Climate Dynamics

, Volume 45, Issue 5–6, pp 1367–1380 | Cite as

Unprecedented recent warming rate and temperature variability over the east Tibetan Plateau inferred from Alpine treeline dendrochronology

  • Chunming Shi
  • Valérie Masson-Delmotte
  • Valérie Daux
  • Zongshan Li
  • Matthieu Carré
  • John C. MooreEmail author


Despite instrumental records showing recent large temperature rises on the Tibetan Plateau (TP), only a few tree-ring temperature reconstructions do capture this warming trend. Here, we sampled 260 trees from seven Alpine treeline locations across the southeast TP. Standardized tree-ring width chronologies of Abies squamata and Sabina squamat were produced following Regional Curve Standardization detrending. The leading principal component of these records is well correlated with the regional summer (JJA) minimum temperature (MinT) (R2 = 0.47, P < 0.001, 1953–2009). Hence we produce a regional summer MinT reconstruction spanning the last 212 years. This reconstruction reveals a long-term persistent warming trend, starting in the 1820s, at a rate of 0.45 ± 0.09 °C/century (1820–2009). This trend is also detected since the 1820s in the Asian summer MinT reconstruction produced by the PAGES 2K project, with a very close warming rate (0.43 ± 0.08 °C/century, 1820–1989). Our record also displays an enhanced multi-decadal variability since the mid-twentieth century. The 1990s–2000s are the warmest of our whole record, due to the superposition of the gradual warming trend and decadal variability during this interval. The strongest decadal cooling occurs during the 1950s and the largest warming trend during the 1970s. The magnitude of warming from 1973 to 2003 was larger than the total warming trend from 1820s to 2009. Extreme events are also more frequent since 1950. The pattern of multi-decadal variability has similarities with the Atlantic multi-decadal oscillation, suggesting common causality. CMIP5 historical simulations fail to capture both the magnitude and timing of this multi-decadal variability. The ensemble CMIP5 average produces a steady warming trend starting in the 1970s, which only accounts for about 60 % of the observed warming trend during this period. We conclude that TP summer temperature could reflect a climate response to increased greenhouse gas concentrations, however modulated by multi-decadal variations common with the Atlantic sector.


Tibetan Plateau Warming Trend Couple Model Intercomparison Project Phase Temperature Reconstruction Atlantic Multidecadal Oscillation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We thank Forestry Department of Sichuan Province, Forestry Administrations of Ganzi Zhou and all counties we sampled for the great helps in field work. The field work was supported by the grant from State Key Laboratory on Earth Surface Processes and Resource Ecology, code: 270403GK. We thank Prof. Ruijie LU and Qijing LIU for providing Lintab® system. We are grateful for the generous help of Duoying JI, Qian MA, Yong LIANG and Jiangzheng WU in terms of data processing and fieldwork assistance. KNMI climate explorer has largely facilitated the processing of CRU and CMIP5 data.

Supplementary material

382_2014_2386_MOESM1_ESM.docx (269 kb)
Supplementary material 1 (DOCX 269 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Chunming Shi
    • 1
    • 5
  • Valérie Masson-Delmotte
    • 2
  • Valérie Daux
    • 2
    • 6
  • Zongshan Li
    • 3
  • Matthieu Carré
    • 4
  • John C. Moore
    • 1
    • 5
    Email author
  1. 1.State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System ScienceBeijing Normal UniversityBeijingChina
  2. 2.Laboratoire des Sciences du Climat et de l’Environnement, UMR CEA-CNRS-UVSQ 8212Institut Pierre Simon LaplaceGif-sur-YvetteFrance
  3. 3.State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijingChina
  4. 4.UM2-CNRS-IRD, Institut des Sciences de l’Evolution de MontpellierUniversité Montpellier 2MontpellierFrance
  5. 5.Arctic CentreUniversity of LaplandRovaniemiFinland
  6. 6.Université de Versailles - Saint QuentinVersaillesFrance

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