PowerPoint Template

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How are seasonal prediction skills related to
models systematic error?
June-Yi Lee and Bin Wang
IPRC, University of Hawaii, USA
In-Sik Kang, Seoul National University, Korea J.
Shukla, George Mason University, USA C.-K. Park,
APCC, Korea
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CliPAS Climate Prediction and Its Application to
Society
The international project, the CliPAS, in support
of APCC is aimed at establishing well-validated
multi-model ensemble (MME) prediction systems for
climate prediction and developing economic and
societal applications.
Acknowledge contributions from the following
CliPAS/APCC Investigators
BMRC O. Alves CES/SNU I.-S. Kang, J.-S. Kug
COLA/GMU J. Shukla, B. Kirtman, J. Kinter, K.
Jin FSU T. Krishnamurti, S. Cocke,
FRCGC/JAMSTEC J. Luo, T. Yamagata (UT)
IAP/CAS T. Zhou, B. Wang KMA W.-T. Yun
NASA/GSFC M. Suarez, S. Schubert, W. Lau
NOAA/GFDL N.-C. Lau, T. Rosati, W. Stern
NOAA/NCEP J. Schemm, A. Kumar UH/IPRC/ICCS B.
Wang, J.-Y. Lee, P. Liu, L. X. Fu
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The Current Status of HFP Production
Two-Tier systems
One-Tier systems
Statistical-Dynamical SST prediction (SNU)
CGCM
AGCM
NASA 80-04,2 times
CFS (NCEP) 81-04,12 times
FSU 79-04, 2 times
GFDL 79-04, 2 times
SNU 80-02, 4 times
CAM2 (UH) 79-03, 4 times
SINTEX-F 82-04, 12 times
SNU/KMA 79-02, 12 times
ECHAM(UH) 79-03, 2 times
UH 82-03, 4 times
IAP 79-04, 4 times
GFDL 79-05,12 times
NCEP 81-04,4 times
POAMA(BMRC) 80-02, 12 times
NCEP two-tier prediction was forced by CFS SST
prediction
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Climate Prediction Models
Multi-Model Ensemble Climate Prediction
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/CliPAS One Tier
APCC/CliPAS Two Tier
DEMETER
NCEP/CFS
FSU
CERFACS
Meteo-France
FRCGC/ SINTEX-F
GFDL
ECMWF
SNU
SNU
Met Office
INVG
Comparison
GFDL
NCEP GFS
MPI
LODYC
POAMA/ BMRC
IAP
UH 1
UH
NASA
UH 2
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Topics
Objective To identify the strengths and
weaknesses of the seasonal prediction models,
especially coupled models, in predicting seasonal
monsoon climate.
(1) The impact of the models systematic errors
in mean state on its performance on seasonal
precipitation prediction
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).
(2) The impact of the systematic errors on
ENSO-monsoon relationship
Improvements in a coupled models 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)
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13 Coupled Climate Models
Institute AGCM Resolution OGCM Resolution Ensemble Member Reference
BMRC POAMA1.5 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 2.5x0.5-2.5 L23 9 Pope et al. (2000) Gordon et al. (2000)
UK Met Office HadAM3 2.5x3.75 L19 GloSea OGCM 1.25x0.3-125 L40 9 Roeckner (1996) Marsland et al. (2003)
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Reconstruction of Annual Cycle in Climate
Prediction
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 month
Feb
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Jan
Jan
1mon
2mon
3mon
4mon
Spring forecast Integrating from February 1st
Forecast lead time
1mon
2mon
3mon
4mon
1mon
2mon
3mon
4mon
Summer forecast Integrating from May 1st
Fall forecast Integrating from August 1st
1mon
2mon
3mon
4mon
Winter forecast Integrating from November 1st
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Current Status of Prediction of Seasonal
Precipitation Temporal Correlation Skill for 13
Coupled Model MME (81-01)
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One-Tier vs Two-Tier MME Prediction of JJA Precp.
/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.

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Performance on Annual Mean
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

• Underestimation over ocean convergence zone
• (2) Overestimation over Maritime continents and
  high elevated terrains where the wind-terrain
  interaction influences annual rainfall.

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Performance on Annual Cycle
Solstice global monsoon mode (71) (JJAS minus
DJFM mean precipitation)
The spring-fall asymmetry is exaggerated over the
entire Indian Ocean, East Asia and South China
Sea-Western North Pacific regions.
The MME predicted a weaker-than-observed Asian
summer monsoon.
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Systematic Errors in JJA Monsoon Climate
Reduced precipitation over BoB, SCS, WNP, and
East Asia Enhance 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 NP Strong 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)
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Performance on Mean States and its Linkage with
Seasonal Prediction
Pattern Correlation over Global Tropics 30S
30N
Combined annual cycle skill of the 1st and 2nd
EOF modes by weighting their eigenvalues
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
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Annual Mode vs Seasonal Precipitation
Prediction/ One-Tier vs Two-Tier MME
(a) Climatology vs IAV
(b) 1st Annual Cycle vs IAV
NCEP CFS
NCEP CFS
NCEP T2
NCEP T2
Metric Anomaly pattern correlation skill over
0-360E, 30S-30N
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One Tier vs Two Tier / The 1st 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.
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Source of Seasonal Predictability of
Precipitation in Couple Model MME
SEOF Modes for Precipitation over Global
Tropics 0-360E, 30S-30N

• How many modes are predictable?

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Systematic 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.

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ENSO Composite / Precipitation (Shaded) SST
(Contoured)
(Normalized anomaly field)

• 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.

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ENSO Composite /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.

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Summary
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 models capacity in
capturing the ENSO teleconnections around the
mature phases of ENSO.
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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.
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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.
3
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.
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Thank You !
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Model Descriptions of CliPAS System
APCC/CliPAS Tier-1 Models
Institute AGCM Resolution OGCM Resolution Ensemble Member Reference
FRCGC ECHAM4 T106 L19 OPA 8.2 2o cos(lat)x2o lon L31 9 Luo et al. (2005)
GFDL R30 R30L14 R30 R30 L18 10 Delworth et al. (2002)
NASA NSIPP1 2o lat x 2.5o lon L34 Poseidon V4 1/3o 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-2 Models
Institute AGCM Resolution Ensemble Member SST BC Reference
FSU FSUGCM T63 L27 10 SNU SST forecast Cocke, 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)
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Current Status of ENSO Prediction / Correlation
Skill of Nino 3.4 SST
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ENSO Composite (Velocity Potential)
(ECMWF model)
Divergence (dashed line)
Convergence (solid line)
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MME predicts weaker-than-observed monsoon
precipitation
Systematic Bias of Model in JJA

• 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.

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Annual Cycle of NCEP Models
SST
Precipitation
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Source of Predictability and Error
Indian Monsoon

• 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

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Monsoon Domain
(red)
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).
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Temporal Correlation and Normalized RMSE of
Precipitation Prediction
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).
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Performance on Mean States and its Linkage with
Seasonal Prediction
Pattern Correlation skill over the A-AM Region
40-160E, 30S-30N
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Performance on Mean States and its Linkage with
Seasonal Prediction
Pattern Correlation skill over the global
Tropics 30S-30N