Towards the creation of a climate database for Catalonia and Andorra (18th - 21th centuries)

1. Towards the creation of a climate database for Catalonia and Andorra (18th - 21th centuries) • Marc Prohom (1) and Pere Esteban (2) • Area of Climatology – Meteorological Service of Catalonia • Snow and Mountain Research Centre (CENMA) – Andorran Research Institute

2. The project During the last two years, the SMC has initiated a project of Identification, cataloguing and digitization of instrumental climate data from Catalan documentary sources, encompassing the period between 18th century and the present. The final goal is to create a complete and high quality database of climate series. The similar project is being initiated by the Snow and Mountain Research Centre (CENMA) in Andorra for Andorran series.

3. Metadata METADEM To identify new sources To catalogue those sources To extract the climatic information Climatic dataBDSCLIM Qualitycontrol Homogeneityanalysis Methodology To analyze the climatic documentary sources, identifying as many as possible meteorological stations or observatories, and the meteorological series associated to these sites. To create a database of the metadata for each of the sites detected: METADEM. To construct a database of the series associated to each one of the meteorological sites (temperature and precipitation): BDSCLIM. Quality control and homogeneity analysis of the series.

4. Geographicalinformation Who is/was in charge? Locationdescription (images) Observers Precipitation: period coveredinstruments used, documentary sources Temperature:period covered,screens used, instruments,units,documentarysources,... Additional information Documentarysources METADEM The metadata information of each site detected is introduced into METADEM (Database of Metadata). Only temperature and rainfall information is by now taken into account.

5. Fabra Observatory: location and tipology of the instruments during the early period (1905-1912) Engolasters WS (Andorra): location of the screen (2007) METADEM By now, the SMC and CENMA are paying attention on trying to obtain as much information as possible from those observatories with complete and good temporal coverage series.

6. Results of the quality control for the maximum and minimum temperature series of the Ebre Observatory. Quality control A quality control process has been defined, according to the bibliography, into four levels: gross errors, tolerance tests, internal consistency, temporal coherency and spatial coherency. The final homogeneity testing process will be defined following the conclusions of the COST action HOME.

7. TEMPERATURE PRECIPITATION Daily Daily Monthly Number of series per decade before the project (in grey) and after the project (in green). Period 1860-2004. First improvements of the project • New data has been detected and incorporated to the database. • Temporal coverage has been improved: for the period previous the Spanish Civil War, 150 new thermopluviometric series have been identified and 200 series has now a wider temporal coverage.

8. Meteorological sites that has a wider temporal coverage (green) Meteorological sites previous to the project New meteorological sites detected (blue) First improvements of the project • Evolution of the spatial coverage

9. Data for HOME’s benchmark • For the HOME Cost action the SMC and CENMA provides the following datasets: • Daily temperature and rainfall series from 17 sites encompassing the period 1905-2007 (although most of the series begin in 1920s), and with a good metadata and spatial coverage. • 13 daily rainfall series from a very dense area (within a same county) encompassing the period 1915-2007. • CENMA provides 3 daily temperature and rainfall series from Andorra encompassing the period 1934-2007, without any gap.

10. Analysis of Catalan, Andorran and French temperature series from the early 20th century to the present using different homogenisation approaches * M.J. Prohom (1), P. Esteban(2), M. Herrero(1), O. Mestre (3), E. Aguilar(4), F.G. Kuglitsch (5) (1) Meteorological Service of Catalonia, Area of Climatology, Barcelona Catalonia, Spain (mprohom@meteocat.com) (2) Snow and Mountain Research Center (CENMA) Andorran Research Institute. St. Julià de Lòria, Principality of Andorra (pesteban.cenma@iea.ad) (3) Météo France, Tolouse, France (olivier.mestre@meteo.fr) (4) Climate Change Research Group, Geography Unit, Universitat Rovira i Virgili, Tarragona, Spain (enric.aguilar@urv.cat) (5) Climatology and Meteorology Research Group Institute of Geography, University of Bern, Switzerland * EGU-2008. CL44. Climate data homogenization and climate trend/variability assessment

11. Framework • Cooperative effort between 5 different institutions • Homogenization of a multi-country dataset • METEOCAT AND CENMA efforts  Metadata & data rescue; qc • Comparison of different homogenization approaches • SNHT  weighted average reference series • Cassinus-Mestre  pairwise comparisons • RHTest  no references • Contribution to COST-HOME action benchmark dataset • Ongoing project

12. 17 Catalan stations • 3 Andorran stations • 11 French stations (Languedoc-Roussillon) • Although some series go back to the 1880s, 1921-2006 period was chosen for bettter comparison The dataset

13. Quality control of the dataset • 17 Catalan + 3 Andorran stations • QC applied to daily data (T) • Calendar and duplicates; gross errors; statistical limits; tn<= tx; interdiurnal differences; identical consecutive values • One entire station was dropped from the analysis • Around 12.000 individual values where flagged as suspicious; around 2/3 where validated and the remaining where corrected from original sources or set to missing • 11 French stations  QC’d monthly values where provided by Météo France

14. Automated Software by Enric Aguilar Homogeneity methods: SNHT

15. Complete diagram of detectedbreak-points for all the series: Yearly max. temp, Tx Yearly min. temp, Tn Yearly mean temp, Tm Yearly temperature range, TRG Automated method by Enric Aguilar Homogeneity methods: SNHT

16. Software by Olivier Mestre Synthesis of the detected changepoints and outliers in the Lleida Tn series (rawdata): the stations are ordered from top to bottom with respect to decreasing values of the standard errors of the residuals (STD); hence, in practice, the reliability of the comparisons increases from top to bottom ( , position of the detected changepoints in the difference series for Lleida versus the other stations; , outliers, missing years in the difference series). Vertical red stripped lines are the most likely change points. Homogeneity methods: Cassinus-Mestre

17. Regression – based Can use reference series Here applied to station data (without reference series) Lleida tn anomaly series (i.e., anomalies to the mean annual cycle of the base series) along with its multi-phase regression model fit. Software and documentation available from Xiaolan Wang and Yang Feng at http://cccma.seos.uvic.ca/ETCCDMI/software.shtml Homogeneity methods: RHTESTv2

18. Procedure • First run of each method for blind detection • Cassinus-Mestre (notice C-M needs human input)  annual • SNHT  annual • RHTest  monthly deseasonalized • Breakpoint detection and validation • Metadata • Data plots and test plots • Comparison of detected breakpoints • Final breakpoints list • New runs of C-M, SNHT, and RhTest forced with breakpoints list

19. SNHT Final listof breakpoints METADATA C-M RHTestv2 Procedure: breakpoints detection

20. Final list of break-points according toavailable METADATA and homogeneitytesting results: • Level 1: the three methods detect the break and metadata confirms the finding. • Level 2: two methods detect the break and metadata confirms the finding. • Level 3: at least two methods detect the break, but metadata does not confirm the finding. • Level 4: at least one method detects the break, and metadata confirms the finding. Procedure: final list of detected break-points

21. Most of the breakpoints detected by SNHT are also detected by CAME. The opposite is not true RHTest and SNHT breakpoints are quite different, specially on TX TX NUMBER OF DETECTED BREAKPOINTS Comparison of detected break-points

22. Good agreement for therecent period (mid-1970s to the present), whilefor the early period morerandom behaviour isreported (there are lessseries available), beingthe RHTest direct (non- forced) the method showing more discrepancies (Tx). Results: aggregated annual series

23. Results: aggregated seasonal series. Tx

24. Results: aggregated seasonal series. Tn

25. ADJUSTED SERIES. AGGREGATED Similar Different Results: differences in trends

26. VILA-SECA: INDIVIDUAL STATIONS SHOW LARGER DIFFERENCES Results: individual seasonal series.

27. Correction factors comparison: CM vs SNHT

28. DIFFERENCES IN TRENDS. Percentage of trends sharing the EQUAL sign and significance; sharing the SIGN of the estimate; DIFFerent Results: comparison of trends signal

29. CONCLUSIONS • Different homogenization methods (and different applications) can produce different breakpoints and different adjustments, leading to different trends. • This work has compared 3 widely used methods (SNHT, RHTEST, Caussinus-Mestre) • Although no assumptions can be made here about which method performs better, we have highlighted obvious differences • There is a need for extended comparison of results for detection and correction approaches • There is a need for extending this comparisons to other elements and daily data • HOME action can contribute to this effort