1. 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

2. 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

3. Run- type Ensemble Members Integration PeriodInitial ConditionsBoundary Conditions SMIP10 May1979~Nov1999 7 months integrations for every year 00Z~12Z of 26Apr~30Apr for every year OISST(NCEP) and AMIP II climatological cycle Sea ice. SNU AGCM Modeling and Climate Prediction ModelResolutionDynamicsPhysics SNU AGCM 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

4. Current Status of Global Climate Models SNUGCM Model Climatology (Summer) (a) Observation Rainfall (c) Observation Sea Level Pressure (b) Model (d) Model

5. Climatology of Summer Rainfall (Various Models)

6. Super-Ensemble Prediction - Superiority of a multi-model ensemble prediction compared to any of single prediction - Applicability of superensemble technique to climate prediction TrainingForecast Conventional Superensemble SVD SVD Mean RMSE Conventional Superensemble Simple Ensemble Superensemble Precipitation RMSE (Global) Yun, Stefanovar and Krishnamurti (2002)

7.  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.

8. ModelInstituteResolutionExperiment Type NCEP T63L17SMIP (10 member) GDAPSKMAT106L21SMIP (10 member) GCPSSNUT63L21SMIP (10 member) NSIPPNASA2 o x2.5 o L43AMIP (9 member) CWBTaiwanT42L18AMIP (1 member)  Participated Model  Target of prediction : Summer (JJA) mean precipitation APCN Multi-Model Climate Prediction System

9. Dynamical prediction Dynamical prediction Dynamical prediction Dynamical Prediction Corrected prediction Corrected prediction Corrected prediction Corrected prediction Corrected prediction Statistical Downscaling (Post-processing) Specio-Ensemble prediction  Multi Model Ensemble prediction  Multi Model Ensemble procedure Statistical Prediction Multi-Model Dynamical-Statistical Ensemble prediction Conventional Multi-Model Ensemble prediction

10.  Prediction skill – before downscaling / JJA Precipitation

11.  Prediction skill – after downscaling

12. Summer Mean Precipitation (30S~60N) Model Comp. Superensemble with MLRM Superensemble with SVD (b) RMSE (a) Pattern Correlation Specio-ensemble prediction  Comparison of prediction skill for individual summer

13. Computational Resources ( based on NEC/SX4 )  Required CPU Time (Best guess, single node) - Seasonal Hindcast experiments AGCM 1 month integration (user time) = 1.6 hours 7 months forecasts for 1 member = 1.6 hrs/month * 7 months = 11.2 hours 10 member ensemble integrations = 11.2 hours/member * 10 member = 112 hours 21 years hindcasts = 112 hours/1year * 21 years = 2352 hours 4 seasons * 2352 hours = 9408 hours  Required Disks and Network Exchange - AGCM Integration 1 month = 0.7 GB) * 7 months * 10 members *21 years = ~ 1.03 TB * 4 seasons = ~ 4.12 TB  9,408 hours (~ 13 months ) CPU Time Needed  4.12 TB Disks Needed Needed For One Prediction Center

14. Development of the SNU-NASA Multi-Model Ensemble Prediction System Tokyo. U NCEP COLA KMA SNU NASA Supported by National Computerization Agency

15. Network structure between SNU and NASA CES 45Mbps Seoul Backbone node USA Web Server Data Server Analysis Server KMA Taejon Backbone node KISTI Super Computer Super Computer NCEP NASA Analysis Server DB Server 2.5Gbps 155Mbps SNU DB Server FTP Server Direct Conn. KOREN StarTap(APII-Test bed) SNU Network 상용인터넷  국내의 KISTI, KMA, 국외의 NASA, NCEP ( 미국 ) 과 국제공동 기후 네트웍 확장 1Gbps

16. Network Traffic 2003. 01. 01 ~ 2003. 06. 30 Traffic amount : 112.13 TB Average Input : 5.28 Mbits Average Output : 1.93 Mbits 초고속 선도망 및 APII-Testbed 활용도

17. 96 Mbps ( 학내 ) 60 Mbps ( 국내 ) 1.2 Mbps ( 미국 ) 네트웍 Speed 개선 8 Mbps ( 학내 ) 5 Mbps ( 국내 ) 0.9 Mbps ( 미국 )  학내는 네트웍 대역폭 확대와 경로단축으로 큰 개선 효과  국외는 네트웍 경로문제로 속도개선이 크게 향상이 안됨 Network Speed after some attempt

18. SNU-NASA Joint Forecast for Washington D.C. Issued at Oct2002

19. Site URL- http://147.46.56.215/cps/index.htmlhttp://147.46.56.215/cps/index.html Provide real-time prediction for global and regional domains Main Page Global Prediction Regional Prediction Web-based Operational Display System

21. ⊙ Cyber Institute for Pacific-Asian Climate System Network

22. ⊙ CIPACS Main Page About CIPACS Members Online Journal Forum Data News Links Member`s Institute

23. The End