Predictability of ENSO-Monsoon Predictability of ENSO-Monsoon Relationship in NCEP CFS Relationship in NCEP CFS Center for Ocean-Land-Atmosphere studies (COLA) Center for Ocean-Land-Atmosphere studies (COLA) George Mason University (GMU) George Mason University (GMU) Emilia K. Jin Emilia K. Jin COLA/GMU, *IPRC/Univ. of Hawaii COLA/GMU, *IPRC/Univ. of Hawaii Thanks to Thanks to J. Kinter, B. Kirtman, J. Shukla, and B. Wang* J. Kinter, B. Kirtman, J. Shukla, and B. Wang* NOAA 32th Climate Diagnostics and Prediction Workshop (CDPW), 22-26 Oct COAPS/FS U, Tallahassee, FL
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Predictability of ENSO-Monsoon Relationship in NCEP CFS Center for Ocean-Land-Atmosphere studies (COLA) George Mason University (GMU) Emilia K. Jin COLA/GMU,
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Predictability of ENSO-Monsoon Predictability of ENSO-Monsoon Relationship in NCEP CFSRelationship in NCEP CFS
Center for Ocean-Land-Atmosphere studies (COLA)Center for Ocean-Land-Atmosphere studies (COLA)George Mason University (GMU)George Mason University (GMU)
Emilia K. JinEmilia K. Jin
COLA/GMU, *IPRC/Univ. of HawaiiCOLA/GMU, *IPRC/Univ. of Hawaii
Thanks toThanks to J. Kinter, B. Kirtman, J. Shukla, and B. Wang* J. Kinter, B. Kirtman, J. Shukla, and B. Wang*
NOAA 32th Climate Diagnostics and Prediction Workshop (CDPW), 22-26 Oct COAPS/FSU, Tallahassee, FL
Contents
ENSO-monsoon relationship in NCEP/CFS forecasts
The role of perfect ocean forcing in coupled systems: CGCM vs. “Pacemaker”
Shortcoming in “Pacemaker”: Decadal change of ENSO-Indian monsoon relationship
The role of air-sea interaction on ENSO-monsoon relationship
JJA Forecast Skill of Rainfall with respect to Lead MonthJJA Forecast Skill of Rainfall with respect to Lead MonthTemporal correlation with respect to lead monthTemporal correlation with respect to lead month
1st month 3rd month 5th month 9th month
Area mean (60-140E, 30S-30N)
Forecast lead month
Co
rre
latio
n
Retrospective forecast (Courtesy of NCEP)
Lead month Run Period
NCEP CFS 9 15 1981-2003 (23 years)
For retrospective forecasts, reconstructed data with respect to lead time (monthly forecast composite) is used.
Extended IMR indexWNPSM index
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.
Relationship between NINO3.4 and Monsoon IndicesRelationship between NINO3.4 and Monsoon IndicesLead-lag correlation with respect to lead monthLead-lag correlation with respect to lead month
Observation1st month forecast8th month forecastCFS long run
NCEP/CFS 52-year long run (Courtesy of Kathy Pegion)
Relationship between NINO3.4 and Monsoon IndicesRelationship between NINO3.4 and Monsoon Indices
Regressed field of 1Regressed field of 1stst SEOF of 850 hPa zonal wind SEOF of 850 hPa zonal wind
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).
Shading: 500 hPa vertical pressure velocity
Contour: 850 hPa winds
COR (PC, NINO3.4) = 0.85
Shading: Rainfall (CMAP and PREC/L)
Contour: SST
1
1
ObservationObservation
Co
rre
latio
n
N34 lead N34 lag
COR (1st PC timeseries of SEOF, N34)
Shading: 500 hPa vertical pressure velocity
Contour: 850 hPa winds
Shading: Rainfall (CMAP and PREC/L)
Contour: SST
1
1
Regressed field of 1Regressed field of 1stst SEOF of 850 hPa zonal wind SEOF of 850 hPa zonal windObservationObservation
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
1 2 3 4 5 6 7 8 9
Pattern Cor. of EOF Eigenvector
COR (1st PC timeseries of SEOF, N34)
Impact of the Model Systematic Errors on ForecastsImpact of the Model Systematic Errors on Forecasts
Forecast lead month
Co
rre
latio
nC
orr
ela
tion
N34 lead N34 lag
Patternl correlation of eigenvector with observation
Pattern correlation of eigenvector with free long run
Observation1st month forecast8th month forecastCFS long run
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.
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.
Ocean forcing?
Atmospheric response?
Air-sea interaction?
…..
In CFS coupled GCM, what is responsible to drop the pIn CFS coupled GCM, what is responsible to drop the predictability of ENSO – monsoon relationship?redictability of ENSO – monsoon relationship?
““Pacemaker” ExperimentsPacemaker” 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.
In this study, the deep tropical eastern Pacific where coupled ocean-atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST.
In this study, the deep tropical eastern Pacific where coupled ocean-atmosphere dynamics produces the ENSO interannual variability, is prescribed by observed SST.
Lead-lag Correlation between NINO3.4 and Monsoon indicesLead-lag Correlation between NINO3.4 and Monsoon indices
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Decadal change of ENSO-Monsoon relationship based on SEOF analysis (Wang et al. 2007)1. 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.
Change of Lead-lag Correlation (Extended IMR, NINO3.4) Change of Lead-lag Correlation (Extended IMR, NINO3.4) 20-year Moving Window during 1950-200420-year Moving Window during 1950-2004
(HadSST and CMAP)
Lag correlation with respect to 20-yr moving window during 55 years
OBS (IMR)
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.
Change of DJF Simultaneous CorrelationChange of DJF Simultaneous Correlation20-year Moving Window during 1950-200420-year Moving Window during 1950-2004
Ensemble spread of PaceEnsemble spread of Control
ObservationPACECONTROL
• Shading denotes ensemble spread among 4 members. Note that correlation for ensemble mean is not the average of correlations for four members.