Predictability of ENSO-Monsoon Relationship in NCEP CFS

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

N34 lead N34 lag N34 lead N34 lag

Observation1st month forecast8th month forecast 0.48

0.200.480.12

1st month8th month

COR(OBS,CFS)WNPSMI EIMR

Extended IMR indexWNPSM index

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.

““Pacemaker” Experimental DesignPacemaker” Experimental Design

Pacemaker region

Outside the pacemaker region

To handle model drift

To merge the pacemaker and non-pacemaker regions

165E-290E, 10S-10N

Weak damping of 15W/m2/Kto observed climatology

No blending

Slab ocean mixed-layer

Mixed-layer depth Zonal mean monthly Levitus climatology

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 Paci

fic (reduced as 1/3)55 yr

(1950-2004) 4

CGCM (CFS) MOM3 52 yr 1

Model and Experimental DesignModel and Experimental Design

Local air-sea interactionLocal air-sea interaction Fully coupled systemFully coupled system

SSTSSTheat flux, wind stress, fresh water fluxheat flux

Mixed layer model + AGCMMixed layer model + AGCM(1950-2004, 4runs)(1950-2004, 4runs)

CGCMCGCM(52 yrs)(52 yrs)

HC

F

dt

dT

p -γTclim

Atmosphere(GFS T62L64)


Ocean(Full dynamics)

Observed SST

Slab ocean (No dynamics and

advection)

PacemakerPacemaker CGCM CGCM

Lead-lag correlation with Nino3.4 IndexLead-lag correlation with Nino3.4 IndexWNPSMI

1st PC timeseries of SEOF

ISMI: U850(5ºN–15ºN, 40ºE–80ºE) minus U850(20ºN–30ºN, 70ºE–90ºE)

EIMR

ISMI

Ensemble spread of 4 members of Pacemaker exp.

ObservationPACECFS


(a) Observation

(b) CFS CGCM (52 year long run)

ENSO Characteristics in CFS CGCM ENSO Characteristics in CFS CGCM NINO3.4 Index during 1950-2005NINO3.4 Index during 1950-2005

Lead-lag correlation with Nino3.4 IndexLead-lag correlation with Nino3.4 IndexWNPSMI



EIMR

ISMI


ObservationPACECFS


Regression of DJF NINO3.4 Index to SST anomaliesRegression of DJF NINO3.4 Index to SST anomalies

(a) Observation

(b) CFS long run

ENSO Characteristics in CFS CGCM ENSO Characteristics in CFS CGCM

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

JJA Regression map of 1JJA Regression map of 1stst SEOF of 850 hPa zonal wind SEOF of 850 hPa zonal wind

850 hPa zonal wind and rainfallDifference from Obs.Total field

850 hPa zonal wind and SST

Obs.

Pace

CFS

Pace-Obs.

CFS-Obs.

Contour: zonal windShading: rainfall

Contour: zonal windShading: SST

Experiment Extratropics Tropics Mixed-layer depth Period Member

PacemakerSlab ocean

mixed-layer withweak damping of

15W/m2/K

Observed interannual

SST

Zonal mean monthly Levitus climatology except Eastern Paci

fic (reduced as 1/3)55 yr

(1950-2004) 4

Control Climatological SSTObserved

interannual SST

none 55 yr(1950-2004) 4

Model and Experimental DesignModel and Experimental Design

No air-sea interactionNo air-sea interaction Local air-sea interactionLocal air-sea interaction

SSTheat flux

AGCMAGCM(1950-2004, 4runs)(1950-2004, 4runs)

Mixed layer model + AGCMMixed layer model + AGCM(1950-2004, 4runs)(1950-2004, 4runs)

HC

F

dt

dT

p -γTclim



Observed SST

Slab ocean (No dynamics and

advection)

Observed SST

ClimatologySST

ControlControl PacemakerPacemaker

Leag-lag correlation with Nino3.4 IndexLeag-lag correlation with Nino3.4 IndexWNPSMI



EIMR

ISMI


ObservationPACEControl


JJA Regression map of 1JJA Regression map of 1stst SEOF of 850 hPa zonal wind SEOF of 850 hPa zonal wind

850 hPa zonal wind and rainfallDifference from Obs.Total field850 hPa zonal wind and SST

Obs

Pace

Ctl

Pace-Obs.

Ctl-Obs.

Contour: zonal windShading: rainfall

Contour: zonal windShading: SST

11stst S-EOF modes: Observation S-EOF modes: Observation1956-761956-76 1977-2004

Lead-lag Correlation between NINO3.4 and Monsoon indicesLead-lag Correlation between NINO3.4 and Monsoon indices

56-7677-04

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.

Summary

Emilia K. [email protected] K. [email protected]

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.

Documents

Predictability of ENSO-Monsoon Relationship in NCEP CFS