Developing long-term homogenized climate Data sets

Olivier Mestre

Météo-France

Ecole Nationale de la MétéorologieUniversité 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 » - 1703

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.

µiClimate factor

+Station factor

+Noise

j1j2

j3j4 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 : 1901-2000 trends

« Before » « After »

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

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!