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The decadal ENSO variability in a Hybrid Coupled Model

Sang-Wook Yeh1

, Jong-Ghap Jhun2

, In-Sik Kang2, Ben P. Kirtman

3

1Center for Ocean-Land-Atmosphere Studies

Institute of Global Environment and Society

4041 Powder Mill Rd., Suite 302

Calverton, MD, 20705

2School of Earth and Environmental Sciences,

Seoul National University

Seoul, Korea

3George Mason University, Fairfax, Virginia, and

Center for Ocean-Land-Atmosphere Studies

Institute of Global Environment and Society

4041 Powder Mill Rd., Suite 302

Calverton, MD 20705

January 2003


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Abstract

In this study, we investigated the characteristics of decadal ENSO variability in

a long (100-year) simulation of a hybrid coupled model (HCM). To exclude the

possibility that the decadal El Nio-Southern Oscillation (ENSO) variability is forced

by midlatitude ocean variability, the atmospheric component model is coupled to an

ocean model that is restricted to the tropical Pacific. The sea surface temperature

anomaly (SSTA) variability from a 100-year run of HCM compares favorably to the

observations and shows fluctuations in the ENSO period and amplitude on decadal time

scales. The spatial structure of the interannual ENSO variability in the HCM is similar

to the observations, whereas on decadal time scales the spatial structure differs

significantly from the observations suggesting the importance of extra-tropical oceanic

processes or deficiencies in the model. The decadal mean of both the SSTA and the

wind stress anomaly is too equatorially confined in the HCM compared to the

observations.

Simple coupled model experiments are performed to determine the source of

decadal ENSO variability in the HCM. These experiments indicate that the slow time

scale variations in the mean state has little effect on the character of the ENSO

variability. The decadal modulation of ENSO is primarily related to the details of

atmospheric noise processes.


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1. Introduction

In recent years, our understanding of decadal variability of El Nio-Southern

Oscillation (ENSO) has developed rapidly due to the accumulation of observations and

improvements in coupled atmosphere-ocean models. There is considerable

observational evidence that decadal variations of ENSO are part of the natural

variability of the tropical Pacific. Trenberth and Shea (1987) pointed out that the

Southern Oscillation was strong from 1880 to 1920 and 1950-1987, and weak from the

mid-1920s to 1950. Wang (1995) found interdecadal changes in the mean background

state between warm events prior to the late 1970s and after the late 1970s. Gu and

Philander (1995) revealed that the amplitude as well as the frequency of the ENSO

exhibits notable variations over the past 130 years by wavelet analysis of the NINO3

SST (5N-5S, 210E-270E) and Southern Oscillation Indices (Wang and Wang, 1996).

Decadal variability is one of the fundamental characteristics of the ENSO cycle.

Gu and Philander (1997) suggested that thermocline ventilation in the midlatitude

oceans is responsible for changes in the tropical mean state. This mechanism results in a

periodic decadal cycle where the period is determined by the time it takes for the

subducted extratropical water to effect the tropical thermocline. However, Schneider et

al. (1999) argued that there was no significant coupling in the Pacific between the

Northern Hemisphere midlatitudes and the equatorial region via advection of thermal

anomalies along the oceanic thermocline.

Recently, Pierce et al. (2000) using a coupled ocean-atmosphere general

circulation model (CGCM), showed that midlatitude SSTAs are strongly correlated with

changes in zonal wind stress on decadal time scales. They suggested that midlatitude

SSTAs drive changes in the trade wind system that alter the east-west slope of the


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tropical thermocline, thereby effecting a decadal time scale change in ENSO activity.

Based on experiments with a hybrid coupled model, Kleeman et al. (1999) found that

the decadal oscillation originating in the midlatitudes may affect the equatorial SST

through heat transport changes in the upper branch of subtropical cell. Many others

(Barnett et al., 1999, Xu et al., 1998, Latif and Barnett, 1994) have argued that the

origin of ENSO decadal variabilitiy is forced from the midlatitudes.

Another possibility is that the tropics act as the source of these decadal

variations in ENSO. Knutson and Manabe (1998) explored a possible mechansim for

the observed decadal variability and showed that the leading mode of internally

generated decadal variability (> 7yr) in their model resembles the observed decadal

variability in terms of pattern and amplitude. They suggested that the decadal variability

has an ENSO-like delayed oscillator mechanism (Suarez and Schopf 1988; Battisti

and Hirst, 1989) that operates on longer time scales.

On the other hand, stochastic forcing in the tropics has been suggested as an

important factor that drives ENSO decadal variability (Flgel and Chang, 1996). The

equatorial coupled system is forced by uncoupled atmospheric noise on monthly or

seasonal mean time scales which has the effect of significantly broadening the spectral

peak of ENSO. Some of this broadening spills into the low frequency domain and hence

generates decadal variability (Kirtman and Schopf, 1998; Blanke et al., 1997; Penland

and Sardeshumukh, 1995). Recently, Timmermann and Jin (2001) argued that the

nonlinearity of the tropical ocean-atmosphere, by itself, gives rise to chaotic modulation

of ENSO on decadal and longer time scales without the extratropics.

As mentioned above, two broad possibilitites are currently suggested for the

origin of ENSO decadal variability: (i) midlatitude-tropical teleconnections (there are


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several different possible mechanisms for these teleconnections) or (ii) internal tropical

dynamics. However, neither the nature nor precise processes determining the decadal

variations of ENSO have yet been identified.

In this paper, we explore the characteristics of decadal ENSO variability in a

HCM. Our results suggest that decadal ENSO variability can have its roots in the tropics

and can be primarily driven by local atmospheric noise processes. As evidence for this,

we examine a 100-yr run of a hybrid coupled model (HCM) which employs an ocean

model that is restricted to the tropical Pacific thereby excluding the generation of

decadal ENSO variability by midlatitude ocean variability. Additional simple coupled

model experiments are performed to diagnose the source of the decadal variability of

ENSO. Typically, HCMs consist of ocean general circulation models (OGCMs) coupled

to either a simplified dynamical or a statistical atmosphere model (Neelin 1990; Barnett

et al. 1993; Davey et al. 1994). However, the HCM used here has a complex

atmospheric general circulation model (AGCM) which is coupled to an intermediate-

level anomaly ocean model in the tropical Pacific region (130E-270W, 19N-19S).

This is the same HCM approach used by Kirtman and Zebiak (1997).

Section 2 contains a brief description of the HCM. In section 3, we describe the

characteristics of the model variability, compare it to available observations, and show

that the model has a credible simulation for both ENSO-scale and decadal-scale

variability. We explore the characteristics of decadal ENSO variability in the coupled

model in section 4 and present our summary in section 5.


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2. HCM description

a. SNUAGCM

This HCM consists of the Seoul National University atmospheric general

circulation model (AGCM) known as SNUAGCM (Kim et al., 1998), coupled to the

ocean component of the Zebiak and Cane (ZC) coupled model. The SNUAGCM has

been developed at Seoul National University. It is a global spectral model with T31

resoultion (approximately 3.5 long * 2.5 lat). There are 17 unevenly spaced sigma-

coordinate vertical levels in the model. The SNUAGCM is based on the CCSR/NIES

AGCM of Tokyo University (Numaguti et al., 1995), but has several major changes

including the land surface process, shallow convection, and PBL processes (Kim et al.,

1998). The land surface parmeterization is the same as in the land surface model (Bonan,

1996) developed in the NCAR Community Climate Model 3 (CCM3). The SNUAGCM

contains non-precipitating shallow convection in diffusion type (Bonan, 1996) and the

non-local PBL/vertical diffusion scheme (Holtslag and Boville, 1993). The radiation

processes are parameterized by the two stream k-distribution method (Nakajima and

Tanaka, 1986). The cumulus parameterization is based on the Relaxed Arkawa-Schubert

scheme (Moorthi and Suarez, 1992).

b. ZC ocean model

The dynamics of the ZC ocean model is described by linear shallow-water

equations, which produce thermocline depth anomalies and depth-averaged baroclinic

currents. A shallow frictional layer of constant depth (50m) is embedded to simulate the

surface intensification of wind-driven currents. The annual cycle is included in the

model by the prescribed mean currents, temperature and thermocline depth. Kirtman


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and Zebiak (1997) argued that the relatively poor job of simulating cold events is likely

due to the ocean component of the HCM. Therefore, the ocean model used in this study

has a new parameterization for the temperature of subsurface water entrained into the

ocean mixed layer (Yeh, 2001). The subsurface temperature anomaly below the mixed

layer (hereafter, referred to the subsurface temperature anomaly) in the ZC ocean model

is estimated based on a hypertangent function of the thermocline depth anomaly. In the

present model, the subsurface temperature anomaly was computed from the Tropical

Ocean and Global Atmosphere-Tropical Atmosphere and Ocean Array (TOGA-TAO)

data based on a similar function used in the ZC model (Yeh, 2001). Surface heat fluxes

are simplified to a form that acts only to damp the SSTA to zero with an e-folding

timescale of 125 days. The integraton time step of the ocean model is 10 days

c. Coupling procedure.

In coupling the SNUAGCM to the ZC ocean model, we follow Kirtman and

Zebiak (1997). Given an SST field, the AGCM produces a total wind stress field that

has been empirically corrected (Huang and Shukla, 1997). The AGCM wind stress

climatology is subtracted and the wind stress anomalies are passed to the ocean

component. The AGCM wind stress climatology is computed with respect to an

uncoupled simulation with observed SST for the period of 1979-1996. Given a wind

stress anomaly, the ZC ocean model produces a predicted SSTA in the tropical Pacific.

The SSTA is superimposed on the observed global annually varying SST climatology

and is then passed to the AGCM. Since the ocean model time step is 10 days, the

atmosphere and ocean components exchange information once every 10 days. The

oceanic and atmospheric grids are not the same and the anomalies are interpolated to the


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corresponding component model grids.

3. Tropical variability in the Hybrid Coupled Model

In this section, we describe the tropical Pacific variability of a 100-yr run of the

HCM and compare it with available observational data. A time series of SSTA averaged

in the NINO3 region (hereafter, NINO3 SST index) from the model is shown in Fig. 1

and that from observations for the period 1950-2000 in Fig. 2. Here, we used observed

SST from 1950 to 2000, which were analyzed by the National Centers for

Environmental Prediction (NCEP; Reynolds and Smith, 1994). The observed SSTA is

defined as the deviation from the mean annual cycle calculated over the entire record

(1950-2000) and the HCM SSTA is the anomaly from the ZC ocean model climatology.

Similar to the observed time series, the simulated SSTA in HCM shows irregular

variability. The amplitude of the warm and cold events from the model are reasonable,

with peaks of1.5 to 2.5C.

Compared to the SSTA simulated by the HCM used in Kirtman and Zebiak

(1997), this model performs better in simulating cold events. This improved simulation

is due to a new subsurface temperature parameterization (Yeh, 2001). Dewitte and

Perigaud (1996) have found that the ZC ocean model with observed wind stress forcing

does a relatively poor job of simulating cold events because of the asymmetry in the

parameterization of entrained temperature at 50m. Yeh (2001) showed that by replacing

the subsurface temperature parameterization in the ZC ocean model the simulation of

cold events improved with observed wind stress forcing.

The dominant observed period of NINO3 SST index is about 44 months with a


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broad spectrum between 25 and 61 months (Fig. 3a). The simulated spectral density in

the HCM is similar with a peak around 40 months (Fig 3b), although it shows less

power both at lower frequencies and at higher frequencies compared to observations.

Overall, the model undergoes realistic ENSO variability, with some periods (model

years 0-20, 50-64, 90-99) consisting of more or less regular warm and cold events and

other periods with relatively little activity (model years 41-50, 65-70, 78-90).

We now document the model ENSO variability in more detail. Figure 4 shows

the leading empirical orthogonal function (EOF) of the SSTA from the HCM over a

100-yr period together with observations. The explained variance is also noted in each

figure. The maximum peak amplitude of the HCM EOF, whose location is similar to the

observation (Fig. 4b), is located further west than that of the coastal-type El Nio

typically simulated by the ZC coupled model (Zebiak and Cane, 1987). This

improvement is due to the difference in the setting of new subsurface temperature

parameter as discussed in Dewitte (2000). The HCM also shows a distinct feature in that

the meridional scale of anomaly is similar to the observation.

It is well known that El Nio and La Nia events have a tendency to be locked

to the end of calender year (Tziperman et al., 1998, Neelin et al., 2000). Figures 5 and 6

show the number of occurrences of warm and cold events in the observations and HCM,

respectively. The warm and cold events are defined by the NINO3 SST index

occurrence above and below one standard deviation over three successive months. The

stardard deviations of the NINO3 SST index are 0.89C and 0.85C in observations

and in the model, respectively. The value plotted for each month in Figs. 5 and 6

corresponds to the center month of the three successive months. The peaks of the El

Nio simulated by the HCM are locked to boreal winter as in the observations; however,


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the peaks of La Nia occur most frequently in boreal fall in the HCM as compared to

the observations.

4. Decadal ENSO variability

a. The decadal variability

In order to document the decadal variability in the HCM, a 10-yr running mean

is applied to the SSTA and zonal wind stress data. Figure 7 shows the leading EOF and

the leading principal component (PC) time series based on the 10-yr running mean

SSTA and zonal wind stress from the HCM over a 100-yr period, respectively. The

spatial structure for the SSTA is similar to that of the leading EOF SSTA mode on the

interannual time scales shown in Fig. 4a, but with a less narrowly confined anomaly

along the equator in the eastern Pacific.

The leading EOF mode for the zonal wind stress shows the largest variability in

the central Pacific. Both PC time series capture the low frequency variability of the

SSTA and zonal wind stress over the Pacific domain with a high correlation coefficient

(0.96). Despite the fact that the model has less power at low frequencies compared to

the observations, significant decadal variability is detected.

As shown in Fig. 1, the HCM simulation also produces a decadal modulation of

the ENSO variance, i.e., warm and cold events occur more regularly with large

amplitude in some active periods and irregularly with small amplitude in quiescent

periods. We compared distinct epochs with a marked difference in the ENSO variability;

two are active ENSO periods (model years 4-13 and 56-65; hereafter, collectively

period A) and the other two are quiescent ENSO periods (model years 41-50 and 79-88;


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hereafter, collectively period Q). Figure 8 shows the NINO3 SST index for each period.

Period A (upper two panels) is dominated by regular larger-amplitude oscillations,

whereas period Q (lower two panels) does not contain any significant warm or cold

events. The standard deviation of NINO3 index is 0.91C and 0.51C for period A and

Q, respectively.

Figures 9a-c show the mean SSTA, wind stress anomaly, and thermocline depth

anomaly from period A and Figs. 9d-f show the same fields for the period Q. Period A is

marked by warm SSTA, westerly wind stress anomalies and consistent thermocline

depth anomalies. Period Q shows a cold SSTA and thermocline shallowing in the east,

however, the magnitude is smaller and the easterly wind stress anomalies in the central

Pacific are not well organized compared to mean westerlies during period A. Figures 7-

9 suggest that the model has decadal variability and decadal modulation of ENSO.

These results suggest that the mechanism reponsible for the decadal ENSO variability

has its seeds in the tropics.

However, the spatial structure of HCM decadal variability is significantly

different from the observed. Figures 10a,b are the same as in Figs. 7a,b except for the

observations. Similar to the HCM, the PC time series also shows decadal variability

although the record is too short to detect any periodicity (Fig. 10b). It is well known that

the leading EOF of observed SSTA variability on decadal time scales has a broad

meridional scale with a triangular shape in the tropics as shown in Fig. 10a (Knutson

and Manabe, 1998; Zhang et al., 1997). Recently, many studies have suggested that

variability in the subtropics and the midlatitudes are closely connected to the cause of

decadal-scale variability in the tropical Pacific Ocean (Zhang et al., 1998; Kleeman et

al., 1999; Pierce et al., 2000; Luo and Yamagata, 2001; Nonaka et al., 2002; Klinger et


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al., 2002). Note that the spatial pattern of the leading EOF SSTA mode for the HCM

shown in Fig. 7a shows an equatorial maximum in the eastern Pacific, which is not

observed. This limitation in the HCM may be due to a fundamental model problem or

may suggests that both in the Northern and Southern Hemisphere can modify the

internal tropical dynamics through ocean teleconnections.

b. A simple model experiment

In this section we address the mechanism of decadal ENSO variability in the

HCM based on simple coupled model experiments. We designed a simple coupled

model which is the same as in the HCM except we use a statistical atmospheric

component. The statistical atmosphere component is modeled following (Kirtman and

Schopf, 1998, hereafter, KS98):

,3),( NINOyxx = ,3),( NINOyxy =

Where NINO3 is the SSTA in the coupled model averaged over the NINO3 region, and

x , y are the zonal and meridional wind stress anomaly, respectively. The structure

functions , were determined by linear regression of time series of the wind stress

anomaly on NINO3 SST inedx simulated for 100 years in the HCM. The structure

functions , are independent of time and are externally prescribed in the coupled

model simulations.

The SSTA variability for the HCM and the control simulation for the simple

coupled model is shown in Figs. 11a and 11b. The simple coupled model was run for

300 years. Figures 11a and 11b show time-longitude cross sections of the SSTA along

the equator, where the HCM field is plotted for model years 0-24 and the control field is


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plotted for simulation years 100-124. The control simulation captures some of the basic

features of the HCM simulation. For example, the control run produces regular

oscillations with a 42-month period compared to a peak of about 40 months in HCM.

However, the simple coupled model produces perfect regular oscillation, whereas the

HCM is irregular and has considerable noise in the SSTA.

KS98 argued that the relatively slow time scale (decadal) variability in the

mean state of the coupled model determines whether the delayed oscillator mechanism

or the noise forcing dominates the interannual SSTA variability. When the decadal mean

anomaly is characterized by westerly wind stress anomalies and warm SSTA in the

coupled model, the model maintains regular ENSO oscillations. When the decadal

mean state is relatively cold the SSTA variability is primarily driven by the noise.

In order to test whether the KS98 mechanism is operating in this model, two

experiments (Exp1 and Exp2) are performed. In these experiments, the effect of decadal

time scale variability in the mean state shown in Figs. 9a-f is prescribed in the simple

coupled model. The simplest way to show the effect of two mean states shown in Fig. 9

is to add a constant wind stress forcing to the coupled model based on the structure

functions , . In two separate 300-yr simulations, the wind stress from Figs. 9b,e is

added to the coupled model, respectively. This is the same procedure KS98 employed.

Figure 12a,b shows the 10-yr NINO3 index from these two simulations with the

control run (solid line in Figs. 12a,b). When mean states of period A are prescribed

(Exp1, dashed line in Fig. 12a), the model maintains interannual ENSO variability with

a warm bias. As shown in Fig. 8a,b, the ENSO behavior during the period A shows a

similar tendency with a larger magnitude and longer duration of warm phases than cold

phases, which is a reflection of the mean bias that is added to the model. Similarly in the


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simulation with prescribed mean states of period Q (Exp2, dashed line in Fig. 12b),

there are no significant changes in the ENSO variability compared to the control run.

This result suggests that the modulation of ENSO is relatively insensitive to change in

the mean state.

One possibility for this relative insensitivity to the cold mean state may be due

to the fact that the easterlies were not well organized (see Fig. 9e). In order to address

this issue we examine a relatively cold mean state in the HCM (model years 20-39),

shown in Fig. 13. The mean state is marked by significantly cold SSTA (Fig. 13a),

easterly wind stress anomalies (Fig. 13b), and enhanced thermocline slope (Fig. 13c)

compared to period Q. In contrast to the small standard deviation of NINO3 SST index

(0.51C) for the period Q, this cold period has relatively strong ENSO variability with a

standard deviation of 0.86C (Fig. 13d), which is similar to the active periods noted

above. When mean easterly anomalies shown in Fig. 13c are prescribed in the simple

coupled model, the NINO3 SST index variability is similar to the result of Exp 1 except

with a cold bias (not shown). Again, it appears that changes in the mean state add a bias

to the model, but have little effect on the character of the variability.

The above results have some similarities, but important differences with KS98.

KS98 found the interannual variability damped out when prescribing a mean easterly

anomaly, here, the model continues to oscillate. This difference is primarily due to the

new parameterization of subsurface temperature anomaly used in the ZC ocean model

coupled to the HCM. When we used the same parameterization as in KS98, our results

mimic KS98. This suggests that the relationship between decadal variability in ENSO

and the mean state is very sensitive to the ocean model physics. This cleary highlights

the importance of using models that accurately simulate the process associated with


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oceanic upwelling.

While it is clear that the KS98 process is not operating in these simulations, the

source of the decadal modulation of ENSO in HCM remains unexplained. In the

following experiment we test whether the noise is responsible for the decadal

modulation of ENSO in the HCM. We first defined the noise field by subtracting the

signal field from the HCM wind stress fields. The signal field is taken from the structure

function , mutiplied by the NINO3 SST index simulated in the HCM. Figures 14 a-

c show the zonal wind stress in the HCM, the zonal wind stress signal and the wind

stress noise for model years 0-9, respectively.

In order to examine the role of the stochastic forcing, the noise time series

calculated from the HCM is directly added to the wind stress used in the control simple

coupled model. Figure 15a shows the time series of the NINO3 SST index from this

simulation along with the HCM time series (Fig. 15b). The 10-yr running NINO3

variance (thick solid in Figs. 15a,b) is also superimposed on the figure and clearly

shows that the decadal modulation of ENSO in the HCM and this noise experiment are

nearly in phase. This in phase relationship is not perfect, but suggests that the noise is

most likely the primary source of the decadal modulation of ENSO in the HCM. This is

consistent with the Fgel and Chang (1996) reuslts except that our model has self-

sustained ENSO oscillations, whereas their model was damped.

5. Summary

We investigated the characteristics of decadal ENSO variability in a long

simulation (100-year run) of a HCM. The HCM excludes the possibility of decadal


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changes being caused by midlatitude ocean variability and the process of oceanic

transport between the tropics and midlatitude.

Even though the coupling between atmosphere and ocean component is

restricted to the tropical Pacific (19N-19S, 130E-90W), the SSTA variability from a

100-year run of HCM is comparable to the observations on interannual time scales and

has significant decadal variability, which is somewhat weak compared to the observed.

The spectral power of NINO3 SST index is similar to the observations but with less

power both at lower and higher frequencies. The peaks of the El Nio simulated by the

HCM are locked to boreal winter as in the observations. The HCM simulations also

produce relatively large modulation of ENSO variability on decadal time scales, with

active ENSO periods and quiescent ENSO periods. This result suggests that midlatitude

oceanic processes are not needed to produce the decadal modulation of ENSO

variability.

However, whereas the spatial structure of interannual ENSO variability is

similar to the observations, the decadal SSTA structure is significantly different from the

observations. The spatial pattern of both SSTA and wind stress anomaly on decadal time

scales are equatorially confined in the HCM. On the other hand, the observations show

a comparable magnitude of decadal mean SSTA in the subtropical Pacific both in the

Northern and Southern Hemisphere. This limitation may be due to fundamental

problems with the HCM, or may suggest the importance of extra-tropical processes in

establishing the structure of tropical decadal variability.

A series of experiments using a simple coupled model were performed to

determine the source of the decadal modulation of ENSO. We separately tested two

potential mechanisms, i.e., the slow time scale change in the mean state and the effect of


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atmospheric noise. A significant change of the mean state had little impact on the

characteristics of the ENSO variability. While this result seems to contradict the results

of KS98, additional experiments indicate that the difference between our results and

KS98 is due to the parameterization of the subsurface temperautre anomaly in the ocean

component model.

In order to examine the effect of the noise, we used the simple coupled model

strategy to separate the signal and the noise from the HCM model output. This noise

was then added into the simple coupled model. In this case the simple coupled model

had the same decadal modulation of ENSO as the HCM. This result suggests that the

decadal modulation of ENSO in the HCM is primarily related to atmospheric noise

processes. Given the contrast of these results with those of KS98, additional coupled

model experiments with ocean components that accurately simulate the processes

associated with upwelling are required in order to determine the source of the decadal

modulation of ENSO.

Acknowledgements : This work was supported by the Korean Governments BK21

Project and Climate Environment System Research Center, Seoul National University.

Also it was partly supported by grants from the National Science Foundation ATM-

9814295 and ATM-0122859, the National Oceanic and Atmospheric Administration

NA16-GP2248 and National Aeronautics and Space Administration NAG5-11656.


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Figure 1. The time series of NINO3 SST index simulated in the HCM for the period of

100 years.


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Figure 2. As in Fig. 2 except from the observations for the period of 1950-2000.


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Figure 3. Power spectra of the (a) observed and (b) simulated NINO3 index. The solid

curve shows the power spectra and the long-dashed curve shows the power

spectra for red noise.


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Figure 4. The first EOF of SST anomaly (a) from the HCM during the 100 yrs and (b)

from the observations during the period of 1950-2000. The contour values are

dimensionless.


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Figure 5. The number of occurrences of warm (a) and cold (b) events in the

observations (1950-2000). The warm and cold events are defined by the NINO 3

index being above and below one standard deviation over three successive

months.


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Figure 6. As in the Fig. 5 except for the SSTA simulated by the HCM.


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Figure 7. The leading EOF (a) and the leading principal component time series (b)

based on the 10-yr running averaged SSTA from the HCM over a 100-yr period.

The leading EOF SSTA mode accounts for 71.2% of the filtered variance.

Dashed for negative and contour interval is 0.01C. (c), (d) as in (a), (b) except

for the zonal wind stress. Contour interval is 0.05 dyn cm-1

. The leading EOF

zonal wind stress mode accounts for 50.7% of the filtered variance.


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Figure 8. The time series of NINO3 index for (a), (b) the period A (model years 4-13,

56-65: upper panel) and for (c), (d) the period Q (model years 41-50, 79-88:

lower panel).


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Figure 9. Decadal mean SSTA, wind stress anomaly, and thermocline depth anomaly for

(a)-(c) the period A (model years 0-9, 56-65) and for (d)-(f) the period Q (model

years 41-50, 79-88).


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Figure 10. As in Fig. 7 (a), (b) except for the observations.


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Figure 11 Time-longitude cross section along the equator of (a) the HCM SSTA and (b)

the control run SSTA. The HCM SSTA is plotted for model years 0-24, and the

control run is plotted for model years 100-124. The contour interval is 0.5C.

Negative values are depicted by dashed lines.


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Figure 12. The 10-yr NINO3 SST index from (a) the control run (solid) and Exp1

(dashed). (b), as in (a) except for the Exp2 (dashed).


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Figure 13. Decadal mean SSTA, wind stress anomaly, and thermocline depth anomaly

for (a)-(c) the model years 20-39 and (d) the time series of NINO3 SST index.


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Figure 14. Time-longitude cross section along the equator of the (a) zonal wind stress in

the HCM, (b) the zonal wind stress in the signal field, and (c) the zonal wind

stress in the noise field for the model years 0-9. The contour interval is 0.1 dyn

cm-1. Negative values are depicted by dashed lines.


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Figure 15. The NINO3 SST index (thin solid) and 10-yr running NINO3 variance from

(a) the noise experiment for the model years 100-199. (b) as in (a) except for

the HCM model result. Note that the amplitude of the running mean is indicated

on the right of the panel.

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