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11 IMSC Session Program Short-term, platform-like inhomogeneities in observed climatic time series Tuesday - Parallel Session 8 Peter Domonkos Centre for Climate Change (C3), Geography Dept., University Rovira i Virgili, Campus Terres de l’Ebre, Tortosa, Spain Any change in technical or environmental conditions of observations may result in bias from the precise values of observed climatic variables. The common name of these biases is inhomogeneity (IH). Large IHs of the candidate time series can easily be identified with appropriate statistical tools when other time series from neighbouring observing stations are available, and they have high correlation with the candidate series. Improving data quality applying homogenisation is an important step towards revealing the real characteristics of climate change and climate variability in the last one-two centuries. In the recent decades big efforts have been put to develop better and better homogenisation methods, and the effectiveness of these methods was tested using artificial datasets with known IH characteristics. There still exists a serious problem that has hardly been considered yet, i.e. characteristics of artificial datasets seem to markedly differ from those of observed datasets at least in case of temperature data. A comparison of real and simulated characteristics of detected temperature IHs using Central European datasets showed that observed datasets contain large number of short-term, platform-like IHs. (Platform means a pair of sudden shifts with the same magnitude and opposite direction). These IHs usually have small direct impact on the time series characteristics, and they cannot be distinguished from the random noise. However, the presence of short-term IHs markedly changes the efficiencies of homogenisation methods. For instance, when 100 year long series include standard white noise process plus randomly situated shifts with 0.05 frequency and mean magnitude of 3.5 standard deviation, the efficiencies of the widely used IH-detection methods (Standard Normal Homogeneity Test, Easterling-Peterson method, Wilcoxon Rank Sum test, etc.) are quasi uniformly high. When instead of solely shifts platforms of 1- 10 years duration are included leaving the mean frequency and magnitude unchanged, all the efficiencies drop dramatically, and the Caussinus - Mestre method, the Multiple Analysis of Series for Homogenization and the Multiple Linear Regression perform clearly better, than the other detection methods examined. Some statistics show that this problem is not specific for Central Europe, and has not been treated well even in the ongoing COST project (COST ES0601: Advances in homogenisation methods of an integrated approach). In the COST project a huge benchmark dataset was built including real and artificial networks for temperature and precipitation data series. In an own-developed, fully automatic homogenisation method (ACMANT = Adapted Caussinus-Mestre Algorithm for Networks of Temperature series) two detection segments were applied, one is for long-term IHs with minimum 3 years duration (main detection), and another one (secondary detection) for detecting the relatively large but short-term biases remained after the correction derived from the main detection. Though the overall frequency of detected IHs is 0.05-0.07 for both the real and simulated networks, only 6% of the IHs was Abstracts 151
11 IMSC Session Program detected with the secondary detection in the simulated networks, while the same rate is 19% for real networks of observed temperature datasets. Abstracts 152
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