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> and Mt<inline-formula><mml:math id="M67" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula> while positive with Elev and are situated in the lower right of the biplot. Drought-tolerant pollen including Amaranthaceae, <italic>Artemisia</italic>, and <italic>Ephedra</italic> are situated at the upper right of the biplot, showing positive correlations with temperature variables and negative correlations with precipitation (Fig. 3).</p>

      <?xmltex \floatpos{p}?><fig id="Ch1.F3" specific-use="star"><?xmltex \currentcnt{3}?><?xmltex \def\figurename{Figure}?><label>Figure 3</label><caption><p id="d1e1305">Redundancy analysis (RDA) of modern pollen samples along with
three climate variables and elevation.</p></caption>
          <?xmltex \igopts{width=412.564961pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f03.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS2">
  <label>4.2</label><title>Sedimentary lithology and chronology</title>
      <p id="d1e1322">The sedimentary lithology of the GAH core is comprised of black silt in the
upper part (0–99 cm), brown clay in the central part (99–240 cm), and
dark-brown fine silt in the lower part (240–329 cm; Fig. 4). Our study
concentrates on the vegetation and environment evolution of the upper 176 cm
due to the extremely low pollen concentrations in the lower part,which are insufficient for statistical analyses.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F4"><?xmltex \currentcnt{4}?><?xmltex \def\figurename{Figure}?><label>Figure 4</label><caption><p id="d1e1327">Age-depth model of the Gahai Lake sediment core derived from <inline-formula><mml:math id="M68" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs, <inline-formula><mml:math id="M69" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb, and <inline-formula><mml:math id="M70" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">14</mml:mn></mml:msup></mml:math></inline-formula>C dating. <bold>(a)</bold> Black line with triangles:
<inline-formula><mml:math id="M71" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">137</mml:mn></mml:msup></mml:math></inline-formula>Cs age; black line with solid circles: <inline-formula><mml:math id="M72" display="inline"><mml:mrow class="chem"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup><mml:mi mathvariant="normal">Pb</mml:mi><mml:msup><mml:mo>:</mml:mo><mml:mn mathvariant="normal">210</mml:mn></mml:msup><mml:msub><mml:mi mathvariant="normal">Pb</mml:mi><mml:mi mathvariant="normal">ex</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> age; black line with squares: mean age based on annual lamination counting. <bold>(b)</bold> Age-depth curve based on a <inline-formula><mml:math id="M73" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb profile of recent sediments and 13 AMS radiocarbon dates from Gahai Lake. The range of the two dashed grey lines indicates the 95 % confidence intervals, and the dashed red lines show the single “best” model based on the weighted mean age for each depth.</p></caption>
          <?xmltex \igopts{width=241.848425pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f04.png"/>

        </fig>

      <p id="d1e1410">The chronology of the upper 20 cm sediment is established by the
<inline-formula><mml:math id="M74" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb-CRS model, with dates falling between 1828 and 2013 CE. The AMS
<inline-formula><mml:math id="M75" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">14</mml:mn></mml:msup></mml:math></inline-formula>C ages of GAH exhibit a linear regression with depth, while there is
a transient inversion between 191 and 279 cm, which is probably due to
increased erosion input to the basin, leading to some old carbon
accumulating in the lake. The ages of the upper 20 cm are calculated based
on their relationship (Table 2), and the age difference between <inline-formula><mml:math id="M76" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">14</mml:mn></mml:msup></mml:math></inline-formula>C and
<inline-formula><mml:math id="M77" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">210</mml:mn></mml:msup></mml:math></inline-formula>Pb of the same depth is considered as the reservoir age. We selected
two depths (6 and 10 cm) to calculate an average to reduce errors and obtained a reservoir age of 483 years. The age-depth model suggests that the
basal age of GAH is about 24 ka BP, with the age of sediments between 191
and 279 cm basically remaining the same, probably because of lake sediment
collapse or rapid input of terrigenous clastic materials since the lithology
also markedly changes between 190 and 280 cm, confirming that the lake
underwent rapid deposition during this phase. The sedimentation rate has been
relatively stable since 15 ka BP, and our research focuses on the vegetation and environmental evolution since 14.2 ka BP (Fig. 4).</p>

<?xmltex \floatpos{t}?><table-wrap id="Ch1.T2"><?xmltex \currentcnt{2}?><label>Table 2</label><caption><p id="d1e1453">AMS radiocarbon dates for Gahai Lake.</p></caption><oasis:table frame="topbot"><oasis:tgroup cols="5">
     <oasis:colspec colnum="1" colname="col1" align="left"/>
     <oasis:colspec colnum="2" colname="col2" align="right"/>
     <oasis:colspec colnum="3" colname="col3" align="right"/>
     <oasis:colspec colnum="4" colname="col4" align="right"/>
     <oasis:colspec colnum="5" colname="col5" align="center"/>
     <oasis:thead>
       <oasis:row>
         <oasis:entry colname="col1">Lab ID</oasis:entry>
         <oasis:entry colname="col2">Depth</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M78" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">13</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>C</oasis:entry>
         <oasis:entry colname="col4"><inline-formula><mml:math id="M79" display="inline"><mml:msup><mml:mi/><mml:mn mathvariant="normal">14</mml:mn></mml:msup></mml:math></inline-formula>C age</oasis:entry>
         <oasis:entry colname="col5">Error</oasis:entry>
       </oasis:row>
       <oasis:row rowsep="1">
         <oasis:entry colname="col1"/>
         <oasis:entry colname="col2">(cm)</oasis:entry>
         <oasis:entry colname="col3">(‰)</oasis:entry>
         <oasis:entry colname="col4">(years BP)</oasis:entry>
         <oasis:entry colname="col5">(<inline-formula><mml:math id="M80" display="inline"><mml:mo lspace="0mm">±</mml:mo></mml:math></inline-formula> years)</oasis:entry>
       </oasis:row>
     </oasis:thead>
     <oasis:tbody>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546102</oasis:entry>
         <oasis:entry colname="col2">10</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M81" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">25.6</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">440</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546103</oasis:entry>
         <oasis:entry colname="col2">25</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M82" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">25.1</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">1740</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-539751</oasis:entry>
         <oasis:entry colname="col2">40</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M83" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">25.7</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">1960</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-539752</oasis:entry>
         <oasis:entry colname="col2">80</oasis:entry>
         <oasis:entry colname="col3">24.8</oasis:entry>
         <oasis:entry colname="col4">3880</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546104</oasis:entry>
         <oasis:entry colname="col2">99</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M84" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">24.3</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">6390</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-539753</oasis:entry>
         <oasis:entry colname="col2">120</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M85" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">22.1</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">8180</oasis:entry>
         <oasis:entry colname="col5">40</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546105</oasis:entry>
         <oasis:entry colname="col2">144</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M86" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">22.9</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">10 240</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-539754</oasis:entry>
         <oasis:entry colname="col2">170</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M87" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">23</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">10 590</oasis:entry>
         <oasis:entry colname="col5">30</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-575823</oasis:entry>
         <oasis:entry colname="col2">191</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M88" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">24.4</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">15 070</oasis:entry>
         <oasis:entry colname="col5">40</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546120</oasis:entry>
         <oasis:entry colname="col2">229</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M89" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">25.4</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">14 870</oasis:entry>
         <oasis:entry colname="col5">50</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-550230</oasis:entry>
         <oasis:entry colname="col2">275</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M90" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">23.5</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">18 930</oasis:entry>
         <oasis:entry colname="col5">60</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546121</oasis:entry>
         <oasis:entry colname="col2">279</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M91" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">22.6</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">15 550</oasis:entry>
         <oasis:entry colname="col5">50</oasis:entry>
       </oasis:row>
       <oasis:row>
         <oasis:entry colname="col1">Beta-546122</oasis:entry>
         <oasis:entry colname="col2">319</oasis:entry>
         <oasis:entry colname="col3"><inline-formula><mml:math id="M92" display="inline"><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">20.5</mml:mn></mml:mrow></mml:math></inline-formula></oasis:entry>
         <oasis:entry colname="col4">19 440</oasis:entry>
         <oasis:entry colname="col5">70</oasis:entry>
       </oasis:row>
     </oasis:tbody>
   </oasis:tgroup></oasis:table></table-wrap>

</sec>
<sec id="Ch1.S4.SS3">
  <label>4.3</label><title>Pollen record of GAH since the last deglaciation</title>
      <p id="d1e1878">In our study, 52 pollen taxa were identified in the 111 samples from the upper part of GAH (0–176 cm), with Cyperaceae, <italic>Pinus</italic>, Asteraceae, and <italic>Artemisia</italic> as dominant taxa, while Poaceae, Ranunculaceae, <italic>Ulmus</italic>, and <italic>Picea</italic> are common taxa. The pollen record can be demarcated into four zones (Fig. 5). Pollen concentration is extremely low (mean 33.5 grains per g) before 7.4 ka BP, and the pollen spectra are dominated by arboreal pollen taxa including <italic>Pinus</italic>, <italic>Picea</italic>, <italic>Ulmus</italic>, and <italic>Betula</italic>, together with abundant drought-tolerant pollen taxa (such as Amaranthaceae and <italic>Ephedra</italic>). Pollen concentrations increase remarkably after 7.4 ka BP, and the percentage of
drought-tolerant pollen taxa decreases while that of <italic>Pinus</italic> increases in the pollen spectra. Between 3.8 and 2.3 ka BP, <italic>Pinus</italic> and <italic>Picea</italic> decrease sharply, while <italic>Artemisia</italic>, Poaceae, Asteraceae, and <italic>Thalictrum</italic> increase significantly. Pollen concentrations increase greatly and the pollen spectra are dominated by Cyperaceae after 2.3 ka BP. Cyperaceae rises sharply and becomes overwhelmingly dominant in the pollen spectra, and the pollen concentration also increases strongly in the last 0.24 ka BP (Fig. 5).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F5" specific-use="star"><?xmltex \currentcnt{5}?><?xmltex \def\figurename{Figure}?><label>Figure 5</label><caption><p id="d1e1927">Pollen diagram of the main fossil pollen taxa in Gahai Lake,
northeast Tibetan Plateau.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f05.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS4">
  <label>4.4</label><title>PCA results</title>
      <p id="d1e1944">The first two axes of the principal component analysis (PCA) explain 72 %
of the total pollen data (axis 1: 59.2 %; axis 2: 12.8 %; Fig. 6a). The PCA divides arboreal pollen taxa (such as <italic>Pinus</italic>, <italic>Picea</italic>, <italic>Betula</italic>, <italic>Ulmus</italic>), alpine steppe taxa (including <italic>Artemisia</italic>, Poaceae, Asteraceae), and meadow taxa (Cyperaceae) into three clear groups. In addition, pollen samples of Zones I and II are consistent with arboreal taxa, pollen samples from Zone III contain abundant steppe taxa, while samples in Zone IV are dominated by Cyperaceae (Fig. 6b).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F6" specific-use="star"><?xmltex \currentcnt{6}?><?xmltex \def\figurename{Figure}?><label>Figure 6</label><caption><p id="d1e1964">Principal component analysis (PCA) of fossil pollen taxa <bold>(a)</bold> and pollen zones <bold>(b)</bold> from Gahai Lake (see Fig. 5 for the pollen zones).</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f06.png"/>

        </fig>

</sec>
<sec id="Ch1.S4.SS5">
  <label>4.5</label><title>Sedimentology and conventional geochemistry</title>
      <p id="d1e1988">The size fractions (volume, %) were classified as clay (<inline-formula><mml:math id="M93" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M94" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>), silt (fine: 4–16 <inline-formula><mml:math id="M95" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>; medium: 16–32 <inline-formula><mml:math id="M96" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>; coarse: 32–63 <inline-formula><mml:math id="M97" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>, combined into one category for the discussion), and sand (<inline-formula><mml:math id="M98" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">63</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M99" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>), and the specific details are shown in Fig. A2. The grain size parameters of GAH include mean grain size, which ranges from 17.5 to 60 <inline-formula><mml:math id="M100" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>. The combined silt fraction (4–63 <inline-formula><mml:math id="M101" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) accounts for the maximum
proportion (58–75 %; mean 66 %) in general (Fig. 7). The clay fraction (15–33 %; mean 23 %) forms the highest proportion during 14.2–10.8 ka BP, then decreases significantly and remains stable after 10.8 ka BP (Fig. 7). The silt fraction (57.6–74.7 %; mean 63.9 %) is lowest during 14.2–10.8 ka BP, then increases and reaches a peak during 7.4–3.8 ka BP, after which the mean value decreases to 65.8 % (Fig. 7). The sand fraction correlates with the silt fraction before 10.8 ka BP, while later the variation is anticorrelated. Mean grain size closely correlates with the sand fraction in general (Fig. 7).</p>
      <p id="d1e2082">The TOC, TN, and <inline-formula><mml:math id="M102" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios fluctuate greatly after 14.2 ka BP, and TOC and TN present simultaneous change trends (Fig. 7). The TOC and TN values are
remarkably low and <inline-formula><mml:math id="M103" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios are lower than 10 between 14.2 and 7 ka BP. TOC, TN, and <inline-formula><mml:math id="M104" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios increase significantly and <inline-formula><mml:math id="M105" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios are higher than 10 between 7 and 3.8 ka BP. TOC, TN, and <inline-formula><mml:math id="M106" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios reduce slightly but are still higher than 10 after 3.8 ka BP. The TOC and TN values increase drastically while <inline-formula><mml:math id="M107" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios have no obvious change during the last 0.24 ka BP (since 1710 CE).</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F7" specific-use="star"><?xmltex \currentcnt{7}?><?xmltex \def\figurename{Figure}?><label>Figure 7</label><caption><p id="d1e2160">Comparison of the multiproxy records from Gahai Lake. <bold>(a)</bold> Total nitrogen (TN); <bold>(b)</bold> total organic carbon (TOC); <bold>(c)</bold> <inline-formula><mml:math id="M108" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratio; <bold>(d)</bold> pollen concentration; <bold>(e–h)</bold> grain size distribution and mean grain size; <bold>(i)</bold> quantitative reconstruction of mean temperature of the warmest month (Mt<inline-formula><mml:math id="M109" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula>). The dark purple curve indicates the reconstruction based on the pollen assemblages including the arboreal pollen and the light purple curve represents the reconstruction based on the pollen assemblages removing the arboreal pollen (before 7.4 ka only); <bold>(j)</bold> the quantitative reconstructions of mean annual precipitation (<inline-formula><mml:math id="M110" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula>). The dark blue curve is the reconstruction based on the pollen assemblages including the arboreal pollen, and the light blue curve is the reconstruction based on the pollen assemblages without the arboreal pollen (before 7.4 ka only). The grey shading denotes the different pollen zones of Gahai Lake.</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f07.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S5">
  <label>5</label><title>Discussion</title>
<sec id="Ch1.S5.SS1">
  <label>5.1</label><title>Patterns and interpretation of the proxies</title>
      <p id="d1e2240">The TOC is a proxy for the abundance of organic matter which originates from
aquatic organisms and terrestrial vegetation, and TN represents the nutritional conditions of the lake. In addition, TOC is an effective index
to evaluate the summer monsoon intensity, where low values reflect a cold
and dry climate (An et al., 2012; Opitz et al., 2012). <inline-formula><mml:math id="M111" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios are used to trace the plant source of the organic matter. <inline-formula><mml:math id="M112" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios of the nonvascular aquatic plants and algae are generally between 4 and 10, and <inline-formula><mml:math id="M113" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios <inline-formula><mml:math id="M114" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">20</mml:mn></mml:mrow></mml:math></inline-formula> indicate that organic matter mainly originates from
terrestrial vascular plants. Ratios ranging from 10 to 20 suggest that the
organic matter is derived from a mixture of aquatic and terrestrial plants
(Meyers and Ishiwatari, 1993; Meyers, 2003; Kasper et al., 2015). High
values of TOC and <inline-formula><mml:math id="M115" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios in the Tibetan Plateau lakes suggest a warm and wet climate (Chen et al., 2021).</p>
      <p id="d1e2301">The grain size composition of lake sediments can be used to trace the source
of clastic particles, aeolian activity, and water level fluctuations, which
reflect the regional climate conditions (Håkanson and Jansson, 1983; Liu
et al., 2016). The sources of lacustrine sediments include clastic materials
carried by inflow rivers, aeolian inputs, and authigenic chemical deposition, and mean grain size reflects the intensity of transport dynamics (Folk and Ward, 1957; Xiao et al., 2013). There have been many particle size analyses from lacustrine sediments or loess deposits on the northeast Tibetan Plateau. For example, Qiang et al. (2014) analysed the grain size of Genggahai lake and propound that the sand fraction (<inline-formula><mml:math id="M116" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">63</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M117" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>)
reflects aeolian activity. Chen et al. (2013) investigated Sugan Lake in the
Qaidam Basin and argue that changes in the <inline-formula><mml:math id="M118" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">63</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M119" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> fraction
reflect the frequencies of dust storms and strong winds. In addition, Wang
et al. (2015) analysed a loess deposit from Ledu on the northeast
Tibetan Plateau and also conclude that a grain size of 60 <inline-formula><mml:math id="M120" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula> is locally transported by strong winds during cold climatic intervals. The sand
fraction (<inline-formula><mml:math id="M121" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">63</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M122" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) is also found in modern river sediments
although the percentage is typically low. A single extreme rain event under
an arid climate could lead to an abrupt sand fraction increase (Ding et al., 2005; Li et al., 2012; Liu et al., 2016; Ota et al., 2017; Zhou et al., 2018). Therefore, the sand fraction is mainly transported by winds and any peak or abnormal increase of the coarse grain size (especially the sand
fraction) is likely related to flood events. In our study, a high proportion
of the sand fraction (&gt;63 <inline-formula><mml:math id="M123" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) mainly represents aeolian activity intensity.</p>
      <p id="d1e2385">Particle size variation can reflect changes in water level or precipitation,
taking into account the different lake recharge types, hydrological
conditions, and lake sizes. There has been debate about how to interpret the
grain size index because the coarse particle fraction is positively
correlated with precipitation and water level in small lakes dominated by
summer rainfall but not in large lakes (Peng et al., 2005; Chen et al., 2021). Gahai Lake is a small, shallow lake and receives most of its
precipitation in summer. The coarse particle fraction reflects a humid
climate and high lake level owing to strong hydrological dynamics
(Håkanson and Jansson, 1983; Peng et al., 2005; Liu et al., 2008). The
silt fraction (4–63 <inline-formula><mml:math id="M124" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) in our study is driven by the medium silt (16–32 <inline-formula><mml:math id="M125" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) fraction, while the fine and coarse silt fractions remain almost unchanged during the Holocene, hence the fine, medium, and coarse silts are combined into the total silt fraction (4–63 <inline-formula><mml:math id="M126" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) for discussion. In addition, the mean grain size is closely related to the sand fraction and poorly reflects the climatic moisture and lake level. Therefore, we speculate that a high silt fraction (4–63 <inline-formula><mml:math id="M127" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) in Gahai Lake reflects an increased lake level, while a high clay fraction (<inline-formula><mml:math id="M128" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula> <inline-formula><mml:math id="M129" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) content reflects a low level.</p>
</sec>
<sec id="Ch1.S5.SS2">
  <label>5.2</label><title>Determination of exogenous pollen grains</title>
      <p id="d1e2457">According to modern pollen research from the Tibetan Plateau and northern
China, <italic>Pinus</italic>, <italic>Picea</italic>, and <italic>Betula</italic> are the dominant pollen taxa in forest samples, and these taxa have a good diffusion capacity with their pollen easily transported for long distances from the source (Lu et al., 2004; Ma et al., 2008). <italic>Ulmus</italic> also has good diffusion and can spread up to 40 km away, and can therefore show up as a regional vegetation component in a pollen assemblage (Xu et al., 2007). In addition, we analysed the non-woodland topsoil samples within 30 km of Gahai
Lake (<inline-formula><mml:math id="M130" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">22</mml:mn></mml:mrow></mml:math></inline-formula>). Results show that arboreal pollen taxa including <italic>Pinus</italic>, <italic>Picea</italic>, and <italic>Betula</italic> are always present (usually at <inline-formula><mml:math id="M131" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">40</mml:mn></mml:mrow></mml:math></inline-formula> %) in the pollen samples, indicating that they have good diffusivity and are easily transported to areas beyond the pollen source (Figs. A3; A4). Therefore, the main arboreal pollen taxa in the GAH core including <italic>Pinus</italic>, <italic>Picea</italic>, <italic>Betula</italic>, and <italic>Ulmus</italic> are highly diffusive
species which may bias the vegetation reconstruction unless the
far distance transport is accounted for.</p>
      <p id="d1e2517">The main pollen taxa have notable spatial distribution characteristics owing
to their ecological environment based on modern pollen research and the
modern pollen dataset. Arboreal taxa including <italic>Pinus</italic>, <italic>Picea</italic>, and <italic>Betula</italic> are mainly
distributed in a warm and humid environment (Lu et al., 2004). <italic>Ulmus</italic> is a drought-tolerant and light-demanding plant which can survive at
precipitation levels lower than 200 mm yr<inline-formula><mml:math id="M132" display="inline"><mml:msup><mml:mi/><mml:mrow><mml:mo>-</mml:mo><mml:mn mathvariant="normal">1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula> (Shen et al., 2005).
Previous modern pollen studies reveal that Amaranthaceae and <italic>Ephedra</italic> are commonly found in the desert, indicating a tolerance for dry climatic conditions (Yu et al., 2001; Huang et al., 2018; Qin, 2021) and our modern pollen dataset for the east Tibetan Plateau suggests that xerophilous taxa, such as <italic>Ephedra</italic> and Amaranthaceae, are restricted to areas with <inline-formula><mml:math id="M133" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> lower than 400 mm, and almost absent in samples with high precipitation (Fig. 2). Fossil
pollen spectra from the Tibetan Plateau with abundant arboreal pollen taxa
together with low pollen concentrations are considered to represent extreme
arid conditions and sparse vegetation (Kramer et al., 2010; Ma et al., 2019). Therefore, we argue that the arboreal pollen including <italic>Pinus</italic>, <italic>Picea</italic>, and
<italic>Ulmus</italic> has been transported by wind from beyond the watershed, and that the high
abundance of drought-tolerant herbaceous taxa (weak dispersal ability) and
low pollen concentrations indicate a sparse vegetation cover around the lake
between 14.2 and 7.4 ka BP, suggesting an extremely arid climate.</p>
</sec>
<sec id="Ch1.S5.SS3">
  <label>5.3</label><title>Evolution of vegetation and climate history since the last deglaciation</title>
      <p id="d1e2579">Palaeo-vegetation and palaeo-climate are reconstructed based on the fossil
pollen, TOC, TN, <inline-formula><mml:math id="M134" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios, and grain size record of Gahai Lake since the last deglaciation.</p>
      <p id="d1e2594">From 14.2 to 10.8 ka BP, alpine steppe or desert covered the study area with the arboreal pollen derived from the surrounding mountains in the south-east of the basin. Pollen-based past <inline-formula><mml:math id="M135" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructions are mainly in the range of higher than 418 mm (excluding arboreal taxa from pollen spectra) but less than 610 mm (including arboreal taxa from pollen spectra). Remarkably, however, there is little difference between the reconstructed Mt<inline-formula><mml:math id="M136" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> based on excluding arboreal taxa (mean 9.1 <inline-formula><mml:math id="M137" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) and including arboreal pollen (mean 9.6 <inline-formula><mml:math id="M138" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C), thus the climate was probably warm and arid during this period (Fig. 7). Quite low TOC and TN
contents, and <inline-formula><mml:math id="M139" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios (<inline-formula><mml:math id="M140" display="inline"><mml:mrow><mml:mo>&lt;</mml:mo><mml:mn mathvariant="normal">4</mml:mn></mml:mrow></mml:math></inline-formula>) suggest that the organic matter is
mainly derived from aquatic plants and little terrestrial biomass
productivity under a dry and cold environment (Fig. 7; Zhu et al., 2015).
The maximum clay fraction and a high sand fraction in the lake sediments reflect
a low water level and intense aeolian activity (Fig. 7). In summary, Gahai Lake was
probably a small and shallow pond during this period, with the surrounding
vegetation dominated by alpine steppe or desert.</p>
      <p id="d1e2658">From 10.8 to 7.4 ka BP, Ranunculaceae and Cyperaceae show a slight increase,
and alpine steppe occurs across the region (Fig. 5). The reconstruction
suggests that Mt<inline-formula><mml:math id="M141" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> (mean: 8.5–9.0 <inline-formula><mml:math id="M142" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) slightly decreases compared with the former stage, whereas reconstructed <inline-formula><mml:math id="M143" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (mean: 468–619 mm) is basically steady but still influenced by the exaggerated
contribution of exogenous arboreal pollen (Fig. 7). The TOC and <inline-formula><mml:math id="M144" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios rise
during the early Holocene, implying an increase in biological productivity
although still mainly from aquatic plants (Fig. 7). The silt fraction
significantly increases while the clay fraction decreases sharply with small
fluctuations in the sand fraction, indicating a slight rise in the water
level and intense aeolian activity during the early Holocene (Fig. 7).
Therefore, we infer that the vegetation of Gahai Basin was covered by alpine
steppe under dry climatic conditions during the early Holocene.</p>
      <p id="d1e2702">The pollen spectra are dominated by <italic>Pinus</italic> and <italic>Picea</italic> while drought-tolerant taxa (such
as Amaranthaceae and <italic>Ephedra</italic>) have low abundances, indicating a vegetation shift
from alpine steppe to montane forest between 7.4 and 3.8 ka BP. In addition,
the pollen concentration increases markedly, reflecting a greatly enhanced
vegetation and moist climate after 7.4 ka BP (Kramer et al., 2010; Ma et
al., 2019). The climate reconstruction shows that <inline-formula><mml:math id="M145" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (mean: 634 mm)
and Mt<inline-formula><mml:math id="M146" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> (mean: 9.3 <inline-formula><mml:math id="M147" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C) reach their peaks, suggesting that
Gahai Lake is under a warm and wet climate optimum during this period (Fig. 7). In addition, the silt fraction significantly increases to a peak (mean: 70 %), and TOC, TN, and <inline-formula><mml:math id="M148" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios (<inline-formula><mml:math id="M149" display="inline"><mml:mrow><mml:mo>&gt;</mml:mo><mml:mn mathvariant="normal">10</mml:mn></mml:mrow></mml:math></inline-formula>) markedly increase suggesting that Gahai Lake was at a high stand with increased terrestrial
organic matter input having grown from a small pond since 7.4 ka BP. At the
same time, the sand fraction decreases to its nadir (mean: 11.7 %),
indicating weakened aeolian activity during this period, which could be
related to the increased vegetation cover and moisture (Fig. 7). In summary,
as Gahai Lake expanded, the surrounding vegetation became montane forest as
seen by a shift in the arboreal pollen from extraregional to within
catchment. To support this vegetation, the climate was warm and wet, while
aeolian activity was weak during the mid-Holocene (Fig. 7).</p>
      <p id="d1e2767">Between 3.8 and 2.3 ka BP, the pollen spectra are characterized by a high
percentage of Poaceae, <italic>Artemisia</italic>, and Asteraceae (major components of alpine steppe),
while arboreal pollen taxa, especially <italic>Pinus</italic> and <italic>Picea</italic>, sharply decrease, indicating a tree-line retreat to a lower elevation and a shift in vegetation type
to alpine steppe (Herzschuh et al., 2010; Shen et al., 2021; Fig. 5).
Reconstructed <inline-formula><mml:math id="M150" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (mean: 547 mm) and Mt<inline-formula><mml:math id="M151" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> (mean: 8.3 <inline-formula><mml:math id="M152" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C)
decrease significantly, suggesting climatic conditions deteriorated (Fig. 7). The TOC, TN, and <inline-formula><mml:math id="M153" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios slightly decrease compared with the previous stage, suggesting that the input of organic matter weakened (Fig. 7). The silt fraction substantially decreases while the sand fraction has an increasing
trend, suggesting the lake level decreased and aeolian activity increased
(Fig. 7). In brief, the climate tended to be arid with enhanced aeolian
activity and deteriorating environmental conditions. Alpine steppe dominated
across the study region during this period.</p>
      <p id="d1e2821">From 2.3 to 0.24 ka BP, the dominant taxa change from alpine steppe
(Poaceae, <italic>Artemisia</italic>, and Asteraceae) (Ma et al., 2017; Qin, 2021) to alpine meadow
(Cyperaceae) components (Herzschuh, 2007; Herzschuh et al., 2010; Fig. 5), and a
decrease in reconstructed <inline-formula><mml:math id="M154" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> (537 mm) and Mt<inline-formula><mml:math id="M155" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula> (7.3 <inline-formula><mml:math id="M156" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C)
suggest an arid and cold environment (Fig. 7). The TOC, TN, and <inline-formula><mml:math id="M157" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula> ratios are
almost unchanged suggesting similar total biogenic productivity to the
previous stage (Fig. 7). The silt fraction decreases while the sand fraction
increases, indicating a lower lake level and stronger aeolian activity than
the former stage. Therefore, in this period, the vegetation turned to alpine
meadow under an arid and cold climate, and the lake level dropped while aeolian activity increased.</p>
      <p id="d1e2868">After 0.24 ka BP (1710 CE), the pollen spectra are dominated by Cyperaceae
(maximum, 95 %; Fig. 5), with the percentage of Poaceae decreasing while
Ranunculaceae increases. Previous vegetation investigations suggest that
overgrazing causes the proportion of Cyperaceae to increase and become the
dominant taxon, and thus could be an indicator of human activities (Yuan et
al., 2004; Miehe et al., 2014; Lin et al., 2016). In addition, modern pollen
research also suggests that pollen assemblages are dominated by Cyperaceae
in overgrazed sites of alpine steppe and alpine meadow (Duan et al., 2021).
According to earlier topsoil studies, Ranunculaceae and Poaceae are
important indicators of grazing activities on the northeast Tibetan
Plateau, with pollen percentages changing significantly in the overgrazed
sites (Wei et al., 2018; Duan et al., 2021). Hence the vegetation during
this period could have been disturbed by human activities. In addition, TOC,
TN, and pollen concentrations notably increase, indicating that the terrestrial
material input strengthened, possibly as a result of increased surface
erosion (silt fraction increases; Fig. 7) due to the destruction of
vegetation by grazing and pastoral activities. Reduced precipitation and
monsoonal activity are also suggested by the increases in TOC, TN, and
pollen concentrations.</p>
</sec>
<sec id="Ch1.S5.SS4">
  <label>5.4</label><title>Comparison of the regional climate and vegetation records from the northeast Tibetan Plateau in the early Holocene</title>
      <p id="d1e2879">Climate and vegetation as revealed by pollen records covering the early
Holocene on the northeast Tibetan Plateau are inconsistent, which may be
due to the following reasons: local factors have a greater effect than
regional climate (Chen et al., 2020), the distance of sampling sites from
forested areas affects the results of vegetation reconstruction (Sun et al., 2017) and different climatic factors influence the regional vegetation
distribution of the eastern Tibetan Plateau (Zhao et al., 2011). Based on
the results of TOC, grain size, and reconstructed precipitation based on
pollen analysis, we infer that Gahai Lake was surrounded by alpine steppe
vegetation under an arid climate, and that the arboreal pollen was mainly
transported by wind from the surrounding mountains during the early Holocene
(Fig. 8a, b, c). Other records from the northeast Tibetan Plateau support
these general features of climate and vegetation during the early Holocene.
For example, reconstructions from adjacent areas show that the climate and
vegetation of the Zoige Basin and Ximencuo Lake based on the pollen records
reached their optimum during the mid-Holocene and had a cooler temperature
and lower humidity during the early Holocene (Fig. 8f, g, h, i; Zhou et al., 2010; Zhao et al., 2011; Sun et al., 2017; Herzschuh et al., 2014).
Multiproxies (e.g. carbonate content, oxygen and carbon stable isotope
compositions of authigenic carbonate) also from Gahai Lake suggest that the
climate was arid, becoming warm during the early Holocene and then on
the whole moist, during the mid-Holocene (Chen et al., 2007). Cheng et al. (2013)
analysed the pollen record of Dalianhai Lake (from 16 ka BP) and conclude
that this region had a dry climate and was covered by steppe desert during
the early Holocene (Fig. 8d). Multiproxy records from Qinghai Lake
including pollen, carbonate, TOC, TN, <inline-formula><mml:math id="M158" display="inline"><mml:mrow><mml:msup><mml:mi mathvariant="italic">δ</mml:mi><mml:mn mathvariant="normal">13</mml:mn></mml:msup></mml:mrow></mml:math></inline-formula>C of organic matter,
redness records, and lake level reveal that this region had a dry climate and weak East Asian
summer monsoon (Fig. 8e; Shen et al., 2005; Ji et al., 2005; Liu et al., 2015; Chen et al., 2016). Similar records are found from Koucha Lake (Fig. 8j, k; <inline-formula><mml:math id="M159" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M160" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> based on the pollen record; Herzschuh et al., 2009), Kuhai Lake (Fig. 8l; <inline-formula><mml:math id="M161" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> based on the pollen record; Wischnewski et
al., 2011), the arid region of central Asian (moisture variation based on
11 records integrated during the early Holocene: Fig. 8m; Chen et al., 2008, 2020), and Luanhaizi Lake (Fig. 8o; <inline-formula><mml:math id="M162" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> based on the pollen record;
Herzschuh et al., 2005, 2010). The pollen assemblages of Donggi Cona Lake
show a high percentage of <italic>Ephedra</italic>, which suggests an arid environment in the early
Holocene, although the quantitative reconstruction (Fig. 8n; <inline-formula><mml:math id="M163" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> based
on the pollen record) shows this period is the wettest stage in the Holocene
(Wang et al., 2014; Huang et al., 2018). Based on the above investigations,
we can conclude that the climate was arid on the northeast Tibetan Plateau
during the early Holocene.</p>

      <?xmltex \floatpos{t}?><fig id="Ch1.F8" specific-use="star"><?xmltex \currentcnt{8}?><?xmltex \def\figurename{Figure}?><label>Figure 8</label><caption><p id="d1e2954">Comparison of the Gahai Lake results with other lake records on the northeast Tibetan Plateau. <bold>(a–c)</bold> Total organic carbon (TOC), silt fraction, and pollen-based precipitation reconstruction of the Gahai Lake record (this paper); <bold>(d)</bold> arboreal pollen percentages of Dalianhai Lake (Cheng et al., 2013); <bold>(e)</bold> arboreal pollen percentages of Qinghai Lake (Shen et al., 2005); <bold>(f)</bold> arboreal pollen percentages of the Hongyuan peatland (Zhou et al., 2010); <bold>(g)</bold> arboreal pollen percentages of the central Zoige basin (Zhao er al., 2011); <bold>(h–i)</bold> <inline-formula><mml:math id="M164" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M165" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructed from pollen records from Ximencuo Lake (Herzschuh et al., 2014); <bold>(j–k)</bold> <inline-formula><mml:math id="M166" display="inline"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> and <inline-formula><mml:math id="M167" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructed from pollen records from Koucha Lake (Herzschuh et al., 2009); <bold>(l)</bold> <inline-formula><mml:math id="M168" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructed from pollen records from Kuhai
Lake (Wischnewski et al., 2011); <bold>(m)</bold> synthesized mean moisture index of arid central Asia (Chen et al., 2008); <bold>(n)</bold> <inline-formula><mml:math id="M169" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructed from pollen records from Donggi Cona Lake (Wang et al., 2014); <bold>(o)</bold> <inline-formula><mml:math id="M170" display="inline"><mml:mrow><mml:msub><mml:mi>P</mml:mi><mml:mi mathvariant="normal">ann</mml:mi></mml:msub></mml:mrow></mml:math></inline-formula> reconstructed from pollen records from Donggi Cona Lake (Herzschuh et al., 2010).</p></caption>
          <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f08.png"/>

        </fig>

</sec>
</sec>
<sec id="Ch1.S6" sec-type="conclusions">
  <label>6</label><title>Conclusions</title>
      <p id="d1e3085">Based on modern pollen investigations for the eastern Tibetan Plateau, arboreal pollen can be determined as exogenous taxa when they appear
together with drought-tolerant taxa and low pollen concentrations in fossil pollen spectra. The Gahai Basin was covered by alpine steppe or desert under
dry climatic conditions during 14.2–7.4 ka BP; montane forest migrated into
the basin and the climate reached an optimum between 7.4 and 3.8 ka BP
according to the evidence of TN, TOC, <inline-formula><mml:math id="M171" display="inline"><mml:mrow class="chem"><mml:mi mathvariant="normal">C</mml:mi><mml:mo>/</mml:mo><mml:mi mathvariant="normal">N</mml:mi></mml:mrow></mml:math></inline-formula>, and grain size records; the
vegetation reverted to alpine steppe owing to a drying climate from 3.8 to
2.3 ka BP, after which steppe was replaced by alpine meadow as the climate
cooled. In addition, the vegetation showed signs of being influenced by
human activity during the last 0.24 ka BP.</p><?xmltex \hack{\newpage}?>
</sec>

      
      </body>
    <back><app-group>

<app id="App1.Ch1.S1">
  <?xmltex \currentcnt{A}?><label>Appendix A</label><title/>
      <p id="d1e3111">As noted in the main text, the silt fraction includes fine silt (4–16 <inline-formula><mml:math id="M172" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>), medium silt (16–32 <inline-formula><mml:math id="M173" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>), and coarse silt (32–63 <inline-formula><mml:math id="M174" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>). The
proportions of the fine and coarse silt remain almost unchanged during the
Holocene, while the medium silt fraction shows the most significant
variation. Therefore, in the following sections we use the whole silt fraction (4–63 <inline-formula><mml:math id="M175" display="inline"><mml:mrow class="unit"><mml:mi mathvariant="normal">µ</mml:mi><mml:mi mathvariant="normal">m</mml:mi></mml:mrow></mml:math></inline-formula>) rather than the different grain sizes of silt fractions.</p>

      <?xmltex \floatpos{h!}?><fig id="App1.Ch1.S1.F9"><?xmltex \currentcnt{A1}?><?xmltex \def\figurename{Figure}?><label>Figure A1</label><caption><p id="d1e3156">Pollen assemblages of the surface sediment samples arranged along
a gradient of climate data from the eastern Tibetan Plateau. Elev: Elevation
(m); Mt<inline-formula><mml:math id="M176" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">co</mml:mi></mml:msub></mml:math></inline-formula>: mean temperature of the coldest month (<inline-formula><mml:math id="M177" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C);
Mt<inline-formula><mml:math id="M178" display="inline"><mml:msub><mml:mi/><mml:mi mathvariant="normal">wa</mml:mi></mml:msub></mml:math></inline-formula>: mean temperature of the warmest month (<inline-formula><mml:math id="M179" display="inline"><mml:msup><mml:mi/><mml:mo>∘</mml:mo></mml:msup></mml:math></inline-formula>C).</p></caption>
        <?xmltex \hack{\hsize\textwidth}?>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f09.png"/>

      </fig>

<?xmltex \hack{\newpage}?><?xmltex \floatpos{t}?><fig id="App1.Ch1.S1.F10" specific-use="star"><?xmltex \currentcnt{A2}?><?xmltex \def\figurename{Figure}?><label>Figure A2</label><caption><p id="d1e3207">The percentage of different grain size components and mean grain
size derived from Gahai Lake since 14.2 ka BP.</p></caption>
        <?xmltex \igopts{width=497.923228pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f10.png"/>

      </fig>

      <?xmltex \floatpos{t}?><fig id="App1.Ch1.S1.F11" specific-use="star"><?xmltex \currentcnt{A3}?><?xmltex \def\figurename{Figure}?><label>Figure A3</label><caption><p id="d1e3218">Location of the modern pollen samples (<inline-formula><mml:math id="M180" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">22</mml:mn></mml:mrow></mml:math></inline-formula>) in the vicinity of Gahai Lake.</p></caption>
        <?xmltex \igopts{width=369.885827pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f11.png"/>

      </fig>

<?xmltex \hack{\clearpage}?><?xmltex \floatpos{h!}?><fig id="App1.Ch1.S1.F12"><?xmltex \currentcnt{A4}?><?xmltex \def\figurename{Figure}?><label>Figure A4</label><caption><p id="d1e3242">Pollen diagram of the modern pollen samples (<inline-formula><mml:math id="M181" display="inline"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mn mathvariant="normal">22</mml:mn></mml:mrow></mml:math></inline-formula>) in the
vicinity of the Gahai Lake.</p></caption>
        <?xmltex \hack{\hsize\textwidth}?>
        <?xmltex \igopts{width=480.851575pt}?><graphic xlink:href="https://cp.copernicus.org/articles/18/2381/2022/cp-18-2381-2022-f12.png"/>

      </fig>

</app>
  </app-group><notes notes-type="dataavailability"><title>Data availability</title>

      <p id="d1e3269">The data used in this study can be obtained from the
corresponding author Xianyong Cao (xcao@itpcas.ac.cn).</p>
  </notes><app-group>
        <supplementary-material position="anchor"><p id="d1e3272">The supplement related to this article is available online at: <inline-supplementary-material xlink:href="https://doi.org/10.5194/cp-18-2381-2022-supplement" xlink:title="zip">https://doi.org/10.5194/cp-18-2381-2022-supplement</inline-supplementary-material>.</p></supplementary-material>
        </app-group><notes notes-type="authorcontribution"><title>Author contributions</title>

      <p id="d1e3281">NW extracted and identified pollen samples, analysed
pollen data and wrote the manuscript. LL, XH, and YZ participated in sample
collecting and data analysis. HW contributed to the detailed comments. XC
designed this study and led the interpretation. All authors commented on and
improved the manuscript.</p>
  </notes><notes notes-type="competinginterests"><title>Competing interests</title>

      <p id="d1e3287">The contact author has declared that none of the authors has any competing interests.</p>
  </notes><notes notes-type="disclaimer"><title>Disclaimer</title>

      <p id="d1e3293">Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p>
  </notes><?xmltex \hack{\newpage}?><?xmltex \hack{~\\[112mm]}?><ack><title>Acknowledgements</title><p id="d1e3302">We would like to thank Cathy Jenks for her help with language editing.</p></ack><notes notes-type="financialsupport"><title>Financial support</title>

      <p id="d1e3307">This study was supported by the Basic Science Center for Tibetan Plateau Earth System (BSCTPES, NSFC project no. 41988101), the National Natural Science Foundation of China (grant no. 41877459) and the CAS Pioneer Hundred Talents Program (Xianyong Cao).</p>
  </notes><notes notes-type="reviewstatement"><title>Review statement</title>

      <p id="d1e3313">This paper was edited by Claudio Latorre and reviewed by two anonymous referees.</p>
  </notes><ref-list>
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