Climatic Change

, Volume 118, Issue 3–4, pp 871–883 | Cite as

Temporal and spatial patterns of modern climatic warming: case study of Northern Eurasia

  • Oleg Anisimov
  • Vasily Kokorev
  • Yelena Zhil’tsova


Century-scale near-surface air temperature data from 744 weather stations in Russia and neighboring countries indicate that the temperature variations have distinct temporal patterns. Two periods, near the beginning and at the end of the 20th century, experienced the largest warming rates. Temperature changes in both periods were not uniform in time or space. We used statistical criteria and applied them to data at the weather stations to define a “tipping point” corresponding to the beginning of the modern climatic period. Results indicate that the position of this point depends on location, and in most cases falls into the interval from the early 1970s through the late 1980s. By means of spatial correlation analysis we delineated regions with coherent air temperature changes and calculated the region-specific rates and magnitudes of changes. We compared the distribution of regional tipping points in time and over space with large-scale atmospheric circulation patterns over northern Eurasia. We analyzed the 20th—early 21st century changes in the relative frequencies of the three circulation forms defined by Vangengheim-Girs classification, and found their qualitative correspondence with the spatial temperature patterns and spread of the tipping points in time. These results improve our knowledge about the regional structure and drivers of modern climate change in northern Eurasia, which is likely to hold the fingerprint of the anthropogenic signal. Findings of this study can be used to obtain insight into regional climatic changes in northern Eurasia over the next few decades.


Atmospheric Circulation Eurasia Temperature Anomaly Online Supplemental Material Circulation Form 
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.



This study was supported by the Russian Foundation for Basic Research, grant 11-05-12011, and by the German-Russian Otto-Schmidt Laboratory for polar and marine research, project OSL-12-02. Authors are thankful to Dr. Valerij Beljazo of the Arctic and Antarctic research institute in St.Petersburg for providing unpublished daily records of the circulation forms. The authors are grateful to Professor Frederick Nelson for editing the manuscript. We are thankful to anonymous reviewers for their thoughtful comments.

Supplementary material

10584_2013_697_MOESM1_ESM.doc (1.7 mb)
ESM 1 (DOC 1.72 mb)


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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Oleg Anisimov
    • 1
  • Vasily Kokorev
    • 1
  • Yelena Zhil’tsova
    • 1
  1. 1.Department of ClimatologyState Hydrological InstituteSt. PetersburgRussia

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