1. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 HOMOGENITY AND HOMOGENIZATION METHODS. A review. Enric Aguilar Climate Change Research Group Geography Department Universitat Rovira i Virgili de Tarragona (Spain) enric.aguilar@urv.cat

2. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 OUTLINE A few questions to start –What does homogeneous (and inhomogeneous) mean? –Why a time series may become inhomogeneous –What does homogeneity assessment mean? –What does homogenization mean? –How does the lack of homogeneity compromise climate analysis? Some general procedrures for homogeneity assessment and homogenization A brief review of techniques. Will briefly introduce these methods (not necessarily in this order) –Craddock + Metadata –Likelihood ratio: SNHT –Regression models –Two-phase regression –Caussinus-Mestre –Vincent’s interpolation of daily factors –Della Marta and Wanner The HOME-COST Action

3. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START What does homogeneous mean? –From. lat. homogenĕus, and from gr. ὁ μογεν ή ς  “of the same nature” –Translating the term to climate time series: A homogeneous climate time series is defined as one where variations are caused only by variations in climate. If a long-term time series is homogeneous, then all variability and change is due to the behavior of the climate system (WMO TD-1186) Conversely, inhomogeneous time series are those presenting any kind of bias, which impacts the recorded values and is not strictly caused by true climatic variability and change

4. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous? –Because a change has been applied or an error has been introduced into the conditions the data are measured, recorded, transmitted, stored and or analyzed resulting in a systematic bias of a particular segment of the time series

5. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example I: errors in temperature units leading to an artificial change in variance

6. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example II: change of rain gauge exposure, leading to an artificial bias in precipitation amount

7. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example III: changes in the way of computing the daily mean temperature, leading to important biases when compared to WMO standard (max+min)/2

8. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example IV: impact of urbanization on temperature series, leading to an artificial enhancement of trends

9. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example V: instrument replacement, leading to an artificial bias (jump) on radiation series

10. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example VI: impact of relocations and changes in environment on wind speed time series

11. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START Why a time series may become inhomogeneous?. Example VII: changes in screen type, leading to an artificial bias in temperature Differences between Simoultaneous temperatures measured in Murcia (Spain). Lines are Stevenson – Montsouris screen. Red is Tmax (much larger temperatures in Montsouris screen lead to negative differences); blue is Tmin (slightly larger values in stevenson screen lead to positive differences); green is Tmean, which balances the effect of Tmax and Tmin into negative differences (i.e. larger temperatures registerd on ancient screens)

12. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START What does homogeneity assessment mean? –To learn if a time series is or is not homogeneous (I) Peterson et al, 2002 Aguilar et al, 2005

13. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START What does homogeneity assessment mean? –To learn if a time series is or is not homogeneous (II) Source: Lucie Vincent Quebec – reference series

14. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START What does homogenization mean? Apply statistical techniques to transform a) into b) and try to eliminate as much as possible non climatic influences biasing the time series a) Quebec City, 1895-2002 (Non adjusted) Source: Lucie Vincent. b) Quebec City, 1895-2002 (Non adjusted)

15. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START How does the lack of homogeneity compromise climate analysis? Trend before homogenization: -0.7°C in 106 years Quebec City, 1895-2002 Source: Lucie Vincent.

16. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A FEW QUESTIONS TO START How does the lack of homogeneity compromise climate analysis? Trend after homogenization: +2.1 °C in 106 years Quebec City, 1895-2002 Source: Lucie Vincent.

17. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SOME GENERAL QUESTIONS Use Quality Controlled Data Detect inhomogeneities (in other words, identify homogeneous subperiods) Adjust to the last homogeneous subperiod Validate results

18. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A GENERAL PROCEDURE FOR HOMOGENEITY ASSESSMENT AND HOMOGENIZATION Detect inhomogeneities (in other words, identify homogeneous subperiods) Metadata Test Visual inspection

19. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SOME GENERAL QUESTIONS Test Over the data? Using reference series? -Identify if period A is different from period B (good when you have reliable metadata) - Identify in which data point the time series is most likely to have breakpoint - Iterate over the series or use a model that allows multiple breaks detection Climate fluctuations may be confused with inhomogeneities Using a model based on a reference series or running the test with the help of a reference series should help to distinguish climate effects from true inhomogeneities

20. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 ON THE USE OF REFERENCE SERIES Using reference series? -Decorrelation (network density) -Homogeneity of the reference series Auer et al, 2005 - Specially problematic at early stages (i.e. 19th century), due the lack of data

21. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 THERE IS A MULTIPLICITY OF TESTS AVAILABLE Simple (but elaborated!) formulatations  Craddock Test + Metadata Caussinus-Mestre Likelihood ratio tests  SNHT and variants Regression model tests  Vincent Two Phase Regression  Wang MASH  will be discused later on by T. Szentimrey For introductory explanations on these and other methods, see Aguilar et al (2003) * : Guidance on Metadata and Homogenization, WMO-TD-1186. * : This is almost 5 years old… see later on slides on COST-HOME action!

22. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 ON THE APLICATION OF THE CRADDOCK TESTS Auer et al (2007)and many others apply the Craddock Test to climatological data The test has a simple formulation and HISTALP heavily relies on metadata and expertise to identify/confirm/reject potential breaks It accumulates the normalized differences between two series (a and b) according to one of the following formulas: Where: s is the sum at the current obs; s-1 the sum at the previous obs; an is the obs at the candidate station; am is the mean of the candidate station; bn is the obs at the reference station; bm is the mean of the reference station

23. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 From Maugheri, M.

24. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 Craddock test - Bologna precipitation record “All’inizio del 1857 a questo pluviometro, ridotto in cattivo stato pel lungo uso, ne venne sostituito un altro di migliore costruzione, e lavorato con molta precisione...” Introduction of a new pluviometer (Fuess recorder): “... fu collocato a cura del prof Bernardo Dessau nel periodo 1900-1903...” Change in data origin: from “Osservatorio Astronomico” to “Istituto Idrografico” News about a damage to the pluviometer. In corrispondence with repairing the damage, the cause of the underestimation of precipitation has been removed for the period 1900-1928 From Maugheri, M.

25. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 ON THE APPLICATION OF CRADDOCK TEST (generalizable to homogeneity work) Auer et al (2007) say: –For the nucleus of homogeneity testing (the comparison of two series) we use Craddock’s normalised accumulated difference/ ratio series (Craddock, 1979), although HOCLIS would allow any method of relative homogeneity testing to be used. The practical experience in our group with a number of such methods tells us that the rejection of break signals due to statistical non-significance (as provided by higher developed methods) is often misleading. Strong breaks may remain in the series simply owing to the fact that the typical length of a homogeneous subinterval (Table I) is short in relation to interannual variability. We try to compensate for the deficits of our method in pure statistical terms by investing much work into metadata analysis, which we regard as the ultimate measure to decide whether a break can be accepted or not.

26. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 ON THE APPLICATION OF CRADDOCK TEST 1. Ignore any previous homogeneity work undertaken for any of the series (i.e. start from the beginning, assuming all series contain potential breaks). 2. Test in small, well-correlated subregions (a maximum of 10 series tested against each other results in a 10 × 10 decision matrix, which enables most breaks detected to be assigned to a most likely candidate series). 3. Choose the most appropriate reference series with a non-affected subinterval for the adjustment of each break detected (i.e. different reference series can be used for each break detected in a candidate series). 4. Avoid erratic monthly precipitation adjustments by smoothing the annual course of adjustment factors. 5. Detect outliers and ‘overshooting adjustments’ using spatial comparisons (by mapping precipitation values both in absolute and relative units) for each month of the study period. 6. Attempt to determine support for homogeneity adjustments when few metadata are available (i.e. contact data providers for more information in difficult cases). Auer et al (2005)

27. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 LIKELIHOOD RATIO TESTS: THE STANDARD NORMAL HOMOGENEITY TEST AND VARIANTS Formulated by Alexandersson (1986) and Alexandersson et al (1997) Critical values derived after MCS Recently Khaliq and Ouarda have recalculated the critical values using improved MCS Widely re-formulated (for example, Reeves et al, 2006) Widely applied (will see example by CCRG)

28. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SHNT Reference Series q-series (data-reference) z-series (standarized q-series) Original Data

29. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SNHT Most Probable Breakpoint: Max of Correction Factor:

30. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SNHT MODIFICATIONS BY REEVES et al (2007) The standarization into z series proposed by Alexandersson : Uses s to estimate the standard deviation of the series, which might be ineficient if the candidate series is inhomogeneous They propose to avoid standarization by using:

31. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 Aguilar, E., Brunet, M., Saladié, O., Sigró, J. : Homogenization of the Spanish Daily Temperature Series. A step forward. QCd daily data of TMax and TMin Screen Bias Minimisation over monthly series of TMax and TMin SDTS Calculation of Monthly Values of TMax and TMin Blind break-point detection over annual, seasonal TMax, Tmin, Tmean with automated SNHT (1997) Breakpoint validation (metadata, plot checks, …) Generation of correction patternApplication to monthly Tmax and Tmin (As described in Aguilar et al, 2002) Monthly, Seasonal, Annual Tmax, Tmin, DTR, TMean Series (STS) Interpolation to daily data (Vincent et al., 2002) Validation of daily corrected values A HOMOGENIZATION PROCEDURE BASED ON THE SNHT TEST (AND OTHER METHODS)

32. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SCREEN BIAS MINIMIZATION

33. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SCREEN BIAS MINIMIZATION CCRG’s SCREEN project (CICYT)  2 replicas of Montsouris Screen, on operation since 2003 Large effect on TMax Much smaller effect on TMin

34. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SCREEN BIAS MINIMIZATION Murcia: TMaxStev = -0.508 + TMaxMont*0.975

35. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 SCREEN BIAS MINIMIZATION Tmax data for Murcia (August) Red: Murcia Original Green: Murcia Screen-Corrected

36. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 BREAK POINT DETECTION BLIND RUN OF AUTOMATED SNHT (see ALEXANDERSON ET AL., 1997) OVER ANNUAL AND SEASONAL VALUES OF TMAX, TMIN, TMEAN AND DTR Reference Series q-series (data-reference) z-series (standarized q-series) Original Data

37. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 BREAK POINT DETECTION BLIND RUN OF AUTOMATED SNHT (see ALEXANDERSON ET AL., 1997) OVER ANNUAL AND SEASONAL VALUES OF TMAX, TMIN, TMEAN AND DTR Most Probable Breakpoint: Max of Correction Factor:

38. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 BREAK POINT DETECTION

39. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 BREAK POINT VALIDATION Tn Detection 03 17 1914 5.51 03 17 1954 -2.58 03 17 1935 2.16 Tm Detection 03 17 1955 -1.89 Tx Detection 03 17 1970 2.54 Green: number of references Red: z-series

40. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CORRECTION PATERN Green: number of references Red: z-series Period: 1880-2006 In 1954 station was relocated from the city center to the airport

41. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CORRECTION RESULTS OVER ANNUAL TMean (BADAJOZ) Original Corrected Red: original; green: corrected (Screen +SNHT)

42. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 APPLICATION TO MONTHLY SERIES BADAJOZ, TMax All Values in 1/10th of ºC

43. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 ADJUSTMENT OF MONTHLY SERIES August TX January TX Red: original; green: adjusted

44. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CONVERTING MONTHLY FACTORS TO DAILY FACTORS Following Vincent et al. (2002), monthly factors are assigned to the 15th of each month to avoid abrupt discontinuities at the end of the month

45. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CLIMATE CHANGE INDICES DERIVED FROM DAILY TIME SERIES Badajoz, TX90p índex Red: oiginal Green: corrected

46. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CLIMATE CHANGE INDICES DERIVED FROM DAILY TIME SERIES Badajoz, TX10p índex Red: oiginal Green: corrected

47. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CLIMATE CHANGE INDICES DERIVED FROM DAILY TIME SERIES Badajoz, TN90p índex Red: oiginal Green: corrected

48. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CLIMATE CHANGE INDICES DERIVED FROM DAILY TIME SERIES Badajoz, TN10p índex Red: oiginal Green: corrected

49. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CAUSSINUS and MESTRE (2004) Use a fairly more complicated penalized log-likelihood procedure to correct groups of stations sharing the same climate signal C-M assume that each series is the sum of climate effect, a station effect and a random white noise. The station effect is constant if the series is reliable (homogeneous). If not, the station effect is piecewise constant between two shifts (except for outliers) The maximization of the statistic, and thus the model selection, implies testing a far too large number of combinations of break-points and oultiers positions

50. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CAUSSINUS and MESTRE (2004) For practical application, pair-wise comparisons across the neighbours are performed by calculating difference series and the arising breaks/outliers are selected over a decission table.

51. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 CAUSSINUS and MESTRE (2004) Expertise and metadata records are used again to decide which break points are retained to be preliminary corrected with a simplified two factors model. Peliminary corrected data is submitted again to pairwise comparison to ensure that no important breakpoints were left untreated and to benefit of the corrections applied to other stations Once all beakpoints and outliers are known for all the stations, the full model is run ¡

52. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) Defined their method in 10 steps: 1.Define HSPs for the candidate and as reference stations as possible (this method does not provide its own detection tool) 2.Starting from the most recent inhomogeneity find a reference station which is highly correlated and has HSP that adequately overlaps both HSP1 and HSP2 of the candidate station

53. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) 3.Model the relationships between the paired candidate and reference observations before the inhomogeneity (i.e. in the period of common overlapping within HSP1 of the candidate, 1988-2003 for Graz-Uni, using Wien)

54. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) 4.Predict the temperature at the candidate station after the inhomogeneity using observations from the reference and the previously obtained model 5.Create a paired difference series between the predicted and the observed model within HSP2 6.Find the probability distribution of the candidate station in HSP1 and HSP2

55. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) 7.Bin each temperature difference in the difference series (step 5), according to its’ associated predicted temperature, in a decile of the probability distribution of the candidate station at HSP1 8.Fit a smoothly varying function between the binned decile differences (step 7) to obtain an estimated adjustment for each percentile 9.Unsing the probability distribution of the candidate in HSP2 (step 5) determine the percentile of each observation in HSP2 and ajust by the amount calculated in step 8 10.Proceed to the remaining HSPs

56. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) Results

57. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 A MORE SOFISTICATED MODEL FOR ADJUSTING DAILY TEMPERATURE DATA: DELLA-MARTA and WANNER (2006) Comparisons

58. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 REGRESSION MODEL TESTS  VINCENT’S TEST Vincent, in 1998, described a new approach based on the fitting of a hierarchy of regression based models to a time series an the analysis of the residuals The model has been, as well as SNHT, widely applied and many variants have appeared

59. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 VINCENT’S TEST -A visual inspection of the plots may tell enough to infer whether the series is homogeneous (top, model accepted, process finished), has an artificial trend (left) or an artificial jump (closer plot) - The Durbin-Watson statistics and the analysis of the correlogram are use to take the final decision

60. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 VINCENT’S TEST Iteration throguh all possible p changepoints. When

61. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 VINCENT’S TEST

62. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 REGRESSION MODEL TESTS  VINCENT AND VARIANTS Reeves et al (2007) describe as follows Vincent’s procedure

63. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 TWO PHASE REGRESSION Another widely used family of methods are those based on two phase regressoin. With pioner applications by Solow (1987) and Peterson and Easterling (1995) and reformulated by Lund and Reeves (2002)

64. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 RH-TEST Wang (2003) defines the following model with commond trend RH-Test is a software package base in an improved version of the former model, widely used at WMO-CCl-ETCCDI workshops and in many other publications Includes an iterative procedure to detect multiple breakpoints and the new statistics account for important aspects as serial autocorrelation Available at: http://cccma.seos.uvic.ca/ETCCD MI/software.shtml

65. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 WHICH IS THE BEST METHOD? There is no agreement There is a sense that MANY methods work well (specially for detection on annual to monthly) if they are applied with care and expertise Many authors have performed statistical comparisons: –Ducré et al (2003) –De Gaetano (2005) –Reeves et al (2007) –... Comparisons (as well as all homogeneity work) depend on several things –The tested data –The test application procedures –The software used –Which quality do we prefer (false detection, false negatives, position, magnitude of the break) –…

66. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 WHICH IS THE BEST METHOD? From Ducré-Robitaille et al

67. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 WHICH IS THE BEST METHOD? From Ducré-Robitaille et al (2003) False detection magnitude and positions over 1000 simultaed series

68. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007

69. COST ACTION HOME. Scientific Activities

70. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 COST ACTION HOME. Working Groups

71. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 FINAL MESSAGE The method is important; its application, even more i.e.: the best method in bad hands will do less a worst method in good hands BEST SCENARIO: good method, good hands.

72. SUMMER SCHOOL ON THE PREPARATION OF CLIMATE ATLAS Sitke (Hungary); 10 - 14 September 2007 Thank you!