Preliminary Results fromPreliminary Results from CliPAS/APCC Multi-Model CliPAS/APCC Multi-Model

Ensemble Hindcast ExperimentsEnsemble Hindcast Experiments

Bin Wang and June-Yi LeeIPRC/ICCS, University of Hawaii, USA

In-Sik KangSeoul National University, Seoul, Korea

Chung-Kyu ParkAPCC, Busan, Korea

Acknowledge contributions from all CliPAS investigators

APEC (Asia-Pacific Economic Cooperation)

APCN(APEC Climate Network)

(APEC Climate Center)

APCC

About APCCAbout APCC

APEC Participating Member EconomiesAPEC Participating Member Economies

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 18-20th November 2005 during the APEC Summit Meeting in Bussan, Korea,.

Background: From APCN to APCCBackground: From APCN to APCC

APCC is an international institute and serves as a hub for

APEC regional climate research and prediction

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 provide core facilities and man powers to accomplish

the vision.

To make an effort toward accomplishing the

WCRP/COPES vision

To make an effort toward accomplishing the

WCRP/COPES vision

APCCAPCC

CliPAS CliPAS Climate Prediction and ItsClimate Prediction and Its

Application to SocietyApplication to Society

A Joint US-Korea Research Project A Joint US-Korea Research Project in Support of APCCin Support of APCC

ObjectivesObjectives 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.

APCCAPCC

NCEPNCEPNCEPNCEP

IPRC- ICCS / UHIPRC- ICCS / UHCES/SNUCES/SNU

NASANASANASANASA

COLACOLACOLACOLA

KMAKMA

Participating Institutions in CliPASParticipating Institutions in CliPAS

FRCGCFRCGCFRCGCFRCGC FSUFSUFSUFSUGFDLGFDLGFDLGFDL

PIPI Bin Wang (UH/IPRC/ICCS)

Co-PI’sCo-PI’s J. Shukla (GMU/COLA), I.-S. Kang (CES/SNU), L. Magaard (ICCS/UH)

Co-IsCo-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)CliPAS Investigators (Oct. 2005)

RESEARCH THRUST AREASRESEARCH THRUST AREASRESEARCH THRUST AREASRESEARCH 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 ProjectAPCC/CliPAS Project

Climate information system model and socio-economic value assessment models

Two-Tier systems

CGCMAGCM

NASANASA CFS/NCEPCFS/NCEP

SNUSNU

FSU FSU GFDLGFDL

ECHAM(UH)ECHAM(UH)

CAM2 (UH)CAM2 (UH)SNU/KMASNU/KMA

SNU SST prediction system

One-Tier systems

SINTEX-FSINTEX-F

HybridCGCM (UH)

HybridCGCM (UH)

1981 – 2004 summer and winter season for 24 years Summer: from May 1 to September 30 Winter: from November 1 to March 31

ExperimentPeriod

ExperimentPeriod

Current CliPAS/APCC MME Hindcast ExperimentsCurrent CliPAS/APCC MME Hindcast Experiments

2m Air Temperature2m Air Temperature

DEMETER MMEPAPCC MMEP

SummerMean

Prediction

WinterMean

Prediction

MME Hindcast Skill: Temporal Correlation/ 1981-2001MME Hindcast Skill: Temporal Correlation/ 1981-2001

2m Air Temperature2m Air TemperatureDEMETER MMEPAPCC MMEP

JJA

DJF

MME Hindcast Skill: Taylor Diagram/ 1981-2001MME Hindcast Skill: Taylor Diagram/ 1981-2001

PrecipitationPrecipitationDEMETER MMEPAPCC MMEP

JJA

DJF

MME Hindcast Skill: Temporal Correlation/ 1981-2001MME Hindcast Skill: Temporal Correlation/ 1981-2001

PrecipitationPrecipitationDEMETER MMEPAPCC MMEP

JJA

DJF

MME Hindcast Skill: Taylor Diagram /1981-2001MME Hindcast Skill: Taylor Diagram /1981-2001

MME Effective Index/ PrecipitationMME Effective Index/ Precipitation

JJA DJF

Precipitation (shading) and SST (contour)

Observation All-Model Composite

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

J J A1997

SON1997

J J A1998

mm/day

Latitu

de

Latitu

de

Latitu

de

Longitude Longitude

Wang et al. (2004)

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

COLA DNM GEOS GFDL IAP IITM MRI NCAR NCEP SNU SUNY Comp

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

- 0.6

- 0.4

- 0.2

0

0.2

0.4

0.6

(a) Southeast Asian and WNP region

J J A97 SON97 J J A98

(b) The rest of the A- AM domain

J J A97 SON97 J J A98

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

COLA DNM GEOS GFDL IAP IITM MRI NCAR NCEP SNU SUNY Comp

Fig. 1

Correlation Coefficients between the observed and 5 AGCM MME hindcasted June-August precipitations (1979-1999)

Wang et al. (2005)

Area averaged correlation coefficients (skills)

El Nino region (10oS-5oN, 80oW-180oW)

WNP (5-30oN, 110-150oE)

Asian-Pacific MNS (5-30oN, 70-150oE)

PrecipitationPrecipitation

DEMETER MMEPAPCC MMEP

JJA

DJF

MME Hindcast Skill in AAM region :1981-2001MME Hindcast Skill in AAM region :1981-2001

Southeast Asian and WNP region: 80-150E, 5-30N

MME Effective Index/ PrecipitationMME Effective Index/ Precipitation

JJA DJF

Southeast Asian and WNP region: 80-150E, 5-30N

One-Tier 1 vs Two-Tier Anomaly PCC over AAM (JJA) One-Tier 1 vs Two-Tier Anomaly PCC over AAM (JJA)

ENSO vs PrecipitationENSO vs Precipitation SST vs. PrecipitationSST vs. Precipitation

Probabilistic forecast for above normal precipitation greater than 0.5 standard deviation

Probabilistic MMEPProbabilistic MMEPRange of Area of ROC Curve/ Above Normal PrecipitationRange of Area of ROC Curve/ Above Normal Precipitation

APCC DEMETER

JJA

DJF

Probabilistic forecast for above normal 2m air temperature greater than 0.5 standard deviation over ENSO Region

APCC DEMETER

Deterministic and Probabilistic MMEPDeterministic and Probabilistic MMEPPotential Economic Value/ Above Normal 2m Air TempPotential Economic Value/ Above Normal 2m Air Temp

Summary of the Preliminary Results

a. The CliPAS blended one- and two-tier MME hindcastshave skills comparable to DEMETER in precipitation andsurface temperature prediction, although their individualmodles’ 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.