1. A sia P acific Economic Cooperation C limate C enter A sia P acific Economic Cooperation C limate C enter Australia Brunei Darussalam Canada Chile People’s Republic of China Hong Kong, China Indonesia Japan Korea Malaysia Mexico New Zealand Papua New Guinea Peru Philippines Russia Singapore Chinese Taipei Thailand United States Viet Nam APEC Climate Center (APCC) Climate Information Service APEC Climate Center (APCC) Climate Information Service APEC Climate Center First AMY/MAHASRI Workshop 8-10th, January, 2007, Tokyo, Japan

2. Vision of APCC  Realizing the APEC vision of regional prosperity  the enhancement of economic opportunities  the reduction of economic loss  the protection of life and property  Realizing the APEC vision of regional prosperity  the enhancement of economic opportunities  the reduction of economic loss  the protection of life and property

3. APEC Climate Center (APCC) ECMWF/EU APCC/APEC Iceland Hungary Ireland Denmark Sweden Norway Spain Portugal Italy Greece Turkey Austria Germany Slovenia Croatia Czech Republic Finland The Netherlands United Kingdom Switzerland France Belgium Luxembourg 1973 2005

4. Goals of APCC  Facilitating the share of high-cost climate data and information  Capacity building in prediction and sustainable social and economic applications of climate information  Accelerating and extending socio-economic innovation  Facilitating the share of high-cost climate data and information  Capacity building in prediction and sustainable social and economic applications of climate information  Accelerating and extending socio-economic innovation

5. Functions of APCC  Developing a value-added reliable climate prediction system  Acting as a center for climate data and related information  Coordinating research toward the development of an APEC integrated climate- environment-socio- economic system model  Developing a value-added reliable climate prediction system  Acting as a center for climate data and related information  Coordinating research toward the development of an APEC integrated climate- environment-socio- economic system model

6. APCC Structure Administration division System division Science division Science Advisory Committee Director Member Working Group Climate Prediction R&D Climate Information Service

7. Climate Prediction: APCC Multi-Model Ensemble APCC

8. APEC Climate Center Multi-Institutional Cooperation

9. APEC Climate Center APCC Participating Models Member Economies AcronymOrganizationModel Resolution AustraliaPOAMABureau of Meteorology Research Centre T47L17 CanadaMSCMeteorological Service of Canada 1.875   1.875  L50 China NCCNational Climate Center/CMAT63L16 IAPInstitute of Atmospheric Physics 4   5  L2 Chinese TaipeiCWBCentral Weather BureauT42L18 JapanJMAJapan Meteorological AgencyT63L40 Korea GDAPS/KMAKorea Meteorological AdministrationT106L21 GCPS/SNUSeoul National UniversityT63L21 METRI/KMAMeteorological Research Institute 4   5  L17 Russia MGOMain Geophysical ObservatoryT42L14 HMCHydrometeorological Centre of Russia 1.12   1.4  L28 USA IRI COLA International Research Institute Center for Ocean-Land-Atmosphere Studies T42L19 T63L18 NCEPNCEP Coupled Forecast SystemT62L64 NSIPP/NASA National Aeronautics and Space Administration 2   2.5  L34

10. APEC Climate Center Observed Analysis Training PhaseForecast Phase MME Real-time Prediction t=0 MME Prediction Method The weights are computed at each grid point by minimizing the function:

11. APEC Climate Center APCC Deterministic MME Forecast

12. APEC Climate Center  Defining terciles using Normal Fitting Method - For the middle/upper tercile boundary : mean plus 0.43 times the standard deviation  - For the lower/middle tercile boundary : mean minus 0.43 times the standard deviation  + 0.43 σ μ  Forecast probability A N B Probability of Above-normal Probability of Near-normal Probability of Below-normal - 0.43 σ μ - Above normal case (For example) Xc -Xc Probabilistic Forecast Method

13. APEC Climate Center  Combine different models Combine : according to each model’s square root of ensemble size Na : num. of above-normal Nn : num. of near-normal Nb : num. of below-normal  Merged 3-category distribution (Chi-square) TEST O : Forecast frequencies E : Hindcast frequencies Under 0.05% significance level Near-normal Below-normal Above-normal 3-Categorical distribution Probabilistic Forecast Method

14. APEC Climate Center APCC Probabilistic MME Forecast

15. APEC Climate Center Bangkok MME-based statistical downscaling: precipitation in Thailand MME-based statistical downscaling: precipitation in Thailand

16. APEC Climate Center Taylor Diagram : 1983~2003, JJA Hindcast Forecast Verification: Deterministic MME

17. APEC Climate Center Anomaly Correlation Coefficient for hindcast in each region (precipitation, JJA, 1983-2003) individual model multi-model ensemble schemes Forecast Verification: Deterministic MME

18. APEC Climate Center Observed Relative Frequency Globe, Above-Normal, Precipitation Hit rate Reliability DiagramROC Curve False alarm rate Forecast Probability Forecast Verification: Probabilistic MME

19. APEC Climate Center Climate Information Service

20. APEC Climate Center El Nino Monitoring Real-time Climate Monitoring

21. APEC Climate Center Data Acquisition Data format Unification Data Dissemination Quality Check Data Service System Transformation MAHASRI MODEL MME OBS ( NCEP, CAMS_OPI, CMAP, JRA-25 ) SATELLITE Visualization openDAP ftp Data Backup Reduce the data size

22. APEC Climate Center 기상청 장기예보지원  2D 그래픽 표출 시스템 구축 및 시험 운영 GDS 기반의 원격 기후 자료 표출 시스템 시범 운영 http://gds.apcc21.net:9090/dodshttp://gds.apcc21.net:9090/dods ( 외부접속 ) http://testbed.apcc21.net:9090/dodshttp://testbed.apcc21.net:9090/dods ( 내부접속 ) 웹에서 제공되는 엔트리 정보를 이용한 기후자료 표출 Data Service System

23. APEC Climate Center Multi-Cumulus convection scheme Ensembles Climatology and zonal mean of precipitation during the boreal summer (JJA) Cloud mass flux Areal rate of cumulus Cloud water in cumulus Snow fall Rain fall Change rate of moisture Heating rate UNIZMMCAKuoSAS (SAS + Kuo + MCA + ZM ) / N, (N=4) (SAS + ZM ) / N, (N=2) Convection scheme of the four different individual models and unified model Research & Development

24. APEC Climate Center EOF of CISO for Precipitation Unified scheme Observation Unified Scheme best captures the CISO evolution Multi-Cumulus convection scheme Ensembles

25. APEC Climate Center 1997 1999 2004 2005 TC activity/monsoon in high-resolution GCM (2007~) Examine high-resoultion GCM performance Compare with SNU, NASA results

26. APEC Climate Center (TC activity/monsoon in high-resolution GCM: cont’) GDAPS T106, T213, and T426 (+ T512 GCPS from SNU) Summer season of 97, 99, 04, 05; prescribed SST Sensitivity of the following on model resolution:  tropical cyclone activity  Monsoon simulation  ISO simulation

27. Improving Quality of Service 1 st phase : Innovative technology development (~2010) 2 nd phase : Socio-economic application technology development (2011~2015) 3 rd phase : Providing climate and application information to the end users as growth engine (2016~2020) Future Plan

28. Collaboration with MAHASRI Development of RCM schemes and application models:  APCC will provide hindcast datasets from participating models and from the CLiPAS project, as well as MME seasonal prediction data for dynamical downscaling efforts of MAHASRI, in order to better understand the predictability of the Monsoon systems and for the development of application models. Data service and management:  APCC will serve as one of the data centers for the intensive observational data during the AMY-2008  Datasets will be made available to model providers for verification and ultimately model improvement Development of RCM schemes and application models:  APCC will provide hindcast datasets from participating models and from the CLiPAS project, as well as MME seasonal prediction data for dynamical downscaling efforts of MAHASRI, in order to better understand the predictability of the Monsoon systems and for the development of application models. Data service and management:  APCC will serve as one of the data centers for the intensive observational data during the AMY-2008  Datasets will be made available to model providers for verification and ultimately model improvement

29. Collaboration with AMY/MAHASRI Expects high resolution (spatial and temporal) station data, LS data, for  high-res model verification  Statistical/RCM downscaling development  application model development  Climate monitoring (intraseasonal, drought, flood, TC, other extreme events) Expects high resolution (spatial and temporal) station data, LS data, for  high-res model verification  Statistical/RCM downscaling development  application model development  Climate monitoring (intraseasonal, drought, flood, TC, other extreme events)

30. Focal points/members in MAHASRI WG:  Prediction & application: C.K. Park/Nohara  RCM downscaling: Tam/Song  Data Management: Tam/Lee  Prediction & application: C.K. Park/Nohara  RCM downscaling: Tam/Song  Data Management: Tam/Lee

31. Thank you