clipas june-yi lee and bin wang how are seasonal prediction skills related to models’ systematic...

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


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


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


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.


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


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


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.


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.


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.


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


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)


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]


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


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.


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?


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.


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)


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)


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


ENSO Composite (Velocity Potential)ENSO Composite (Velocity Potential)

Divergence (dashed line)

Convergence (solid line)

(ECMWF model)


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.


SST Precipitation

Annual Cycle of NCEP Models


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


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]


CliPASCliPAS

Pattern Correlation skill over the global Tropics [30S-30N]


clipas june-yi lee and bin wang how are seasonal prediction skills related to models’ systematic...

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