1. After HOME : Progress in the practical application of statistical homogenisation Peter Domonkos Dimitrios Efthymiadis Centre for Climate Change University Rovira i Virgili, Campus Terres de l’Ebre, Tortosa, Spain

2. The topic Homogenisation of monthly temperature and precipitation series with statistical methods We have a large number of methods. What are the best ones? Two main problems in monthly homogenisation: There is no perfect reference series Existence of multiple inhomogeneities (modelled by shifts in the means [=breaks] or gradual changes in the means)

3. Sections of the presentation (1) Characterisation of multiple break problem (2) Facts about COST ES0601 [HOME] (3) Homogenisation in practice, 2013 (4) Analysis of the causes of the large difference between proven theoretical results and practice. (5) Recommendations

4. (1) Relation between detected and true break frequencies I. ( C-M = PRODIGE, MAS = MASH, MLR = Multiple Linear Regression, SNH = Standard Normal Homogeneity Test)

5. (1) Relation between detected and true break frequencies II. Short-term biases can be detected only when their magnitude is exceptionally large.

6. (2) COST ES0601 [HOME] Blind test of homogenisation methods Benchmark mimics well the true climate in Europe both temporally and spatially Inhomogeneity properties of Benchmark were set by the expert team of HOME: frequency: 5-6 per 100yr (low), size: normal distribution, standard deviation: 0.8°C (large). Participants: Most of the European countries + guests from overseas First results: early 2010 Closing study: early 2012 (Climate of the Past, 8, 89-115). Five methods, i.e. MASH, PRODIGE, ACMANT, Craddock-test, USHCN were found to be significantly more effective than the other methods.

7. (2) COST ES0601 [HOME] Four of the recommended 5 methods detect and correct multiple breaks directly, i.e. assessing their joint effects instead of applying hierarchic procedure of single-break treatments. One of them (Craddock-test) is a fundamentally subjective procedure, therefore it is recommended only for skilled homogenisers and for homogenising not too large datasets. MASH, PRODIGE, ACMANT and the final HOME-product HOMER (Idöjárás, 117 (2013), 47-67) are all easily applicable multiple break methods. The success of multiple break methods in HOME tests was predictable due to the mathematical properties of these methods, which are well tailored to the task of the elimination of biases caused by multiple breaks.

8. (2) COST ES0601 [HOME] One of the HOME-recommended method is not a multiple- break method. It is the USHCN (Homogenisation of United States Historical Climatic Network) method. Although the efficiency of USHCN was slightly lower than the efficiency of multiple break methods in terms of the residual RMSE and trend bias of homogenised time series, USHCN produced the lowest false alarm rate among all the participated method and was the most effective among hierarchic methods and also fully prepared for the automatic homogenisation of huge datasets. Nevertheless the partial success of USHCN should not blur the even higher success of multiple break methods.

9. (3) Homogenisation in practice, 2013: Statistics of climatic studies Application of homogenisation methods in issues of 2013, or available on-line until 30-June-2013 in six leading climatic journals: BAMS Climate of the Past Climatic Change Int. J. Climatol. J. Climate Theor. Appl. Climatol.

10. (3) Statistics of climatic studies, 2013 Papers selected (include homogenisation or the topic would require its inclusion) : 181 Refer to global datasets: 26 (remains 155) Do not address the problem of homogeneity: 60 (remains 95) The method is unknown: 15 (remains 80) Based on parallel measurements: 2 (remains 78) Purely visual: 2 (remains 76) Purely metadata-based: 2 (remains 74) Statistical tests applied in 74 studies, 120 actions

11. (3) Statistics of climatic studies, 2013 SNHT21 MASH3Lee and H.1 RHtest12 moving t-test3Levene1 Double mass 10 USHCN3PRODIGE1 Buishand82-phase regr.2PCA1 FTP8Bayesian2Reg. vector1 Pettitt7Maronna-Y.2Run-test1 Von Neum.7Bartlett1Shapiro-W.1 MLR5Climatol1Thom test1 AnClim3Cum. resid.1THOMAS1 Craddock3Gallagher1Wald-W.1 Kruskal-W.3Higher order1Worsley1 Hubert1

12. (4) Climatic studies, 2013 The main findings of HOME are widely known by climatologists and its closing study (written by 31 authors) is often referred. There still exists large difference between the HOME recommendation and the practical use of homogenisation methods. While the closing study itself is often referred, its recommendation is not. Authors of climatic studies when they select and use a homogenisation method do not discuss the possible effect of their specific choice, or if yes, they do not put it into the context of the proven high performance of multiple break methods and USHCN. We think that this discrepancy is a consequence of the communication failure of HOME results.

13. (4) Three mistakes, 1 Differences between efficiencies according to the selected methods are often thought to be small (Peterson et al. 1998, Int. J. Climatol.; Auer et al. 2005; Domonkos 2011, Theor. Appl. Clim.) Residual bias of network-mean trends was scattered between 0.19 - 0.70°C/100yr in HOME (with the exclusion of the absolute method, whose residual bias was much larger).

14. (4) Three mistakes, 2 Multiple break methods are thought to be difficult and/or time consuming to use. It is not true, MASH and ACMANT have fully automated versions and even the use of HOMER is rather straightforward. Their web addresses are accessible e.g. through HOME- webpage (www.homogenisation.org)

15. (4) Three mistakes, 3 Recently, the focus of debates has shifted towards daily homogenisation and the homogenisation of higher moments. The problems arising in these topics give the feeling that the principles of time series homogenisation are generally doubtful. The study of new problems and new aspects should not make to forget proven results.

16. (4) The closing study of HOME (uppermost) is long and readers find relatively little discussion about the theoretical background of the recommended methods. Theoretical analyses were published in other HOME studies, which, unfortunately, have remained less known for the climatological community. Venema, V. and 30 co-authors, 2012: Benchmarking monthly homogenization algorithms. Climate of the Past, 8, 89-115. Domonkos, P., Venema, V., Auer, I., Mestre, O. and Brunetti, M. 2012: The historical pathway towards more accurate homogenisation. Adv. Sci. Res, 8, 45-52. Domonkos, P., Venema, V. and Mestre, O. 2011: Efficiencies of homogenisation methods: our present knowledge and its limitation. In: Seventh Seminar for Homogenisation, WCDMP-78, WMO, Geneva, 11- 24. Szentimrey, T. 2011: Theoretical aspects of homogenization. In: Seventh Seminar for Homogenisation, WCDMP-78, WMO, Geneva, 2-10.

17. Recommendations We should use and advertise the use of multiple break methods We should make more tests with realistic test datasets, at least for automatic methods The other option: “Metadata + software without mathematics = Stone age + Bill Gates” (Tamás Szentimrey, 2 nd Homogenisation Seminar, WCDMP 41, 1998, page 46)