1. The Role of Initial and Boundary Conditions for Sub-Seasonal Atmospheric Predictability Thomas Reichler Scripps Institution of Oceanography University of California San Diego La Jolla, CA (now at: NOAA-GFDL / Princeton University, Princeton NJ)

2. Outline 1.Motivation and Goal 2.Methodology 3.Predictability temporal evolution horizontal distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

3. Elements of predictability Initial conditions (ICs) Boundary conditions (BCs) Physical model

4. Goal of this study  Sub-seasonal (2 weeks to 2 months) predictability of the atmosphere = IC (weather) + BC (climate) prediction problem ICsinitially very strong, but rapid decrease in time classical predictability range: ~ 2 weeks beyond that: weak or zero IC influence!?  persistent features (e.g. blocking, major modes, stratosphere)  periodic features (e.g. MJO) BCseffects are weak, require long time averaging recent studies: mostly seasonal and longer, impacts of ENSO sub-seasonal range: relatively short averaging period  ocean & land  tropics & extratropics

5. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution spatial distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

6. Experimental Design  AGCM with prescribed SSTs  Different “qualities” of ICs and BCs, find out how important they are  Base runs observed (2x) or climatological SST continuously over many years to produce ICs for subsequent experiments  Experiments branching off from base runs 107 days: DJFM and JJAS (start on the 15 th ) 10-20 members, from perturbed ICs (breeding) 22 years (1979-2000) different combinations of ICs and BCs

7. Experiments BCICBC’IC’ ii ME ICBC  iBC  BC  0  IC  0  CC  00  ICBC-r  rean   00 experiments (IC’=0: initial conditions from base run with BC’=0)

8. Verification Strategy  verification10-member ensemble-mean of experiment against 1 member of “observation”  “observation”a. one realization of ICBC (perfect model skill) repeat 20 times and average no model errors > upper limit of predictability (this is what I mostly show) b. NCEP reanalysis (real world skill)  measure of skill correlation of geopotential spatial or temporal (year-to-year)

9. The Model NCEP seasonal forecasting model (e.g. Kanamitsu et al. 2002) originates from MRF, similar to reanalysis-2 model T42 (300km) L28 RAS Convection: Moorthi and Suarez (1992) SW: Chow (1992) LW: Chow & Suarez (1994) Clouds: Slingo (1987) Gravity wave drag: Alpert et al. (1988) 2-layer soil model: Pan & Mahrt (1987) Orography: smoothed Ozone: zonal mean climatology 10 18  extratropical tropopause

10. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution spatial distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

11. Classical predictability evolution of spatial AC for global Z500 during DJFM CC vs. CC (IC’=0, BC’=0) correlation lead time (days) correlation

12. Effects of IC’  initial condition effect has very long time scale  anomalous initial conditions (IC’) lead to prolonged predictability  possible reason: excitation of low-frequency modes by BC’ lead time (days) correlation 30 day averages IC vs. IC CC vs. CC evolution of spatial AC for global Z500 during DJFM

13. Effects of IC’ and BC’ evolution of spatial AC for NH Z500 during DJFM verified against ICBC instantaneous30 days90 days  4 weeks ICs dominate for first 4 weeks (3 weeks during ENSO, 5 weeks during neutral) lead time (days) correlation

14. Southern Hemisphere  7 weeks evolution of spatial AC for SH Z500 during DJFM verified against ICBC instantaneous30 days90 days

15. Tropics  3 weeks evolution of spatial AC of tropical Z200 during DJFM verified against ICBC instantaneous 30 days 90 days

16. Summary: Effects of IC’ and BC’ Time scale for: IC = BC

17. Effect of model uncertainty evolution of spatial AC of NH Z500 during DJFM ICBC/ICBC vs. ICBC-r/reanalysis 90 days averages = model error

18. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution horizontal distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

19. Horizontal structure I ICBC January monthly mean (week 3-6), Z500, temporal correlation temporal correlation Pacific South American region (PSA) Pacific North American region (PNA) Antarctica Tropics longitude latitude

20. Horizontal structure II ICBC iBC BC IC January monthly mean (week 3-6), Z500, temporal correlation

21. Effects of persistence persistence Z500 (Jan) ICBC IC predictability Z500 (Jan) ICBC IC persistent boundary forcing atmospheric persistence ICBC major modes Z500 (JFM) AAO SO NAO PNA NA

22. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution horizontal structure vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

23. Vertical structure I Jan Feb Mar ICBC: temporal correlations of monthly and zonal mean geopotential temporal correlation latitude height

24. Vertical structure II Jan Feb Mar ICBC IC- ICBC BC- ICBC

25. Vertical structure III: neutral ENSO Jan Feb Mar ICBC IC- ICBC BC- ICBC

26. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution spatial distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

27. Antarctic Oscillation (AAO) ICBC-B (0.81) EOF1 (59%) ICBC-A IC (0.80) BC (0.10) January, Z500

28. AAO index (Jan 1) and forecast skill (Jan) AAO index (Jan 1) El Nino La Nina ICBC (0.53) iBC (0.05) BC (-0.15) spatial AC for SH Z500 during January, verified against ICBC AAO index (Jan 1) IC (0.75)

29. Outline 1.Motivation and Background 2.Methodology 3.Predictability temporal evolution spatial distribution vertical structure 4.The initial condition effect and the Antarctic oscillation 5.Summary

30. Summary  The effects of ICs on forecast skill were detectable for ca. 8 week, were more important than BCs for ca. 4 weeks, were particularly important over Antarctica, the Tropics, and the lower stratosphere.  Regions of large skill coincided with regions of major modes.  Total skill (ICBC) can be understood as the sum of IC and BC produced skill (ICBC=BC+IC).  IC produced skill came mostly from atmospheric persistence in relationship with major modes.  Conclusion: Do not underestimate the importance of ICs for seasonal to sub-seasonal forecasts.

32. Scale variations 0-4 d 4-10 10-2020-4040-100 ICBC Saturation of spectral error energy globally, Z500, DJFM Maximum gain from ICBC IC BC m (zonal) n (total)

33. Perfect ENSO JFM Z JAN FEB MAR ICBC IC- ICBC BC- ICBC

34. Real world JFM Z JAN FEB MAR ICBC BC- ICBC

35. Perfect JAS Z JUL AUG SEP ICBC IC- ICBC BC- ICBC

36. Vertical structure II ICBC IC iBC- ICBC BC- ICBC Jan Feb Mar latitude

37. Predictability of MJO 30-70 day filtered 200 hPa velocity potential lead time (days) correlation initial conditions are crucial boundary conditions are important ~ 4 weeks

38. Real world, Z500, DJFM 30 days 90 days NH SH = model error verified against NCEP/NCAR reanalysis

39. BC IC Temporal correlation: Z500JAN (week 3-6) FEB (week 7-10) MAR (week 11-14) significant IC influence ICBC

40. BC ICBC IC ICBC Perfect world: JFM JAN FEB MAR Zonal mean temporal correlation: Z500 BC IC ICBC

41. BC ICBC IC ICBC Perfect world: JAS JUL AUG SEP Zonal mean temporal correlation: Z500 IC BC ICBC

42. BC Real world: JFM JAN FEB MAR ICBC Zonal mean temporal correlation: Z200 BC ICBC

43. AAO, JFM, perfect

44. AAO, JFM, real

45. AAO, JAS, perfect

46. AO, JFM, perfect

47. AO, JFM, real

48. AO, JAS, perfect

49. Outline I. Introduction II. Experimental Design III. Results a. Time evolution of skill and scale variations b. Regional variations and vertical structure c. Antarctic oscillation d. Tropical predictability IV. Summary

50. U850 (10N-10S) time (d) 0 107 Atl Ind W Pac Atl temporal correlation ICBC IC BC-ICBC