Efficiency evaluation for detecting inhomogeneities by objective homogenisation methods

Evaluation and comparison of efficiencies of widely used objective homogenisation methods (OHOMs) are presented relying on some test-datasets and efficiency measures. Problems related to the choice of efficiency measure, creation of appropriate test-datasets and use of OHOM parameterisation are discussed. The detection parts of the OHOMs are examined only. Power of detection, false alarm rate, detection skill and skill of linear trend estimation are calculated and compared for eight OHOMs and six test-datasets. Each test-dataset comprises 10,000 100 year-long artificially simulated time series. In the simplest test dataset, each time series contains one inhomogeneity (IH), while a structure of inhomogeneities that is similar to that of real central European temperature time series is included in the most complex simulated dataset. Distinct attention is given to OHOMs that contain (1) cutting algorithm, (2) semihierarchic algorithm, (3) direct detection of multiple IHs, (4) detection of change-point and trend-line shaped IHs. Results show that Caussinus-Mestre method and Multiple Analysis of Series for Homogenization are the most powerful tools in detecting and correcting IHs in climatic time series.