1. Development and testing of homogenisation methods: Moving parameter experiments Peter Domonkos and Dimitrios Efthymiadis Centre for Climate Change University Rovira i Virgili, Campus Terres de l’Ebre, Tortosa, Spain, peter.domonkos@urv.cat 12th Annual Meating of EMS, Lodz, 2012.

2. Introduction: ACMANT, HOME, moving parameter experiments ACMANT = Adapted Caussinus Mestre Algorithm for homogenising Networks of monthly Temperature data (Domonkos, 2011, Int. J. Geosci, 2, 293-309). Fully automatic. Its outstandingly high efficiency has been proved by the Benchmark-homogenisation of COST- ES0601 “HOME” (www.homogenisation.org). Benchmark Surrogated Temperature Dataset has been used in the experiments of the present study.

3. Introduction: moving parameter experiments Moving parameter experiments : variation of parameterisation in test datasets or in the method itself. Sensitivity-tests moving 1 parameter only (e.g. Gruber and Haimberger, 2008, Meteor. Zeits., 17, 631-643) or ensemble tests varying several parameters at the same time (e.g. Williams et al., 2012, J. Geophys. Res. -Atmos., 117, D05116) for examining the stability of the results.

4. ACMANT: Main properties Optimal segmentation (as in PRODIGE, Caussinus and Mestre, 2004, J. Roy. Stat. Soc., C53, 405-425 and HOMER, www.homogenisation.org) Caussinus-Lyazrhi criterion (as in PRODIGE) ANOVA for corrections (as in PRODIGE and HOMER) Pre-homogenisation with excluding the double use of the same spatial relation Reference series by Peterson and Easterling, 1994, Int. J. Climatol. 14, 671-679

5. ACMANT: Main properties Multiple reference series when not all the series of observations cover the same period Specific coordination of the works on different time- scales (from multiyear to month, partly as in HOMER) Recent innovations in ACMANT ANOVA is applied also in pre-homogenisation periods (of 2-24 months) of outliers are filtered along with common outlier filtering

6. Moving parameter experiments 17 parameters, 6 arbitrary values for each within fairly wide ranges Ensemble experiments, varying all parameters randomly in each realisation Number of experiments (sample size) n = 2000 Results: RMSE in homogenised series. Comparison for the 6 values of a chosen parameter allows to make sensitivity analysis.

7. Sensitivity to “c”: monthly means RMSE of raw series: 0.61°C

8. Annual means RMSE of raw series: 0.61°C

9. Trend bias for individual series RMSE of raw series: 1.23°C

10. Network-mean trends, 1925-1999 RMSE of raw series: 0.49°C

11. Sensitivity to “d”: monthly means RMSE of raw series: 0.61°C

12. Comparison with HOME results ACMANT results are shown in two versions: i) 7 values from the 6*17 = 102 parameter values are excluded, because that values are obviously suboptimal choices and affected the results significantly. – Remaining sample size: n = 496 ii) 4 further values are excluded arbitrarily – remaining sample size: n = 197 See the original HOME results in: Venema et al., 2012, Climate of the Past, 8, 89-115

13. Network-mean trends, 1925-1999 RMSE of raw series: 0.49°C

14. Trend bias for individual series RMSE of raw series: 1.23°C

15. Monthly means RMSE of raw series: 0.61°C

16. Annual means RMSE of raw series: 0.61°C

17. Concluding remarks Performance of automatic methods can be checked with moving parameter experiments. Test datasets mimicking well the observed data are necessary: more kinds of and larger datasets. ACMANT homogenises a network of 10 time series of 100yr data in ~10 sec. (on normal PC) In interactive methods the segments of best performing automatic methods should be included (as e.g. in HOMER)

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