1. Predictability of ENSO-Monsoon Relationship in NCEP CFS
Emilia K. Jin
Center for Ocean-Land-Atmosphere studies (COLA)
George Mason University (GMU)
Hi!
I’m Emilia Jin in COLA and George Mason University.
Today, I’d like to talk about Predictability of ENSO-Monsoon Relationship in NCEP CFS.
They are my colleagues in COLA.
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), Oct COAPS/FSU, Tallahassee, FL

2. 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
In this study we will show you the several characteristics of simulation of ENSO-monsoon relationship by comparing different experiments.

3. 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
This is forecast skill of monsoon rainfall with respect to lead month.
In CFS forecasts, the monsoon predictability decrease with respect to lead month.
Forecast lead month
Retrospective forecast (Courtesy of NCEP)
Lead month
Run
Period
NCEP CFS 9 15
(23 years)

4. Relationship between NINO3.4 and Monsoon Indices
Lead-lag correlation with respect to lead month
WNPSM index
Extended IMR index
N34 lead N34 lag
N34 lead N34 lag
COR(OBS,CFS)
Observation
1st month forecast
8th month forecast
WNPSMI
EIMR
If we focus on the predictability of ENSO-monsoon relationship, monsoon index can be good indication.
This is the lead-lag correlation of western North pacific summer monsoon index and extended Indian monsoon index.
Black is for observation, red is for 1st month forecast, and blue for 8th month forecast.
At first forecast month, the relationship is quite similar to observed.
However, it show moderate skill in 8th month for both cases.
1st month
8th month
0.48
0.20
0.48
0.12
Western North Pacific Summer Monsoon Index (Wang and Fan, 1999)
WNPSMI : U850(5ºN–15ºN, 100ºE–130ºE) minus U850(20ºN–30ºN, 110ºE–140ºE)
Extended Indian Monsoon Rainfall Index (Wu and Kirtman 2004)
EIMR: Rainfall (5ºN–25ºN, 60ºE–100ºE)
Green line denotes 95% significant level.

5. Relationship between NINO3.4 and Monsoon Indices
WNPSM index
Extended IMR index
Observation
1st month forecast
8th month forecast
CFS long run
If we compare this result with index from CFS long run simulation, purple line her, some feature shown in the 8th forecast looks similar to it.
It means model’s systematic error has a role to degrading the forecast skill with respect to lead month.
NCEP/CFS 52-year long run (Courtesy of Kathy Pegion)

6. 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 (PC, NINO3.4) = 0.85
To summarize the model’s monsoon property associated with ENSO, we used the seasonal EOF analysis.
SEOF is developed to describe the seasonally evolving anomalies throughout a full calendar year
From summer to next spring, a covariance matrix was constructed using four consecutive seasonal mean anomalies for each year.
In this case, we use 850 hap zonal wind as primary variable to obtain the PC timeseries and Principal Component time series shown here.
The simultaneous correlation coefficient between PC1 and the ENSO index- the NINO 3.4 SST anomaly reaches 0.85, indicating that the seasonally evolving patterns of the leading mode concur with ENSO’s turnabout from a warming to a cooling phase.
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 ENSO’s turnabout from a warming to a cooling phase (Wang et al. 2007).

7. 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)
1) Reversal of the monsoon anomalies from JJA(0) to JJA(1)
2) Two prominent off-equator anticyclonic anomalies: SIO and WNP
The lead-lag relationship between PC time series and NINO3.4 index is shown like this.
If we look at the circulation, there are two dominant off-equator anticyclonic anomalies over the Southern Indian Ocean and Western North Pacific for the preceding and following summer of ENSO.
In patterns, significant rainfall anomalies in various regions of the A-AM are associated with the evolution of these two anoamlous anticyclones.
Correlation
N34 lead N34 lag

8. 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)
In CFS forecasts, this mode is also well simulated in first month forecast while show moderate skill for 8th lead month.
This feature also suggest that the influence of model deficiency on forecast drop the skill in the case of monsoon associated with ENSO.
Pattern Correlation with respect to lead month show this feature more clearly.
With respect to lead month, forecast monsoon mode is much similar to that of free long run, while far from the observed feature.
Correlation
Observation
1st month forecast
8th month forecast
CFS long run
N34 lead N34 lag

9. In CFS coupled GCM, what is responsible to drop the predictability of ENSO – monsoon relationship?
Ocean forcing?
Atmospheric response?
Air-sea interaction?
…..
In CFS coupled GCM, what is responsible to drop the predictability of ENSO – monsoon relationship?
It must be the imperfect ocean forcing, and imperfect atmospheric response, and so on.

10. “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.
To investigate this problem, we performed the ideal experiment called “pacemaker” and compared with coupled model.
In our experiment, tropical Pacific SST is prescribed from observation as perfect SST, but coupled air-sea feedbacks are maintained in the other ocean basins by using slab ocean.

11. “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.
Pacemaker region
165E-290E, 10S-10N
To merge the pacemaker and
non-pacemaker regions
No blending
Outside the pacemaker region
Slab ocean mixed-layer
This is simple description of
To handle model drift
Weak damping of 15W/m2/K
to observed climatology
Mixed-layer depth
Zonal mean monthly
Levitus climatology

12. Model and Experimental Design
Pacemaker
Atmosphere
(GFS T62L64)
CGCM
Atmosphere
(GFS T62L64)
Local air-sea interaction
Fully coupled system
Ocean
(Full dynamics)
SST
SST
heat flux, wind stress, fresh water flux
Observed
SST
heat flux
-γTclim
Slab ocean
(No dynamics and
advection)
Mixed layer model + AGCM
( , 4runs)
CGCM
(52 yrs)
This is simple summary of experiments.
We performed 4 member ensemble simulation during 55 years and compared with CFS long run
Experiment
Extratropics
Tropics
Mixed-layer depth
Period
Member
Pacemaker
Slab ocean mixed-layer with
weak damping of 15W/m2/K
Observed interannual SST
Zonal mean monthly Levitus climatology except Eastern Pacific (reduced as 1/3)
55 yr
( ) 4
CGCM (CFS)
MOM3
52 yr 1

13. Lead-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
This is lead-lag correlation between monsoon indices and Nino3.4 index.
Yellow band denotes the ensemble spread of 4 pacemaker simulations.
In general, pacemaker shows some accordance with observed relationship suggesting the atmospheric response to the perfect eastern Pacific SST is reasonable in this model.
While, CFS long run shows different results.
For western North Pacific summer monsoon, it shows the delay of positive correlation associated with its long ENSO life cycle.
While, in the case of indian monsoon indices, it shows the insignificant relationship with ENSO.
So the imperfect ocean forcing in CFS must be responsible for this discrepancies.
Observation
PACE
CFS
N34 lead N34 lag
N34 lead N34 lag
ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE)
Ensemble spread of 4 members of Pacemaker exp.

14. ENSO Characteristics in CFS CGCM
NINO3.4 Index during
(a) Observation
(b) CFS CGCM (52 year long run)
For the delay of relationship, it must be associated with long life cycle of ENSO and associated summer peak in this model.

15. Lead-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
This is lead-lag correlation between monsoon indices and Nino3.4 index.
Yellow band denotes the ensemble spread of 4 pacemaker simulations.
In general, pacemaker shows some accordance with observed relationship suggesting the atmospheric response to the perfect eastern Pacific SST is reasonable in this model.
While, CFS long run shows different results.
For western North Pacific summer monsoon, it shows the delay of positive correlation associated with its long ENSO life cycle.
While, in the case of indian monsoon indices, it shows the insignificant relationship with ENSO.
So the imperfect ocean forcing in CFS must be responsible for this discrepancies.
Observation
PACE
CFS
N34 lead N34 lag
N34 lead N34 lag
ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE)
Ensemble spread of 4 members of Pacemaker exp.

16. ENSO Characteristics in CFS CGCM
Regression of DJF NINO3.4 Index to SST anomalies
(a) Observation
(b) CFS long run
And the spatial pattern of ENSO SST anomalies showing the westward penetration with narrow band comparing to the observed and so sst structure ove the Indian Ocean is somewhat different from oberved.
It must be associated with the Indian monsoon.
In CGCM, ENSO SST anomalies show westward penetration with narrow band comparing to the observed.

17. JJA Regression map of 1st SEOF of 850 hPa zonal wind
Total field
Difference from Obs.
850 hPa zonal wind and rainfall
850 hPa zonal wind and SST
Obs.
Pace
Pace-Obs.
This is summertime regression map of 1st SEOF for observation and simulations.
Shading is for rainfall and contour is the zonal wind anomalies.
In left panel, the westerlies associated with SST forcing is well related with rainfall pattern.
In CFS, cooling over Indian Ocean due to westward shift of SST pattern is related with easterly anomalies over here
and so dry region occurs southeastward comparing to observation.
In pacemaker, it has warm bias over maritime continent but it has relatively small influence on Indian continent.
CFS
CFS-Obs.
Contour: zonal wind
Shading: rainfall
Contour: zonal wind
Shading: SST

18. Model and Experimental Design
Control
Atmosphere
(GFS T62L64)
Pacemaker
Atmosphere
(GFS T62L64)
No air-sea interaction
Local air-sea interaction
SST
Observed
SST
Observed
SST
-γTclim
heat flux
Climatology
SST
Slab ocean
(No dynamics and
advection)
AGCM
( , 4runs)
Mixed layer model + AGCM
( , 4runs)
To investigate the role of air-sea interaction, we also performed the control run having pacemaker with climatological SST outside of tropics.
We performed 4 member ensemble simulation during 55 years.
Experiment
Extratropics
Tropics
Mixed-layer depth
Period
Member
Pacemaker
Slab ocean mixed-layer with
weak damping of 15W/m2/K
Observed interannual SST
Zonal mean monthly Levitus climatology except Eastern Pacific (reduced as 1/3)
55 yr
( ) 4
Control
Climatological SST
none

19. Leag-lag correlation with Nino3.4 Index
WNPSMI
EIMR
1st PC timeseries of SEOF
ISMI
This is lead-lag correlation between monsoon indices and Nino3.4 index.
Blue line is for control run and blue band is for ensemble spread.
Because of perfect SST over the tropics, the SEOF PC has identical relationship with observed.
It has also reasonable relationship of wnpsmi, but the indian monsoon relatinship is very weak,
Observation
PACE
Control
N34 lead N34 lag
N34 lead N34 lag
ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE)
Ensemble spread of 4 members of Pacemaker exp.

20. JJA Regression map of 1st SEOF of 850 hPa zonal wind
Total field
Difference from Obs.
850 hPa zonal wind and rainfall
850 hPa zonal wind and SST
Obs
Pace
Pace-Obs.
Because this control run doesn’t have SST anomalies outside the Pacific, regressed pattern of difference shows the opposite sign of observed SST anomalies.
Without air-sea interaction and indian ocean dipole pattern over here, the wind pattern altered toward westward.
As a result, the rainfall over Indian continent show reversed sign.
In pacemaker, warm bias over the maritime continent also weaken the Indian dipole pattern over the Indian Ocean, but the intensity is relatively small than control run.
Ctl-Obs.
Ctl
Contour: zonal wind
Shading: rainfall
Contour: zonal wind
Shading: SST

21. 1st S-EOF modes: Observation
Even though pacemaker show better relationship comparing to other experiment,
there is a clear shortcoming in terms of decadal change and it can give a key to understand the ENSO-monsoon relationship.
This is observed decadal change of monsoon variability for two period.
Most dominant change of two period is strength of WNPM and weakening of SIOM associated with change of ENSO.

22. 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.
Pacemaker mimic the enhancement of wnpm but fail to reproduce the weakening of Indian monsoon.
Considering the cause of decadal change based on SEOF analysis, even though pacemaker has a change of remote forcing,
the absence of mechanism to generate the change of warm-ocean interaction with fixed mixed-layer depth and climatology through damping
While, the reproduction of change of WNPMI suggest that the decadal change of WNPMI is strongly related with change of remote forcing.
It also supported by previous results showing reasonable relationship in control run without air-sea interaction.

23. Change of Lead-lag Correlation (Extended IMR, NINO3.4)
20-year Moving Window during
OBS (IMR)
(HadSST and CMAP)
Lag correlation with respect to 20-yr moving window during 55 years
This is the evolution of lead-lag relationship between indian monsoon rainfall and nino3.4 index with 20-year moving window.
Accordance with previous plot, observation show weakening of relationship for recent years but pacemaker show strong relationship all the time.

24. 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.
systematic errors of couple models is major factor of limiting predictability
(mean error, phase error, amplitude error, seasonal cycle)
investigate the model capability in long simulation is the one key to understand the behavior of forecast error.
more quantitative examination to distinguish the influence of initial condition and model capability will be followed.

25. Thank You !
Emilia K. Jin

26. Change of DJF Simultaneous Correlation
20-year Moving Window during
Focusing on the simultaneous correlation, it is more clear.
Black is for observed imr, red for pacemaker, and blue for control run.
Till 70’s, the relationship in pacemaker is quite identical with observation.
However, it cannot reproduce the weakening of relationship after climate shift near 1976
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.