1. Predictability and predictions of Indian summer Monsoon rainfall using dynamical models Michael K. Tippett (IRI), Andrew Robertson (IRI), Makarand Kulkarni (IIT), O.P. Sreejith (IMD), Palash Sinha (IIT), Kripan Ghosh, Dave DeWitt (IRI), Donna Lee(IRI)

2. Outline Predictable components (S/N EOFs) of CFS JJAS Asian Indo-Pacific precipitation (Liang et al 2009) –Modified calculation procedure –Diagnose S/N EOFs Compare to: –ECHAM 4.5 forced with CFS SST. –ECHAM 4.5 forced CFS Pacific SST, MLM elsewhere.

3. S/N EOF calculation Analysis similar to Liang et al. 2009 –May forecasts of JJAS 1982-2008 Total (Ens. Member) = Signal (Ens. Mean) + Noise. Find components that maximize –signal to noise ratio Express predictable component in basis of noise EOFs –signal to total variance ratio (used here) Express predictable component in basis of total EOFs Calculation requires an EOF expansion and truncation

4. S/N depends on number of EOFs Each curve uses different # of EOFs S/N increases as more EOFs are included. Significance? F-test is “univariate” Monte Carlo is multivariate ~3 “significant” predictable patterns

5. How to make the S/N EOF calculation more objective? S/N EOFs = CCA –between ensemble members and ensemble mean CCA = regression –predicting ensemble members from ensemble mean EOFs in regression model = EOFS in S/N EOF calculation –Pick EOF truncation that maximizes cross-validated prediction skill 3 S/N EOFs for JJAS CFS precipitation.

6. S/N EOF 1(ENSO)

8. S/N EOF 2 (ENSO-1)

9. S/N EOF 3 (IOD)

11. CFS skill and perfect model predictability India average Correlation with observations = 0.47 Perfect model correlation = 0.48 Obs = CPC Merged Analysis Perfect model skillReal skill

12. ECHAM 4.5 forced with CFS SST

13. S/N EOF 1 (ENSO) Correlation with S/N EOF PC 1

14. S/N EOF 2 (ENSO-1) Correlation with S/N EOF PC 2

15. S/N EOF 3 (IOD) Correlation with S/N EOF PC 3

16. SST-forced ECHAM 4.5 skill and perfect model predictability India average Correlation with observations = 0.15 Perfect model correlation = 0.67 Obs = CPC Merged Analysis Real skillPerfect model skill

17. ECHAM-GML (DeWitt & Lee) Atmosphere ECHAM v4.5 T42grid Ocean Mixed Layer Observed damped persistence Prescribed with CFS SST forecasts

18. S/N EOF 1 (ENSO) Correlation with S/N EOF PC 1

19. S/N EOF 2 (ENSO-1) Correlation with S/N EOF PC 2

20. S/N EOF 3 (IOD) Correlation with S/N EOF PC 3

21. CFS skill and perfect model predictability India average Correlation with observations = 0.55 Perfect model correlation = 0.73 Obs = CPC Merged Analysis Real skillPerfect model skill

22. Summary An improvement in S/N EOF calculation objectivity Find 3 predictable components in the CFS JJAS precipitation –ENSO –ENSO-1, warm Indian ocean –Indian ocean dipole Coupled model precipitation response differs from atmosphere-only and atmosphere+mixed layer, particularly over land Coupled model perfect model predictability resembles observed skill. Models being used in real-time as part of the Extended Range Forecasting and Agricultural Risk Management project.