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Monsoon Predictive Capabilities next 5-10 years

Monsoon Intraseasonal-Interannual Variability and Prediction Harry Hendon BMRC (also CLIVAR AAMP) Acknowledge contributions: Oscar Alves, Eunpa Lim, Guomin Wang, Hongyan Zhu (BMRC) David Anderson (ECMWF) Daehyun Kim (SNU/US CLIVAR MJO Metrics Working Group).

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Monsoon Predictive Capabilities next 5-10 years

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  1. Monsoon Intraseasonal-Interannual Variability and PredictionHarry Hendon BMRC (also CLIVAR AAMP)Acknowledge contributions:Oscar Alves, Eunpa Lim, Guomin Wang, Hongyan Zhu (BMRC) David Anderson (ECMWF) Daehyun Kim (SNU/US CLIVAR MJO Metrics Working Group)

  2. Monsoon Predictive Capabilities next 5-10 years from BMRC Dynamical Seasonal Prediction Strategic Plan 30 day forecasts(atmospheric initial conditions) broad scale onset/active/break individual MJO events regional rainfall (accumulations/probability) severe weather episodes (probability of TC genesis, extreme rainfall accumulation, heat wave, floods ) 1-9 month prediction(ocean/land initial conditions) ENSO and its monsoon teleconnection IOD and its monsoon teleconnection MJO activity Delayed/early onset Seasonal rainfall accumulation Directly tie dynamical model output into applications stream flow models, crop models,…..

  3. Current situation: along way to go / unachievable? To progress intraseasonal/seasonal monsoon prediction: Improve understanding of monsoon variability and predictability process studies/theory/predictability studies Improve modeling systems model physics/initial conditions

  4. Determination of the limits of predictability and causes of the loss of predictability (for the coupled systems as a whole) Improved understanding of ENSO and its teleconnection Role of intra-seasonal variability (esp. MJO) in the evolution of Monsoon (and ENSO) and its impact on predictability Understanding of Indian Ocean variability, it’s predictability and it’s impact on Monsoons Decadal variability, principally ENSO and IOD and their teleconnections into monsoon Impact of climate change on seasonal climate forecasts Key Research Areas for Improved Understanding of Monsoon Intraseasonal-Seasonal Predictability

  5. Improved representation of tropical convection (not just limited to MJO) Reduced coupled model drift/bias iii) Improved initialisation of the coupled system (including land surface) through advanced data assimilation systems that initialise the coupled model as one system (iv) Improved modelling of oceanic processes particularly tropical thermocline structure, boundary currents, and instability waves (v) Improved modelling of the land surface (vI) Inclusion of changing greenhouse gases/aerosols Model System Development Foci from BMRC Dynamical Seasonal Prediction Strategic Plan

  6. POAMA: Coupled AGCM/OGCM together with ocean data assimilation system T47L17 AGCM coupled to OGCM MOM2 0.5 x 2 deg Ocean Initial conditions: 2-d OI assimilation subsurface T and SST (soon to be updated to EKF) Atmosphere: latest global NWP initial conditions Runs operationally (9 mnth forecast everyday) Current Status of Dynamical Forecast System at BMRC

  7. Skill (ACC) from hindcasts 1987-2001 (all months) • +1 • +3 • +5 • +7 Sfc Zonal wind SST Thermocline Not much better than persistence

  8. Skill for Mean DJF Monsoon Rainfall POAMA 1982-2005 (correlation coef blue neg/red pos) LT 0 Similar results for Indian/Asian Monsoon No skill with current system! LT 3 LT 6

  9. Skill Dipole Mode Index 1982-2005 POAMA Start month POAMA 1.5aPersistence Nov Lead time Lead time

  10. ECMWF System 3 (courtesy David Anderson) UKMO Unified Model version 6 (soon to be atmospheric component of BoM Coupled Model) Current Status of MJO Simulation/Prediction

  11. w-k power spectra U850 1979-88 Observed UKMO Unified Model Version 6 1979-88 AMIP

  12. Power Spectrum Velocity Pot 200 hPa as function of longitude along equator Anderson et al 2007

  13. Compare convective behavior in 2 runs of NCAR CAM Multi Model Framework - Randel CSU (super-parameterization: 2 d cloud resolving model at each grid box) Parameterized convection (Zhang and MacFarlane) Diagnostic study of representation of MJO/organized convection in forecast/climate models

  14. Power Spectra Precipitation MMF CAM MMF Power Spectra U850 CAM

  15. Scatter between precipitation and saturation fraction MMF CAM Reality apparently somewhere in between (Bretherton et al 2004)

  16. Correlation between precipitation and relative humility anomaly (at 992hPa ) MMF CAM

  17. Focus on improved representation of convection in models (commit resources tomodel development) Design diagnostic studies for behavior of convection in models Make appropriate observations to support model improvement of convection Enhance atmospheric and oceanic observing system especially in Indian Ocean to improve atmos/ocean initial conditions Develop coupled ocean/atmosphere/land data assimilation Promote/design model/observation studies for understanding predictability of monsoon Impact of land/ocean initialization Recommendations for AMY08-YTC Intraseasonal-Interannual Prediction

  18. Correlation U850’ NCEP1 and ERA40 1979-2001 20-120 day 2-10 day Deahyun Kim

  19. Coherence (w-k) U850 with OLR 1979-2002 ERA40-NCEP1 ERA 40 NCEP1

  20. Rainfall Potential Predictability (% variance) ANOVA for Ensemble of AGCM forced with observed SST 1982-2002 Observed rainfall correlation withNino4 Seasonal mean monsoon anomaly is unpredictable? Reflects low sigma/mean

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