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Collaboration between CLIVAR/AAMP and GEWEX/MAHASRI A proposal to foster interaction

Collaboration between CLIVAR/AAMP and GEWEX/MAHASRI A proposal to foster interaction. Coordinated GCM/RCM Process study on Monsoon ISO Multi-RCM Downscaling Experiment for seasonal prediction. Proposed Activity I: Coordinated GCM/RCM Process study (AAMP-MAHASRI). Why? How?.

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Collaboration between CLIVAR/AAMP and GEWEX/MAHASRI A proposal to foster interaction

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  1. Collaboration between CLIVAR/AAMP and GEWEX/MAHASRIA proposal to foster interaction • Coordinated GCM/RCMProcess study on Monsoon ISO • Multi-RCM Downscaling Experiment for seasonal prediction

  2. Proposed Activity I:Coordinated GCM/RCMProcess study (AAMP-MAHASRI) Why? How?

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

  4. 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. Need to understand Monsoon ISO: Multi-Scale Interrelation Slingo2006: THORPEX/WCRP Workshop report

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

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

  10. Given observed SST forcingCan AGCMssimulate A-AM precipitation anomalies? 11 AGCMsAMIP type 10-member ensemble simulationObserved SST and sea ice as LB forcing2-year period (9/1996-8/1998)

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

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

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

  15. MME Downscaling Seasonal Prediction Experiment Develop effective strategy and methodology for 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. Physical Basis for Monsoon Prediction: A challenge to Two-tier approach 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. a. 5-AGCM ensemble hindcast skill b. OBS SST-rainfall correlation c. Model SST-rainfall correlation Wang, et al. 2005

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