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Predictability of Indian monsoon rainfall variability

Predictability of Indian monsoon rainfall variability. Michael K. Tippett, IRI Timothy DelSole, COLA. Issues & Questions. Interannual variability of Indian monsoon rainfall has a large societal impact. How predictable is IMR given SST? GCM simulations forced by observed SST.

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Predictability of Indian monsoon rainfall variability

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  1. Predictability of Indian monsoon rainfall variability Michael K. Tippett, IRI Timothy DelSole, COLA

  2. Issues & Questions • Interannual variability of Indian monsoon rainfall has a large societal impact. • How predictable is IMR given SST? • GCM simulations forced by observed SST. • Ability to reproduce observations is limited by • predictability • GCM deficiencies • How to account for (and correct) systematic model error in seasonal forecasts?

  3. Seasonal forecasting Two-tier seasonal forecast system • (1) predict SST. • (2) predict response to SST • GCM ensembles forced with dynamical/statistical predicted and persisted SST anomalies. • Grid-point post-processing to account for model error. • Categorical probability forecasts for temperature and precipitation.

  4. GCM JJAS anomaly correlation Observed SST 1950-1998

  5. IRI Net Assessment 2002

  6. Predictability and imperfect models Inherent lack of predictability vs. model deficiency • Is predictability being obscured by systematic errors? • Can additional information be extracted from the model? Information theory approach (DelSole 2003). • Account for systematic model error by finding the expected outcome given a forecast. • Predictable component analysis of the regression forecast equivalent to CCA between model outputs and observations. • Correction tool.

  7. Seasonal forecasting Two-tier seasonal forecast system • (1) predict SST. • (2) predict response to SST • GCM ensembles forced with dynamical/statistical predicted and persisted SST anomalies. • Pattern-based statistical correction. • Grid-point post-processing to account for model error. • Categorical probability forecasts for temperature and precipitation.

  8. Relating model and observations Which model variables? • Regional (India) precipitation? • Pacific precipitation? • Vertical wind shear (dynamical monsoon indices)? • Zonal component? • Meridional component? • How many predictable patterns (CCA modes)?

  9. Model selection by cross-validation • Use cross-validation to choose predictors and number of modes • Leave-1-out • Leave-10-out • Pacific GCM precipitation has most skill. • Avoids region where heat flux inconsistency may be an issue.

  10. Correlation of local SST with Observed precipitation Model precipitation Local SST Local SST

  11. First CCA mode Observations GCM precip. pattern

  12. Correction skill estimates Leave-1-out CV Hindcasts made May 1 using persisted (Apr) SST. Not cross-validated. 1950-1998 1968-1998

  13. Categorical probability forecasts 2002 2003 Corrected May forecasts using forecast SST.

  14. Observations 2002 2003 From Monsoon On Line

  15. Conclusions • Some GCM deficiencies can be corrected. • Enhanced skill levels for IMR. • Care required with statistics. • Forecast corrections dependent on SST forecast. • Potential to predict other variables. • hydrology • Agriculture • Next: identify dynamical mechanisms associated with predictable components.

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