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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 10 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 be not helpful. This can be understood by considering that in case of multiple breakpoints also a random segmentation explains about half of the break variance. We derive analytical equations for the explained noise and break 15 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 station trends 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.
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