Predictability of ENSOMonsoon Relationship in NCEP CFS

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Predictability of ENSO-Monsoon Relationship in
NCEP CFS
Emilia K. Jin
Center for Ocean-Land-Atmosphere studies
(COLA) George Mason University (GMU)
Thanks to J. Kinter, B. Kirtman, J. Shukla, and
B. Wang
COLA/GMU, IPRC/Univ. of Hawaii
NOAA 32th Climate Diagnostics and Prediction
Workshop (CDPW), 22-26 Oct COAPS/FSU,
Tallahassee, FL
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Contents

• ENSO-monsoon relationship in NCEP/CFS forecasts

• The role of perfect ocean forcing in coupled
  systems CGCM vs. Pacemaker

• The role of air-sea interaction on ENSO-monsoon
  relationship

• Shortcoming in Pacemaker Decadal change of
  ENSO-Indian monsoon relationship

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JJA Forecast Skill of Rainfall with respect to
Lead Month
Temporal correlation with respect to lead month
1st month
3rd month
5th month
9th month
Area mean (60-140E, 30S-30N)
For retrospective forecasts, reconstructed data
with respect to lead time (monthly forecast
composite) is used.
Correlation
Forecast lead month

• Retrospective forecast (Courtesy of NCEP)

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Relationship between NINO3.4 and Monsoon Indices
Lead-lag correlation with respect to lead month
Extended IMR index
WNPSM index
N34 lead N34 lag
N34 lead N34 lag
COR(OBS,CFS)
Observation 1st month forecast 8th month forecast
EIMR
WNPSMI
0.48 0.20
0.48 0.12
1st month 8th month

• Western North Pacific Summer Monsoon Index (Wang
  and Fan, 1999)
• WNPSMI U850(5ºN15ºN, 100ºE130ºE) minus
  U850(20ºN30ºN, 110ºE140ºE)
• Extended Indian Monsoon Rainfall Index (Wu and
  Kirtman 2004)
• EIMR Rainfall (5ºN25ºN, 60ºE100ºE)
• Green line denotes 95 significant level.

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Relationship between NINO3.4 and Monsoon Indices
Extended IMR index
WNPSM index
Observation 1st month forecast 8th month
forecast CFS long run

• NCEP/CFS 52-year long run (Courtesy of Kathy
  Pegion)

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Regressed field of 1st SEOF of 850 hPa zonal wind
Observation
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Shading 500 hPa vertical pressure velocity
Contour 850 hPa winds
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Shading Rainfall (CMAP and PREC/L) Contour SST
COR (PC, NINO3.4) 0.85

• From the summer of Year 0, referred to as
  JJA(0), to the spring of the following year,
  called MAM(1), a covariance matrix was
  constructed using four consecutive seasonal mean
  anomalies for each year.
• SEOF (Wang and An 2005) of 850 hPa zonal wind
  over 40E-160E, 40S-40N
• High-pass filter of eight years
• The seasonally evolving patterns of the leading
  mode concur with ENSOs turnabout from a warming
  to a cooling phase (Wang et al. 2007).

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Regressed field of 1st SEOF of 850 hPa zonal wind
Observation
1
Shading 500 hPa vertical pressure velocity
Contour 850 hPa winds
1
Shading Rainfall (CMAP and PREC/L) Contour SST
COR (1st PC timeseries of SEOF, N34)
Correlation
N34 lead N34 lag
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Impact of the Model Systematic Errors on Forecasts
Pattern Cor. of EOF Eigenvector
Patternl correlation of eigenvector with
observation Pattern correlation of eigenvector
with free long run
Correlation

• With respect to the increase of lead month,
  forecast monsoon mode associated with ENSO is
  much similar to that of long run, while far from
  the observed feature.

Forecast lead month
COR (1st PC timeseries of SEOF, N34)
Correlation
Observation 1st month forecast 8th month
forecast CFS long run
N34 lead N34 lag
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In CFS coupled GCM, what is responsible to drop
the predictability of ENSO monsoon relationship?

• Ocean forcing?
• Atmospheric response?
• Air-sea interaction?
• ..

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Pacemaker Experiments

• The challenge is to design numerical experiments
  that reproduce the important aspects of this
  air-sea coupling while maintaining the
  flexibility to attempt to simulate the observed
  climate of the 20th century.
• Pacemaker tropical Pacific SST is prescribed
  from observations, but coupled air-sea feedbacks
  are maintained in the other ocean basins (e.g.
  Lau and Nath, 2003).
• Anecdotal evidence indicates that pacemaker
  experiments reproduce the timing of the forced
  response to El Niño and the Southern Oscillation
  (ENSO), but also much of the co-variability that
  is missing when global SST is prescribed.
• In this study, we use NCEP/GFS T62 L64 AGCM
  mainly.

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Pacemaker Experimental Design
In this study, the deep tropical eastern Pacific
where coupled ocean-atmosphere dynamics produces
the ENSO interannual variability, is prescribed
by observed SST.
165E-290E, 10S-10N
No blending
Slab ocean mixed-layer
Weak damping of 15W/m2/K to observed climatology
Zonal mean monthly Levitus climatology
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Model and Experimental Design
Pacemaker
CGCM
Atmosphere (GFS T62L64)
Atmosphere (GFS T62L64)
Local air-sea interaction
Fully coupled system
Ocean (Full dynamics)
SST
SST
heat flux, wind stress, fresh water flux
-?Tclim
Observed SST
heat flux
Slab ocean (No dynamics and advection)
Mixed layer model AGCM (1950-2004, 4runs)
CGCM (52 yrs)
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Lead-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
Observation PACE CFS
N34 lead N34 lag
N34 lead N34 lag
ISMI U850(5ºN15ºN, 40ºE80ºE) minus
U850(20ºN30ºN, 70ºE90ºE)
Ensemble spread of 4 members of Pacemaker exp.
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ENSO Characteristics in CFS CGCM
NINO3.4 Index during 1950-2005
(a) Observation
(b) CFS CGCM (52 year long run)
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Lead-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
Observation PACE CFS
N34 lead N34 lag
N34 lead N34 lag
ISMI U850(5ºN15ºN, 40ºE80ºE) minus
U850(20ºN30ºN, 70ºE90ºE)
Ensemble spread of 4 members of Pacemaker exp.
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ENSO Characteristics in CFS CGCM
Regression of DJF NINO3.4 Index to SST anomalies
(a) Observation
(b) CFS long run

• In CGCM, ENSO SST anomalies show westward
  penetration with narrow band comparing to the
  observed.

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JJA Regression map of 1st SEOF of 850 hPa zonal
wind
Difference from Obs.
Total field
850 hPa zonal wind and SST
850 hPa zonal wind and rainfall
Obs.
Pace
Pace-Obs.
CFS
CFS-Obs.
Contour zonal wind Shading SST
Contour zonal wind Shading rainfall
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Model and Experimental Design
Control
Pacemaker
Atmosphere (GFS T62L64)
Atmosphere (GFS T62L64)
No air-sea interaction
Local air-sea interaction
SST
-?Tclim
Observed SST
Observed SST
heat flux
Slab ocean (No dynamics and advection)
Climatology SST
Mixed layer model AGCM (1950-2004, 4runs)
AGCM (1950-2004, 4runs)
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Leag-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
Observation PACE Control
N34 lead N34 lag
N34 lead N34 lag
ISMI U850(5ºN15ºN, 40ºE80ºE) minus
U850(20ºN30ºN, 70ºE90ºE)
Ensemble spread of 4 members of Pacemaker exp.
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JJA Regression map of 1st SEOF of 850 hPa zonal
wind
Difference from Obs.
Total field
850 hPa zonal wind and rainfall
850 hPa zonal wind and SST
Obs
Pace-Obs.
Pace
Ctl-Obs.
Ctl
Contour zonal wind Shading SST
Contour zonal wind Shading rainfall
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1st S-EOF modes Observation
1956-76
1977-2004
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Lead-lag Correlation between NINO3.4 and Monsoon
indices
56-76 77-04

• Decadal change of ENSO-Monsoon relationship based
  on SEOF analysis (Wang et al. 2007)
• Remote El Niño/La Niña forcing is the major
  factor that affects A-AM variability.
• The mismatch between NINO3.4 SST and the
  evolution of the two major A-AM circulation
  anomalies suggests that El Niño cannot solely
  force these anomalies.
• 2. The monsoon-warm pool ocean interaction is
  also regards as a cause (a positive feedback
  between moist atmospheric Rossby waves and the
  underlying SST dipole anomalies)
• The enhanced ENSO variability in the recent
  period has increased the strength of the
  monsoon-warm pool interaction and the Indian
  Ocean dipole SST anomalies, which has
  strengthened the summer westerly monsoon across
  South Asia, thus weakening the negative linkage
  between the Indian summer monsoon rainfall and
  the eastern Pacific SST anomaly.
• ? However, in pacemaker, the strengthen of the
  Indian Ocean dipole SST anomalies is not shown
  due to fixed mixed-layer depth and SST
  climatology.

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Change of Lead-lag Correlation (Extended IMR,
NINO3.4)
20-year Moving Window during 1950-2004
OBS (IMR)
(HadSST and CMAP)
Lag correlation with respect to 20-yr moving
window during 55 years
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Summary

• In CFS CGCM, the predictability of lead-lag
  ENSO-monsoon relationship drops with respect to
  lead month due to systematic errors of ENSO and
  its response.

• To improve the predictability, pacemaker
  experiment is designed and conducted to reproduce
  the important aspects of air-sea coupling while
  maintaining the flexibility to attempt to
  simulate the observed climate.

• Surprisingly, pacemaker mimics the realistic
  ENSO-monsoon relationship compared to other
  experiments including control and coupled (CGCM).

• However, the recent change of ENSO-Indian
  monsoon relationship is missed in pacemaker,
  possibly associated with the Indian Ocean
  dynamics, while the decadal change of western
  North Pacific summer monsoon is well related with
  that of eastern tropical Pacific SST anomalies.
• To find out the cause of this discrepancy,
  supplementary pacemaker experiments can be
  performed based on this shortcoming.

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Thank You !
Emilia K. Jin kjin_at_cola.iges.org
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Change of DJF Simultaneous Correlation
20-year Moving Window during 1950-2004
Observation PACE CONTROL
Ensemble spread of Pace Ensemble spread of Control

• Shading denotes ensemble spread among 4 members.
  Note that correlation for ensemble mean is not
  the average of correlations for four members.

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