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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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