1. Monthly Air Temperature Homogenization over France An example in department Vendée Anne – Marie WIECZOREK METEO – FRANCE

2. Context : Homogenization  First Homogenization 1901–2000 –O. Mestre (Thesis) 70 Temperature series – 1901-2000 Trends : 0,07 to 0,11 °C/decade  Work extended to other parameters –M. Schneider : Pmer 1901-2000 (25) –C. Canellas : Insolation 1931-2000 (20) –Students works : RR 1901-2000 (300)  But numerous non covered areas Mean temperature Trends (1901-2000)

3. Context : Period and area gaps  Homogenized 70 Temperature Series –Gaps : World War (1914 – 1918 & 1939 – 1945) 37 non covered dept on 95 ~ 39% Graph of series number function of Percentage of missing data in 1901-2000 period Spatial Coverture (1901-2000)

4. Context : Now  New Homogenization Program –Obtain ~ 200 Tp series over France (~ 2 stations /departement) Period : 2° half 20° century (1950-2007) More homogenous coverture More control (data quality) Series with less than 5% missing values –Example in non covered area : Vendée  Data & Metadata (rescue program in connection with –Most of data series begin in 1959 in the BDCLIM database –Metadata aim to validate shifts in original series

5. Overview  Methodology of Caussinus –Mestre Technique  Choice of the climatic « homogenous » area (Vendée) and choice of stations with control  Break Detection and homogenization  Homogenization Results : Annual and Seasonal Trends  Conclusion

6. Methodology  Mestre – Caussinus method : –Treat an unknow number of breaks –gaussian noise –Break detected in series of Tp difference (comparison 2 by 2 stations)  Iterative processus shift detection – data correction –Shifts validated by metadatas –Data Correction with surrounding stations series correlated at minimum 0.8 correction depends of selected stations –Climatologist expertise to aim in good homogenization results  Final homogenization

7. Overview  Methodology of Caussinus –Mestre Technique  Choice of the climatic « homogenous » area (Vendée) and choice of stations with control  Break Detection and homogenization  Homogenization Results : Annual and Seasonal Trends  Conclusion

8. Vendee example : Choice of stations  Parameters: monthly Tn & Tx –Tn : 1952-2007 –Tx : 1951-2007  Choice of stations –Data beginning in 1950-1952 (17 candidate stations) via database BDCLIM –Concatenation to build up « La Rochelle » series Aerod – 1950-1954 Bout Blanc – 1955-today  Quality Control –pb data quality in 1950-1960 –only 9 stations retained

9. Vendee Example : Data Control  Example of data control in Sainte Gemme la Plaine on Tx A  Temporal Control  Spatial Control Realized on annual mean anomalies

10. Vendee Example : Break/Shift Detection  Example of 2 x 2 stations comparison : La Mothe Achard with Bouguenais on Tx

11. Overview  Methodology of Caussinus –Mestre Technique  Choice of the climatic « homogenous » area (Vendée) and choice of stations with control  Break Detection and homogenization  Homogenization Results : Annual and Seasonal Trends  Conclusion

12. Vendee Example  Break detection : La Mothe Achard  After homogenization Shelterchanging automatisation

13. Vendee Example  Break detection : La Rochelle  After homogenization Concatenationhees cut

14. Vendee Example : Final Results  Mean rupture number per station (1952-2007) –2,2for Tn –2for Tx  ~ 1 shift on 2 is validated by metadatas –Shelterchanging –Deplacement –Automation  < 5% missing or reconstructed data  best control quality except 1950 – 1960

15. Overview  Methodology of Caussinus –Mestre Technique  Choice of the climatic « homogenous » area (Vendée) and choice of stations with control  Break Detection and homogenization  Homogenization Results : Annual and Seasonal Trends  Conclusion

16. Annual Temperature Trend Analysis Tn Tx OriginalHomogenized 0,42°C/decade 0,24°C/decade 0,07°C/decade0,28°C/decade Acceleration of Warming

17. Annual Temperature Trend Analysis  Homogenization seems to be easier for Tx than for Tn  Good comparison with other homogenization works done in the West of France  Grad is East –West (Land-Ocean) for Tn, opposite (West – East) for Tx

18. Seasonal Temperature Trend Analysis Summer & Winter Trends

19.  Seasonal trend values are higher than the annual ones but theirs values depend little bit on choice of stations  Grad is still the same as the annual, except Winter where there is an additional grad(T) oriented N-S relative to latitude of the station  Seasonal trend values (most Summer) seem to be very high :  So, we have less confidence for seasonal trends than the annual ones

20. Conclusion  2° half century homogenization is encouraging  But metadatas (most lacking) are essential to validate series shifts. A data rescue program is still active. Great efforts in departemental stations to collect the metadatas  In 2008, 1/2 France coverture will be homogenized for the period 1950-2007

21. Conclusion  Annual trends in 1951 – 2007 (Vendée): –[ 0,22; 0,27] °C/decade for homog Tn –[0.25; 0.33] °C/decade for homog Tx –Values consistant to other works and are more than twice as high as the 1901-2000 (0.07 to 0.11).  Acceleration of Warming is observed after 1985  Seasonal trends must be treated with carefulness (data quality) : control has been done on annual values, not monthly values (which can be erroneous)  For Vendée study, homogenization is very efficient and seems easier for Tx than for Tn.

22. Thank you for your attention Any questions ?