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

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Woo-Sung Lee / APCN Secretariat

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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

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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

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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

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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

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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

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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

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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