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International Workshop on Monthly-to-Seasonal Climate Prediction National Taiwan Normal Univ., 25-26 October 2003. Evaluation of the APCN Multi-Model Ensemble Prediction System. AP EC C limate N etwork. Woo-Sung Lee / APCN Secretariat. Contents. Objectives - PowerPoint PPT Presentation
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International Workshop International Workshop on Monthly-to-Seasonal Climate Prediction on Monthly-to-Seasonal Climate Prediction
National Taiwan Normal Univ., 25-26 October 2003National Taiwan Normal Univ., 25-26 October 2003
Evaluation of the APCN Multi-Model Ensemble Prediction System
AP
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Woo-Sung Lee / APCN Secretariat
AP
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ObjectivesObjectives
Participating ModelsParticipating Models
VerificationVerification
- Climatology- Climatology
- Variability- Variability
- Predictability- Predictability
SummarySummary
Contents
Is multi-model ensemble prediction superior to single model prediction?
Where do we stand?Performance of the APCN MME System in terms of:
Multi-model Ensemble
Reduce bias in model formulation
Reduce bias in Initial condition
climatology variability Predictability
Objectives
AP
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Member Economie
sAcronym Organization
Model Resolution
Seasonal Prediction Data
Hindcast Data
2001 2002 2003 SMIP AMIP
ChinaNCC
National Climate Center/China Meteorological
AdministrationT63L16 Ⓟ ⓅⓉⒽ
IAP Institute of Atmospheric Physics 4 5 L2 ⓅⓉⒽ ⓅⓉⒽ
Chinese Taipei CWB Central Weather Bureau T42L18 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-1999
Japan JMA Japan Meteorological Agency T63L40 ⓅⓉⒽ 1979-1999
Korea
GDAPS/KMA
Korea Meteorological Administration T106L21 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-1999 1979-1995
GCPS/KMA Korea Meteorological Administration T63L21 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-1999 1979-1998
METRI/KMA
Meteorological Research Institute
/ Korea Meteorological Administration
4 5 L17 Ⓟ ⓅⓉⒽ ⓅⓉⒽ
Russia MGO Main Geophysical Observatory T42L14 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-1999
USANCEP
Climate Prediction Center/National Centers for
Environmental PredictionT63L17 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-1999
(7 months)
NSIPP/NASA
National Aeronautics and Space Administration 2 2.5 L34 Ⓟ ⓅⓉⒽ ⓅⓉⒽ 1979-2000
Variable Name Reference
Precipitation CMAP/NCEP Xie and Arkin, 1997
850hPa Temperature NCEP/NCAR reanalysis Kalnay et al., 1996500hPa GPH
Participating Models
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Composite (global mean: 2.79)
OBS (global mean: 2.77)
Individual Model
Global Mean
Bias
3.25 0.48
3.19 0.42
2.91 0.14
2.81 0.04
2.72 -0.05
2.66 -0.11
2.49 -0.28
2.32 -0.45
Composite
2.79 0.02
Climatology: JJA mean Precipitation
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Composite-OBS
Climatology: JJA mean Precipitation
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Composite (global mean: 9.14)
OBS (global mean: 9.16)
Individual Model
Global Mean
Bias
11.09 1.93
10.03 0.87
9.07 -0.09
9.07 -0.09
8.58 -0.58
8.42 -0.74
7.73 -1.43
Composite
9.14 -0.02
Climatology: JJA mean 850hPa Temperature
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OBS
Composite
Climatology: JJA mean 850hPa Temperature_Eddy
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Composite-OBS
Climatology: JJA mean 850hPa Temperature
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Global Mean
1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
JJA
OBS
Comp
Climatology: JJA mean Temperature & Precipitation
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1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
Climatology: JJA mean Temperature & Precipitation
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I. Asian Monsoon Region II. Indian Monsoon Region
III
IV
II
I
III. Western North Pacific Region IV. East Asian Monsoon Region
Precipitation
1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
850hPa Temperature
Variability: Space-Time Variability (Global)
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SDSD RMSE
Corr.
Precipitation
1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
1 CWB 2 GCPS 3 GDAPS 4 IAP 5 JMA
6 METRI 7 MGO 8 NCC 9 NCEP 10 NSIPP
850hPa Temperature
Variability: Space-Time Variability (Asian Monsoon Region)
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January
April
July
October
Composite
CMAP
First HarmonicOf Precipitation
Variability: Annual Cycle (Global)
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COMP OBS
Variability: Annual Cycle(Asian Monsoon Region)
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Precipitation Anomaly (mm/day) at Equator
Precipitation Anomaly (mm/day) at Equator
OBS Comp CWB GCPS GDAPS JMA MGO NSIPP
Variability: Inter-annual Variability (Equator)
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Empirical Orthogonal Function(1st Mode)
Empirical Orthogonal Function(1st Mode)
Year
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0CWB GCPS GDAPS JMA MGO NCEP NSIPP COMP
OBS
Pricipal component(1st Mode)
Pricipal component(1st Mode)
OBS(24.4%) COMP(30.8%) CWB(40.8%) GCPS(42.5%)
NCEP(49.9%)MGO(28.8%)JMA(22.0%)GDAPS(24.9%)
NSIPP(34.3%)
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Variability: Inter-annual Variability (Asian Monsoon Region)
MME IMME I Simple composite
MME IIMME II Singular Value Decomposition
MME IIIMME III Composite after statistical downscaling bias correction (Coupled Pattern Projection Method).
Used Model CWB, NSIPP GCPS, NCEP, JMA, GDAPS
Period• 21-year hindcasts from 1979 to 1999• 2001/2002/2003 summer forecasts
Variable Precipitation, 850hPa Temperature
Multi-Model Ensemble Technique
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Year
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
AC
C
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
Mean 2002
Year
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
AC
C
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8CWBGCPS
GDAPS JMA NSIPP NCEP
MME1 MME2 MME3
Mean 2002
Precipitation (Global Mean)Precipitation (Global Mean)
850hPa Temperature (Global)850hPa Temperature (Global)
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Pattern Correlation: 1979-1999 Hindcast, 2002 JJA forecast
Year
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
AC
C
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
Mean 2002
Year
79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99
AC
C
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7CWB
GCPS
GDAPS
JMA
NSIPP
NCEP
MME1
MME2
MME3
Mean 2002
Precipitation (Asian Monsoon Region)
Precipitation (Asian Monsoon Region)
850hPa Temperature (Asian Monsoon Region)
850hPa Temperature (Asian Monsoon Region)
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Pattern Correlation: 1979-1999 Hindcast, 2002 JJA forecast
Precipitation Anomaly (JJA mean)
Precipitation Anomaly (JJA mean)
MME1(0.42) MME2(0.41) MME3(0.46)
CWB(0.06) GCPS(0.21)
GDAPS(0.07) NCEP(0.41) NSIPP(0.35)
OBS
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2002 Summer Forecast
850hPa Temperature Anomaly (JJA mean)
850hPa Temperature Anomaly (JJA mean)
MME1(0.40) MME2(0.40) MME3(0.51)
CWB(0.1) GCPS(0.39)
GDAPS(0.12) NCEP(0.25) NSIPP(0.33)
OBS
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2002 Summer Forecast
Composite of the APCN participating models reproduces major features of the observation, while there is a considerable diversity among the models. In the global sense, APCN MME system provides superior performance to any single model prediction in term of climatology, variability and predictability.
Statistical bias correct of individual models prior to multi-model ensemble(MME3) enhances predictability compare to simple model composite(MME1) or SVD superensemble(MME2)
However, most of the models show significant deficiency in simulating regional climate over the Asian monsoon region. Thus the MMEs are relatively not effective.
Model physics needs to be improved for better Asian monsoon prediction.
Summary
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Model Number
654321
AC
C
0.32
0.36
0.40
0.44
0.48
0.52
0.56
0.60
MME3
Predictability according to participating model numbers
Predictability according to participating model numbers
Global mean Precipitation
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Predictability