Serhat Sensoy Chief of Climate & Climate Change Division Vice-President of WMO CCl

1. Climate Indices Serhat Sensoy Chief of Climate & Climate Change DivisionVice-President of WMO CCl Turkish State Meteorological Service (TSMS)

2. How can we detect Climate Change?-Climate Indices

3. The background • WMO Commission for Climatology / CLIVAR Working Group on Climate Change Detection meets and try to find answer: “What could a small group of volunteers do to further global climate change detection?” The answers are: • Internationally coordinate a suite of indices • Mainly highlighting changes in extremes • Derived from daily data • Hold regional climate change workshops

4. In 2001 two workshops were held • In Kingston, Jamaica for the Caribbean • Produced a workshop report • Produced a multi-authored JGR paper • Released all daily data used in the analysis • Released suite of indices • In Casablanca, Morocco for various countries in Africa • Produced a workshop report 4

5. In 2003,CCl/CLIVAR Expert Team on Climate Change Detection Monitoring and Indices. The ET has met in Norwich UK, in November, 2003 and has coordinated improved indices and additional workshops ET was renewed again in 2010. 5

7. CCL-XV OPACE-2 Joint CCl/Clivar/JCOMM Expert Team On Climate Change Detection And Indices Members 1. Albert Klein-Tank ( The Netherlands) (Co-Lead) 2. Clivar member (Co-Lead) 3. Blair Trewin (Australia) 4. Matilde Rusticucci (Argenatina) 5. Zhai PanMao (China)

8. Terms of Reference: 1. Provide international coordination and help organize collaboration on climate change detection and indices; 2.Further develop and publicize indices and indicators of climate variability and change and related methodologies, from the surface and subsurface ocean to the stratosphere, with international consensus; 3. Encourage the comparison of modeled data and observations, perhaps via the development of indices appropriate for both sources of information; 4.Coordinate these and other relevant activities the ET chooses to engage in with other appropriate working bodies including of those affiliated under OPACE-4, WCRP and JCOMM as well as others such as GCOS, CBS, CIMO, CAgM, CHy, IPCC and START; and regional associations; 5.Explore, document and make recommendations for addressing the needs for capacity-building in each region, pertinent to this topic with consideration of the GFCS requirements; and 6.Submit reports in accordance with timetables established by the OPACE 2 co-chairs

9. Global analyses of changes in extremes used in the IPCC TAR Did not represent nearly half of the world. (Frich et al)

10. Six regional workshop were held to fill the gap in the global extreme analyses.

11. The workshop was composed combination of seminars and hands-on data analysis 11

12. Workshop Agenda was modeled as • Introductions to the issues • Data Quality Control • Calculating indices • Testing data homogeneity • Making sense out of the results • Country reports • Regional evaluation • Post workshop planning • Peer-reviewed articles, etc. 12

13. Indices software • Workshop suitable software (RClimDex) produced on behalf of the ET by Xuebin Zhang from Environment Canada • http://cccma.seos.uvic.ca/ETCCDMI/ • RClimDex uses the free “R” statistical package Workshop results • 6 regional workshop peer-review papers submitted – after careful post workshop analyses • One global peer-review paper was prepared newly by Alexander L. et al These papers have been input for IPCC AR4 13

14. 2002 Less Coverage 2005 ImprovedCoverage

15. What is the characteristic of extremes? • Trends in extreme events Can't be characterized by the size of their societal or economic impacts • Trends in “very rare” extreme events can’t be analyzed by the parameters of extreme value distributions • Trends in observational series of phenomena is theindicators of extremes

16. Careful post-Workshop Analysis Addressed Data Problems • Many stations’ digital record were too short to use in this analysis (at least 30 years daily data is needed for extreme analyses) • QC: a wide variety of checks, including looking for: • Extreme values due to digitizing errors • Incorrect English/metric units • Runs of the same value • Tmax < Tmin • Missing precipitation set to 0 • Homogeneity • Evaluation of time series of the indices to weed out inhomogeneous data 16

17. Climate Data Homogenization By Enric Aguilar A homogeneous climate time series is defined as one where variations are caused only by variations in climate (WMO-TD No. 1186) Difference between Quebec City and a reference series Adjustment Station is located on the roof of the main building 1942-1960 1931 1942 1960 1931: station relocated to the college with change in exposure 1942: station relocated from college to airport Station is locatedon the ground after 1960 17

18. Figure shows homogeneity test of annual minimum temperature for station Rize, Turkey. The discontinuity in 1995 is reflected in metadata which shows that the station relocated in this year. Data homogeneity is assessed using R-based program, RHtest, developed at the Meteorological Service of Canada. It is based on two-phase regression model with a linear trend for the entire base series (Wang, 2003)

19. Advantages of Indices versus Data • Indices are information derived from data • It represents the data • More readily released than data • Are not reproducible without the data • Useful in a wide variety of climate change analyses • Useful forModel – observations comparisons • Useful foranalyses of extremes 19

20. prec. p.

21. “warm nights” “cold nights” Percentage based Indices After: Jones et al. (Climatic Change, 1999) Yan et al. (…, 2002, IMPROVE- issue) upper 10-ptile 1961-1990 the year 1996 lower 10-ptile 1961-1990 21

22. Quality Control • If precipitation value is (–), it is assumed as missing value(-99.9) • If Tmax < Tmin both are assumed as missing value(-99.9) • If the data outside of threshold (mean ±4*STD) it is problematic value. 22

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24. Indices Plots Locally weighted regression Linear (least square) fit Kendall’s tau based slope estimator has been used to compute the trends since this method doesn’t assume a distribution for the residuals and is robust to the effect of outliers in the series. If slope error greater than slope estimate we can’t trust slope estimate. If PValue is less than 0.05 this trend is significant at 95% level of confidence This indices show that frost days will be decreasing 26.8 days in 100 years. 24

25. Climate Indices Study in Turkey 25

26. Numbers of Frost Days have been increasing mainly in Black Sea and Marmara Region. 53 stations have decreasing trend while 32 are increasing. Average decreasing is 28 days in 100 years. Although Istanbul, Elazığ, Diyarbakır and Hakkari show opposite trend with their located regions, they trends are not linear and have some breakpoint.

27. Numbers of Summer Days have been increasing all over Turkey especially northern part stations have greatest trends. Average increasing is 59 days in 100 years. Most of the trends are statistically significant at the 5% level

28. Numbers of Ice Days have been decreasing all over Turkeyexcept 6 stations. Inland stations have greatest trends. There is no ice day in the Mediterranean region. Average decreasing is 20 days in 100 years. Although Bilecik, Tekirdağ and Hakkari show opposite trend with their located regions, they trends are not significant and have some breakpoint.

29. Numbers of Tropical Nights have been increasing except Euphrates Basin. Elazığ has significant decreasing trend after Keban Dam constructed. Diyarbakır has non significant decreasing. Especially coastal stations have greatest trends. Average increasing is 47 days in 100 years. Most of the trends are statistically significant at the 5% level.

30. Global Indices Analyses From Alexander, L. et all • Locations of • temperature and • precipitation stations available for this study. The colours represent the different data sources that are used. 30

31. Trends in (a) cold nights (TN10p), (b) warm nights (TN90p), (c) cold days (TX10p) and (d) warm days (TX90p). Trends were calculated only for the grid boxes with sufficient data (at least 40 years of data. Black lines enclose regions where trends are significant at the 95% confidence of level. The red curves on the plots are non-linear trend estimates obtained by smoothing using a 21-term binomial filter. 31

32. precipitation indices • R10 in days, • R95pT (i.e. (R95p/PRCPTOT)*100) in %, • CDD • SDII 32

33. Conclusion The results show that numbers of summer days and tropical nights have been increasing all over Turkey while ice days and frost days decreasing. Summer days have increased about 6 days per decade. Most of the trends are statistically significant at the 5% level. Extreme temperature both maximum and minimum have increased at most stations. Warm days and warm nights have been increasing all over Turkey while cool days and cool nights have been decreasing. Warm spells have increased while cold spells have decreased. Diurnal temperature range has increased in most inland stations while it has decreased along coastal areas. Trends in simple daily intensity index have been increasing in most of the stations even mean annual total precipitation declined in 30 stations located in the Aegean and inland Anatolia. The number of heavy precipitations days have been increasing especially in the Black Sea and Mediterranean regions and usually cause extreme flood events. The maximum one-day and 5 days precipitation have also increased except eastern Marmara and southeast Anatolia region. Unfortunately consecutive dry days have been increasing in Aegean and Black Sea, Diyarbakır, Batman and central Anatolia while decreasing Eastern Aegean, Mediterranean and East Anatolia Region. Average increasing is 25 days in 100 years . Consecutive Wet Days have been increasing especially in Eastern part of the Marmara and around of Burdur, Nigde, Nevşehir, Sinop, Sivas, Rize and Muş but decreasing in Aegean and Konya. Average increasing is 2 and decreasing is 2 days in 100 years. In summary, in general there are large coherent patterns of warming across in the country affecting both maximum and minimum temperatures but there is a much more mixed pattern of change in precipitation. 33

34. Thank you for your attention Serhat Sensoy Chief of Climate & Climate Change DivisionVice-President of WMO CCl Turkish State Meteorological Service