APCC/CliPAS Bin Wang Multi-Model Ensemble Seasonal Prediction System Development IPRC, University of...

APCC/CliPASAPCC/CliPAS

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

2007 APCC International Research Project


Executive Summary

The Climate Prediction and its Application to Society (CliPAS) team is an international

research project of the Asia-Pacific Economic Cooperation (APEC) Climate Center

(APCC).

Its goals is to provide APCC with frontier research in climate predictability and

prediction and to facilitate APCC’s effort in developing first-rate model tools and

technologies and continuously improving APCC operational forecast system.

The strategy of APCC/CliPAS is to coordinate leading climate scientists in 12 institutions

through well designed research projects and to share their expertise in climate

prediction and its application.

The Climate Prediction and its Application to Society (CliPAS) team is an international

research project of the Asia-Pacific Economic Cooperation (APEC) Climate Center

(APCC).

Its goals is to provide APCC with frontier research in climate predictability and

prediction and to facilitate APCC’s effort in developing first-rate model tools and

technologies and continuously improving APCC operational forecast system.

The strategy of APCC/CliPAS is to coordinate leading climate scientists in 12 institutions

through well designed research projects and to share their expertise in climate

prediction and its application.

In 2007 APCC international project, the APCC/CliPAS team has devoted to improving

APCC operational multi-model ensemble (MME) seasonal prediction system through

(1) implementing innovated MME schemes to APCC and

(2) providing one-tier predictions for 2007 winter using coupled models developed by

three non-operational institutions in APCC/CliPAS team.

The APCC/liPAS team has strive to address forefront climate issues through coordinated

multi-institutional retrospective forecast experiments and analysis of 21 models’ two-

decade long hindcast.

In 2007 APCC international project, the APCC/CliPAS team has devoted to improving

APCC operational multi-model ensemble (MME) seasonal prediction system through

(1) implementing innovated MME schemes to APCC and

(2) providing one-tier predictions for 2007 winter using coupled models developed by

three non-operational institutions in APCC/CliPAS team.

The APCC/liPAS team has strive to address forefront climate issues through coordinated

multi-institutional retrospective forecast experiments and analysis of 21 models’ two-

decade long hindcast.


Executive Summary

Plan Progress AchievementImprovement of MME method

Test and Evaluation of the MME 3.1 (the MME method based on SPPM v2) using APCC operational predictionsOperationalized MME 3.1 code need to be checked.

Kug, Lee, Kang, Wang, and Park (2007, submitted to GRL)Draft manuscript for APCC operational prediction

Case Study of the causes of the seasonal forecast for which most models failed

Case study was done for DJF 1989/90, MAM 1994, SON 2003, DJF 2003/04 using APCC operational prediction

Draft manuscript for APCC operational prediction

Evaluating APCC operational models with a newly designed hierarchy of metrics

A hierarchy of metrics were designed to evaluate intraseasonal-to-seasonal prediction.APCC operational prediction has been evaluated using a hierarchy of metrics on mean states (annual mean and cycle) and interannual variability of Equatorial SST, A-AM Monsoon, and ENSO-monsoon relationship. Practical predictability of APCC MME prediction on global Tropical precipitation was also investigated.

Technical report on evaluating APCC operational prediction will be provided by Dec. 22


Executive Summary

Plan Progress AchievementMulti-institutional retrospective forecast experiments

The multi-institutional retrospective forecast experiments were updated and completed for four seasons. APCC CliPAS team collected 7 one-tier and 7 two-tier predictions

Wang, Lee, Kang, Shukla, Park and co authors (2007, will be submitted to J. Climate)

APCC operational two-tier MME and APCC/CliPAS one-tier MME prediction

The APCC operational two-tier MME prediction was compared with APCC/CliPAS one-tier MME prediction for the Boreal summer precipitation and atmospheric circulation

Draft manuscript for comparison between one-tier and two-tier MME prediction

Predictability of ENSO and predictability of global precipitation in coupled models

Predictability of ENSO and global precipitation in coupled models were investigated.

Jin, Kinter, Wang, and co authors (2007, accepted to Clim. Dyn.)Jin and Kinter (2007, submitted to Clim. Dyn.)Wang, Lee, Kang, Shukla, Hameed, Park (2007, CLIVAR Exchanges 12, 4, 17-18)

Experimental prediction of ISO with a hybrid coupled model

Experimental hindcasts of MJO and Boreal summer ISO have been produced using UH hybrid coupled GCM

Fu, Wang, Bao, Liu, and Yang (2007, submitted to GRL)


Implemented MME-S in APCC

SPPM and MME-S was tested on

prediction of 850 hPa temperature

precipitation using APCC hindcast

data for the period 1983-2003 and

operational forecast data for 2006

and 2007. SPPM code was transferred to

APCC and is now part of the

Automated Forecast System.

SPPM and MME-S was tested on

prediction of 850 hPa temperature

precipitation using APCC hindcast

data for the period 1983-2003 and

operational forecast data for 2006

and 2007. SPPM code was transferred to

APCC and is now part of the

Automated Forecast System.

(1) Prior prediction selection

STEP 1: Applying statistical correction using SPPM to individual models

STEP 1: Applying statistical correction using SPPM to individual models

(2) Second Step: Pattern Projection

(3) Optimal choice of prediction

STEP 2: Simple multi-model composite using available predictions

STEP 2: Simple multi-model composite using available predictions

MME-S Procedure

CPPM- OLD version : 72 hoursCPPM – New version : 12-15 hours

SPPM v2 : 5 hours(suggestion: If you use 8 cpu simultaneously, it takes 10 hours for all models’ hindcast and forecast and two variables)

Advantage to using SPPM2

(1) Computational Estimates(per 1 model, 1 variable, 22 years)

(2) Improved skill, especially for precipitation


Implemented SPPM and MME-S in APCC

JJA Precipitation JJA Temperature at 850 hPa

Temporal Correlation Skill of APCC MME Prediction for the period 1983-2003

Anomaly Pattern Correlation Skill

Root Mean Square Error

Hindcast (83-03) and Forecast (2006) skills for four seasons


Prediction of JJA T850 in 2007

Observed and Predicted Anomaly of JJA 850 hPa Temperature in 2007

PCC for JJA2007 prediction MME I MME III MME IV New MME

Global (0-360, 60S-60N) 0.35 0.38 0.39 0.42

East Asia (80-180E, 10-60N) 0.52 0.55 0.51 0.57


6-month lead coupled predictions initiated from Nov 1, 2007

3 non-operational coupled models made real-time 6-month lead prediction initiated from November 1,

2007 from FRCGC in Japan, SNU in Korea, and UH in USA. This implementation is expecting to improve APCC operational MME prediction because the scientific

results of 2006-2007 APCC international project show that the one-tier models have better skill than

two-tier models in general

3 non-operational coupled models made real-time 6-month lead prediction initiated from November 1,

2007 from FRCGC in Japan, SNU in Korea, and UH in USA. This implementation is expecting to improve APCC operational MME prediction because the scientific

results of 2006-2007 APCC international project show that the one-tier models have better skill than

two-tier models in general

DJF Temperature DJF Precipitation


Description of APCC Operational Models

Institute AGCM Resolution MAM JJA SON DJF

China NCC T63L16 O O O O

Chinese Taipei CWB T42L18 X O O X

Japan JMA T63L40 O O X O

Korea

GDAPS/KMA T106L21 O O O O

GCPS/SNU T63L21 O O X X

METRI/KMA 4ox5o L17 O O O O

RussiaMGO T42L14 O O O O

HMC 1.12ox1.4o L28 O O X O

USA IRI T42L19 O O O X

NCEP T62L64 O O O O

Total number of model being used 9 10 7 7


Case study of the causes of the seasonal forecast for which most models failed

Forecast skill was very poor for

most models during DJF

1989/90, MAM 1994, SON 2003,

and DJF 2003/04 in which SST

anomaly is very weak over

equatorial Central and Eastern

Pacific. It is found that most of coupled

models failed to predict SST

anomalies over Tropical Oceans

as well as extratropical Oceans,

resulting in the failure in

predicting atmospheric

circulation and precipitation. It is interestingly noted that

prediction skill in winter season is

strongly related to that in

previous fall season during

recent decade.

Forecast skill was very poor for

most models during DJF

1989/90, MAM 1994, SON 2003,

and DJF 2003/04 in which SST

anomaly is very weak over

equatorial Central and Eastern

Pacific. It is found that most of coupled

models failed to predict SST

anomalies over Tropical Oceans

as well as extratropical Oceans,

resulting in the failure in

predicting atmospheric

circulation and precipitation. It is interestingly noted that

prediction skill in winter season is

strongly related to that in

previous fall season during

recent decade.

Anomaly Pattern Correlation Skill

Lee, June-Yi, J.-S. Kug, B. Wang, C.-K. Park, K.-H. AN, Saji H., and H. Kang, 2007: Assessment of APCC MME retrospective and realtime forecast for seasonal climate. Will be submitted to Clim. Dyn.


Case study of the causes of the seasonal forecast for which most models failed

The observed warm

anomalies over Equatorial

Central Pacific and North

Pacific were very difficult to

be captured by current

climate models.

For all cases, the spatial

pattern of predicted

anomalies was quite

different among models,

resulting in very weak

anomalies of MME prediction

all over the globe.

The observed warm

anomalies over Equatorial

Central Pacific and North

Pacific were very difficult to

be captured by current

climate models.

For all cases, the spatial

pattern of predicted

anomalies was quite

different among models,

resulting in very weak

anomalies of MME prediction

all over the globe.


Design a hierarchy of metrics to evaluate climate models’ performance on intraseasonal-to-seasonal prediction

We design a hierarchy of metrics to evaluate climate models’ performance on

intraseasonal-to-seasonal prediction We published and submitted several papers on evaluating climate models using the

metrics.

Wang et al, 2007: Assessment of APCC/CliPAS 14-model ensemble retrospective

seasonal prediction. To be submitted to J. Climate

Wang et al, 2007: How accurately do coupled climate models predict the leading modes of

A-AM interannual variability? Clim. Dyn. DOI:10.007/s00382-007-0310-5

Wang et al., 2007: Coupled predictability of seasonal tropical precipitation. CLIVAR

Exchanges 12, 4, 17-18

Kim et al., 2007: Simulation of intraseasonal variability and its predictability in climate

predictio models. Clim. Dyn. DOI 10. 1007/S00382-007-0292-3

We design a hierarchy of metrics to evaluate climate models’ performance on

intraseasonal-to-seasonal prediction We published and submitted several papers on evaluating climate models using the

metrics.

Wang et al, 2007: Assessment of APCC/CliPAS 14-model ensemble retrospective

seasonal prediction. To be submitted to J. Climate

Wang et al, 2007: How accurately do coupled climate models predict the leading modes of

A-AM interannual variability? Clim. Dyn. DOI:10.007/s00382-007-0310-5

Wang et al., 2007: Coupled predictability of seasonal tropical precipitation. CLIVAR

Exchanges 12, 4, 17-18

Kim et al., 2007: Simulation of intraseasonal variability and its predictability in climate

predictio models. Clim. Dyn. DOI 10. 1007/S00382-007-0292-3

We will make technical report to evaluating APCC operational prediction using some part of

the metrics including mean states and interannual variability of monsoon and ENSO-

monsoon relationship. The upper limit of practical predictability of global precipitation using

APCC operational prediction will be also investigated.

We will make technical report to evaluating APCC operational prediction using some part of

the metrics including mean states and interannual variability of monsoon and ENSO-

monsoon relationship. The upper limit of practical predictability of global precipitation using

APCC operational prediction will be also investigated.


Updated and completed the four-season multi-institutional retrospective forecast experiments

Current Status of Multi-Institutional Retrospective Forecast Experiments

The APCC/CliPAS team completed the four-season multi-institutional retrospective forecast

experiments for the period 1979-2004 for advancing our understanding of climate predictability

and determining the capability and limitations of the MME prediction. We collected 7 two-tier and

7 one-tier predictions from 12 institutions in Korea, USA, Japan, China, and Australia.

The APCC/CliPAS team completed the four-season multi-institutional retrospective forecast

experiments for the period 1979-2004 for advancing our understanding of climate predictability

and determining the capability and limitations of the MME prediction. We collected 7 two-tier and

7 one-tier predictions from 12 institutions in Korea, USA, Japan, China, and Australia.


Institute AGCM Resolution OGCM Resolution Ensemble Member Reference

BMRC BAM v3.0d T47L17 ACOM2 0.5-1.5o latx 2o lon L25 10 Zhong et al., 2005

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

GFDL AM2.1 2olatx2.5olon L24 MOM4 1/3olatx1olon L50 10Delworth et al.

(2006)

NASA NSIPP1 2o latx2.5o 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


TS PREC U850 V850 U200 V200 TS2M Z500

NCEP(JJA,DJF, 17GB/Sea)

o o o o

GFDL(MAM,JJA,SON,DJF, 40GB/Sea)

o o o o o

NASA(JJA,DJF, 5GB/Sea)

o o o o o o o

SNU T1(JJA,DJF, 8GB/Sea)

o o

SNU T2(JJA, DJF, 11GB/Sea)

o o o o o o o o0

UH – CAM2(JJA,DJF, 14GB/Sea)

0 0 0 0 0 0

FSU(JJA,DJF, 14GB/Sea)

o

Variables

CliPAS/APCC HFP Daily DATACliPAS/APCC HFP Daily DATA

Institution


One-tier and two-tier MME

predictions have been compared

using 7 one-tier and 7 two-tier

predictions in APCC/CliPAS

project. In JJA, the one-tier MME

system has better skill than two-

tier MME for seasonal climate

prediction as well as simulation of

mean and annual cycle. On the

contrary, the skill difference

between two MME system is very

small in DJF. NCEP two-tier prediction was

forced by predicted SST using

NCEP one-tier system. The

comparative results between

NCEP one-tier and two-tier

prediction support the necessity to

use one-tier system for predicting

summer rainfall.

One-tier and two-tier MME

predictions have been compared

using 7 one-tier and 7 two-tier

predictions in APCC/CliPAS

project. In JJA, the one-tier MME

system has better skill than two-

tier MME for seasonal climate

prediction as well as simulation of

mean and annual cycle. On the

contrary, the skill difference

between two MME system is very

small in DJF. NCEP two-tier prediction was

forced by predicted SST using

NCEP one-tier system. The

comparative results between

NCEP one-tier and two-tier

prediction support the necessity to

use one-tier system for predicting

summer rainfall.

Comparative Assessment of the One-Tier and Two Tier MME predictions 1

A-AM Region ENSO Region

(a) Climatology vs IAV (b) 1st Annual Cycle vs IAV

NCEP CFS

NCEP T2

NCEP CFS

NCEP T2

Seasonal prediction skill of JJA precipitation over A-AM and ENSO region

Performance of Annual Mode vs Seasonal Prediction Skill

June-Yi Lee, Bin Wang, and co authors, 2007: Forecast skill comparison between one-tier and two-tier multi-model ensemble prediction. To be submitted to J. Climate


Comparative Assessment of the One-Tier and APCC Two Tier MME predictions

Temporal Correlation Skill of JJA precipitation

Skill Difference between APCC T2 MME and T1 MME in (a) CliPAS and (b)

DEMETER

0.21

0.27

0.27


Comparative Assessment of the One-Tier and APCC Two Tier MME predictions

Temporal Correlation Skill of DJF precipitation

Skill Difference between APCC T2 MME and T1 MME in (a) CliPAS and (b)

DEMETER

0.27

0.31

0.32


Correlation between local SST and precipitation

SAPI

WNPPI

Lead-lag relationship between

Nino 3.4 SST and JJA PRCP Index


One-tier prediction shows increased feedback from local SST to some extent, although it

bears similar systematic error as two-tier, especially over East China Sea and Western North Pacific. One-tier prediction shows improved ENSO-monsoon teleconnection over Indian Ocean, while it

exhibits unrealistic impact of JJA precipitation over Western North Pacific on Nino 3.4 SST following

SON and DJF.

One-tier prediction shows increased feedback from local SST to some extent, although it

bears similar systematic error as two-tier, especially over East China Sea and Western North Pacific. One-tier prediction shows improved ENSO-monsoon teleconnection over Indian Ocean, while it

exhibits unrealistic impact of JJA precipitation over Western North Pacific on Nino 3.4 SST following

SON and DJF.



Two-tier MME shows distinctive

difference from one-tier

prediction during El Nino onset

and decaying summers.

Precipitation error is large over

South Asia in one-tier

prediction during El Nino

onset summers.

Two-tier MME has large

error over the same region

during El Nino decaying

summers.

Two-tier MME shows distinctive

difference from one-tier

prediction during El Nino onset

and decaying summers.

Precipitation error is large over

South Asia in one-tier

prediction during El Nino

onset summers.

Two-tier MME has large

error over the same region

during El Nino decaying

summers.

Velocity Potential at 850 hPa (shaded) and 200 hPa (contoured)

Divergence(dashed line)

Convergence(dashed line)


Prediction of Equatorial SST

The equatorial sea surface temperature

(SS) anomalies are the primary sources

of climate predictablity worldwide. The

7-coupled GCMs’ MME SST forecast

skills beat the SNU dynamical-statistical

model’s performance and far better than

persistence forecast.

In particular, the current MME capture

the temporal variation of the two leading

modes realistically. However, the spatial

westward shift of MME prediction

between the dateline and 120E could

potentially cause errors of global

teleconnection that is associated with

equatorial SSTA, degrading seasonal

climate prediction skills over both tropics

and extratropics.

The equatorial sea surface temperature

(SS) anomalies are the primary sources

of climate predictablity worldwide. The

7-coupled GCMs’ MME SST forecast

skills beat the SNU dynamical-statistical

model’s performance and far better than

persistence forecast.

In particular, the current MME capture

the temporal variation of the two leading

modes realistically. However, the spatial

westward shift of MME prediction

between the dateline and 120E could

potentially cause errors of global

teleconnection that is associated with

equatorial SSTA, degrading seasonal

climate prediction skills over both tropics

and extratropics.

EOF/ Equatorial SST [10S-5N]

Wang, Bin, June-Yi Lee, J. Shukla, I.-S. Kang, C.-K. Park and coauthors, 2007: Assessment of APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). To be submitted to J. Climate


Prediction of Indian Ocean SST

The temporal correlation skill (TCC) for SST predictions over the equatorial eastern Indian Ocean (EIO)

reaches about 0.68 at a 6-month lead forecast. The prediction for the equatorial western Indian Ocean

(WIO) SST is about 0.8 for November initiation but drops below 0.5 at the 4-month lead for May initiation.

However, the TCC skill for IOD index (SST at EIO minus SST at WIO) drops below 0.4 at the 3-month lead

forecast for both the May and November initiations. There exist a July prediction barrier and a severe,

unrecoverable January prediction barriers for IOD index prediction.

The temporal correlation skill (TCC) for SST predictions over the equatorial eastern Indian Ocean (EIO)

reaches about 0.68 at a 6-month lead forecast. The prediction for the equatorial western Indian Ocean

(WIO) SST is about 0.8 for November initiation but drops below 0.5 at the 4-month lead for May initiation.

However, the TCC skill for IOD index (SST at EIO minus SST at WIO) drops below 0.4 at the 3-month lead

forecast for both the May and November initiations. There exist a July prediction barrier and a severe,

unrecoverable January prediction barriers for IOD index prediction.

Wang, Bin, June-Yi Lee, J. Shukla, I.-S. Kang, C.-K. Park and coauthors, 2007: Assessment of APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). To be submitted to J. Climate

Correlation Skill of Indian Ocean SSTA


Predictability of Global Tropical Precipitation

Percentage Variance

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

Prediction skill of each mode

The first two SEOF modes are very well predicted. The third are also reasonably well predicted. But all other higher modes are not predictable as shown by the insignificant correlation skill in the spatial structures and temporal variation. We defined the first three modes are predictable part of the interannual variation using the current coupled MME prediction system.

54.3% (CMAP)83% (MME)


Upper limit of predictability if there is no other prediction source in MME system

We can quantify the “predictability” by the fractional variance that is accounted for by the “predictable” leading modes in the observations. Such “predictable” modes can be determined by examining models’ hindcast results

0.4 correlation is correspondent to 16% of fractional variance. (d) will be same as (a) If there is no systematic anomaly errors for the “predictable modes” in MME prediction.

Predictability of Global Tropical Precipitation


Experimental hindcasts of MJO have been produced using UH hybrid coupled GCM for 4 months of

the TOGA-COARE program in 1992-1993. The model was initialized with observations from January 1,

1993, and allowed to run freely for 2 months. A comparison of daily rainfall from the observations

(left) and from a 100-ensemble-mean model output (right) reveals that the model was able to

“forecast” the eastward movement and associated rainfall of the MJO beyond one month fairly

accurately.

Experimental hindcasts of MJO have been produced using UH hybrid coupled GCM for 4 months of

the TOGA-COARE program in 1992-1993. The model was initialized with observations from January 1,

1993, and allowed to run freely for 2 months. A comparison of daily rainfall from the observations

(left) and from a 100-ensemble-mean model output (right) reveals that the model was able to

“forecast” the eastward movement and associated rainfall of the MJO beyond one month fairly

accurately.

Experimental hindcast of MJO with the UH hybrid coupled model

Observed (left) and forecast (right) rainfall (mm/day) averaged over 10oS–10oN. For convenience observed rainfall (contours) are overlaid on the forecast in the right panel.

Fu, Xiouhua, Bin Wang, Q. Bao, P. Liu, and B. Yang, 2007: Experimental dynamical forecast of an MJJO event observed during TOGA-COARE period. Submitted to GRL


Experimental hindcastsof Boreal summer monsoon ISO have been produced using UH hybrid

coupled GCM for summer of 2006. The model was initialized with NCEP reanalysis data on June 11,

2006. A comparison of daily rainfall from the observations (left) and from a 100-ensemble-mean

model output (right) reveals that the model was able to “forecast” the northward movement and

associated rainfall of the ISO beyond one month fairly accurately.

Experimental hindcastsof Boreal summer monsoon ISO have been produced using UH hybrid

coupled GCM for summer of 2006. The model was initialized with NCEP reanalysis data on June 11,

2006. A comparison of daily rainfall from the observations (left) and from a 100-ensemble-mean

model output (right) reveals that the model was able to “forecast” the northward movement and

associated rainfall of the ISO beyond one month fairly accurately.

Experimental hindcast of ISO with the UH hybrid coupled model

Observed (left) and forecast (right) rainfall (mm/day) averaged over 60oE–120oE. For convenience observed rainfall (contours) are overlaid on the forecast in the right panel.


The impact of the systematic errors on ENSO-monsoon relationship

The errors in El Nino amplitude, phase, and maximum location of variability in coupled models

are related with mean state errors such as colder equatorial Pacific SST and stronger easterly

wind over western equatorial Pacific. The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst

the MME produce clear negative relationship mainly related to SST anomaly bias . The anomalous precipitation and circulation are predicted better in the ENSO decaying JJA than

ENSO developing JJA.

The errors in El Nino amplitude, phase, and maximum location of variability in coupled models

are related with mean state errors such as colder equatorial Pacific SST and stronger easterly

wind over western equatorial Pacific. The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst

the MME produce clear negative relationship mainly related to SST anomaly bias . The anomalous precipitation and circulation are predicted better in the ENSO decaying JJA than

ENSO developing JJA.

Precipitation (shading) and SST (contour) AnomalySystematic and Anomaly Errors of JJA SST Forecast

June-Yi Lee and Bin Wang, 2007: How is ENSO-monsoon relationship in coupled prediction affected by model’s systematic mean error? To be submitted to GRL


Paper PreparationPaper Preparation

Wang, Bin, June-Yi Lee, I.-S. Kang, J. Shukla, J.-S. Kug, A. Kumar, J. Schemm, J.-J. Luo, T. Yamagata, and C.-K. Park, 2007: How accurately do coupled climate models predict the leading modes of Asian-Australian monsoon interannual variability? Clim. Dyn. DOI: 10.1007/s00382-007-0310-5

Published (or in press)

Kim, H.-M., I.-S. Kang, B. Wang, and J.-Y. Lee, 2007: Simulation of intraseasonal variability and its predictability in climate prediction models. Clim. Dyn., DOI 10. 1007/S00382-007-0292-3.

Wang, Bin and Qinghua Ding, 2007: The global monsoon: Major modes of annual variation in tropical precipitation and circulation. Dynamics of Atmospheres and Oceans. In press.

Wang, Bin, June-Yi Lee, I.-S. Kang, J. Shukla, S. N. Hameed, and C.-K. Park, 2007: Coupled predictability of seasonal tropical precipitation. CLIVAR Exchanges, Vol. 12 No. 4. 17-18.

In revision

Jin, E. K, J. L. Kinter III, B. Wang and Co Authors, 2007: Current status of ENSO prediction skill in coupled ocean-atmosphere model. Climate Dynamics


Paper PreparationPaper Preparation

Kug, J.-S., J.-Y. Lee, I.-S. Kang, B. Wang, and C.-K. Park, 2007: Optimal multi-model ensemble method in seasonal climate prediction. Submitted to Geophys Res Lett

Submitted

Fu, Xiouhua, Bin Wang, Qing Bao, Ping Liu, and Bo Yang, 2007: Experimental dynamical forecast of an MJO event observed during TOGA-COARE period. Submitted to GRL

Emilia K. Jin and James L. Kinter III, 2007: Characteristics of Tropical Pacific SST Predictability in Coupled GCM Forecasts Using the NCEP CFS. Submitted to Clim Dyn

Emilia K. Jin, James L. Kinter III, and Ben P. Kirtman, 2007: Impact of Tropical SST on the Asian-Australian Monsoon in GCM experiments. Submitted to Geo. Res. Let.

To be submittedWang, Bin, J.-Y. Lee, J. Shukla, I.-S. Kang, C.-K. Park and co authors, 2007: Assessment of APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980-2004). To be submitted to J. ClimateLee, June-Yi, Bin Wang, I.-S. Kang, J. Shukla, C.-K. Park and co authors, 2007: Performance of climate prediction models on annual modes of precipitation and its relation with seasonal prediction. To be submitted to Clim. Dyn.

Lee, June-Yi and Bin Wang, 2007: How is ENSO-monsoon relationship in coupled prediction affected by model’s systematic mean errors? To be submitted to GRL.

Lee, June-Yi, J.-S. Kug, B. Wang, C.-K. Park, K.-H. An, Saji H., H. Kang and co authors, 2007: Assessment of APCC MME retrospective and realtime forecast for seasonal climate. To be submitted to Clim Dyn.


Conclusions (2)

The MME captures the first two leading modes of precipitation variability with high fidelity.

Potential to capture the precursors of ENSO in the A-AM domain.

The MME underestimates the total variances of the two modes and the biennial tendency of the first mode.

The correlation skill for the first principal component remains about 0.9 up to six months before it drops rapidly, but the spatial pattern forecast exhibits a drop across the boreal spring.

The coupled models’ MME predictions capture the first two leading modes of variability better than those captured by the ERA-40 and NCEP-2 reanalysis datasets.

Future reanalysis should be carried out with coupled atmosphere and ocean models.


Challenges

Physical basis/Strategy Correction of coupled model systematic errors

in annual cycle Improvement of the slow coupled modes Improvement of coupled model initialization Determine the roles of land-atmosphere

interaction Sub-seasonal prediction Predictability of extreme events


Directions

Improvement of models’ physics representation and correcting systematic errors.

Development of Multi-model one-tier system, including coupled data assimilation and reanalysis.

Improving slow coupled physics is a key for long-lead seasonal forecast.

Urgent need is to determine the role of land-atmosphere interaction in monsoon predictability.

Development of High resolution global models for prediction of TC and other extreme events.

Determine predictability of ISO and improve monthly prediction.


Any Questions and Comments?

APCC/CliPAS Bin Wang Multi-Model Ensemble Seasonal Prediction System Development IPRC, University of...

Documents

Transcript of APCC/CliPAS Bin Wang Multi-Model Ensemble Seasonal Prediction System Development IPRC, University of...