Potential Predictability and Extended Range Prediction of Monsoon ISO’s

1. International Asian Monsoon Symposium, Honolulu, Hawaii Potential Predictability and Extended Range Prediction of Monsoon ISO’s B.N. Goswami Centre for Atmospheric and Oceanic Sciences Indian Institute of Science, Bangalore. B.N. Goswami and Prince K. Xavier, 2003, Geophys. Res. Lett., 30(18), 1966

2. Basis for Potential Predictability of monsoon ISOs Convectively coupled ; large-scale spatial structure Rainfall (shaded, mm/day) and 850 hPa wind anomalies associated with a strong (right) and weak (left) phases of monsoon ISO.

3. Basis for Potential Predictability of monsoon ISOs Existence of low frequency quasi-periodicity Time series of daily rainfall anomaly (mm/day) over central India (blue) during 1 June – 30 Sept. for three years and 10-90 day filtered (red) rainfall.

4. What could possibly be predicted with lead time of more than 10 days? Large-scale low frequency component of intraseasonal variability (10-90 day) of rainfall • What is the usefulness of such predictions? • Would predict the dry and wet spells. Planning for sowing, harvesting, water management. • As ISOs also cluster thesynoptic variability,it would also giveprobability of occurrenceof high or low rainfall.

5. Goswami, et al. 2003, GRL Frequency distribution of genesis of low pressure systems (LPS) as a function of normalized monsoon intraseasonal index (MISI) based on 40 years of data

6. Spatial clustering of tracks of LPS during active ISO phase, MISI > +1, during 1954-1983. Few LPS and their tracks during weak (‘break’) ISO phase, MISI <-1, during 1954-1983.

7. Long term seasonal mean (JJAS) winds at 850 hPa (m/s) and associated relative vorticity (10-6 s-1) Active - Weak composite wind anomalies at 850 hPa (m/s) and associated relative vorticity (10-6 s-1) based on 40 year (1954-1993)

8. How can we make an estimate of potential predictability for active and break conditions from observations? • A simple procedure is described to make such an estimate from observations

9. Data Used • Daily rainfall over Indian continent from rain gauge stations (1971-1989) • CMAP pentad data (linearly interpolated to daily values) , 1979-2001 • NCEP/NCAR Reanalysis daily winds ; 1979 – 2001 • NOAA daily OLR ; 1979-2001 • 10-90 day band-pass Lanczos filter is used to isolate ISO

10. Regions over which potential predictability of precipitation is examined

11. 10-90 day filtered precipitation (CMAP) averaged over Box I normalized by its own standard deviation shown here for 10 summers (1 June- 30 Sept.). Blue circles  peak wet spells (active conditions); red squares  peak dry spells (break in monsoon).

12. CMAP Box-I 2 2 2

13. Same as the previous figure (a), but for the precipitation averaged over the eastern equatorial Indian ocean Box II Same as in (a) but for rainfall data from rain gauge stations averaged over monsoon trough region.

14. Same as in (a) but for relative vorticity averaged over the monsoon trough (70E-90E, 15N-25N). Active and break dates were taken from precipitation. Same as (a) but for Zonal winds at 850 hPa averaged over 80E-95E, 12N-18N. Active and break dates were taken from precipitation. Thus, the differences in divergence between transitions from active to break and break to active is similar in circulation and rainfall.

15. Conclusion: Predictability The transition from break to active conditions is intrinsically more chaotic than transitions from active to break conditions. A fundamental property of monsoon ISOs. Why? Break Active – convective instability—fast error growth ActiveBreak – dying convection -- slow error growth due to slow oscillation Consequence, The potential predictability limit for monsoon breaks is about 20 days while that for monsoon active conditions is only about 10 days

16. Empirical Extended Range Prediction of Monsoon ISOs

17. Lo and Hendon (2000) Mon. Wea. Rev., 128, 2528–2543. The prediction of MJO in the OLR and 200 hPa streamfunction was attempted by using a simple multiple linear regression model. The predictors were OLR and 200 hPa streamfunction themselves. The predictants were two leading Principal Components (PC) of OLR and three leading PCs of 200 hPa streamfunction. Skillful forecasts of the MJO in OLR and 200 hPa streamfunction were achieved out to about 15 days. The model performed well when the MJO is active at the initial condition but not so well when it is inactive. They also found that the empirical forecasts were better than the DERFs for lead times longer than one week.

18. First two EOFs of CMAP pentad (interpolated to daily) rainfall for JJAS (1979-2001)

19. Predictors PC1-4 of 10-90 day filtered CMAP PC1-2 of Surface Pressure Predictants PC1-4 of filtered rainfall Predicted rainfall at lead time 

20. Model developed on 1 June-30 Sept. data for 1979-1995 Model is tested on independent data for 1996-2001 15-day predictions and verifications of rain anoms ave(70E-90E)

22. Mean of 18-day predictions of breaks (mm/day) Mean of 57 verifications of the 18-day predictions from CMAP (mm/day) (Mean of an ensemble of 57 such predictions starting from initial conditions around active conditions)

23. Mean of 18-day predictions of Active conditions (mm/day) Mean of an ensemble of 54 such predictions starting from initial conditions around active conditions Mean of 54 verifications of the 18-day predictions from CMAP (mm/day)

24. Correlation between 18-day predictions of active conditions starting from break conditions with corresponding verifications (n=54) Correlation between 18-day predictions of breaks starting from active conditions with corresponding verifications (n=57).

25. ------------------------------------------------------------------------------------------------------------------------------------------ Lead Time Prediction Prediction of Breaks of Active --------------------------------------------------------------------- 15 days 0.65** 0.38* 18 days 0.56** 0.43* ---------------------------------------------------------------------- Correlations between predictions and observations of rainfall averaged over the monsoon trough region (70E-85E, 10N-22N)

26. 18-day predictions of rainfall over the monsoon trough (red) together with actual observations (blue) for the period June 1-Sept. 30, of 2000 and 2001.

27. Conclusion The model demonstrates useful skill of prediction of breaks up to 18 days in advance. However, the useful skill for prediction of active conditions is limited to about 10 days. Scope for improvement: preparation for real-time prediction employ different method of empirical prediction

30. Table 1. Errors and Skill of model forecasts and persistence of anomalies averaged over 70-85E, 10-22N.

31. 15-day predictions and verifications of rain anoms ave(70-90E)

34. Empirical prediction of dry and wet spells of rain The multiple linear regression model i - Regression coefficients  - Lead time N - Number of Predictors

35. Predictors PC1-4 of 10-90 day filtered CMAP PC1-2 of Surface Pressure Predictants PC1-4 of filtered rainfall Predictors are added one by one and the model gives optimum performance with the above 6 predictors The model is developed on 17 monsoon seasons (1979-1995) and they are tested on the next 5 years (1996-2001).

36. Having generated the predicted values of the first four PCs of rainfall, the predicted rainfall anomalies (P) are constructed using Where, PC is the Principal components E is the EOFs