The joint influence of break and noise variance on the break detection capability in time series homogenization
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Published:2018-08-22
Issue:1/2
Volume:4
Page:1-18
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ISSN:2364-3587
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Container-title:Advances in Statistical Climatology, Meteorology and Oceanography
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language:en
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Short-container-title:Adv. Stat. Clim. Meteorol. Oceanogr.
Author:
Lindau Ralf, Venema Victor Karel ChristiaanORCID
Abstract
Abstract. Instrumental climate records of the last centuries suffer from
multiple breaks due to relocations and changes in measurement techniques.
These breaks are detected by relative homogenization algorithms using the
difference time series between a candidate and a reference. Modern multiple
changepoint methods use a decomposition approach where the segmentation
explaining most variance defines the breakpoints, while a stop criterion
restricts the number of breaks. In this study a pairwise multiple breakpoint
algorithm consisting of these two components is tested with simulated data
for a range of signal-to-noise ratios (SNRs) found in monthly temperature
station datasets. The results for low SNRs obtained by this algorithm do not
differ much from random segmentations; simply increasing the stop criterion
to reduce the number of breaks is shown to not be helpful. This can be
understood by considering that, in case of multiple breakpoints, even a
random segmentation can explain about half of the break variance. We derive
analytical equations for the explained noise and break variance for random
and optimal segmentations. From these we conclude that reliable break
detection at low but realistic SNRs needs a new approach. The problem is
relevant because the uncertainty of the trends of individual stations is
shown to be climatologically significant also for these small SNRs. An
important side result is a new method to determine the break variance and the
number of breaks in a difference time series by studying the explained
variance for random break positions. We further discuss the changes from
monthly to annual scale which increase the SNR by more than a factor of 3.
Publisher
Copernicus GmbH
Subject
Applied Mathematics,Atmospheric Science,Statistics and Probability,Oceanography
Reference32 articles.
1. Alexandersson, H. and Moberg, A.: Homogenization of Swedish temperature data. Part I: Homogeneity test for linear trends, Int. J. Climatol., 17, 25–34, 1997. 2. Auer, I., Böhm, R., Jurkovic, A., Lipa, W., Orlik, A., Potzmann, R., Schöner, W., Ungersböck, M., Matulla, C., Briffa, K., Jones, P., Efthymiadis, D., Brunetti, M., Nanni, T., Maugeri, M., Mercalli, L., Mestre, O., Moisselin, J. M., Begert, M., Müller-Westermeier, G., Kveton, V., Bochnicek, O., Stastny, P., Lapin, M., Szalai, S., Szentimrey, T., Cegnar, T., Dolinar, M., Gajic-Capka, M., Zaninovic, K., Majstorovic, Z., and Nieplova, E.: HISTALP – Historical Instrumental climatological surface rime series of the greater Alpine region, Int. J. Climatol., 27, 17–46, https://doi.org/10.1002/joc.1377, 2007. 3. 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. 4. 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. 5. 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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