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Correction to: The complexity of climate reconstructions using the coexistence approach on Qinghai–Tibetan Plateau

The Original Article was published on 08 January 2019

Correction to: J Palaeogeogr (2019) 8:5

https://doi.org/10.1186/s42501-018-0016-0

After publication of this article (Zhang et al., 2019), it is noticed the article contains some error:

Incorrect versions of Figs. 1, 2, 3, 4 and 5 were used during typesetting. Hence we provide the most updated versions of Figs. 1, 2, 3, 4 and 5 below.

Fig. 1
figure 1

a Location of the study sites. The sites (closed black squares, numbered as S1–S44) are superimposed on maps showing the digital elevation. Please see sites details in Zhang et al. 2015; be Climatic gradients for the sampling sites. MAP Mean annual precipitation, MAT Mean annual temperature, MTWA Mean temperature of the warmest month, MTCO Mean temperature of the coldest month. The climate data from 1814 meteorological stations across China were interpolated to the 1-km resolution grid using smoothing spline interpolation (ANUSPLIN version 4.36; Hancock and Hutchinson 2006) and the Shuttle Radar Topography Mission (SRTM) 1-km digital elevation model (Farr et al. 2007)

Fig. 2
figure 2

Anemophilous tree pollen with long-distance transportation in Qinghai–Tibetan Plateau above topographic map. Red squares indicate the sites containing the anemophilous tree pollen. Black circles indicate locations of the parent plants corresponding to the anemophilous tree taxa, which are determined by Wu and Ding (1999). Dashed arrows indicate the possible pathways of anemophilous tree pollen carried by the Asian summer monsoon

Fig. 3
figure 3

Comparison difference between the weighted reconstruction (WR) and observations with difference between the non-trees reconstruction (NTR) and observations for MAP, MAT, MTWA and MTCO. MAP Mean annual precipitation, MAT Mean annual temperature, MTWA Mean temperature of the warmest month, MTCO Mean temperature of the coldest month. Dashed line is 1:1 line. Solid line indicates the linear regression line

Fig. 4
figure 4

Distributions of variability of the mean winter snow depth (cm). Redrawn from Qian et al. (2003). The sampling sites are the same as in Fig. 1. Areas with different colors indicate the variability of the mean winter snow depth

Fig. 5
figure 5

Schematic illustration of the possible explanation of discrepancy between the reconstructed MTWA (mean temperature of the warmest month) and observed values. The MTWA reconstruction result is redrawn from non-trees reconstructions of Zhang et al. 2015. Vertical bars with black points indicate the coexistence intervals and their medians for reconstructed MTWA. The observations of MTWA are indicated as red points. The altitudes of sampling sites are also shown in blue. The blossoming of shrubs and herbs in Qinghai–Tibetan Plateau may response to the air temperature somewhere less than this height, rather than the air temperature at 1.5 m. Here it is named as the blossoming temperature. Air temperature and soil temperature at 5 cm depth during the growth period of plants are from Gu et al. 2005

The original article has been updated accordingly. We apologize for the inconvenience caused.

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Correspondence to Zhi-Yong Zhang or Cheng-Sen Li.

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The original article can be found online at https://doi.org/10.1186/s42501-018-0016-0

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Zhang, ZY., Cheng, DM., Li, CS. et al. Correction to: The complexity of climate reconstructions using the coexistence approach on Qinghai–Tibetan Plateau. J. Palaeogeogr. 8, 9 (2019). https://doi.org/10.1186/s42501-019-0025-7

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