1. The observational dataset most RT’s are waiting for: the WP5.1 daily high-resolution gridded datasets HadGHCND – daily Tmax Caesar et al., 2001 GPCC - monthly precipitation Rudolf, 2005 MARS – daily Tmin Genovese, 2001

2. Why? …developing daily high-resolution gridded observational datasets for Europe? 1. Evaluation of the ENSEMBLES simulation/prediction system 2. Scenario construction 3. Impact assessment 4. Analysis of climate extremes

3. 1. KNMI, Lisette Klok & Albert Klein Tank 2. MeteoSwiss, Evelyn Zenklusen & Michael Begert 3. University of East Anglia, Malcolm Haylock & Phil Jones 4. University of Oxford, Nynke Hofstra & Mark New Project partners

4. Overview Variables and grid Stations and series Homogeneity Interpolation Data availability

5. Variables and grid Domain daily observations Tmax, Tmin, Tmean, RR, mslp, snow depth regular 0.25 degree grid and/or an equal area grid 1960-2004 or present

6. Station locations ECA&D coverage 2004 Data sources: ECA&D EMULATE STARDEX GSN GHCN - daily MAP project MARS 2033 stations

7. Series 1831 precipitation 1384 Tmax 1388 Tmin 1244 Tmean 317 mslp 180 snow depth  quality controlled  updated with SYNOP data

8. Homogeneity – results of the absolute test following the method of Wijngaard et al., 2004 Homogeneous for periods > 10 years absolute test Homogeneous for periods > 40 years absolute test Precipitation78%35% Temperature64%20% Air pressure80%41% Snow depth89%77%

9. Homogeneity – results of the relative test Vera-QC (Begert et al., in preparation) Only complete series Period: 1960-2000 Number of break- points detected: - 0( ) - 1( ) - 2( ) - 3( ) - >4( ) - undefined ( ) mean temperature precipitation

10. Homogeneity – results of the relative test Homogeneous for periods > 10 years absolute test Homogeneous for periods > 40 years absolute test Homogeneous over 1960 – 2000 relative test Precipitation78%35%32% Temperature64%20%Tmean: 23% Tmax: 22% Tmin: 12% Air pressure80%41%9% Snow depth89%77%-

11. Homogeneity – results of the relative test [°C] Frequency distribution of shift dimensions for temperature

12. Interpolation methods Natural Neighbour Interpolation Angular Distance Weighting Thin Plate Splines Kriging Conditional Interpolation (only rainfall)

13. Selection of best method and validation Cross validation Remove one station and interpolate to location of that station Compare results with observed values and calculate skill scores (e.g. RMSE, LEPS) Comparison with grids from high resolution station series E.g. UK 5*5 km rainfall, Switzerland rainfall and Norway Compare results with gridded datasets and calculate skill scores

14. Interpolation results LEPS skill scores averaged across all methods

15. Uncertainties in the interpolation results Still looking for appropriate method to determine uncertainties Will be in the form of uncertainty bands around the interpolated value Different uncertainty bands for every grid for every day

16. Data availability Gridded datasets (September 2007): http://www.ensembles-eu.org/ >> RT5 site Daily station series (if public!): http://eca.knmi.nl