Efficiency evaluations for change point Detection methods used in nine major Objective Homogenization Methods (DOHMs) are presented. The evaluations are conducted using ten different simulated datasets and four efficiency measures: detection skill, skill of linear trend estimation, sum of squared error, and a combined efficiency measure. Test datasets applied have a diverse set of inhomogeneity (IH) characteristics and include one dataset that is similar to the monthly benchmark temperature dataset of the European benchmarking effort known by the acronym COST HOME. The performance of DOHMs is highly dependent on the characteristics of test datasets and efficiency measures. Measures of skills differ markedly according to the frequency and mean duration of inhomogeneities and vary with the ratio of IH-magnitudes and background noise. The study focuses on cases when high quality relative time series (i.e., the difference between a candidate and reference series) can be created, but the frequency and intensity of inhomogeneities are high. Results show that in these cases the Caussinus-Mestre method is the most effective, although appreciably good results can also be achieved by the use of several other DOHMs, such as the Multiple Analysis of Series for Homogenisation, Bayes method, Multiple Linear Regression, and the Standard Normal Homogeneity Test. 1. Introduction The underlying climate signal in observed in situ climatic data is often masked either by changes in observational practices, exposure, and instrumentation or by local changes in the environment where the observations are taken. If these changes (called inhomogeneities (IH)) have a significant impact on the statistical characteristics of the observed data, then the time series are inhomogeneous, and their usefulness is limited in assessments of observed climate change. Given that almost any long climate series is potentially inhomogeneous, various techniques have been developed to detect and adjust series where necessary (see, among others, the seminar series of Homogenisation and Quality Control in Climatological Databases, WMO-HMS [1–6]). There are several options to eliminate the IHs from observed time series. The timing and cause of many potential IHs are documented in network management documents (so-called metadata). Good metadata information greatly facilitates the development of appropriate corrections for inhomogeneous time series, so that the time series can be made more suitable for climate studies. However, metadata is generally incomplete ([7–12], etc.) or at least cannot be assumed
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