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A kernel-based Bayesian approach to climatic reconstruction

Godwin Institute for Quaternary Research, University of Cambridge, Cambridge CB2 3SA, UK; Department of Geography, University of Wales at Swansea, Swan-sea SA2 8PP, UK

D. Lucy

Department of Archaeological Sciences, University of Bradford, Bradford BD7 1DP, UK

L. Baxter

Department of Archaeological Sciences, University of Bradford, Bradford BD7 1DP, UK; National Fingerprint Laboratory, New Scotland Yard, Broadway, London SW1H 0BG, UK

A. M. Pollard

Department of Archaeological Sciences, University of Bradford, Bradford BD7 1DP, UK

R. G. Aykroyd

Department of Statistics, School of Mathematics, University of Leeds, Leeds LS2 9JT, UK

A. C. Barker

Environmental Science Research Centre, Anglia Polytechnic University, Cambridge CB1 1PT, UK

A. H.C. Carter

V. R. Switsur

Environmental Science Research Centre, Anglia Polytechnic University, Cambridge CB1 1PT, UK; Godwin Institute for Quaternary Research, University of Cambridge, Cambridge CB2 3SA, UK

J. S. Waterhouse

Environmental Science Research Centre, Anglia Polytechnic University, Cambridge CB1 1PT, UK

To understand recent climatic trends and possible future climatic change, it is necessary to examine the nature of past climatic variability. Proxy measures of past climatic fluctuations can be used to extend this record beyond the limited period of instrumental measurements. Regression-based techniques are generally used to define transfer functions, which describe the statistical relationship between these proxy estimates of past climate and measured climatic parameters. Although these regression-based techniques have been extremely successful, they can engender bias in the estimates if not used with care. More significantly, we show that if regression errors are explicitly calculated they are often similar in magnitude to the total range of the parameter being estimated, implying that such reconstructions of past climate cannot be regarded as truly precise. A novel approach based upon Bayes' theorem is introduced which appears to increase the statisti cal veracity of such climatic reconstructions.

Key Words: Climatic reconstruction • regression analysis • Bayes' theorem • kernel-density estimation • carbon isotopes • dendroclimatology

The Holocene, Vol. 9, No. 4, 495-500 (1999)
DOI: 10.1191/095968399676373488


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