Development and Testing of ACMANT Homogenisation Methods through Moving Parameter Experiments

1. Development and testing of homogenisation methods:Moving parameter experiments Peter Domonkos and Dimitrios EfthymiadisCentre for Climate ChangeUniversity Rovira i Virgili, Campus Terres del’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. Itsoutstandingly 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, 6arbitrary 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 meansRMSE of raw series: 0.61°C

8. Annual meansRMSE of raw series: 0.61°C

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

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

11. Sensitivity to “d”: monthly meansRMSE 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, 1925-1999RMSE of raw series: 0.49°C

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

15. Monthly meansRMSE of raw series: 0.61°C

16. Annual meansRMSE 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)

18. Thank you for your attention!