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

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

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

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 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 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 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 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 Model and CDF. Inhomogeneity in 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 Model and CDF. Inhomogeneity in 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 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 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) FIRST YEARSERIESLTVTESUTV 1854MADRID_CCRG_ANNUAL_MAX MADRID_HOMA_ANNUAL_MAX MADRID_HOMB_ANNUAL_MAX MADRID_ORIG_ANNUAL_MAX MADRID_CCRG_ANNUAL_MAX MADRID_HOMA_ANNUAL_MAX MADRID_HOMB_ANNUAL_MAX MADRID_ORIG_ANNUAL_MAX MADRID_CCRG_ANNUAL_MAX MADRID_HOMA_ANNUAL_MAX MADRID_HOMB_ANNUAL_MAX MADRID_ORIG_ANNUAL_MAX MADRID_CCRG_ANNUAL_MAX MADRID_HOMA_ANNUAL_MAX MADRID_HOMB_ANNUAL_MAX MADRID_ORIG_ANNUAL_MAX

SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING Same for TX90p FIRST YEARSERIESLTVTESUTV 1854MADRID_CCRG_TX90p MADRID_HOMA_TX90p MADRID_HOMB_TX90p MADRID_ORIG_TX90p MADRID_CCRG_TX90p MADRID_HOMA_TX90p MADRID_HOMB_TX90p MADRID_ORIG_TX90p MADRID_CCRG_TX90p MADRID_HOMA_TX90p MADRID_HOMB_TX90p MADRID_ORIG_TX90p MADRID_CCRG_TX90p MADRID_HOMA_TX90p MADRID_HOMB_TX90p MADRID_ORIG_TX90p

SIXTH SEMINAR FOR HOMOGENIZATION AND QUALITY CONTROL IN CLIMATOLOGICAL DATABASES AND COST ES-0601 “HOME” ACTION MANAGEMENT COMMITTEE AND WORKING GROUPS MEETING Same for TX10p FIRST YEARSERIESLTVTESUTV 1854MADRID_CCRG_TX10p MADRID_HOMA_TX10p MADRID_HOMB_TX10p MADRID_ORIG_TX10p MADRID_CCRG_TX10p MADRID_HOMA_TX10p MADRID_HOMB_TX10p MADRID_ORIG_TX10p MADRID_CCRG_TX10p MADRID_HOMA_TX10p MADRID_HOMB_TX10p MADRID_ORIG_TX10p MADRID_CCRG_TX10p MADRID_HOMA_TX10p MADRID_HOMB_TX10p MADRID_ORIG_TX10p

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