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)

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,…..

Current situation: along way to go / unachievable? To progress intraseasonal/seasonal monsoon prediction: 1)Improve understanding of monsoon variability and predictability process studies/theory/predictability studies 1)Improve modeling systems model physics/initial conditions

Key Research Areas for Improved Understanding of Monsoon Intraseasonal-Seasonal Predictability  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

Model System Development Foci from BMRC Dynamical Seasonal Prediction Strategic Plan i)Improved representation of tropical convection (not just limited to MJO) ii)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

Current Status of Dynamical Forecast System at BMRC  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)

Sfc Zonal wind Thermocline SST Skill (ACC) from hindcasts (all months) Not much better than persistence

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

POAMA 1.5a Persistence Nov Skill Dipole Mode Index POAMA Start month Lead time

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

w-k power spectra U Observed UKMO Unified Model Version AMIP

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

Diagnostic study of representation of MJO/organized convection in forecast/climate models 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)

Power Spectra Precipitation MMF CAM MMF Power Spectra U850 CAM

Scatter between precipitation and saturation fraction MMFCAM Reality apparently somewhere in between (Bretherton et al 2004)

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

Recommendations for AMY08-YTC Intraseasonal-Interannual Prediction  Focus on improved representation of convection in models (commit resources to model 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

Correlation U850’ NCEP1 and ERA day 2-10 day Deahyun Kim

Coherence (w-k) U850 with OLR ERA 40 NCEP1 ERA40-NCEP1

Rainfall Potential Predictability (% variance) ANOVA for Ensemble of AGCM forced with observed SST Observed rainfall correlation with Nino4 Seasonal mean monsoon anomaly is unpredictable? Reflects low sigma/mean