preliminary results from clipas/apcc multi-model ensemble hindcast experiments bin wang and june-yi...
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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
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
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.