In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea Chung-Kyu Park and Dong-Il Lee Korea Meteorological Administration • Current Status of Global Climate Models • Multi-model ensemble prediction system • Computation and network environments • SNU-NASA multi-model prediction • Cyber Institute for Pacific-Asian Climate System Multi-model Climate Prediction
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In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea
Multi-model Climate Prediction. In-Sik Kang Climate and Environment System Research Center Seoul National University, Korea Chung-Kyu Park and Dong-Il Lee Korea Meteorological Administration. Current Status of Global Climate Models Multi-model ensemble prediction system - PowerPoint PPT Presentation
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In-Sik KangClimate and Environment System Research Center
Seoul National University, Korea
Chung-Kyu Park and Dong-Il LeeKorea Meteorological Administration
• Current Status of Global Climate Models• Multi-model ensemble prediction system• Computation and network environments • SNU-NASA multi-model prediction• Cyber Institute for Pacific-Asian Climate System
Multi-model Climate Prediction
Numerical Simulation of Earth Climate Atmospheric General Circulation Models (AGCMs)• Widely-used tools for Numerical Reproduction of Weather and Climate• Adapted to Seasonal Prediction Problem with the advance of High-performance Super Computing• Dynamic Equation Set
• Numerical Representation
• Super Computing
Run-type
Ensemble Members Integration Period Initial Conditions Boundary Conditions
SMIP 10May1979~Nov1999
7 months integrations for every year
00Z~12Z of 26Apr~30Apr for
every year
OISST(NCEP) and AMIP II climatological cycl
e Sea ice.
SNU AGCM Modeling and Climate Prediction
Model Resolution Dynamics Physics
SNUAGCM
T63L21 hybrid
vertical coordinate
Spectral model using semi-implicit
method
• 2-stream k-distribution radiation scheme (Nakajima and Tanaka 1986)• Simplified Arakawa-Schubert cumulus convection scheme based on RAS scheme (Moorthi and Suarez 1992)• Orographic gravity-wave drag (McFarlane 1987)• Dry adiabatic adjustment• Bona’s land surface model (Bonan 1996)• Mon-local PBL/vertical diffusion (Holtslag and Boville 1993)• Diffusion-type shallow convection• Modified CCM3 slab ocean/sea-ice.model
Experimental design for Seasonal Ensemble Prediction
SNU (Seoul National University ) AGCM description
Current Status of Global Climate ModelsSNUGCM Model Climatology (Summer)
(a) ObservationRainfall
(c) ObservationSea Level Pressure
(b) Model (d) Model
Climatology of Summer Rainfall (Various Models)
Super-Ensemble Prediction
- Superiority of a multi-model ensemble prediction compared to any of single prediction
- Applicability of superensemble technique to climate prediction
Training ForecastConventional Superensemble SVD
SVD Mean RMSE
Conventional Superensemble
Simple Ensemble
Superensemble Precipitation RMSE (Global)
Yun, Stefanovar and Krishnamurti (2002)
)(1
,
n
iitiit FFaOS
Asia-Pacific Climate Network (APCN)
To develop and maintain an infrastructure of a well-validated multi-model ensemble system (MMES) to produce the seasonal climate Prediction for Asian Pacific Economic Cooperation (APEC) member countries and to use it as an economic tool to effectively manage future weather and climate risks
The APCN-MMES will produce real-time seasonal forecasts and disseminated the forecast products to member countries.