1. Preliminary Results from CliPAS/APCC Multi-Model Ensemble Hindcast Experiments Bin Wang and June-Yi Lee IPRC/ICCS, University of Hawaii, USA In-Sik Kang Seoul National University, Seoul, Korea Chung-Kyu Park APCC, Busan, Korea Acknowledge contributions from all CliPAS investigators

2. APEC (Asia-Pacific Economic Cooperation) APCN (APEC Climate Network) (APEC Climate Center) APCC About APCC

3. APEC Participating Member Economies

4. APCN (1999-2004) APCN (1999-2004) APCN, “The APEC Climate Network,” is a regional climate program aimed at realizing the APEC vision of regional prosperity through mitigation of economic losses induced by abnormal climate. APCN produces real-time operational climate prediction information based on a well-validated multi- model ensemble system (MMES). APCC (2005- APCC (2005- In order to enhance the activities of APCN, Korea proposed and the APEC Science and Technology Ministry endorsed establishment of APEC Climate Center (APCC) in Korea with a core staff of scientists and computing facilities. The APCC Opening Ceremony will be held on 18- 20 th November 2005 during the APEC Summit Meeting in Bussan, Korea,. Background: From APCN to APCC

5. APCC is an international institute and serves as a hub for APEC regional climate research and prediction To provide core facilities and man powers to accomplish the vision. To make an effort toward accomplishing the WCRP/COPES vision APCC APCC

6. CliPAS Climate Prediction and Its Application to Society A Joint US-Korea Research Project in Support of APCC Objectives I nvestigate a set of core scientific problems on multi- model ensemble (MME) climate prediction Establish well-validated MME prediction systems for intraseasonal and seasonal prediction Develop economic and societal application models.

7. APCC NCEP IPRC- ICCS / UH CES/SNU NASA COLA KMA Participating Institutions in CliPAS FRCGCFSU GFDL

8. PI Bin Wang (UH/IPRC/ICCS) Co-PI’s J. Shukla (GMU/COLA), I.-S. Kang (CES/SNU), L. Magaard (ICCS/UH) Co-Is J.-Y. Lee (UH/ICCS) B. Kirtman, J. Kinter (GMU/COLA) T. Krishnamurti, Steven Cocke (FSU), N.C. Lau, T. Rosati, W. Stern (NOAA/GFDL), M. Suarez, S. Schubert, W. Lau (NASA/GSFC), A. Kumar, J. Schemm (NOAA/NCEP), J.-S. Kug (CES/SNU), W.-T. Yun (KMA) C.-K. Park (APCC), S, Kar (APCC), J.-J. Luo (FRCGC/JAMSTEC), T. Yamagata (UT) J. Marsh (UH/ICCS), W.-D. Grossmann (GKSS/ICCS) CliPAS Investigators (Oct. 2005)

9. RESEARCH THRUST AREAS  Establish a pilot operational APCC-MME SPS  New methodology for integrating MME predictions  Strategy for Intraseasonal prediction  Coupled model initialization and data assimilation  Perturbed physics experiments  Interactive multi-model ensemble prediction experiment APCC/CliPAS Project  Climate information system model and socio-economic value assessment models

10. Two-Tier systems CGCM AGCM NASACFS/NCEP SNU FSU GFDL ECHAM(UH) CAM2 (UH) SNU/KMA SNU SST prediction system One-Tier systems SINTEX-F Hybrid CGCM (UH)  1981 – 2004 summer and winter season for 24 years  Summer: from May 1 to September 30  Winter: from November 1 to March 31 Experiment Period Current CliPAS/APCC MME Hindcast Experiments

11. 2m Air Temperature DEMETER MMEP APCC MMEP Summer Mean Prediction Winter Mean Prediction MME Hindcast Skill: Temporal Correlation/ 1981-2001

13. 2m Air Temperature DEMETER MMEPAPCC MMEP JJA DJF MME Hindcast Skill: Taylor Diagram/ 1981-2001

14. Precipitation DEMETER MMEP APCC MMEP JJA DJF MME Hindcast Skill: Temporal Correlation/ 1981-2001

15. Precipitation DEMETER MMEP APCC MMEP JJA DJF MME Hindcast Skill: Taylor Diagram /1981-2001

16. MME Effective Index/ Precipitation JJA DJF

17. Wang et al. (2004)

19. Fig. 1 Correlation Coefficients between the observed and 5 AGCM MME hindcasted June-August precipitations (1979-1999) Wang et al. (2005)

20. Area averaged correlation coefficients (skills) El Nino region (10 o S-5 o N, 80 o W-180 o W) WNP (5-30 o N, 110-150 o E) Asian-Pacific MNS (5-30 o N, 70-150 o E)

21. Precipitation DEMETER MMEP APCC MMEP JJA DJF MME Hindcast Skill in AAM region :1981-2001 Southeast Asian and WNP region: 80-150E, 5-30N

22. MME Effective Index/ Precipitation JJA DJF Southeast Asian and WNP region: 80-150E, 5-30N

23. One-Tier 1 vs Two-Tier Anomaly PCC over AAM (JJA) ENSO vs PrecipitationSST vs. Precipitation ENSO vs PrecipitationSST vs. Precipitation

24. Probabilistic forecast for above normal precipitation greater than 0.5 standard deviation Probabilistic MMEP Range of Area of ROC Curve/ Above Normal Precipitation APCC DEMETER JJA DJF

25. Probabilistic forecast for above normal 2m air temperature greater than 0.5 standard deviation over ENSO Region APCC DEMETER Deterministic and Probabilistic MMEP Potential Economic Value/ Above Normal 2m Air Temp

26. Summary of the Preliminary Results a.The CliPAS blended one- and two-tier MME hindcasts have skills comparable to DEMETER in precipitation and surface temperature prediction, although their individual modles’ performance is lower that those of DEMETERs. b. The CliPAS MME is more effective due to their larger mutual independence as evidenced from their larger range of their skills. c. The MME is more effective when and where individual models have moderate performances while potential predictability is large. MME is more applicable to the summer monsoon regions. d. In A-A summer monsoon heavy precipitation regions, one-tier is superior to two-tier system due to increased feedback from the local surface SST and improved ENSO teleconnections.