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Potential Predictability and Extended Range Prediction of Monsoon ISO’s

INDOCLIM-February 23-27, 2004 Pune, India. Potential Predictability and Extended Range Prediction of Monsoon ISO’s. Prince K. Xavier Centre for Atmospheric and Oceanic Sciences Indian Institute of Science, Bangalore.

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Potential Predictability and Extended Range Prediction of Monsoon ISO’s

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  1. INDOCLIM-February 23-27, 2004 Pune, India Potential Predictability and Extended Range Prediction of Monsoon ISO’s Prince K. Xavier 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. Prediction of active and break phases of Indian summer monsoon are important in agricultural planning and water management. What is the basis for predictability of intraseasonal variability? Large scale convectively coupled nature Quasi periodicity

  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. We make a quantitative estimate of potential predictability of active and weak spells from observations A simple empirical model is used to examine the potential predictability.

  5. 10-90 day filtered precipitation (CMAP) averaged over monsoon trough 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). The rate of transition, the magnitude of the next peak are quite different for the active and weak phases. Predictability depends on the degree of regularity of transitions.

  6. Spread in evolutions from peak wet and from peak dry conditions (represented as standard deviation) may be considered as ‘growth of errors’ Signal is the average amplitude of the ISO over a period of 50 days. Errors from dry peak grows larger than the signal in about 8 days, whereas errors from wet peak takes about 20 days to cross the signal. 2 2 2

  7. Same as the previous figure, but for the precipitation averaged over the eastern equatorial Indian ocean Same as in (c) but forrainfall data from rain gauge stationsaveraged over monsoon trough region.

  8. 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.

  9. 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 are about 20 days while that for monsoon active conditions is only about 10 days

  10. Empirical Extended Range Prediction of Monsoon ISOs

  11. 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.

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

  13. 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) Phase and northward propagation are predicted with useful skill

  14. 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)

  15. 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)

  16. 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).

  17. ------------------------------------------------------------------------------------------------------------------------------------------ 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)

  18. 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.

  19. 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 methods of empirical prediction

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

  21. 15-day predictions and verifications of rain anoms ave(70-90E)

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

  23. 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).

  24. 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

  25. 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

  26. 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.

  27. 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)

  28. 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

  29. 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

  30. 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

  31. 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.

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

  33. 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.

  34. I III II Regions over which potential predictability of precipitation is examined

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