1. Developing long-term homogenized climate Data sets Olivier Mestre Météo-France Ecole Nationale de la Météorologie Université Paul Sabatier, Toulouse

2. The introduction you ever dreamed of…

3. « State of fear » (Michael Crichton)

4. Homogenisation : why? Example of Pau temperature series  1912 : Lescar primary school  2007 : Pau-Uzein Airport

5. Pau: raw maximum temperatures (TX)

6. Homogenisation : a very old problem!  « Comptes-rendus de l’Académie Royale des Sciences » - 1703

7. Usual method: relative homogeneity PRINCIPLE : removing the climatic signal to put into evidence artificial shifts in the series minus Tested series Reference series COMPARISON series

8. Shifts detection  Dynamic programming algorithm + penalized likelihood  Multiple comparisons of non-homogeneous series  Metadata!

9. Shifts detection

10. Correction  ANOVA model : correction of multiple non-homogenous series, provided change-point positions are well known. µiµi Climate factor + Station factor + Noise j1 j2 j3 j4 j5

11. Correction Climate signal estimation + Bias estimation in the station effects (monthly scale)  Correction+reconstitution of missing data Absolutely no assumption is made concerning the evolution of the climate signal

12. Correction of Pau maximum temperatures  « Before »  « After »

13. Maximum temperatures : 1901-2000 trends  « Before »  « After »

14. Developments in Homogenisation  COST ACTION ES0601 : « Advances in HOmogenisation MEthods for climate series : an integrated approach » (HOME) http://www.homogenisation.org  Daily data homogenisation : study of extreme events

15. Requirements in terms of data digitization  Fill the gaps and complete the target series as far as possible  Quality control and homogenisation techniques require complete neighbouring series : digitize every data, not only target series!  Metadata, station histories are as important as data itself Digitize metadata along with corresponding data!