LASG/IAPLASG/IAP

Collaboration between CLIVAR/AAMP Collaboration between CLIVAR/AAMP and GEWEX/MAHASRIand GEWEX/MAHASRI

A proposal to foster interactionA proposal to foster interaction

Coordinated GCM/RCMCoordinated GCM/RCMProcess study on Monsoon ISOProcess study on Monsoon ISO

Multi-RCM Downscaling Experiment Multi-RCM Downscaling Experiment for seasonal predictionfor seasonal prediction

LASG/IAPLASG/IAP

Proposed Activity I:Proposed Activity I:Coordinated GCM/RCMCoordinated GCM/RCM

Process study (AAMP-MAHASRI)Process study (AAMP-MAHASRI)

Why?Why?

How?How?

LASG/IAPLASG/IAP

Diurnal cycle biasesDiurnal cycle biases (Yang and Slingo 2001)(Yang and Slingo 2001)

UKMO Unified Model

Satellite

Local time of peak precipitation

Satellite shows early evening peak over land, early morning peak Satellite shows early evening peak over land, early morning peak over ocean ITCZ.over ocean ITCZ.

Models show late morning peak over land, midnight peak over ocean.Models show late morning peak over land, midnight peak over ocean.

LASG/IAPLASG/IAP

•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)

LASG/IAPLASG/IAPSlingo 2006: THORPEX/WCRP Workshop report

Need to understand Monsoon ISO: Multi-Scale Interrelation

Satellite View of Composite life cycle of ISO (42 cases,1998-2004)

rain rate (contour) & SST (shading)

Wang, Webster, Kikuchi, Yasunari, 2006, Climate Wang, Webster, Kikuchi, Yasunari, 2006, Climate DynamicsDynamics

1 2 3

3

4

4

1

1

1 2 3

3

4

4

1

1

Schematic evolution of tropical ISO rain anomalies (May-October)

LASG/IAPLASG/IAP

MAHASRI:MAHASRI:Coordinated RCM Process studyCoordinated RCM Process study

Integration of observation and Integration of observation and modelling, Meteorology and Hydrology modelling, Meteorology and Hydrology

Domain: MAHASRI tropics—Critical Domain: MAHASRI tropics—Critical region for monsoon ISO influenceregion for monsoon ISO influence

Phenomenon and Issues: ISO, diurnal Phenomenon and Issues: ISO, diurnal cycle, meso-scale and synoptic scale cycle, meso-scale and synoptic scale regulation, Onset of monsoon (summer)regulation, Onset of monsoon (summer)

Design: Driving field, Output, validation Design: Driving field, Output, validation strategy and Data,…strategy and Data,…

Participating model groups: minimum 5Participating model groups: minimum 5

LASG/IAPLASG/IAP

Proposal II:Proposal II:Multi-RCM Downscaling Multi-RCM Downscaling

Experiment for seasonal prediction Experiment for seasonal prediction (AAMP-MAHASRI)(AAMP-MAHASRI)

Why?Why?

How?How?

Given observed SST forcingCan AGCMs

simulate A-AM precipitation anomalies?

11 AGCMsAMIP type 10-member ensemble simulation

Observed SST and sea ice as LB forcing2-year period (9/1996-8/1998)

Precipitation (shading) and SST (contour)

Observation All-Model Composite

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

mm/day

Latit

ude

Latit

ude

Latit

ude

Longitude Longitude

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

COLA DNM GEOS GFDL IAP IITM MRI NCAR NCEP SNU SUNY Comp

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

(a) Southeast Asian and WNP region

J J A97 SON97 J J A98

(b) The rest of the A- AM domain

J J A97 SON97 J J A98

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

COLA DNM GEOS GFDL IAP IITM MRI NCAR NCEP SNU SUNY Comp

SE Asia Summer monsoon prediction is most challengeSE Asia Summer monsoon prediction is most challenge

Wang, Kang, Lee 2004 J. Climate

LASG/IAPLASG/IAP

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..

LASG/IAPLASG/IAP

Sources of Low Prediction SkillSources of Low Prediction Skill Limit of the monsoon rainfall Limit of the monsoon rainfall

predictability (internal chaotic predictability (internal chaotic dynamics) (ensemble)dynamics) (ensemble)

Model physical parameterization (Multi-Model physical parameterization (Multi-model)model)

Model representation of the slow Model representation of the slow coupled processes: A-O-L Interaction coupled processes: A-O-L Interaction

Initialization: Ocean, Land surface Initialization: Ocean, Land surface Resolution of topography, land surface Resolution of topography, land surface

properties…(RCM come into play?)properties…(RCM come into play?)

LASG/IAPLASG/IAP Koster et al. 2004

Hot places of land surface feedback

LASG/IAPLASG/IAP

MME Downscaling Seasonal MME Downscaling Seasonal Prediction ExperimentPrediction ExperimentDevelop effective strategy and Develop effective strategy and

methodology for downscalingmethodology for downscaling Assess the added value of MME Assess the added value of MME

downscaling downscaling Determine the predictability of Determine the predictability of

monsoon precipitationmonsoon precipitationLarge scale driving: 10 CGCM Large scale driving: 10 CGCM

from DEMETER and APCC/CliPAS from DEMETER and APCC/CliPAS modelsmodels

LASG/IAPLASG/IAP

a. 5-AGCM ensemble hindcast skill

b. OBS SST-rainfall correlation c. Model SST-rainfall correlation

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.

Physical Basis for Monsoon Prediction:A challenge to Two-tier approach

Wang, et al. 2005