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SCIENCE CHINA Earth Sciences, Volume 63 , Issue 8 : 1144-1160(2020) https://doi.org/10.1007/s11430-019-9599-y

Pollen-based Holocene quantitative temperature reconstruction on the eastern Tibetan Plateau using a comprehensive method framework

More info
  • ReceivedAug 2, 2019
  • AcceptedMar 17, 2020
  • PublishedApr 26, 2020

Abstract


Funded by

National Key Research and Development Program of China(Grant,No.,2016YFA0600501)

the National Natural Science Foundation of China(Grant,Nos.,41690113,41888101,41471169)

the Strategic Priority Research Program of Chinese Academy of Sciences(Grant,No.,XDA20070101)


Acknowledgment

This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600501), the National Natural Science Foundation of China (Grant Nos. 41690113, 41888101 and 41471169) and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20070101).


References

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  • Figure 1

    Location of modern pollen samples and vegetation distribution. (a) Sites of modern samples, different colors indicating different types. The study region is marked by the black rectangle. (b) Vegetation types in the Zoige Basin and its surrounding mountains. Brown dots indicate the three fossil pollen records for quantitative reconstruction.

  • Figure 2

    Pollen diagrams for the three cores of Zoige Basin, showing the percentage values for tree pollen taxa (green), herbs (orange) and tree pollen in total (blue). (a) ZB08-C1 (Zhao et al., 2011); (b) ZB10-C9 (Li, 2015); (c) ZB10-C14 (Sun et al., 2017). The gray bars show the clay sediment layers.

  • Figure 3

    Schematic diagram of the taxa coverage of modern samples and fossil samples. (a) Scatter plot of maximum taxa abundant in modern dataset against that in fossil pollen assemblages. The red circle indicated the taxa with maximum abundant in fossil sample>maximum abundant in modern dataset. (b) The box plot of the percentage coverage of the major pollen taxa (red: fossil samples, yellow: modern samples).

  • Figure 4

    Correlation analysis of 6 climate variables. Abbreviation for environmental variables: MTco=Mean temperature of coldest month; MTwa=Mean temperature of warmest month; MAT=Mean annual temperature; MPco=Mean precipitation of coldest month; MPwa=Mean precipitationof warmest month; MAP=Mean annual precipitation.

  • Figure 5

    Time-track analysis based on canonical correspondence analysis (CCA). It demonstrates the relationships between fossil samples in 3 cores and 4 environmental variables (arrows). (a) CCA analysis showing the relationships between the calibration set samples and 4 environmental variables. (b) The scatter plot of fossil samples projected onto the CCA axis established by calibration set.

  • Figure 6

    Time-series of reconstructed mean temperature in warmest month (MTwa) and MAT based on the pollen sequences of three cores in the Zoige Basin. (a) ZB08-C1; (b) ZB10-C9; (c) ZB10-C14. Orange lines (with error in yellow zones): MTwa; blue lines (with error in gray zones): MAT; green lines: tree pollen abundances. The clay layers are indicated by gray bands.

  • Figure 7

    Spectral analysis and wavelet power spectrum of the integrated MTwa data. (a) Spectral analysis result, in which green and red line means the 95% confidence level and Monte Carlo simulation. (b) Wavelet analysis result, in which high power is represented by red while low power is indicated by blue. The area marked by the black line shows a confidence level greater than 95%. The translucent shaded regions indicate the cone of influence where edge effects become significant. The MTwa data were resampled at equally spaced 60-yr intervals and detrended prior to analysis.

  • Figure 8

    Scatter plots of squared chord distance between each fossil sample and nearest modern analogue. (a) ZB08-C1; (b) ZB10-C9; (c) ZB10-C14. Green, good analogue; blue, fair good analogue; red, no analogue.

  • Figure 9

    Geographical distribution of calibration set selected by analogues and distance respectively. (a) Calibration set selected by analogues, number of analogues=30; (b) calibration set selected by distance, radii distance=500 km. The right panel shows the MTwa distribution pattern of the calibration set and the modern meteorological observation in the study region (black dashed line).

  • Figure 10

    Scatter plots of observed MTwa vs. predicted MTwa using WA-PLS model for the three cores from the Zoige. (a) ZB08-C1; (b) ZB10-C9; (c) ZB10-C14. The bottom panel shows the residual of the model.

  • Figure 11

    Significant test of pollen-based climate reconstructions for the three cores from the Zoige Basin. (a) ZB08-C1; (b) ZB10-C9; (c) ZB10-C14. Gray bars indicate the histogram of the proportion of variance explained by 999 calibration functions trained with random environmental data. The red dotted line indicates the proportion of the variance below which 95% of the random data-trained calibration functions could explain; the black dotted line indicates the proportion of the variance explained by the first axis of a PCA of the fossil data.

  • Figure 12

    Optima and tolerances of MAP, MAT and MTwa for selected pollen taxa based on weighted averaging. (a) MAP; (b) MAT; (c) MTwa. The modern pollen data are from the calibration dataset. The black dash lines indicate the mean climate optima of pollen taxa.

  • Figure 13

    Comparison of inferred mean warmest month temperature in eastern Tibetan Plateau with multiple Holocene records. (a) Reconstructed MTwa of ZB08-C1 (yellow dashed line), ZB10-C9 (blue dashed line), ZB10-C14 (green dashed line) and integrated MTwa from this study (pink line); (b) pollen-based mean temperature of warmest month in Koucha Lake, eastern Tibetan Plateau (gray line) with smooth (blue dotted line, Herzschuh et al., 2009); (c) temperature reconstruction based on ice core in Greenland (green line, Kobashi et al., 2017); (d) globally stacked temperature anomalies by the 5°×5° area-weighted mean calculation for 30°–90°N (purple line, Marcott et al., 2013); (e) summer insolation at 30°N (brown line) and 60°N (red line) latitudes; (f) melt water flux into the North Atlantic (blue histogram, Liu et al., 2014).

  • Figure 14

    Comparison of the cyclicity of MTwa in Zoige Basin and solar activity (total solar irradiance, TSI; Steinhilber et al. 2012). (a) Cross-wavelet transform of MTwa and TSI. Arrows pointing to the right indicate that the two variables are in phase. Black boundaries mark the 95% significance level. (b) Time series of normalized MTwa (orange) and TSI (green) after removing the trend. The 400–600-yr bandpass filter with the rectangular window are shown in gray lines.

  • Table 1   Table 1 Summary statistics for Variance Inflation Factors (VIF) with 6 variables and the explained variance of each climate variables as sole prediction of samples in calibration seta)

    Climate variables

    VIF Run 1

    (all variables)

    VIF Run 2

    (without MTwa)

    VIF Run 3

    (without MTco)

    VIF Run 4

    (3 variables)

    Climatic variables as sole predictor

    Explained variance

    p-value

    MPco

    2.054

    2.05396

    2.054052

    2.343

    0.005

    MPwa

    8.049

    8.04255

    8.040067

    3.18

    0.005

    MAP

    11.194

    10.50667

    10.496896

    2.09881

    3.491

    0.005

    MTco

    208.666

    7.54003

    2.899

    0.005

    MTwa

    117.666

    4.251812

    3.719867

    4.556

    0.005

    MAT

    486.248

    5.937843

    6.574035

    5.427129

    4.053

    0.005

    Run 1–4 illustrates the process of variable simplification. Run 1, with all variables; Run 2, without the variable of MTwa; Run 3, remove the variables MTco; Run 3, remove the variables MTco, MPco and MPwa.

  • Table 2   Table 2 Error estimates for different reconstruction models of three coresa)

    Model

    Parameter

    ZB08-C1

    ZB10-C9

    ZB10-C14

    Sample number

    RMSEP

    R2

    Sample number

    RMSEP

    R2

    Sample number

    RMSEP

    R2

    Modern Analogue Technique

    k=4

    3.095

    0.836

    3.117

    0.837

    3.207

    0.851

    WA classical

    500 km

    332

    4.205

    0.607

    369

    3.942

    0.612

    326

    4.171

    0.708

    1000 km

    1063

    5.190

    0.723

    1052

    5.165

    0.711

    1078

    5.341

    0.713

    1500 km

    1529

    5.128

    0.814

    1519

    5.316

    0.810

    1517

    5.095

    0.817

    20-analogues

    161

    3.540

    0.667

    153

    3.617

    0.680

    137

    3.543

    0.685

    30-analogues

    175

    3.352

    0.730

    204

    3.194

    0.724

    193

    3.247

    0.731

    40-analogues

    213

    3.836

    0.673

    235

    4.005

    0.674

    259

    3.808

    0.679

    50-analogues

    290

    4.211

    0.646

    304

    4.300

    0.650

    321

    4.385

    0.646

    100-analogues

    352

    4.277

    0.642

    348

    4.245

    0.647

    407

    4.340

    0.652

    WA inverse

    500 km

    332

    2.617

    0.607

    369

    2.556

    0.669

    326

    2.668

    0.605

    1000 km

    1063

    3.682

    0.723

    1052

    3.618

    0.709

    1078

    3.504

    0.728

    1500 km

    1529

    3.953

    0.814

    1519

    3.931

    0.818

    1517

    3.992

    0.802

    20-analogues

    161

    2.367

    0.667

    153

    2.269

    0.671

    137

    2.419

    0.673

    30-analogues

    175

    2.394

    0.730

    204

    2.330

    0.719

    193

    2.402

    0.719

    40-analogues

    213

    2.583

    0.673

    235

    2.671

    0.661

    259

    2.413

    0.658

    50-analogues

    290

    2.748

    0.646

    304

    2.602

    0.651

    321

    2.747

    0.652

    100-analogues

    352

    2.777

    0.642

    348

    2.886

    0.637

    407

    2.735

    0.638

    WA non-linear

    500 km

    332

    2.561

    0.633

    369

    2.595

    0.632

    326

    2.540

    0.613

    1000 km

    1063

    3.567

    0.754

    1052

    3.617

    0.744

    1078

    3.506

    0.743

    1500 km

    1529

    3.873

    0.830

    1519

    3.967

    0.830

    1517

    3.812

    0.812

    20-analogues

    161

    2.256

    0.718

    153

    2.351

    0.712

    137

    2.112

    0.710

    30-analogues

    175

    2.217

    0.800

    204

    2.269

    0.802

    193

    2.337

    0.785

    40-analogues

    213

    2.349

    0.768

    235

    2.222

    0.763

    259

    2.306

    0.761

    50-analogues

    290

    2.628

    0.695

    304

    2.625

    0.701

    321

    2.770

    0.695

    100-analogues

    352

    2.664

    0.688

    348

    2.767

    0.695

    407

    2.555

    0.697

    WAPLS Component 2

    500 km

    332

    2.522

    0.651

    369

    2.528

    0.667

    326

    2.591

    0.655

    1000 km

    1063

    3.465

    0.780

    1052

    3.639

    0.793

    1078

    3.490

    0.781

    1500 km

    1529

    3.686

    0.767

    1519

    3.636

    0.767

    1517

    3.725

    0.780

    20-analogues

    161

    2.164

    0.758

    153

    2.154

    0.772

    137

    1.951

    0.768

    30-analogues

    175

    2.045

    0.831

    204

    2.071

    0.819

    193

    1.915

    0.824

    40-analogues

    213

    2.215

    0.818

    235

    2.119

    0.822

    259

    2.173

    0.823

    50-analogues

    290

    2.390

    0.785

    304

    2.239

    0.800

    321

    2.361

    0.795

    100-analogues

    352

    2.533

    0.739

    348

    2.400

    0.755

    407

    2.574

    0.733

    LWWA

    k=20

    3.190

    0.825

    3.163

    0.817

    3.088

    0.805

    k=30

    3.196

    0.824

    3.205

    0.822

    3.270

    0.812

    k=40

    3.233

    0.820

    3.390

    0.809

    3.172

    0.820

    k=50

    3.270

    0.816

    3.260

    0.812

    3.306

    0.827

    k=100

    3.378

    0.804

    3.549

    0.786

    3.133

    0.801

    LWWAPLS

    k=20

    3.374

    0.804

    3.319

    0.784

    3.509

    0.810

    k=30

    3.375

    0.804

    3.261

    0.792

    3.519

    0.795

    k=40

    3.383

    0.803

    3.410

    0.791

    3.543

    0.793

    k=50

    3.397

    0.801

    3.263

    0.784

    3.411

    0.800

    k=100

    3.482

    0.791

    3.586

    0.806

    3.509

    0.792

    Model errors are given as Root Mean Square Errors of Prediction (RMSEP) calculated by bootstrapping (boots). In addition, the adjusted coefficients of determination R2 between boots predicted and observed values are given.