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Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS )

Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS ). Aguilar, E., Brunet, M., Sigró, J. Climate Change Research Group, Universitat Rovira i Virgili, Tarragona, Spain. MOTIVATION.

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Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS )

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  1. Different approaches for the homogenisation of the Spanish Daily Temperature Series (SDATS) Aguilar, E., Brunet, M., Sigró, J. Climate Change Research Group, Universitat Rovira i Virgili, Tarragona, Spain SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  2. MOTIVATION • SDATS dataset included only the “longest and most reliable series”, leading to a low density network • CCRG is involved in a coordinated project (EXPICA) that wants to relate temperature and precipitation extrems to circulation patterns over the Iberian Peninsula • Can our current homogenization procedure for daily data feed temperatures to EXPICA? • Can we apply other procedures with the current network? (i.e. HOM) • Do we have to expand it? • CAFIDEXPI subproject  re-homogenization on a daily bases of SDATS and calculation of extreme indices SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  3. Spanish Daily Temperature Series • 22 Stations • Unevenly distributed across Spain SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  4. HOMOGENIZATION STEPS QCd daily data of TMax and TMin Calculation of Monthly Values of TMax and TMin Screen Bias Minimisation over monthly series of TMax and TMin Blind break-point detection over annual, seasonal TMax, Tmin, Tmean with automated SNHT (1997) Breakpoint validation (metadata, plot checks, …) Application to monthly Tmax and Tmin (As described in Aguilar et al, 2002) Generation of correction pattern Monthly, Seasonal, Annual Tmax, Tmin, DTR, TMean Series (STS) Validation of daily corrected values Interpolation to daily data (Vincent et al., 2002) SDTS SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  5. SCREEN BIAS MINIMIZATION Large effect on TMax Much smaller effect on TMin CCRG’s SCREEN project (CICYT)  2 replicas of Montsouris Screen, on operation since 2003 SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  6. SCREEN BIAS MINIMIZATION New Estimation (Murcia): TMaxStev = -0.508 + TMaxMont*0.975 SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  7. The homogenization methods. SNHT Automated Software by Enric Aguilar. Available under request SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  8. INTERPOLATION TO DAILY DATA SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  9. THE HOM METHOD CONCEPT • 1) DEFINE HSPs for the candidates and reference stations • 2) Identify highly correlated ref station that overlaps HSP1 and HSP2 of the reference • 3) Model (LOESS) the relations in HSP1 • 4) Predict the temperature at the candidate in HSP2 using observations from the reference series in HSP2 • 5) Create a paired difference between predicted and observed temperatures in HSP2 • 6) Find the probability distribution (L-Moments, 6 distributions) of the candidate in HSP1 and HSP2 • 7) Bin each difference in 5) according to the associated predicted temperature according the distribution of HSP1 • 8) Fit a smoothly varying function between the binned differences to obtain adjustments for each percentile • 9) Using the probability distribution of the candidate in HSP2 , determine the percentile of each observation and adjust accordingly to the value obtained in 8) SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  10. SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  11. PRELIMINARY APPLICATION OF HOM METHOD TO LA CORUÑA, MADRID, MURCIA • We compare the results obtained with CCRG procedure with the HOM method • HOM is applied to raw data (with no screen adjustments) using the breakpoints detected through the CCRG’s procedure. • We use 3 series: Madrid, Murcia and La Coruña, analyzing the impacts of the different approaches over annual trends in TMIN and TMAX and on four extreme indices: warm days (TX90p); cold days (TX10p), warm nights (TN90p) and cold nights (TN10p SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  12. LA CORUÑA • The method cannot be applied to this station with the current dataset • Correlations with other series are too low • Best candidates do not have overlapping HSPs. For example, San Sebastian • Introduction of new stations (Gijón, Oviedo, shorter Galician stations) should improve this situation SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  13. MADRID • Changes in screen around 1893  can HOM capture this kind of problems? • Artificial trend (urban) between 1893 and 1960  this can be a problem for HOM, as we’re modelling HSPs and 1893-1960 won’t be exactly an HSP. To try to tackle this we are using to schemes for Madrid • 1893,1960 • -1893, 1920,1940 (understanding the urban trend as a succession of same sign shifts) • Jump in 1960 SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  14. Black = raw; Red CCRG; Blue HOM-1break; Green HOM-3breaks SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  15. Model and CDF. Inhomogeneity in 1893. HOM-1break. TMAX. August. Larger values are evident in HSP2 (pre-1893) represented by dashed lines. The adjustments capture this jump SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  16. Model and CDF. Inhomogeneity in 1893. HOM-1break. TMAX. April Change in variance and in mean. Lower percentiles need more correction than upper percentiles. Is this what we should expect from the source of inhomogeneity we know (i.e. change in screen)? SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  17. SOMETHING I’VE HIDDING FROM YOU! • Reference chosen among the available stations with a reasonable number of pairs and a reasonable correlation: • Reference for April is Badajoz • Reference for August is Cádiz (!) • This is far from optimum; there is little chance to find closer neighbors for this part of the record… SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  18. Trends for annual TMAX compared to trends from CCRG original approach(bold italic, different sign of point estimate; bold different sign in the confidence interval) SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  19. Same for TX90p SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  20. Same for TX10p SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  21. MURCIA • Murcia presents a change in SCREEN around 1912 • And relocations • 1939 • 1954 • 1984 SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  22. Annual values derived from daily homogenized data. Black lines: original data; red lines: CCRG procedure (correcting change of screen in 1912 and relocations in 1939, 1954 and 1984); green lines HOM adjustments using 1863-1912; 1913-1939; 1940-1954 and 1955-2006 as HSPs. Notice the excellent agreement between methods in the highlithed area of the plot SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  23. ADJUSTMENTS FOR MURCIA. Break 1984. May (USING ALICANTE, now this is good!!) Wide range of adjustments; from slightly negative to about +1ºC in the higher percentiles SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  24. Histograms of differences between CCRG adjustments and ORIGinal data (left); HOM adjustments and ORIginal data (center) and CCRG and HOM adjustments (right) for different months (rows). Due the nature of the two sets of adjustments, notice a largest gamma of adjustment values when HOM is implied in the differencing. The pairs of series, show significant changes in variance. SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

  25. CONCLUSIONS AND FUTURE WORK • There is a strong consensus about the need of improving the homogenization of climatological time series, specially on daily and sub-daily scales The CCRG has been homogenizing daily values using an effective combination of an adapted version of SNHT + interpolation of monthly factors to daily values • The HOM method provides a powerful tool to adjust daily datasets accounting for Higher Order Moments inhomogeneities • Although HOM method and CCRG procedures can show very similar adjustments when annual values are re-computed from homogenized daily values, in some ocasions adjustments can show large differences. This differences – enlarged when seasonal or monthly series are analyzed, can be partially attributed to the lack of good references to produces overlapping HSPs or – in other cases – to non identified breakpoints. But they could also derive from the larger range of corrections applied to daily values for each month • In the near future, several projects by the CCRG – specially the CAFIDEXPI (Changes in Frequency Intensity and Duration of EXtremes in the Iberian Peninsula) and CLICAL - will introduce new series to SDATS for the compilation of a new version of. The application HOM method – when applicable – will continue to be explored. SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING

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