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Developing long-term homogenized climate Data sets
Olivier Mestre
Météo-France
Ecole Nationale de la MétéorologieUniversité Paul Sabatier, Toulouse
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The introduction you ever dreamed of…
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« State of fear » (Michael Crichton)
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Homogenisation : why?Example of Pau temperature series
1912 : Lescar primary school 2007 : Pau-Uzein Airport
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Pau: raw maximum temperatures (TX)
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Homogenisation : a very old problem!
« Comptes-rendus de l’Académie Royale des Sciences » - 1703
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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
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Shifts detection
Dynamic programming algorithm + penalized likelihood Multiple comparisons of non-homogeneous series Metadata!
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Shifts detection
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Correction
ANOVA model : correction of multiple non-homogenous series, provided change-point positions are well known.
µiClimate factor
+Station factor
+Noise
j1j2
j3j4 j5
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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
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Correction of Pau maximum temperatures
« Before » « After »
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Maximum temperatures : 1901-2000 trends
« Before » « After »
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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
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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!