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

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Presentation transcript:

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

The introduction you ever dreamed of…

« State of fear » (Michael Crichton)

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

Pau: raw maximum temperatures (TX)

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

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

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

Shifts detection

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

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

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

Maximum temperatures : trends  « Before »  « After »

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

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!