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
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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)
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
Correlation between local SST and precipitation
SAPI
WNPPI
Lead-lag relationship between
Nino 3.4 SST and JJA PRCP Index
Comparative Assessment of the One-Tier and Two Tier MME predictions 2
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.
APCC/CliPASAPCC/CliPAS
Comparative Assessment of the One-Tier and Two Tier MME predictions 3
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)
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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)
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
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
APCC/CliPASAPCC/CliPAS
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.
APCC/CliPASAPCC/CliPAS
Any Questions and Comments?