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Detection and correction of artificial shifts in climate series

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  • Henri Caussinus
  • Olivier Mestre

Abstract

Summary. Many long instrumental climate records are available and might provide useful information in climate research. These series are usually affected by artificial shifts, due to changes in the conditions of measurement and various kinds of spurious data. A comparison with surrounding weather‐stations by means of a suitable two‐factor model allows us to check the reliability of the series. An adapted penalized log‐likelihood procedure is used to detect an unknown number of breaks and outliers. An example concerning temperature series from France confirms that a systematic comparison of the series together is valuable and allows us to correct the data even when no reliable series can be taken as a reference.

Suggested Citation

  • Henri Caussinus & Olivier Mestre, 2004. "Detection and correction of artificial shifts in climate series," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 53(3), pages 405-425, August.
    • Handle: RePEc:bla:jorssc:v:53:y:2004:i:3:p:405-425
    DOI: 10.1111/j.1467-9876.2004.05155.x
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    Cited by:

    1. Jie Shen & Colin M. Gallagher & QiQi Lu, 2014. "Detection of multiple undocumented change-points using adaptive Lasso," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1161-1173, June.
    2. Taugourdeau, Olivier & Caraglio, Yves & Sabatier, Sylvie & Guédon, Yann, 2015. "Characterizing the respective importance of ontogeny and environmental constraints in forest tree development using growth phase duration distributions," Ecological Modelling, Elsevier, vol. 300(C), pages 61-72.
    3. Picard, F. & Lebarbier, E. & Budinskà, E. & Robin, S., 2011. "Joint segmentation of multivariate Gaussian processes using mixed linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1160-1170, February.
    4. Azaïs, Jean-Marc & Ribes, Aurélien, 2016. "Multivariate spline analysis for multiplicative models: Estimation, testing and application to climate change," Journal of Multivariate Analysis, Elsevier, vol. 144(C), pages 38-53.
    5. S.M. Vicente-Serrano & A. El Kenawy & C. Azorin-Molina & O. Chura & F. Trujillo & E. Aguilar & N. Martín-Hernández & J.I. López-Moreno & A. Sanchez-Lorenzo & E. Moran-Tejeda & J. Revuelto & P. Ycaza &, 2016. "Average monthly and annual climate maps for Bolivia," Journal of Maps, Taylor & Francis Journals, vol. 12(2), pages 295-310, March.
    6. Venkata Jandhyala & Stergios Fotopoulos & Ian MacNeill & Pengyu Liu, 2013. "Inference for single and multiple change-points in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(4), pages 423-446, July.
    7. Renate Wilcke & Thomas Mendlik & Andreas Gobiet, 2013. "Multi-variable error correction of regional climate models," Climatic Change, Springer, vol. 120(4), pages 871-887, October.
    8. S. Dahech & G. Beltrando, 2012. "Observed temperature evolution in the City of Sfax (Middle Eastern Tunisia) for the period 1950–2007," Climatic Change, Springer, vol. 114(3), pages 689-706, October.

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