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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