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Data collation for the ENSEMBLES grid

EU-FP6 project: Ensemble-based predictions of climate changes and their impacts. Data collation for the ENSEMBLES grid. Lisette Klok KNMI. Project aim – ENSEMBLES work package 5.1. Development of daily high-resolution gridded observational datasets for Europe. Overview.

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Data collation for the ENSEMBLES grid

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  1. EU-FP6 project: Ensemble-based predictions of climate changes and their impacts Data collation for the ENSEMBLES grid Lisette Klok KNMI

  2. Project aim – ENSEMBLES work package 5.1 Development of dailyhigh-resolution gridded observational datasets for Europe

  3. Overview • Background • Daily series • Quality Control/Homogeneity

  4. Background – on gridded datasets • daily values • Tmax, Tmin, P, slp, snowcover • 25 km • >45 years Spatial domain:

  5. Background – on project partners • KNMI, Albert Klein Tank & Lisette Klok • MeteoSwiss, Evelyn Zenklusen & Michael Begert • University of East Anglia, Malcolm Haylock & Phil Jones • University of Oxford, Mark New & Nynke Hofstra

  6. Background – on data availability • Daily time series (if public!): website of European Climate Assessment & Dataset http://eca.knmi.nl • Gridded datasets (in 2007): http://www.ensembles-eu.org/

  7. Daily series – data sources • ECA&D (~409 stations) • EMULATE (~78 stations) • STARDEX (~236 stations) • GCOS Surface Network (~48 stations) • Global Historical Climate Network (~645 stations) • MAP project (~110) • SYNOP data for updating the series Current status: ~1526 stations

  8. ECA&D coverage 2004 Daily series –station density

  9. Daily series – number of precipitation series

  10. Daily series – number of max temperature series

  11. Daily series – number of air pressure series

  12. Daily series – number of snow depth series

  13. anomalous values outliers inconsistencies repetitiveness Quality Control – as in ECA&D

  14. Homogeneity – example of an inhomogeneity

  15. Homogeneity – tests as in ECA&D • Wijngaard et al., 2003 • Temperature and precipitation • Absolute test • Classification: useful, doubtful, suspect

  16. Homogeneity – preliminary results

  17. …more about data quality by Evelyn Zenklusen

  18. Homogeneity - inhomogeneity Annual mean temperature and DTR, Groningen (NL) 1948: change of observation hut 1951: relocation 1959: change in sensor height

  19. Homogeneity– preliminary results • * Indices • snow day count (sd > 0) • annual mean air pressure

  20. Homogeneity - Wijngaard et al., 2003 • Three testing variables: • annual mean of daily temperature range (dtr) • annual mean of absolute day-to-day differences of dtr • wet day count (> 1 mm) • Four test methods: (1) Standard normal homogeneity test, (2) Buishand range test, (3) Pettit test, (4) Von Neumann ratio test • Classification: useful, doubtful, suspect depending on number of tests rejecting the null hypothesis (respectively 0-1,2,3)

  21. Extra Number of series • rr: 1597 • tx: 1089 • tn: 1088 • pp: 258 • sd: 129 Air pressure series: 21% does not show a trend at 5% level

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