Climate Dynamics

, Volume 31, Issue 2–3, pp 263–281 | Cite as

Evaluation of proxy-based millennial reconstruction methods

Article

Abstract

A range of existing statistical approaches for reconstructing historical temperature variations from proxy data are compared using both climate model data and real-world paleoclimate proxy data. We also propose a new method for reconstruction that is based on a state-space time series model and Kalman filter algorithm. The state-space modelling approach and the recently developed RegEM method generally perform better than their competitors when reconstructing interannual variations in Northern Hemispheric mean surface air temperature. On the other hand, a variety of methods are seen to perform well when reconstructing surface air temperature variability on decadal time scales. An advantage of the new method is that it can incorporate additional, non-temperature, information into the reconstruction, such as the estimated response to external forcing, thereby permitting a simultaneous reconstruction and detection analysis as well as future projection. An application of these extensions is also demonstrated in the paper.

Keywords

Kalman filter State-space model Temperature reconstruction 

Notes

Acknowledgments

We thank Caspar Ammann, Gabriele Hegerl and Eduardo Zorita for providing their data for use in this study. We also thank Gabriele Hegerl for helpful and constructive discussion. We gratefully acknowledge that Terry Lee was supported by the Canadian Foundation for Climate and Atmospheric Science through the Canadian CLIVAR Research Network. Work by Min Tsao was supported by the Natural Sciences and Engineering Research Council through a Discovery Grant. This paper was improved by insightful and helpful comments provided by Scott Rutherford, Walter Skinner, Xuebin Zhang and an anonymous referree.

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Copyright information

© Springer-Verlag 2007

Authors and Affiliations

  • Terry C. K. Lee
    • 1
  • Francis W. Zwiers
    • 2
  • Min Tsao
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of VictoriaVictoriaCanada
  2. 2.Climate Research DivisionEnvironment CanadaTorontoCanada

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