1. How to link large-scale circulation structures to local extremes ? Frank Selten and Deb Panja Royal Netherlands Meteorological Institute “Extreme Associated Functions: Optimally Linking Local Extremes to Large-scale Atmospheric Circulation Structures” In discussion in Atmospheric Chemistry and Physics Discussions

2. Motivation Local weather extremes are usually connected to typical large-scale circulation anomalies Examples: extreme rainfall in the UK daily mean summer temperatures in the Netherlands

3. Floods in the UK Average rainfall May – July 2007

4. Impressions British always make the best of it …..

5. UK July rainfall and Z500 anomalies

6. Teleconnection Z500

7. Daily JA temperatures in Holland Z500 anomaly

8. Motivation Local weather extremes are usually connected to typical large-scale circulation anomalies Probability of occurrence of these structures impact probability of the local extremes

9. Motivation Local weather extremes are usually connected to typical large-scale circulation anomalies Probability of occurrence of these structures impact probability of the local extremes Future probability of local extremes depends on the response of circulation to the CO2 forcing

10. Motivation Local weather extremes are usually connected to typical large-scale circulation anomalies Probability of occurrence of these structures impact probability of the local extremes Future probability of local extremes depends on the response of circulation to the CO2 forcing Uncertainties in circulation changes lead to uncertainties in local weather extremes

11. Identification of circulation structures that are optimally linked to local extremes enables: model evaluation against observations diagnosis of cause of discrepancies; maybe not due to circulation but clouds, soil-moisture or radiation deficiencies evaluation and intercomparison of simulated changes in extremes dynamical understanding of circulation changes which enhances faith in simulated changes

12. Use information on the extremes Example: July and August daily mean temperatures in De Bilt and 500 hPa geopotential height fields over the Euro- Atlantic region EOF 1 (12.8 %) EOF 2 ( 11.6 %)

13. Daily values 1958-2000 No apparent clusters by simple visual inspection …

14. Temperature anomaly ~ EOF1 Clear dependence …

15. Extreme Associated Functions Linear combinations of first L EOF amplitudes that have maximum ‘tilt’ r in scatter plot with local temperature (or wind, rain, …) Interpretation: find the pattern that for a one standard deviation change gives the largest change in the local temperature n an adjustable power to emphasize the more extreme anomalies p time average over positive anomalies only bamplitude of the new pattern

16. Two possibilities: find c’s that maximize r 2 by variational analysis: = 0 Or find the least-squares solution of the multiple linear regression problem: The solution is:

17. Comparison Linear regression T and Z500 Composite of 5 % hottest days EAF 1

18. Temperature ~ EAF 1

19. Robustness Taking only the 30% maximum temperature anomalies leads to the same EAFs Varying the power from 1 to 3 leads to qualitatively similar EAFs Choosing a smaller geographical region leads to the same EAFs

20. Other patterns ???? Test: synthetic temperature timeseries T(t) = a1(t) + a2(t)

21. EAF1 : sum of both patterns

22. Linear regression pattern Does not reproduce the original patterns as well

23. Conclusion EAFs are a robust method to link large-scale circulation structures to local extremes; all contributing patterns are sumarized into one Next application: validate climate simulations for present day and assess changes in climate scenario simulation

24. Application to simulated data “ESSENCE project: a 17 member ensemble of climate SRES A1b scenario simulations from perturbed initial conditions using the ECHAM5-MPI-OM model ” 1850195020002100 historical concentrations of GHGs and sulphate aerosols. GHG according to SRES A1b 17 simulations random perturbations in atmospheric temperatures (< 0.1 K ) initial state from pre- industrial control integration

25. Streamfunction at 500 hPa Mean Standard deviation Mean Standard deviation ERA JA 1958-2000 ESSENCE JA 1958-2000 Mean Standard deviation

26. Streamfunction 500 hPa EOFs 1 ERA ESSENCE 2

27. Streamfunction 500 hPa EOFs 3 4 ERA ESSENCE

28. Streamfunction EAFs ERA ESSENCE 1

29. T2m versus EAF1 amplitude ERA ESSENCE

30. Climate change in ESSENCE Compare 2071-2100 period with 1958-2000 Average across all 17 ensemble members

31. Temperature at 2m Mean Standard deviation ESSENCE JA climate change

32. Streamfunction at 500 hPa Mean Standard deviation

33. Streamfunction 500 hPa EOFs 1 ESSENCE 1958-2000 2 ESSENCE 2051-2100

34. ESSENCE 1958-2000 ESSENCE 2051-2100 Streamfunction 500 hPa EOFs 3 4

35. Streamfunction EAFs ESSENCE 1958-2000ESSENCE 2050-2100 1 2

36. EAF 1 Netherlands present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; mere shift of PDF present future PDF of EAF amplitudes

37. Netherlands EAF 1 Mean change included Mean change subtracted

38. EAF 1 France present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; mere shift of PDF present future PDF of EAF amplitudes

39. France EAF 1 Mean change included Mean change subtracted

40. EAF 1 Spain present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern bit changed; PDF changes present future PDF of EAF amplitudes

41. Spain EAF 1 Mean change included Mean change subtracted

42. EAF 1 Greece present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern bit changed; PDF changes present future PDF of EAF amplitudes

43. Greece EAF 1 Mean change included Mean change subtracted

44. EAF 1 Romania present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF changes present future PDF of EAF amplitudes

45. Romania EAF 1 Mean change included Mean change subtracted

46. EAF 1 Moscow present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF mere shift present future PDF of EAF amplitudes

47. Moscow EAF 1 Mean change included Mean change subtracted

48. EAF 1 Poland present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF slight change present future PDF of EAF amplitudes

49. Moscow EAF 1 Mean change included Mean change subtracted

50. EAF 1 Hamar present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF bit changed present future PDF of EAF amplitudes

51. Hamar EAF 1 Mean change included Mean change subtracted

52. EAF 1 UK present future present EAF projected on future future wrt future climate present EAF projected on future wrt future climate Pattern not changed; PDF bit changed present future PDF of EAF amplitudes

53. UK EAF 1 Mean change included Mean change subtracted

54. Conclusions EAF method is an objective, robust tool to relate local extremes to large-scale circulation structures Useful tool to evaluate climate simulations