1. Homogenization of monthly Benchmark temperature series of network no. 3 – using ProClimDB software COST Benchmark meeting in Zürich 13-14 September 2010 – Lars Andresen

2. Norwegian Meteorological Institute met.no Software package –AnClim Homogeneity analysis (using txt-files) –ProClimDB Automating the homogenization procedure (using mainly dbf-files) Petr Štěpánek

3. Norwegian Meteorological Institute met.no Normal homogenization procedure Original Data Quality control Reconstruction of series Homogeneity testing Adjusting Data Reference series (40 years, 10 years overlap) from correl. / weights SNHT (Alexandersson test)Assessment of hom. results Standardization to base station (AVG/STD) Stations within 10 kmDemands on data coverage Merging of different series Iteration process Reference series (10 years around inhomogeneity) from distances Standardization to base station (AVG/STD) Smoothing monthly adjustments / Demands on corr. after adjustm. Rank of monthly valuesComparing with neighbours Replacing suspicious valuesDist. / Stand. to alt. / Outliers

4. Norwegian Meteorological Institute met.no Detecting breaks of network 3 (15 series) Outliers removed from manipulated series –10 outliers from 8 stations Testing settings of ProClimDB –40 year periods, 10 years overlap versus 20 years –Excluding breaks closer than 4 years to edge of series or to nearest break –Finding the more distinct breaks before the less distinct ones

5. Norwegian Meteorological Institute met.no Removing outliers Station 01400 Value of 5/1978 changed from 14.8°C (outlier) to 10.8°C (true) 1976, 14.3/14.3 1977, 11.5/11.5 1978, 10.8/14.8 1979, 13.2/13.2 1980, 8.8/8.8

6. Norwegian Meteorological Institute met.no Consequences by changing overlap years – A case study, using SNHT method 0.3°0.5°0.7° Single shift of +/- 0.5° 2, 4, 9, 19 years from edge of a homogeneous temperature series of 40 years Single shift of +/- 0.3, 0.5, 0.7° Each pair 9 and 19 years from edge of the series

7. Norwegian Meteorological Institute met.no Criteria for detection Approved –Correct year (two years involved, both correct) –Adjustment within ± 0.1 degrees, e.g. 0.5 ± 0.1 –T 0 ≥ 8.1 (40 years – significance level 95%) Nearly approved –Correct year, T 0 ≥ 8.1, Adj = 0.5 ± 0.3 degrees –Correct year ± 1, T 0 ≥ 8.1, Adj = 0.5 ± 0.2 –Correct year, T 0 ≥ 7.0 (s.l.90%), Adj = 0.5 ± 0.1 Fault –Significant break not approved or nearly approved

8. Norwegian Meteorological Institute met.no Network 3 – comparing 46 breaks B:Breaks detected, M: Missing detection, F: Fault detection After 0 1 2 iterations Overlap 10 years 20 years Y_Poss ≥30Y_Poss ≥25Y_Poss ≥20

9. Norwegian Meteorological Institute met.no Left: ”Official result” (46 breaks) Y_Poss ≥15, no iteration Y_Poss ≥30, 25 and 20, 2 iterations Case study

10. Norwegian Meteorological Institute met.no Discussion – 1 Homogeneity analysis Reference series for finding breaks Using correlations Using distances Weighting of neighbour values (0.5 or 1.0?) Period (40 years) / Overlap (10 or 20 years?) Processing of results Method (SNHT alone or in combination with others?) Finding most probable breaks (Y_POSSIBLE). How? Weighting of month, season, year (1, 2, 5) Metadata (improving?) Nearness to begin/end/other breaks (2 or 4 years?)

11. Norwegian Meteorological Institute met.no Discussion – 2 Adjustments of the series Reference series for making adjustments Using distance alone (limitation on distance) Using distance and correlation (limitations on distance and correlation) Smoothing monthly adjustments Gauss filter (0~no smoothing, 2~period of 5 values is recommended, other?) Checking correlation after adjustments Keep smoothed adjustment if correlation improvement between candidate and neighbours (Corr+value) ≥ 0.005 or ≥ 0.000 ?

12. Norwegian Meteorological Institute met.no Discussion – 3 Iterations Using adjusted file for new analysis How finding most probable breaks –More stringent criteria when automating procedure (depends on metadata and Y_POSSIBLE)?

13. Norwegian Meteorological Institute met.no Conclusion It is reason for concern about the high number of fault detections Use of metadata is necessary in homogenization! Using metadata allows lower values of Y_Possible It’s important to find the optimal conditions of a procedure before comparing methods Homogenization has no correct answer !