1. LASG/IAP Collaboration between CLIVAR/AAMP and GEWEX/MAHASRI A proposal to foster interaction l Coordinated GCM/RCM Process study on Monsoon ISO l Multi-RCM Downscaling Experiment for seasonal prediction

2. LASG/IAP Proposed Activity I: Coordinated GCM/RCM Process study (AAMP-MAHASRI) Why?How?

3. LASG/IAP Diurnal cycle biases (Yang and Slingo 2001) UKMO Unified Model Satellite Local time of peak precipitation l Satellite shows early evening peak over land, early morning peak over ocean ITCZ. l Models show late morning peak over land, midnight peak over ocean.

4. LASG/IAP ISV Variance is too small MJO variance does not come from pronounced spectral peak but from over reddened spectrum: too strong persistence of equatorial precipitation (13/14)

5. LASG/IAP Slingo 2006: THORPEX/WCRP Workshop report Need to understand Monsoon ISO: Multi-Scale Interrelation

6. Satellite View of Composite life cycle of ISO (42 cases,1998-2004) rain rate (contour) & SST (shading) Wang, Webster, Kikuchi, Yasunari, 2006, Climate Dynamics

7. Schematic evolution of tropical ISO rain anomalies (May-October)

8. LASG/IAP MAHASRI: Coordinated RCM Process study l Integration of observation and modelling, Meteorology and Hydrology l Domain: MAHASRI tropics—Critical region for monsoon ISO influence l Phenomenon and Issues: ISO, diurnal cycle, meso-scale and synoptic scale regulation, Onset of monsoon (summer) l Design: Driving field, Output, validation strategy and Data,… l Participating model groups: minimum 5

9. LASG/IAP Proposal II: Multi-RCM Downscaling Experiment for seasonal prediction (AAMP-MAHASRI) Why?How?

10. Given observed SST forcing Can AGCMs simulate A-AM precipitation anomalies? 11 AGCMs AMIP type 10-member ensemble simulation Observed SST and sea ice as LB forcing 2-year period (9/1996-8/1998)

11. SE Asia Summer monsoon prediction is most challenge Wang, Kang, Lee 2004 J. Climate

12. LASG/IAP Current status of seasonal prediction of precipitation: Temporal Correlation skill (1981-2001) Two MMEs correlation skill are comparable.( DEMETER 7 one-tier models, CliPAS 5 one-tier and 5 two-tier models) Land regions are lacking skills. During DJF ENSO impacts extends to Land..

13. LASG/IAP Sources of Low Prediction Skill l Limit of the monsoon rainfall predictability (internal chaotic dynamics) (ensemble) l Model physical parameterization (Multi- model) l Model representation of the slow coupled processes: A-O-L Interaction l Initialization: Ocean, Land surface l Resolution of topography, land surface properties…(RCM come into play?)

14. LASG/IAP Koster et al. 2004 Hot places of land surface feedback

15. LASG/IAP MME Downscaling Seasonal Prediction Experiment Develop effective strategy and methodology for downscaling Assess the added value of MME downscaling Assess the added value of MME downscaling Determine the predictability of monsoon precipitation Large scale driving: 10 CGCM from DEMETER and APCC/CliPAS models

16. LASG/IAP a. 5-AGCM ensemble hindcast skill b. OBS SST-rainfall correlation c. Model SST-rainfall correlation State-of-the-art AGCMs, when forced by observed SST, are unable to simulate Asian- Pacific summer monsoon rainfall (Fig. a). The models tend to yield positive SST-rainfall correlations in the summer monsoon region (Fig. c) that are at odds with observation (Fig.b). Treating monsoon as a slave to prescribed SST results in the models’ failure, which suggests inadequacy of the tier-2 climate prediction system for summer monsoon prediction. Physical Basis for Monsoon Prediction: A challenge to Two-tier approach Wang, et al. 2005