CliPASCliPAS

Bin WangBin Wang

Multi-Model Ensemble Multi-Model Ensemble Seasonal Prediction System Seasonal Prediction System

DevelopmentDevelopment

IPRC, University of Hawaii, USAIPRC, University of Hawaii, USA

APCC International Research Project

CliPASCliPAS

This Year AchievementsThis Year Achievements

A coordinated research community is extended consisting of twelve institutions and a large group of leading scientist in the field of climate prediction from USA, Korea, Japan, Australia, and China.

A coordinated research community is extended consisting of twelve institutions and a large group of leading scientist in the field of climate prediction from USA, Korea, Japan, Australia, and China.

The second CliPAS project meeting was successfully held at University of Hawaii on 9-11th January, 2006.The second CliPAS project meeting was successfully held at University of Hawaii on 9-11th January, 2006.

24-year (1981-2004) MME hindcast experimental dataset are produced for 4 seasons. The dataset consists of 6 one-tier and 7 two-tier model systems (for 4 seasons from 8 models and 2 seasons from 5 models).

24-year (1981-2004) MME hindcast experimental dataset are produced for 4 seasons. The dataset consists of 6 one-tier and 7 two-tier model systems (for 4 seasons from 8 models and 2 seasons from 5 models).

Scientific achievements are made on seasonal climate prediction and predictability.Scientific achievements are made on seasonal climate prediction and predictability.

Metrics for validating hindcast has been designed.Metrics for validating hindcast has been designed.

CliPASCliPAS

The Current Status of HFP ProductionThe Current Status of HFP Production

Two-Tier systems

CGCMAGCM

NASA2 seasons

NASA2 seasons

CFS (NCEP)4 seasons

CFS (NCEP)4 seasons

SNU4 seasons

SNU4 seasons

FSU2 seasons

FSU2 seasons

GFDL2 seasons

GFDL2 seasons

ECHAM(UH)2 seasons

ECHAM(UH)2 seasons

CAM2 (UH)4 seasons

CAM2 (UH)4 seasons

SNU/KMA4 seasons

SNU/KMA4 seasons

Statistical-Dynamical SST

prediction (SNU)

One-Tier systems

SINTEX-F4 seasons

SINTEX-F4 seasons

UH Hybrid2 seasons

UH Hybrid2 seasons

IAP4 seasons

IAP4 seasons

GFDL4 seasons

GFDL4 seasons

*NCEP4 seasons*NCEP

4 seasons

* NCEP two-tier prediction was forced by CFS SST prediction

CliPASCliPAS

Institute AGCM Resolution OGCM Resolution Ensemble Member Reference

FRCGC ECHAM4 T106 L19 OPA 8.2 2o cos(lat)x2o lon L31 9 Luo et al. (2005)

GFDL R30 R30L14 R30 R30 L18 10 Delworth et al. (2002)

NASA NSIPP12o lat x 2.5o lon

L34Poseidon

V41/3o lat x 5/8o lon L27 3 Vintzileos et al. (2005)

NCEP GFS T62 L64 MOM3 1/3o lat x 1o lon L40 15 Saha et al. (2005)

SNU SNU T42 L21 MOM2.2 1/3o lat x 1o lon L32 6 Kug et al. (2005)

UH ECHAM4 T31 L19 UH Ocean 1o lat x 2o lon L2 10 Fu and Wang (2001)

APCC/CliPAS Tier-1 Models

Model Descriptions of CliPAS SystemModel Descriptions of CliPAS System

Institute AGCM Resolution Ensemble Member SST BC Reference

FSU FSUGCM T63 L27 10 SNU SST forecastCocke, S. and T.E.

LaRow (2000)

GFDL AM2 2o lat x 2.5o lon L24 10 SNU SST forecast Anderson et al. (2004)

IAP LASG 2.8o lat x 2.8o lon L26 6 SNU SST forecast Wang et al. (2004)

NCEP GFS T62 L64 15 CFS SST forecast Kanamitsu et al. (2002)

SNU/KMA GCPS T63 L21 6 SNU SST forecast Kang et al. (2004)

UH CAM2 T42 L26 10 SNU SST forecast Liu et al. (2005)

UH ECHAM4 T31 L19 10 SNU SST forecast Roeckner et al. (1996)

APCC/CliPAS Tier-2 Models

CliPASCliPAS

Scientific Achievements

Part I. Assessment of the current status of the climate model’s performances

Part I. Assessment of the current status of the climate model’s performances

Part II. Improvement of the MME techniques Part II. Improvement of the MME techniques

Part III. Predictability of coupled GCM forecast Part III. Predictability of coupled GCM forecast

Part IV. Intraseasonal Prediction and Predictability Part IV. Intraseasonal Prediction and Predictability

Current status of simulating Long-term mean and annual cycle of precipitation Current Skills of MME one-month lead seasonal forecast: NINO 3.4 SST, Rainfall, temperature Impacts of systematic errors on ENSO and Tropical Precipitation

Predictability of coupled model for precipitation, ENSO, Asian-Australian Monsoon

CliPASCliPAS

Background: Climate Prediction

Theories and ReviewsTheories and Reviews

Charney and Shukla (1977, 1981), Lorenz (1982) Palmer (1993), Palmer and Shukla (2000), Palmer and Hagedorn (2006), Kang and Shukla (2006), Waliser (2006)

Break through in ENSO forecast: Cane and Zebiak (1985)Statistical approaches: Barnston 1994), Hastenrass (1995)

Two-tier: AGCM forced by predicted SST Bengtsson et al. (1993), Barnet et al. (1994), Levezey et al. (1996), Wang et al. (2005),

One-Tier: Coupled A-OGCM: Ji et al. (1996), Stockdale et al. (1998)MME: Krishnamurti et al. (1999, 2006), Doblas-Reyers et al. (2000)Dynamical vs statistical prediction: Oldenborgh et al. (2003 for ECMWF system), Saha et al. (2006 for NCEP system)

MilestonesMilestones

Projects for MME PredictionProjects for MME Prediction

PROVOST (EU), DSP (USA), SMIP (WCRP), CTB (USA), DEMETER (EU), CliPAS (APCC)

Operational MME predictionOperational MME prediction

ECMWF, IRI , APCC

CliPASCliPAS

Part I. Assessment of the current status Part I. Assessment of the current status of the climate model’s performancesof the climate model’s performances

Annual Cycle and its Linkage with Seasonal Prediction skillAnnual Cycle and its Linkage with Seasonal Prediction skill Monsoon Domain and Rainy Season Evolution over Asian Monsoon Domain and Rainy Season Evolution over Asian

Sub-monsoon RegionsSub-monsoon Regions Prediction Skills of NINO 3.4 SSTPrediction Skills of NINO 3.4 SST Prediction Skills of Temperature and PrecipitationPrediction Skills of Temperature and Precipitation Impact of Model Systematic Error for SST and PrecipitationImpact of Model Systematic Error for SST and Precipitation One-Tier vs Two-Tier MME predictionOne-Tier vs Two-Tier MME prediction

CliPASCliPAS

Annual Mean Precipitation

The models’ performance in simulating and forecasting seasonal mean states is closely related to the models’ capability in predicting seasonal anomalies.

The models’ performance in simulating and forecasting seasonal mean states is closely related to the models’ capability in predicting seasonal anomalies.

Performance on Annual Cycle Performance on Annual Cycle and its Linkage with Seasonal Prediction skilland its Linkage with Seasonal Prediction skill

Performance on Annual mean & Annual Cycle

Linkage to Seasonal prediction skill Pattern Correlation Skill over the Global Tropics (0-360E, 30S-30N) Precipitation

CliPASCliPAS

Monsoon Domain and Rainy Season evolution Monsoon Domain and Rainy Season evolution over Asian Sub-monsoon Regionsover Asian Sub-monsoon Regions

[5-30N, 60-105E]

[5-20N, 105-160E]

[20-45N, 110-140E]

CliPASCliPAS

Prediction Skills of NINO 3.4 SSTPrediction Skills of NINO 3.4 SST

Forecast lead month

An

om

aly

Co

rre

lati

on

Tier-1 MMEDynamic-Statistical ModelPersistence

< 13 Tier-1 Models >

FebMayAugNov

El Nino GrowthLa Nina GrowthEl Nino DecayLa Nina DecayNormal

Seasonal Initial ConditionsENSO Phase of

Initial month

Tier-1 MME Forecast

Overall Skill

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Temporal Correlation Skill of 2m Air Temperature

Performance of MMEs Performance of MMEs in Hindcast Global Temperaturein Hindcast Global Temperature

JJA

DJF

MME seasonal prediction with 1-month lead time using 17 climate models which participate in CliPAS and DEMETER

CliPASCliPAS

Precipitation

Dry

Dry

DryDry

Wet

Wet

Dry

Dry

DryDry

Wet

Wet

Wet

Wet

Dry

Wet

* Impact of El-Nino on Global Climate from NOAA (based on Ropelewski and Halpert (1987), Halpert and Ropelewski (1992), and Rasmusson and Carpenter (1982)

Temporal Correlation Skill of Precipitation

Performance of MMEs Performance of MMEs in Hindcast Global Precipitationin Hindcast Global Precipitation

CliPASCliPAS

Impact of Model Systematic ErrorImpact of Model Systematic Error

1st mode SEOF of SST (Low frequency mode)

Obs. long run1st month

9th

month5th

month

NCEP CFSJJA

SINTEX-FMAM

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9

Forecast lead month

Co

rrel

atio

n

Temporal correlation coeff. of PC time series with observation

Pattern correlation coeff. of eigenvector with free coupled run

SINTEX-FNCEP CFS

SINTEX-FNCEP CFS

With increase of the lead month, the forecast ENSO mode progressively approaches to the model intrinsic mode in free coupled run and departs from the observed.

With increase of the lead month, the forecast ENSO mode progressively approaches to the model intrinsic mode in free coupled run and departs from the observed.

CliPASCliPAS

Impact of Model Systematic Error Impact of Model Systematic Error

Pattern Correlation Skill for the first two AC modesJJAS minus DJFM mean Precipitation

The first Annual Cycle mode

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Part II. Part II. Improvement of the MME techniques Improvement of the MME techniques

MME EffectivenessMME Effectiveness Optimal MME TechniqueOptimal MME Technique Deterministic vs Probabilistic ForecastDeterministic vs Probabilistic Forecast

CliPASCliPAS

Multi-Model Ensemble (MME) Multi-Model Ensemble (MME)

JJA precipitation over Indo-Pacific Region [40-280E, 30S-30N]

MME is produced using 17 climate models which participate in CliPAS and DEMETER.

JJA precipitation over Indo-Pacific Region [40-280E, 30S-30N]

MME is produced using 17 climate models which participate in CliPAS and DEMETER.

Forecast Skill of JJA PrecipitationOptimal Selection of a Subgroup of Models

Example: East Asian Domain [105-145E, 20-45N] The best MME skill is obtained using 4 models.

CliPASCliPAS

Optimal MME TechniqueOptimal MME Technique

Number of Model for MME

Mea

n Te

mpora

l Correl

atio

n

MME1

MME2

MME3

MME3.1

Temporal Correlation Skillof MMEs using 15 models

Temporal Correlation Skill as a Function of number of models

Over the globe [0-360E, 60S-60N]

* The MME3.1 is based on a new statistical downscaling method, which is named stepwise pattern projection model (SPPM), combined with prior procedure of predictor selection and posterior procedure of multi-model average with equal weight.

(a) Simple composite (b) Superensemble using SVD

(c) MME using SPPM1 (d) MME using SPPM2

CliPASCliPAS

Optimal MME TechniqueOptimal MME Technique

Number of Model for MME

Mea

n Te

mpora

l Correl

atio

n

MME1

MME2

MME3

MME3.1

Temporal Correlation Skillof MMEs using 15 models

Temporal Correlation Skill as a Function of number of models

Over the globe [0-360E, 60S-60N]

* The MME3.1 is based on a new statistical downscaling method, which is named stepwise pattern projection model (SPPM), combined with prior procedure of predictor selection and posterior procedure of multi-model average with equal weight.

(MME-S)

CliPASCliPAS

Deterministic vs Probabilistic ForecastDeterministic vs Probabilistic Forecast

JJA

DJF

* Temporal correlation: Contour (0.5, 0.7)* Area under ROC curve is the averaged value for that of three categorical events , contour (0.65, 0.75)

Temporal Correlation Skill Area under ROC curve (Aroc)for three categorical events

correlation

Aroc

CliPASCliPAS

Part III. Part III. Predictability of Coupled GCM Forecast Predictability of Coupled GCM Forecast

ENSO Predictability and How to Improve itENSO Predictability and How to Improve it Precipitation Over Global TropicsPrecipitation Over Global Tropics A-AM Monsoon PredictabilityA-AM Monsoon Predictability

CliPASCliPAS

ENSO Predictability and How to Improve itENSO Predictability and How to Improve it

(a) NCEP CFS

(c) ECMWF (d) UKMO

(b) SINTEX-F

Forecast Error of Ensemble MeanLorenz Curve of Ensemble Mean

Mean of Forecast Error of Each MemberMean of Lorenz Curve of Each member

Forecast Error of Each MemberLorenz Curve of Each Member

Forecast error: skill of “current” forecast. Lorenz curve: upper bound of predictability, the growth of initial error defined

as the difference between two forecasts valid at the same time (Lorenz 1982)

Forecast error: skill of “current” forecast. Lorenz curve: upper bound of predictability, the growth of initial error defined

as the difference between two forecasts valid at the same time (Lorenz 1982)

Forecast lead month

RM

S e

rro

r

Lorenz Curve of Ensemble Mean is not growing

Initial error growth is saturated within first two months followed by an level-off.Most significant improvement of ENSO prediction can be obtained by reducing the forecast error in the first month.

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Predictability in Couple Model MMEPredictability in Couple Model MME

SEOF Modes for Precipitation over Global Tropics[0-360E, 30S-40N]

How many modes are predictable?

variance ratio

10 15 20 25 30 40 50 60 70 80 % variance

First Four: 59.3%

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Asian-Australian Monsoon PredictabilityAsian-Australian Monsoon Predictability

S-EOF of Seasonal Mean Precipitation Anomalies

The First Mode: 30% The Second Mode: 13%

Forecast Skills of the Leading Modes of AA-M

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Part IV. Part IV. Intraseasonal Prediction and Predictability Intraseasonal Prediction and Predictability

The Current Status of ISO PredictionThe Current Status of ISO Prediction Potential PredictabilityPotential Predictability Effect of Air-Sea Coupling Effect of Air-Sea Coupling

CliPASCliPAS

Pattern Corr of climatological Summer Mean Prcp.

vs. ISO activity (40-180,20S-30N)

• Models which represent the pattern of climatological mean state reasonably well (bad) can also represent the pattern of ISO activity well (bad). • Proper simulation of mean basic state is crucial to the simulation of the intensity of intraseasonal variations and vice versa.

CERFECMWINGVLODYMAXPMETFUKMO

SNU1NCEPNASASNU2FSUUHCAM2

CERFECMWINGVLODYMAXPMETFUKMO

SNU1NCEPNASASNU2FSUUHCAM2

Averaged Pattern Correlation for 21 years / 60E-150E, EQ-25N

ISO PredictionISO Prediction

CliPASCliPAS

Signal To Noise Ratio at Indian Ocean and Western Pacific

IO : 60E-100E, 10S-20NWP: 120E-140E, EQ-20N

CERFECMWINGVLODYMAXPMETFUKMO

SNU-T1NCEP-T1SNU – T2FSU – T2UHCAM2-T2

ISO Potential PredictabilityISO Potential Predictability

CliPASCliPAS

ISO Potential PredictabilityISO Potential Predictability

Signal CPL Forecast Error

ATM Forecast Error

Air-Sea Coupling Extends the Predictability of Monsoon Intraseasonal Oscillation

ATM: 17 days, CPL: 24 days Fu et al. 2006

CliPASCliPAS

Prediction Strategy: One-Tier vs Two-Tier Prediction Strategy: One-Tier vs Two-Tier

Consistency between hindcast and forecast is very important.

One-Tier prediction has better skill than two-tier prediction. These models have no skills in the heavily precipitating summer monsoon regions. Coupled atmosphere-ocean models, on the other hand, can produce qualitatively correct local lead/lag SST-rainfall correlations, enhance the ENSO-monsoon connection, and provide improved skill in summer monsoon precipitation.

CliPASCliPAS

Precipitation

Prediction Strategy: SNU One-Tier vs Two-Tier Prediction Strategy: SNU One-Tier vs Two-Tier

CliPASCliPAS

One-Tier vs Two-Tier MME PredictionOne-Tier vs Two-Tier MME Prediction

A-AM Region ENSO Region

It is documented that the prediction skill of tier-1 systems is superior to the tier-2 seasonal prediction system in boreal summer over both A-AM and ENSO regions.

CliPASCliPAS

Paper PreparationPaper Preparation

(1) Bin Wang, J. Shukla, In-Sik Kang, June-Yi Lee, C.-K Park, E. K. Jin, J.-S. Kug, P. Liu, X. Fu, J. Schemm, A. Kumar, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Multi-model ensemble dynamic seasonal prediction of APCC/CliPAS and DEMETER. Will be submitted to Journal of Climate

(2) June-Yi Lee, Bin Wang, In-Sik Kang, Jong-Seong Kug, J. Shukla, E. K. Jin, C.-K. Park, J. Schemm, A. Kumar, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Performance of climate prediction models on annual modes of precipitation and its linkage with seasonal prediction. Will be submitted to Journal of Climate

(3) Bin Wang, June-Yi Lee, In-Sik Kang, Jong-Seong Kug, J. Shukla, C.-K. Park, J.-J. Luo, and J. Schemm: Interannual variability of Asian-Australian monsoon in observation and multi-model ensemble seasonal prediction. Will be submitted to Journal of Climate

(4) Bin Wang and Qinghua Ding: The global monsoon: Major modes of annual variation in tropical precipitation and circulation. Will be submitted to Journal of Climate

(5) Jong-Seong Kug and In-Sik Kang, 2006: Seasonal climate prediction with SNU tier-one and tier-two systems. submitted to Climate Dynamics

(6) June-Yi Lee, Bin Wang, A. Kumar, In-Sik Kang, Jong-Seong Kug, J. Shukla, E. K. Jin, C.-K. Park, J. Schemm,, J.-J. Luo, J. Kinter, B. Kirtman, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, W.-T. Yun, and T. Yamagata: Forecast skill comparison between one-tier and two-tier multi-model ensemble prediction. Will be submitted to Journal of Climate

CliPASCliPAS

Paper PreparationPaper Preparation

(7) H.-M. Kim, I.-S. Kang and coauthors: Simulation of intraseasonal variability and its predictability in climate prediction models. Will be submitted to Journal of Climate (8) E. K. Jin, J. L. Kinter, J. Shukla, B. Kirtman, B. Wang, J.-Y. Lee, I.-S. Kang, J.-S. Kug, C.-K. Park, J. Schemm, A. Kumar, J.-J. Luo, T. Krishnamurti, S. Cocke, N. C. Lau, T. Rosati, W. Stern, M. Suarez, S. Schubert, W. Lau, T. Yamagata, and W.-T. Yun: Impact of model systematic errors on CGCM forecast skills. Will be submitted to Journal of Climate

(9) E. K. Jin, J. L. Kinter III, and B. Wang: Current status of ENSO prediction skill in coupled ocean-atmosphere model. Will be submitted to Journal of Climate

(10) E. K. Jin, J. L. Kinter III, and B. Wang: Predictability of coupled GCM forecasts: Error growth and its implication on seasonal forecast. Will be submitted to Journal of Climate

CliPASCliPAS

Conclusions (1)

1. MME prediction beats any individual model. The highest skill may be achievable by an optimal choice of a subgroup of models, drawing upon individual models’ skills and their mutual independence.

2. Correlation skill of the CGCM MME forecast of NINO3.4 SST reaches 0.86 at a 6-month lead. The forecast skills depend strongly on the phase of ENSO, the initial time (season), and the strength of ENSO.

3. MME prediction of air temperature is considerably superior to the persistence skill in the warm pool oceans. The precipitation skill is better than what the empirical relationships indicated, especially in the tropical Pacific in JJA and East Asian monsoon region during DJF.

4. Precipitation predictability in the coupled climate models can be quantified by the fractional variance of the “predictable” leading modes. The MME’s prediction skill primarily comes from these predictable modes.

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Conclusions (2)

5. Most significant improvement of ENSO prediction can be achieved by reducing the forecast error in the first month.

6. Coupled model MME captures the first two leading modes of AA-M variability better than those by the reanalyses (ERA 40 and NCEP-2).

7. Model errors, such as biases in the amplitude, spectral peak, and phase locking to the annual cycle, are factors of degrading forecast skills especially at long lead times.

8. Seasonal prediction skills are positively correlated to their performance on both the annual mean and annual cycle of the coupled model.

9. Atmosphere–ocean coupling can extend the intraseasonal predictability by about a week.

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Challenges and Recommendations

1. Rainfall forecasts in A-AM region remains moderate. Over land is particularly poor. There is an urgent need to determine to what extent the intrinsic internal variability of monsoon limits its predictability.

2 Poor performance over land monsoon region may be partially due to poor quality of the land surface initial conditions and the models’ deficiencies in the representation of atmosphere-land interaction. Global land surface data assimilation is an urgent need. Need to determine to what extent improved land processes can contribute to improved predictive skill.

3. The MME can only capture a moderate portion of the precipitation variability. Improvement of the MME skill relies on good models. Improvement of models is essential and remains a long-term goal.

4. Continuing improvement to the models’ representation of the slow coupled dynamics (e.g., properties of ENSO mode) is essential for improving ENSO and long-lead seasonal predictions. Correction of systematic errors also holds a key.

6. The accuracy and consistency of the initial conditions of the coupled ocean-atmosphere system is important for improving short-lead seasonal prediction.

7. The notion that the summer monsoon can be modeled and predicted by prescribing the lower boundary conditions is questionable. Need to reshape our strategy in validating models and predicting summer monsoon rainfall.

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Any Questions and Comments?