Articles | Volume 4, issue 1/2
https://doi.org/10.5194/ascmo-4-1-2018
https://doi.org/10.5194/ascmo-4-1-2018
22 Aug 2018
 | 22 Aug 2018

The joint influence of break and noise variance on the break detection capability in time series homogenization

Ralf Lindau and Victor Karel Christiaan Venema

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Cited articles

Alexandersson, H. and Moberg, A.: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends, Int. J. Climatol., 17, 25–34, 1997. 
Bellman, R.: The theory of dynamic programming, B. Am. Math. Soc., 60, 503–516, https://doi.org/10.1090/S0002-9904-1954-09848-8, 1954. 
Brunetti, M., Maugeri, M., Monti, F., and Nanni, T.: Temperature and precipitation variability in Italy in the last two centuries from homogenized instrumental time series, Int. J. Climatol., 26, 345–381, 2006. 
Caussinus, H. and Lyazrhi, F.: Choosing a linear model with a random number of change-points and outliers, Ann. I. Stat. Math., 49, 761–775, 1997. 
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Short summary
Climate data contain spurious breaks, e.g., by relocation of stations, which makes it difficult to infer the secular temperature trend. Homogenization algorithms use the difference time series of neighboring stations to detect and eliminate this spurious break signal. For low signal-to-noise ratios, i.e., large distances between stations, the correct break positions may not only remain undetected, but segmentations explaining mainly the noise can be erroneously assessed as significant and true.