Climatic Change

, Volume 117, Issue 4, pp 843–858 | Cite as

Statistical modeling of extreme value behavior in North American tree-ring density series

  • Elizabeth MannshardtEmail author
  • Peter F. Craigmile
  • Martin P. Tingley


Many analyses of the paleoclimate record include conclusions about extremes, with a focus on the unprecedented nature of recent climate events. While the use of extreme value theory is becoming common in the analysis of the instrumental climate record, applications of this framework to the spatio-temporal analysis of paleoclimate records remain limited. This article develops a Bayesian hierarchical model to investigate spatially varying trends and dependencies in the parameters characterizing the distribution of extremes of a proxy data set, and applies it to the site-wise decadal maxima and minima of a gridded network of temperature sensitive tree ring density time series over northern North America. The statistical analysis reveals significant spatial associations in the temporal trends of the location parameters of the generalized extreme value distributions: maxima are increasing as a function of time, with stronger increases in the north and east of North America; minima are significantly increasing in the west, possibly decreasing in the east, and exhibit no changes in the center of the region. Results indicate that the distribution varies as a function of both space and time, with tree ring density maxima becoming more extreme as a function of time and minima having diverging temporal trends, by spatial location. Results of this proxy-only analysis are a first step towards directly reconstructing extremal climate behavior, as opposed to mean climate behavior, by linking extremes in the proxy record to extremes in the instrumental record.


Posterior Distribution Tree Ring Generalize Extreme Value Generalize Extreme Value Distribution Tree Ring Series 
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 collaboration was formed during the 2009–2010 Program on Space-time modeling for Epidemiology, Climate Change, and Environmental Mapping, organized by the Statistical and Applied Mathematical Sciences Institute and supported by the National Science Foundation (NSF) under Grant DMS-0635449. ECM is supported in part by The Program in Spatial Statistics and Environmental Statistics at The Ohio State University. PFC is supported in part by the NSF under grants DMS-0604963, DMS-0906864 and SES-1024709, and MPT by the NSF under Grants ATM-0724828 and ATM-0902374. We thank Peter Guttorp, Richard Smith, Michael Stein, an Associate Editor, and three anonymous reviewers for suggestions that improved this work.

Supplementary material

10584_2012_575_MOESM1_ESM.pdf (312 kb)
(PDF 312 KB)


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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  • Elizabeth Mannshardt
    • 1
    Email author
  • Peter F. Craigmile
    • 2
    • 3
  • Martin P. Tingley
    • 4
  1. 1.Department of StatisticsNorth Carolina State UniversityRaleighUSA
  2. 2.Department of StatisticsOhio State UniversityColumbusUSA
  3. 3.School of Mathematics and StatisticsUniversity of GlasgowGlasgowScotland
  4. 4.Department of Earth and Planetary SciencesHarvard UniversityCambridgeUSA

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