WG4:Testing methods for various applications

1. COST733WG4CTs vs Teleconnection indices and Precipitation over SpainMaría Jesús CasadoMaría Asunción PastorSub. Gral. Climatología y AplicacionesState Meteorological Agency (AEMet)

2. WG4:Testing methods for various applications Question: “Which are the best classifications for the selected applications?” • Evaluation based on the comparison of characteristics of events in the classifications • Teleconnection indices and Circulation Types (CTs) • Influence of Circulation Types on Precipitation over SPAIN

3. COST733 D00-D09

4. 1. Evaluation • Version 1.1 of the catalogue • Domains D00 and D09 • Extended winter (DJFM) We analyze the behaviour of the classifications about the distribution of events, the mean lifetime, the percentage of time spending in events lasting 4 or more days and the number of 1-day events

5. 1. Evaluation

6. 1. Evaluation

7. 1. Evaluation For D00 • HBGWL,TPCA07 and SANDRAS exhibit the higher percentages of time spent in events lasting 4 or more days, similar behaviour for the mean residence time • SCHUEEPP, LITTC and LWT2 exhibit the shorter percentage of time spent in events lasting 4 or more days • The classifications with the shorter mean residence time have a large proportion of 1-day events For D09 • PCACA and NNW exhibit the higher percentages of time spent in events lasting 4 or more days, similar behaviour for the mean residence time • LWT2, WLKC733 and P27 exhibit the shorter percentage of time spent in events lasting 4 or more days • PCACA and NNW exhibit the most noticeable changes with respect to the results obtained for D00 (both classifications suffer a considerable reduction in the number of CTs in D09)

8. 2.- Teleconnection indices/CTs Which classifications are the best for discriminating NAO phases? 2.1.- Frequency of NAO+ and NAO- of each CT and classification 2.2.- Discrimination of classifications using χ2 statistic Which classifications are the best for discriminating teleconnection indices? 2.3.- Discrimination of classifications using R2

9. 2.1 NAO+/NAO- • We analyse the relationship between NAO phases and CTs, using the winter NAO daily index, from the Climate Prediction Center (CPC), after its standardization • We define NAO+ as the values greater than 1.0, and NAO- as the values lesser than -1.0 • For each CT we analyse the number of days which are NAO+ and NAO- respectively

10. 2.1 NAO+/NAO- D00

11. 2.1 NAO+/NAO- D00 The largest frequency values are detected for NAO- in: HBGWL (CT14), 75% of the days NNW (CT2), 75% of the days OGWL (CT14,CT15), 65% of the days The largest frequency values are detected for NAO+ in: SANDRA (CT15,CT16), 50% of the days SANDRAS (CT23), 50% of the days TPCA07 (CT1), >40% of the days

12. 2.1 NAO+/NAO- D09

13. 2.1 NAO+/NAO- D09 The largest frequency values are detected for NAO- in: SANDRA (CT19), 50% of the days LITTC (CT13), 50% of the days TPCA07 (CT5), 40% of the days The largest frequency values are detected for NAO+ in: SANDRAS (CT16), 40% of the days PCAXTR (CT9), 40% of the days PCAXTRKM (CT9), 40% of the days

14. 2.2 X2 statistics The X2statistics: pi teor =(ni/N)*(K/N) ki number of days of NAO+ (NAO-) for each CT and classification ni total number of days for each CT and classification K total number of days NAO+ (NAO-) N total number of days for the period Dec 1957 to Mar 2002 (5456 days) I number of CTs for each classification Criteria: the higher values of X2 the best discrimination

15. 2.2 χ2 statistics NAO+ D00 D09 SANDRAS SANDRAS SANDRA CEC NNW SANDRA P27 LITTC OGWL PCACA NAO- D00 D09 SANDRAS SANDRAS CEC SANDRA OGWL CEC SANDRA LITTC NNW PCACA

16. 2.2 Ranking of classifications for NAO+/NAO- using χ2statistics

17. 2.3 Teleconnection indices • Principal Component analysis (PCA) in S-mode followed by an orthogonal rotation (varimax), (Richman, 1986) is applied to the daily winter 500-hPa geopotential height from ERA40 (2.5º x 2.5º). • Four PCs rotated determined by the Log-Eigenvalue diagram. • Cumulative percentage of variance explained by the four PCs rotated: 62.2%. • Teleconnection indices identified: NAO, SCAN, EA and EU2 • Spatial domain: Euro-Atlantic region: 250N - 700N 450W - 500E (D00: 300N - 760N 370W - 580E) • 3.- METHODOLOGY • Principal Component analysis (PCA) in T-mode followed by a varimax • rotation, (Richman, 1986) applied to ERA40. • Four PCs rotated determined by the Log-Eigenvalue diagram. • Cumulative percentage of variance explained by the four PCs rotated: 65.5%. • CGCM2 loadings obtained from the four ERA40 PC score patterns projected onto CGCM2 simulations. • Each day is classified on the PC with the highest loading, the higher the loading the greater the similarity (Huth, 1996). • As loadings may be either positive or negative, twice as many CTs as PCs rotated are obtained. In this case eight CTs provided by the four PCs rotated. • CTs are the composites of the maps assigned to each CT. • 3.- METHODOLOGY • Principal Component analysis (PCA) in T-mode followed by a varimax • rotation, (Richman, 1986) applied to ERA40. • Four PCs rotated determined by the Log-Eigenvalue diagram. • Cumulative percentage of variance explained by the four PCs rotated: 65.5%. • CGCM2 loadings obtained from the four ERA40 PC score patterns projected onto CGCM2 simulations. • Each day is classified on the PC with the highest loading, the higher the loading the greater the similarity (Huth, 1996). • As loadings may be either positive or negative, twice as many CTs as PCs rotated are obtained. In this case eight CTs provided by the four PCs rotated. • CTs are the composites of the maps assigned to each CT. • 3.- METHODOLOGY • Principal Component analysis (PCA) in T-mode followed by a varimax • rotation, (Richman, 1986) applied to ERA40. • Four PCs rotated determined by the Log-Eigenvalue diagram. • Cumulative percentage of variance explained by the four PCs rotated: 65.5%. • CGCM2 loadings obtained from the four ERA40 PC score patterns projected onto CGCM2 simulations. • Each day is classified on the PC with the highest loading, the higher the loading the greater the similarity (Huth, 1996). • As loadings may be either positive or negative, twice as many CTs as PCs rotated are obtained. In this case eight CTs provided by the four PCs rotated. • CTs are the composites of the maps assigned to each CT. 3.- METHODOLOGY • Principal Component analysis (PCA) in T-mode followed by a varimax rotation, (Richman, 1986) applied to ERA40. • Four PCs rotated determined by the Log-Eigenvalue diagram. • Cumulative percentage of variance explained by the four PCs rotated: 65.5%. • CGCM2 loadings obtained from the four ERA40 PC score patterns projected onto CGCM2 simulations. • Each day is classified on the PC with the highest loading, the higher the loading the greater the similarity (Huth, 1996). • As loadings may be either positive or negative, twice as many CTs as PCs rotated are obtained. In this case eight CTs provided by the four PCs rotated. • CTs are the composites of the maps assigned to each CT.

18. 2.3 R2 Discrimination • The medians of the four teleconnection indices series for each classification are sorted in ascending order • The linear trend and the coefficient of determination (R2) are calculated • R2 valueis used for discriminating classifications. The higher R2 values, the best discrimination Example

19. 2.3 R2 Discrimination

20. 2.3 R2 Discrimination

21. 2.3 Ranking of classifications for teleconnection indices using R2

22. 2.3 Concluding remarks For D00 • The R2 highest value is shown in EA • LITTC,LWT2,SCHUEEPP and CEC show similar R2 values for all the teleconnection indices For D09 • The R2 highest value is shown in EU2 • SCAN show small R2 values for a great number of classifications • LITTC, LWT2 show similar R2 values for all the teleconnection indices

23. 3. Influence of Circulation Types on Precipitation over Spain 3.1.- Data 3.2.- Precipitation percentage for each CT and classification 3.3.- Discrimination of classifications using the standard deviation of the precipitation percentage

24. 3.1 Data • Daily gridded Precipitation data from INM Climatological Data Base • Temporal domain: extended winter (DJFM) from 1961-1990 • Spatial domain: Spain

25. 3.1 Data 203 grid points (50kmx60km)

26. 3.2 Precipitation Percentage D00

27. 3.2 Precipitation Percentage D09

28. 3.2 Precipitation Percentage • GWT • LITADVE • LITTC • LUND • LWT2 • NNW • P27 • PCACA • PCAXTRKM • PCAXTR • PETISCO • SANDRAS • SANDRA • TPCA07 • TPCAV • WLKC733 • HBGWL • OGWL • PECZELY • PERRET • SCHUEEPP • ZAMG classifications The red box is limited by : 1st quartile, median and 3rd quartile

29. 3.2 Precipitation Percentage • GWT • LITADVE • LITTC • LUND • LWT2 • NNW • P27 • PCACA • PCAXTRKM • PCAXTR • PETISCO • SANDRAS • SANDRA • TPCA07 • TPCAV • WLKC733 classifications The red box is limited by : 1st quartile, median and 3rd quartile

30. 3.2 Precipitation Percentage (Box-plots) For D00 • The classifications with larger interquantile range are: LUND, PCACA and PECZELY • The maximum appears in PETISCO followed by TPCA07 • The classifications with larger medians are: TPCA07,LITADVE and LUND For D09 • The classifications with larger interquantile range are: PCACA, TPCAV and TPCA07 • The maximum appears in PCACA followed by PCAXTR and PCAXTRKM • The classifications with larger medians are: PCACA, TPCA07 and LITADVE

31. 3.3 Standard Deviation of Precip.Percentage The Standard Deviation of Precipitation percentage: j grid-point xij precipitation percentage at gridpoint j for a CT i and classification mean of the precipitation percentage at gridpoint j for a given classification N number of CTs for each classification Criteria: the higher values of STD the best discrimination This way, we have spatial patterns of the ‘performance’ of classifications

32. 3.3 STD of precipitation percentage DOO

33. 3.3 STD of precipitation percentage DO9

34. 3.3 STD of precipitation percentage (mean value)

35. 3.3 Ranking of classifications using STD (Prec.Perc.)

36. 3.-Concluding remarks Percentage of precipitation For D00 • Highest percentages for: PETISCO, PCACA,TPCA07 and PCAXTR (15-40%) • Smallest percentages for: P27,OGWL, LITTC, PERRET and SCHUEEPP (<10%) For D09 • Highest percentages for: PCACA, PCAXTRKM, LITADVE, LUND, NNW, PECZELY and PETISCO (15-50%) • Smallest percentages for: OGWL and LITTC (<10%)

37. 3.-Concluding remarks STD of precipitation percentage For D00 • TPCA07 and LUND capture the three main regions of precipitation over Iberian Peninsula: Atlantic region, Cantabrian region and Mediterranean coast. In a lesser extent, PETISCO, PECZELY, TPCAV and LITADVE For D09 • PCACA is to a large extent the best classification in capturing the three above-mentionned regions of precipitation in Iberian Peninsula.

38. MANY THANKS FOR YOUR ATTENTION