CliPASCliPAS
June-Yi Lee and Bin WangJune-Yi Lee and Bin Wang
How are seasonal prediction skills How are seasonal prediction skills related to models’ systematic related to models’ systematic
error?error?
IPRC, University of Hawaii, USAIPRC, University of Hawaii, USAIn-Sik Kang, Seoul National University, In-Sik Kang, Seoul National University, KoreaKoreaJ. Shukla, George Mason University, USAJ. Shukla, George Mason University, USAC.-K. Park, APCC, KoreaC.-K. Park, APCC, Korea
APCC/CliPASAPCC/CliPAS
Acknowledge contributions from the following CliPAS/APCC Investigators
CliPAS: Climate Prediction and ItsCliPAS: Climate Prediction and ItsApplication to SocietyApplication to Society
The international project, the CliPAS, in support of APCC is aimed at establishing well-validated multi-model ensemble establishing well-validated multi-model ensemble (MME) prediction systems(MME) prediction systems for climate prediction and developing economic and societal applications.
BMRCBMRC: O. Alves: O. Alves
CES/SNUCES/SNU: I.-S. Kang, J.-S. Kug : I.-S. Kang, J.-S. Kug
COLA/GMUCOLA/GMU: J. Shukla, B. Kirtman, J. Kinter, K. Jin: J. Shukla, B. Kirtman, J. Kinter, K. Jin
FSUFSU: T. Krishnamurti, S. Cocke, : T. Krishnamurti, S. Cocke,
FRCGC/JAMSTECFRCGC/JAMSTEC: J. Luo, T. Yamagata (UT) : J. Luo, T. Yamagata (UT)
IAP/CASIAP/CAS: T. Zhou, B. Wang : T. Zhou, B. Wang
KMAKMA: W.-T. Yun : W.-T. Yun
NASA/GSFCNASA/GSFC: M. Suarez, S. Schubert, W. Lau : M. Suarez, S. Schubert, W. Lau
NOAA/GFDLNOAA/GFDL: N.-C. Lau, T. Rosati, W. Stern : N.-C. Lau, T. Rosati, W. Stern
NOAA/NCEPNOAA/NCEP: J. Schemm, A. Kumar : J. Schemm, A. Kumar
UH/IPRC/ICCSUH/IPRC/ICCS: B. Wang, J.-Y. Lee, P. Liu, L. X. Fu : B. Wang, J.-Y. Lee, P. Liu, L. X. Fu
APCC/CliPASAPCC/CliPAS
The Current Status of HFP ProductionThe Current Status of HFP Production
Two-Tier systems
CGCMAGCM
NASA80-04,2 times
NASA80-04,2 times
CFS (NCEP)81-04,12 timesCFS (NCEP)81-04,12 times
SNU80-02, 4 times
SNU80-02, 4 times
FSU79-04, 2 times
FSU79-04, 2 times
GFDL79-04, 2 times
GFDL79-04, 2 times
ECHAM(UH)79-03, 2 timesECHAM(UH)79-03, 2 times
CAM2 (UH)79-03, 4 timesCAM2 (UH)79-03, 4 times
SNU/KMA79-02, 12 times
SNU/KMA79-02, 12 times
Statistical-Dynamical SST
prediction (SNU)
One-Tier systems
SINTEX-F82-04, 12 times
SINTEX-F82-04, 12 times
UH82-03, 4 times
UH82-03, 4 times
IAP79-04, 4 times
IAP79-04, 4 times
GFDL79-05,12 times
GFDL79-05,12 times
*NCEP81-04,4 times
*NCEP81-04,4 times
* NCEP two-tier prediction was forced by CFS SST prediction
POAMA(BMRC)80-02, 12 times
POAMA(BMRC)80-02, 12 times
APCC/CliPASAPCC/CliPAS
Multi-Model Ensemble Climate Prediction
APCC/CliPASAPCC/CliPASOne TierOne Tier NCEP/CFS
DEMETERDEMETER
FRCGC/ SINTEX-F
SNU
CERFACS
ECMWF
INVG
LODYC
Meteo-France
Met Office
MPI
13 coupled model retrospective forecasts for 1981-2001 targeting seasonal climate prediction with 4 initial conditions starting from February 1st, May 1st, August 1st, and November 1st
APCC/CliPASAPCC/CliPASTwo TierTwo Tier
FSU
GFDL
SNU
Comparison
NASA
UH 1
UH 2
Climate Prediction ModelsClimate Prediction Models
GFDL
POAMA/BMRC
UH
IAP
NCEP GFS
APCC/CliPASAPCC/CliPAS
TopicsTopics
The fidelity of a model simulation of interannual variability has a close link to its ability in simulation of climatology (Shukla 1984; Fennessy et al. 1994, Sperber and Palmer 1996; Kang et al. 2002; Wang et al. 2004) and seasonal migration of rain belt (Gadgil and Sajani 1998).
(1) The impact of the models’ systematic errors in mean state on its performance on seasonal precipitation prediction
Improvements in a coupled model’s mean climatology generally lead to a more realistic simulation of ENSO-monsoon teleconnection (Lau and Nath 2000; Annamalai and Liu 2005; Turner et al. 2005; Annamalai et al. 2007)
(2) The impact of the systematic errors on ENSO-monsoon relationship
Objective: To identify the strengths and weaknesses of the seasonal prediction models, especially coupled models, in predicting seasonal monsoon climate.
APCC/CliPASAPCC/CliPAS
Institute AGCM Resolution OGCM ResolutionEnsembl
e Member
Reference
BMRCPOAMA1.5 BAM 3.0d T47 L17 ACOM3 0.5-1.5olat x
2olon L32 10 Zhong et al. (2005)
FRCGC ECHAM4 T106 L19 OPA 8.2 2ocos(lat) x 2o lon L31 9 Luo et al. (2005)
GFDL AM2.1 2olat x 2.5olon L24 MOM4 1/3olat x 1olon
L50 10 Delworth et al. (2006)
NCEP GFS T62 L64 MOM3 1/3olat x 5/8olon L27 15 Vintzileos et al. (2005)
Saha et al. (2006)
SNU SNU T42 L21 MOM2.2 1/3olat x 1olon L40 6 Kug et al. (2005)
UH ECHAM4 T31 L19 UH Ocean 1olat x 2olon L2 10 Kug et al. (2005)
CERFACS ARPEGE T63 L31 OPA8.2 2.0o x 2.0o L31 9 Deque (2001)
Delecluse and Madec (1999)
ECMWF IFS T95 L 40 HOPE-E 1.4x0.3-1.4 L29 9 Gregory et al. (2000)Wolff et al. (1997)
INGV ECHAM-4 T42 L19 OPA 8.1 2.0x0.5-1.5 L29 9 Roeckner (1996)
Madec et al. (1998)
LODYC IFS T95 L40 OPA 8.2 2.0x2.0 L29 9 Gregory et al. (2000)
Delecluse and Madec (1999)
Meteo-France
ARPEGE T63 L31 OPA 8.0 182GPx152GP L31 9 Deque (2001)
Madec et al. (1997)
MPI ECHAM-5 T42 L19 MPI-OM1 2.5x0.5-2.5L23 9 Pope et al. (2000)
Gordon et al. (2000)UK Met Office HadAM3 2.5x3.75 L19 GloSea
OGCM1.25x0.3-125
L40 9 Roeckner (1996)Marsland et al. (2003)
13 Coupled Climate Models13 Coupled Climate Models
APCC/CliPASAPCC/CliPAS
Reconstruction of Annual Cycle in Climate PredictionReconstruction of Annual Cycle in Climate Prediction
Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec JanJan
1mon 2mon 3mon 4mon
1mon 2mon 3mon 4mon
1mon 2mon 3mon 4mon
1mon 2mon 3mon 4mon
Feb
Forecast lead timeSpring forecast Integrating from
February 1st
Spring forecast Integrating from
February 1st Summer forecast
Integrating from May 1st
Summer forecast Integrating from
May 1st Fall forecast Integrating from
August 1st
Fall forecast Integrating from
August 1st Winter forecast Integrating from November 1st
Winter forecast Integrating from November 1st
Annual cycle of prediction is reconstructed using retrospective forecasts for 4 initial conditions starting from 1 February, 1 May, 1 August, and 1 November. Thus, each month has different forecast lead time. 2-month forecast is used for March, June, September, and December, 3-month forecast for April, July, October, and January, and 4-month forecast for May, August, November, and February.
Reconstruction of annual cycle using different forecast lead time for each monthReconstruction of annual cycle using different forecast lead time for each month
APCC/CliPASAPCC/CliPAS
Current Status of Prediction of Seasonal Precipitation: Temporal Correlation Skill for 13 Coupled Model MME (81-01)
The prediction skills for precipitation vary with space and season. The variations in the spatial patterns and the seasonality of the correlation skills suggest that ENSO variability is the primary source of the global seasonal prediction skill. Prediction in DJF, SON and MAM is evidently better than JJA due to the model’s capacity in capturing the ENSO teleconnections around the mature phases of ENSO. Precipitation predictions over land and local summer monsoon region have little skills.
APCC/CliPASAPCC/CliPAS
One-Tier vs Two-Tier MME Prediction of JJA Precp. One-Tier vs Two-Tier MME Prediction of JJA Precp. /Anomaly Pattern Correlation & Normalized RMSE/Anomaly Pattern Correlation & Normalized RMSE
A-AM Region ENSO Region
It is documented that the prediction skill of one-tier systems is better than the two-tier seasonal prediction system in boreal summer over both A-AM [40-160E, 30S-30N] and ENSO [160-280E, 30S-30N] regions in terms of anomaly pattern correlation skill and normalized RMS error.
APCC/CliPASAPCC/CliPAS
Performance on Annual MeanPerformance on Annual Mean
mmday-1
mmday-1
MME prediction reproduces the observed features which include (1) the major oceanic convergence zones over the Tropics, (2) the Major precipitation zones in the extratropical Pacific and Atlantic and (3) remarkable longitudinal and latitudinal asymmetries
MME prediction reproduces the observed features which include (1) the major oceanic convergence zones over the Tropics, (2) the Major precipitation zones in the extratropical Pacific and Atlantic and (3) remarkable longitudinal and latitudinal asymmetries
(1) Underestimation over ocean convergence zone
(2) Overestimation over Maritime continents and high elevated terrains where the wind-terrain interaction influences annual rainfall.
(1) Underestimation over ocean convergence zone
(2) Overestimation over Maritime continents and high elevated terrains where the wind-terrain interaction influences annual rainfall.
APCC/CliPASAPCC/CliPAS
The MME predicted a
weaker-than-observed
Asian summer
monsoon.
The MME predicted a
weaker-than-observed
Asian summer
monsoon.
The spring-fall asymmetry is exaggerated
over the entire Indian Ocean, East Asia and South China Sea-Western North Pacific
regions.
The spring-fall asymmetry is exaggerated
over the entire Indian Ocean, East Asia and South China Sea-Western North Pacific
regions.
mm/day
mm/day
Solstice global
monsoon mode (71%) (JJAS minus DJFM mean
precipitation)
Performance on Annual CyclePerformance on Annual Cycle
Equinox asymmetric mode (13%) (AM minus ON mean
precipitation)
Forecast SkillForecast Skill
APCC/CliPASAPCC/CliPAS
Systematic Errors in JJA Monsoon Climate
Reduced precipitation over BoB, SCS, WNP, and East AsiaEnhance precipitation over MC, WIO and TP
Strong warm bias over land and cold bias over ocean enhancing the zonal and meridional land-sea thermal contrast
Enhanced AC over IO and MC and northward shifted AC over NPStrong low level div. over India and weakening of V over BoB and SCS, Strong conv. over MC
Weakening of divergence and anti cyclonic circulation in upper level monsoon flow
(a) Precipitation ( mmday-1) (2) 2m air temperature (degree) (3) stream function (shading, 1x106m2s-1) and wind (vector,ms-1) at 850 hPa, (d) stream function (shading) and velocity potential (contour, 2x106m2s-1)
APCC/CliPASAPCC/CliPAS
Performance on Mean States Performance on Mean States and its Linkage with Seasonal Predictionand its Linkage with Seasonal Prediction
The seasonal prediction skills are positively correlated with their performances on both the annual mean and annual cycle in the coupled climate models. The MME prediction has much better skill than individual model predictions for all metrics
The seasonal prediction skills are positively correlated with their performances on both the annual mean and annual cycle in the coupled climate models. The MME prediction has much better skill than individual model predictions for all metrics
Combined annual cycle skill of the 1st and 2nd EOF modes by weighting their eigenvalues
Pattern Correlation over Global Tropics [30S – 30N]
APCC/CliPASAPCC/CliPAS
Annual Mode vs Seasonal Precipitation PredictionAnnual Mode vs Seasonal Precipitation Prediction/ One-Tier vs Two-Tier MME/ One-Tier vs Two-Tier MME
Metric: Anomaly pattern correlation skill over 0-360E, 30S-30N
(a) Climatology vs IAV (b) 1st Annual Cycle vs IAV
NCEP CFS
NCEP T2
NCEP CFS
NCEP T2
APCC/CliPASAPCC/CliPAS
One Tier vs Two Tier / The 1One Tier vs Two Tier / The 1stst Annual Cycle Mode Annual Cycle Mode
Mean biases against CMAP precipitation
Model spread against multi-
model ensemble
mean
The spatial distribution of mean biases in one-tier MME is quite similar to that in two-tier MME except few regions, although the biases are much alleviated. The common biases in the two types of systems may arise from uncertain model physics and problematic land surface processes.
The spatial distribution of mean biases in one-tier MME is quite similar to that in two-tier MME except few regions, although the biases are much alleviated. The common biases in the two types of systems may arise from uncertain model physics and problematic land surface processes.
APCC/CliPASAPCC/CliPAS
Source of Seasonal Predictability of Precipitation in Source of Seasonal Predictability of Precipitation in Couple Model MMECouple Model MME
SEOF Modes for Precipitation over Global Tropics[0-360E, 30S-30N]
How many modes are predictable?
APCC/CliPASAPCC/CliPAS
Systematic and Anomaly Errors of JJA SST ForecastSystematic and Anomaly Errors of JJA SST Forecast
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.
APCC/CliPASAPCC/CliPAS
ENSO Composite ENSO Composite / Precipitation (Shaded) & SST (Contoured)/ Precipitation (Shaded) & SST (Contoured)
The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst the MME produce clear negative relationship. The anomalous precipitation and circulation are predicted better in the ENSO decaying JJA than ENSO developing JJA.
(Normalized anomaly field)
APCC/CliPASAPCC/CliPAS
ENSO Composite ENSO Composite //Velocity Potential at 850 (shaded) and 200 hPa (contoured)Velocity Potential at 850 (shaded) and 200 hPa (contoured)
Divergence (Dashed line)
Convergence (Solid line)
The shift of variability centers in onset summers and exaggerated variability in decay summers are evident in the atmospheric circulation field.
(63,68,72)
(82,91,97)
APCC/CliPASAPCC/CliPAS
SummarySummary
1
The state-of-the art coupled models can reproduce realistically the observed features of long-term annual mean precipitation. However, these models have common biases over the oceanic convergence zones where SST bias exists and the regions where the wind-terrain interaction is likely to produce annual rainfall.
2
3
4
The seasonal prediction skills are positively correlated with their performances on mean states in the coupled climate models. The MME prediction has much better skill than individual model predictions.
The errors in amplitude, phase, and maximum location of El Nino variability in model are associated with mean state errors such as colder equatorial Pacific SST and stronger easterly wind over western equatorial Pacific, resulting in errors in ENSO-Monsoon teleconnection. The breaking relationship between ENSO and Indian monsoon is evident in observation, whilst the MME produce clear negative relationship.
The skills of one-month lead MME prediction of seasonal mean precipitation vary with space and season. The variations in the spatial patterns and the seasonality of the correlation skills suggest that ENSO variability is the primary source of the global seasonal prediction skill. Prediction in DJF, SON, and MAM is evidently better than JJA due to the model’s capacity in capturing the ENSO teleconnections around the mature phases of ENSO.
CliPASCliPAS
CliPASCliPAS
Institute AGCM Resolutio
n OGCM ResolutionEnsembl
e Member
Reference
FRCGC ECHAM4 T106 L19 OPA 8.22o cos(lat)x2o lon
L319 Luo et al. (2005)
GFDL R30 R30L14 R30 R30 L18 10Delworth et al.
(2002)
NASA NSIPP12o lat x 2.5o
lon L34Poseidon
V41/3o lat x 5/8o lon
L273
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 10Fu and Wang
(2001)
APCC/CliPAS Tier-1 Models
Model Descriptions of CliPAS SystemModel Descriptions of CliPAS System
Institute AGCM ResolutionEnsembl
e 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 forecastAnderson 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 forecastKanamitsu 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 forecastRoeckner et al.
(1996)
APCC/CliPAS Tier-2 Models
CliPASCliPAS
Current Status of ENSO PredictionCurrent Status of ENSO Prediction/ Correlation Skill of Nino 3.4 SST/ Correlation Skill of Nino 3.4 SST
APCC/CliPASAPCC/CliPAS
ENSO Composite (Velocity Potential)ENSO Composite (Velocity Potential)
Divergence (dashed line)
Convergence (solid line)
(ECMWF model)
APCC/CliPASAPCC/CliPAS
Systematic Bias of Model in JJA
MME predicts weaker-than-observed monsoon precipitation
Strong warm bias over land and cold bias over ocean enhance the zonal and meridional land-sea thermal contrast in the prediction models.
Oceanic anticyclones are enhanced especially over Indian Ocean and maritime continent. North Pacific anticyclone is shifted northward.
Associated with enhanced anticyclones, cross-equatorial meridional wind is weaken over east of maritime continent and South China Sea. Meridional wind over Bay of Bengal is also weaken.
Precipitation is reduced over Bay of Bengal, SCS, WNP, and east Asian monsoon region and enhanced over maritime continent and western North Indian Ocean.
Reduced precipitation over SCS-WNP region results in weakening of divergence over same region and anticyclone over Indian Ocean at 200 hPa.
APCC/CliPASAPCC/CliPAS
SST Precipitation
Annual Cycle of NCEP Models
APCC/CliPASAPCC/CliPAS
Indian Monsoon
Source of Predictability and Error
MME system predicts realistic annual cycle of precipitation over the Indian monsoon region, while it has no skill in seasonal anomaly prediction of precipitation.
Systematic Bias: Cold bias of SST over the entire North Indian Ocean Weak upper level easterly
Major error source: Systematic bias in ENSO-Indian monsoon teleconnection
SCS-WNP Monsoon MME system has large systematic bias in annual cycle of precipitation, it has moderate skill in seasonal anomaly prediction
Systematic Bias: Cold bias of SST Enhance precipitation in cold seasons and reduced one in warm season Weak mean precipitation and its variance in JJA Weak upper level divergence
Predictability source: ENSO (MME reproduce realistic ENSO-WNP relationship) Error source: unrealistic simulation of ISO in models is related to weak mean precipitation and its weak variance
APCC/CliPASAPCC/CliPAS
The definition of monsoon domain
The regions in which the annual range (summer mean minus winter mean) exceeds 2mm/day and the local summer monsoon precipitation exceeds 35% of annual rainfall. Here, summer means JJA in the NH and DJF in the SH (Wang and Ding 2006).
The definition of monsoon domain
The regions in which the annual range (summer mean minus winter mean) exceeds 2mm/day and the local summer monsoon precipitation exceeds 35% of annual rainfall. Here, summer means JJA in the NH and DJF in the SH (Wang and Ding 2006).
Monsoon DomainMonsoon Domain
(red)
CliPASCliPAS
Figure 4. Temporal correlation coefficients (upper panels) and normalized RMSE (lower panels) of precipitation between observation and one-month lead seasonal prediction obtained from APCC/CliPAS MME system in summer (left-hand panels) and winter (right-hand panels) seasons, respectively. In (a) and (b), dashed line is for 0.3 and solid line is for 0.5 correlation coefficient. Solid contour indicates 0.9 in (b) and (d).
Temporal Correlation and Normalized RMSE of Temporal Correlation and Normalized RMSE of Precipitation PredictionPrecipitation Prediction
CliPASCliPAS
Pattern Correlation skill over the A-AM Region [40-160E, 30S-30N]
Performance on Mean States Performance on Mean States and its Linkage with Seasonal Predictionand its Linkage with Seasonal Prediction
CliPASCliPAS
Pattern Correlation skill over the global Tropics [30S-30N]
Performance on Mean States Performance on Mean States and its Linkage with Seasonal Predictionand its Linkage with Seasonal Prediction