1. User experience with extended range forecasts -- climatic aspects of ECMWF products Christof Appenzeller Wolfgang Müller Heike Kunz Mark Liniger ERA-40 compared to Swiss observations Verification of seas. forecasts monthly forecasts in electricity market seas. forecasts in media

2. 2 ERA-40 T2 Upscaling Z ERA-40 - Z Schw.Stat. ~ 40m

3. 3 ERA-40 T2 seasonal Cycle Temperature[°C] Temperature: ERA-40, Swiss Stations 16 14 12 10 8 6 4 2 0 - 2 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC T[°C] Temperature difference: ERA-40 – Swiss Stations 1.2 0.8 0.4 0.0 -0.4 -0.8 JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ERA-40 in Summer 0.8°C too high ERA-40 in Winter 0.4°C too low

4. 4 ERA-40 & GPS IWV: Correlation Morland et al., submitted

5. 5 IWV: Height dependent bias Morland et al., submitted

6. 6 System 2: Climate variability ERA-40 1959 - 2001 System 2 Nov. Fcsts. 1987 - 2001 Müller et al. (2005), Clim. Dyn. DJF North Atlantic Oscillation (NAO) EOF Z500

7. 7 Predictability of NAO Müller et al. (2005), Clim. Dyn. regression of Z500 fields

8. 8 Impact of climate variability ERA-40 1959 - 2001 System 2 Nov. Fcsts. 1987 - 2001 Müller et al. (2005), Clim. Dyn. DJF North Atlantic Oscillation (NAO) SVD Z500 & T2m

9. 9 Predictability of NAO Impact Müller et al. (2005), Clim. Dyn. regression of T2m fields

10. 10 Verification grid point RPSSd T2m, 1987 – 2002, fc234, 3 cat. Schwierz et al., subm. against ERA-40 Perfect model approach

11. 11 extended range forecast products at MeteoSwiss seasonal forecasts T2m, Precip. Klimagramms probability maps monthly forecasts T2m, Precip, Geopot. tercile maps Klimagramm

12. 12 Weather risk application months temperature anomaly Seasonal Forecast Probabilistic Downscaling local weather risk

13. 13 Electricity Market Electricity company in Southern Switzerland Monthly forecasts

14. 14 Using monthly forecasts Contracts at EEX (European Energy Exchange) Lowermost tercile for S-Germany (spatial avg). EEX Eu/MWh lowermost tercile % Lead time Contracts for peak load in week 12 (21.-27.3.2005) T2 Zurich persistence? other forecasts? ?

15. 15 Summer 2005 in Switzerland Probability T2m > norm Klimagramm for Swiss-Temperature

16. 16 Media Basis MeteoSwiss does not intend to publish seasonal forecasts for Switzerland. Public presentation of Swiss climate research programme. Interview Probability of Temperature in JJA > norm. Signal: 40-50%. Norm value: 17 o C. (JJA daily avg). Hit rate around 55 to 60%. Ocean information is important. Published Headline: MeteoSwiss: Summer will be cool 40% used as signal. Hit rate compared to skill of 84% for next day forecast. Map of current buoys as basis for forecasts.

17. 17 same and next days: prime time news on national television (French and German). several radio interviews. copied by several news paper (without feedback). scientific facts and context somewhat distorted and wrongly quoted. main message: cool summer

18. 18

19. 19 Competing Sunday Press Journal Front pageSummer 05: Weather-forecast destroys tourism! Tourism directors criticize meteorologists HeadlineWeather forecasters drive away tourists Short street interviews people would use seasonal forecasts for holiday planning. Tourism industry: Other countries would never have the idea to talk badly about their own products Forecasts are reason for low earnings in tourism industry ?! Would the forecasts predict a nice summer, we would be happy

20. 20 Conclusions for handling press JJA average of 17 o C is cool. Give clear numbers, no ranges/choices extremes will be chosen Available actual forecasts will be published and used. A scientific context or experimental label can get lost. Observations are preferred to models as technological basis for forecasts. Should seasonal forecasts be published for the public? if yes, how? Bulletin, press release, selected interviews, individually on request Tourism industry: stronger fluctuations due to weather and climate forecasts? Tourists (would) use forecasts for booking, industry seems not to.