1. Zentralanstalt für Meteorologie und Geodynamik 1. Comparison of HOM, SPLIDHOM and INTERP 2. Ideas for the daily benchmark dataset (temperature) Christine Gruber, Ingeborg Auer

2. Zentralanstalt für Meteorologie und Geodynamik Intercomparison experiments  Comparison of:  Della-Marta and Wanner, 2006 (HOM)  Mestre et al., ???? (SPLIDHOM)  Vincent et al., 2002; Brunetti et al., 2006 (INTERP)  I. Semi-synthetic data  Use of parallel measurements  Combination of series:  artificial but realistic breaks  the truth is known for evaluation of the methods  II. (Preliminary) Application of the methods to a test dataset (Lower Austria)  Uncertainty estimation using bootstrap temperature dependent adjustments

3. Zentralanstalt für Meteorologie und Geodynamik Semi-synthetic data  Parallel measurement breaks  Realistic inhomogeneities (relocation, screen change,..)  Not only temperature dependence included  Can be combined at given break point  known position In Austria not enough stations with long parallel measurements available…

4. Zentralanstalt für Meteorologie und Geodynamik Results for 5 Stations, TMIN/TMAX, 4 seasons=40 series Absolute differences of percentiles Homogenized-truth RAW-truth

5. Zentralanstalt für Meteorologie und Geodynamik Benefit of the homogenization HOM SPLIDHOM INTERP Q10Q50Q90 Q10Q50Q90Q10Q50Q90 TMIN TMAX

6. Zentralanstalt für Meteorologie und Geodynamik Conclusions  For evaluation parallel measurement data is used + realistic breaks -only 40 time series homogenized (*20 different samples) -Many time series too small inhomogeneities, less temperature dependence  HOM and SPLIDHOM  similar, main differences for extreme values  Improvement of HOM/SPLIDHOM compared to INTERP, in the case that:  Highly correlated reference stations available  Inhomogeneity is temperature dependent

7. Zentralanstalt für Meteorologie und Geodynamik Lower Austria- Experiment  Preliminary analysis of the Lower Austria temperatures  Mainly to see how the methods work for real data  Influence of reference stations, magnitude of the breaks,…  Testing a bootstrap approach for estimating uncertainties  Break detection with HOCLIS and PRODIGE (annual means)  Homogenization with SPLIDHOM (HOM)

8. Zentralanstalt für Meteorologie und Geodynamik Lower Austria- Experiment TMAXPRODIGEHOCLISMETA HOHhomogen 1971 05Station relocation KRM 1997 197101, 198210, 199604 197101, 198210, 199604 Station relocation Change to automated station RET1951 1985 1994 1983 11 1987 01 1995 06 1983 11 1995 06 Station relocation Change to automated station SPO 1978 1955 09 1971 01 1979 04 1994 01 1955 09 1971 01 1979 04 1994 01 Station relocation 21  19 Uhr Station relocation Change to automated station WIE1951/52 1985 1994 1953 01 1971 01 1980 01 1993 01 1953 01 1971 01 1993 01 Station relocation 21  19 Uhr Change to automated station WMA1956 1985 (1989) 1956 01 1990 03 1956 01 1990 03 Station relocation ZWE19711971 01 1980 01 1994 08 1971 01 1980 01 1994 08 21--> 19 Uhr Station relocation Change to automated station

9. Zentralanstalt für Meteorologie und Geodynamik WIE summer, SPLIDHOM Ref=KRM Ref=HOH Ref=WMA Influence of undetected breakpoints (higher order moments) in REF? Too short HSPs for KRM, WMA! 1993198019711953 adjustment [°C] temperature [°C]

10. Zentralanstalt für Meteorologie und Geodynamik Adjustments Vienna Error growth!!!? 199319801971 HOM SPLIDHOM How many values are required that breaks can be adjusted reliably? Comparison of different methods useful Uncertainty of the adjustments seems to be reduced for earlier breaks Introduction of a “model”  easier to adjust in the following (earlier) breaks

11. Zentralanstalt für Meteorologie und Geodynamik WIE winter SPLIDHOM Ref=KRM Ref=HOH Ref=WMA

12. Zentralanstalt für Meteorologie und Geodynamik WIE (ref=HOH) Q10 Q90 Annual mean All data estimate Mean of bootstrap sample 0.9 confidence interval Original Uncertainties in extremes of the adjustments have hardly any influence (in this case)

13. Zentralanstalt für Meteorologie und Geodynamik WIE (ref=KRM) All data estimate Mean of bootstrap sample 0.9 confidence interval Original Q10 Q90 Annual mean

14. Zentralanstalt für Meteorologie und Geodynamik WIE (ref=WMA) All data estimate Mean of bootstrap sample 0.9 confidence interval Original Q10 Q90 Annual mean

15. Zentralanstalt für Meteorologie und Geodynamik Example for usefulness of uncertainty estimates Q10 No effect of the adjustments on the 0.1 percentile But information about the (minimum) uncertainty of the time series

16. Zentralanstalt für Meteorologie und Geodynamik Example for usefulness of uncertainty estimates Annual mean

17. Zentralanstalt für Meteorologie und Geodynamik Open questions  Requirements for reference stations?  correlation  length of HSPs  Detection of “higher order moment”- breaks?  Is it possible to adjust higher order moments?  Problems due to micro-scale climate changes (test-reference station distribution change)  Uncertainty assessment (especially for extreme values)  method uncertainty  sampling uncertainty  representativeness (references)

18. Zentralanstalt für Meteorologie und Geodynamik Benchmark daily data

19. Zentralanstalt für Meteorologie und Geodynamik The nature of the problem  Extreme value studies  homogenization of daily data necessary  Adjusting inhomogeneities in dependence of the weather type, physical reasons (primary effect)  Adjustments as function of wind, sunshine duration, global radiation… (difficult due to data availability)  Adjustment of the temperature dependence of the inhomogeneities (secondary effect)  Adjusting the temperature distribution (e.g. Della-Marta and Wanner, 2006)  Effect of inhomogeneities on temperature percentiles/extremes is reduced (that’s what we want in extreme value studies) In a first step: Shall we take into account only temperature dependent breaks in the daily benchmark?

20. Zentralanstalt für Meteorologie und Geodynamik Significance of temperature dependence How often significant temperature dependence occurs? Typical pattern and range of the magnitude  pattern for synthetic inhomogeneities

21. Zentralanstalt für Meteorologie und Geodynamik Possible working steps I.Case study Real dataset, metadata (availability?) Classification of inhomogeneities due to their source Examination of temperature dependence? (e.g. HOM) Other dependencies (wind, radiation,…) Typical pattern  benchmark II.Semi-synthetic (parallel measurement) series Realistic inhomogeneities, but truth is known for evaluation Dependencies to other elements could be studied (wind, radiation?) Data availability? (too few stations in Austria) III.Surrogate Based on typical inhomogeneity pattern (temperature dependent) (If other dependencies shall be treated as well  benchmark multiple series???? (  new adjustment-method multi-parameter???) Typical pattern? We must learn more about the problem