1. IPILPS Workshop ANSTO 18-22 April 2005 IPILPS Forcing & REMOiso Performance by Dr Matt Fischer and Kristof Sturm

2. Topics History ECHAM Forcing Variables Performance - Europe - South America - Australia FAR interpolation

3. History PILPS, 8 forcing variables: 2 radiation, pressure, 2 wind, temp., r’fall, humidity IPILPS, where to get isotope var’s from? Observations?: high resolution data rare, & no information on spatial variance Models? - MUGCM - GISS - ECHAM - REMOiso Resolution ? Climate ? Isotopes ? Locations ?

4. ECHAM ECMWF model, aimed at climate simulations ECHAMiso: Spectral resolution : T30, i.e. 3.75º (~ 450 km) Vertical resolution: 19 levels REMOiso - RCM nested in ECHAMiso, run in Europe & South America

5. List

7. Forcing VariablesForcing Variables

8. Tumbarumba EQY1: Radiation List

9. Tumbarumba EQY1: Rainfall

10. Tumbarumba BC24: variance

12. REMOiso was run for 4 years (except Manaus), at =< 5 minute resolution EQY1 Equilibrium experiment BC24 experiment FAR interpolation (for Manaus) REMOiso

13. Topics History ECHAM Forcing Variables Performance - Europe - South America - Australia FAR interpolation

14. Locations Mid-latitude deciduous forest eg. Munich 48°N 16°E Tropical rainforest eg. Manaus 3°S 60°W Mid-latitude eucalypt forest eg.Tumbarumba 35°S 148°E

15. Nordeney  18 O from April 97 to Feb 99  18 O  18 O as good, or bad, as precipitation amounts (Sturm et al., 2004)  18 O Obs REMO Europe

16. Europe

17. South America 5 minute simulations, written for 25 cells: Manaus x9 Zongo x9 Rocafuerte x9 Belem x1 Izobamba x1

18.  18 O in precipitation

19. Manaus

20. Australia First experiment for this domain GNIP Stations: Perth, Darwin, Alice Springs, Brisbane, Adelaide, Cape Grim, Melbourne Tumbarumba x9 Padthaway x9

21.  18 O Australia January

22. July  18 O Australia

23. Temperature

24. Rainfall

25. Rainfall

26.  18 O

27. D-excess

28. Summary so far... REMOiso simulations of Europe and South America compare with data from GNIP stations and general climatology REMOiso simulations of Australia are too wet or dry for some sites, but generally compare with GNIP data, excpet perhaps for D-excess

29. Topics History ECHAM Forcing Variables Performance - Europe - South America - Australia FAR interpolation

30. Functional autoregression (FAR) y t =  y t-1 +  x t +  t but, we can replace x & y by orthonormal vector subsets. y is 5 minute data, x is 6 hour data Estimation: of the parameters , ,  ( ,  ) using method of Damon & Guillas 2001 Interpolation: Eigenvectors of x & y are ‘stretched & pulled’ over a new set of 6 hour data to form a new set of 5 min data. Applications: NCEP

31. Temperature & Wind FAR 5 min. Linear 5 m. Original 5 m.

32. Temperature & Wind

33. FAR 5 min. Linear 5 m. Original 5 m.

34. Rainfall FAR 5 min. Linear 5 m. Original 5 m.

35. Rainfall

36. Rainfall FAR 5 min. Linear 5 m. Original 5 m.

37. Future : more comparisons! REMOiso comparison of: the stochastic properties of storms (duration, intensity, rainout) with Australian BOM data (40 yrs of 6 minute rainfall data, Capital cities only) Isotopic rainout of individual storms, for BOM data this is an inverse problem using storm data + monthly GNIP data

38. Conclusions REMOiso, a regional climate model, produces monthly isotope values in 3 domains which compare with GNIP data, except, perhaps for D-excess FAR can be used to downscale observational data; and is based on functional relationships between different timescales, calibrated with an isotope model.

39. model

40. FAR FAR 5 min. Linear 5 m. Original 5 m.

41. FAR FAR 5 min. Linear 5 m. Original 5 m.

43. ‘ List