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Statistical Methods for Change-Point Detection in Surface Temperature Records

Published

Author(s)

Adam L. Pintar, Antonio M. Possolo, Nien F. Zhang

Abstract

We describe several statistical methods to detect possible change-points in a time series of values of surface temperature measured at a meteorological station, and to assess the statistical significance of such changes, taking into account the natural variability of the measurements, and the autocorrelations between them. These methods serve to determine whether the record may suffer from biases unrelated to the climate signal, hence whether there may be a need for adjustments as considered by M. J. Menne and C. N. Williams (2009) “Homogenization of Temperature Series via Pairwise Comparisons”, Journal of Climate 22 (7), 1700–1717. We also review methods to characterize patterns of seasonality (seasonal decomposition using monthly medians or robust local regression), and explain the role they play in the imputation of missing values, and in enabling robust decompositions of the measurements into a seasonal component, a possible climate signal, and a station-specific remainder. The methods for change-point detection that we describe include statistical process control, wavelet multi-resolution analysis, adaptive weights smoothing, and a Bayesian procedure, all of which are applicable to single station records.
Proceedings Title
Statistical Methods for Change-Point Detection in Surface Temperature Records
Volume
1552
Conference Dates
March 19-23, 2012
Conference Location
Anaheim, CA
Conference Title
9th International Temperature Symposium

Keywords

Adaptive Weights Smoothing, Autocorrelation, Bayesian Methods, Bootstrap, Change-Point, Control Charting, Temperature Series, Wavelets.

Citation

Pintar, A. , Possolo, A. and Zhang, N. (2013), Statistical Methods for Change-Point Detection in Surface Temperature Records, Statistical Methods for Change-Point Detection in Surface Temperature Records, Anaheim, CA, [online], https://doi.org/10.1063/1.4819688 (Accessed December 1, 2023)
Created September 12, 2013, Updated January 27, 2020