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Data collation for the ENSEMBLES grid

The ENSEMBLES project focuses on producing daily high-resolution gridded observational datasets for Europe, crucial for understanding climate change and its impacts. Led by Lisette Klok from KNMI, this project collates data from multiple partners, including MeteoSwiss and the University of East Anglia, covering over 45 years of climate records. The datasets encompass essential climate variables like maximum and minimum temperatures, precipitation, snow cover, and air pressure. Quality control measures are established to ensure data homogeneity and accuracy, facilitating reliable climate change predictions.

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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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