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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 yearsfrom BMRC Dynamical Seasonal Prediction Strategic Plan
30 day forecasts (atmospheric initial conditions)broad scale onset/active/breakindividual 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 teleconnectionIOD and its monsoon teleconnectionMJO activityDelayed/early onsetSeasonal rainfall accumulation
Directly tie dynamical model output into applicationsstream 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 1987-2001 (all months)
+1
+3
+5
+7
Not much better than persistence
LT 0
LT 3
LT 6
Skill for Mean DJF Monsoon Rainfall POAMA 1982-2005
(correlation coef blue neg/red pos)
Similar results for Indian/Asian Monsoon
No skill with current system!
DMI (Reynolds)
-0.2-0.1
00.10.2
0.30.4
0.50.60.7
0.80.9
0 1 2 3 4 5 6 7 8
Lead Time (month)
Co
rr.
Co
eff.
POAMA 1.5
Persistence
POAMA 1.5a Persistence
Nov
Skill Dipole Mode Index 1982-2005 POAMA
Start month
Lead time 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 U850 1979-88
Observed
UKMO Unified Model Version 6
1979-88 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
MMF CAM
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 ERA40 1979-2001
20-120 day
2-10 day
Deahyun Kim
Coherence (w-k) U850 with OLR 1979-2002
ERA 40 NCEP1 ERA40-NCEP1
Rainfall Potential Predictability
(% variance)
ANOVA for Ensemble of AGCM forced with observed SST 1982-2002
Observed rainfall correlation with Nino4
Seasonal mean monsoon anomaly is unpredictable?
Reflects low sigma/mean