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