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Preliminary Results from CliPAS/APCC Multi-Model Ensemble Hindcast Experiments

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.

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Preliminary Results from CliPAS/APCC Multi-Model Ensemble Hindcast Experiments

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  1. Preliminary Results fromCliPAS/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. About APCC APEC (Asia-Pacific Economic Cooperation) APCN (APEC Climate Network) APCC (APEC Climate Center)

  3. APEC Participating Member Economies

  4. Background: From APCN to APCC • 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- 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 18-20th November 2005 during the APEC Summit Meeting in Bussan, Korea,.

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

  6. CliPAS Climate Prediction and Its Application to Society A Joint US-Korea Research Project in Support of APCC Objectives Investigate 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. Participating Institutions in CliPAS APCC IPRC- ICCS / UH KMA CES/SNU NCEP COLA GFDL NASA FRCGC FSU

  8. CliPAS Investigators (Oct. 2005)

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

  10. Current CliPAS/APCC MME Hindcast Experiments One-Tier systems Two-Tier systems SNU SST prediction system CGCM AGCM FSU NASA CFS/NCEP GFDL CAM2 (UH) SINTEX-F SNU SNU/KMA Hybrid CGCM (UH) ECHAM(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

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

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

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

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

  15. MME Effective Index/ Precipitation DJF JJA

  16. Wang et al. (2004)

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

  18. El Nino region(10oS-5oN, 80oW-180oW) WNP (5-30oN, 110-150oE) Asian-Pacific MNS (5-30oN, 70-150oE) Area averaged correlation coefficients (skills)

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

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

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

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

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

  24. Summary of the Preliminary Results • 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.

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