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VOLUME 19
Properties of El Niño–Southern Oscillation in Different Equilibrium Climates
with HadCM3
THOMAS TONIAZZO
Hadley Centre for Climate Prediction and Research, Met Office, Exeter, United Kingdom
(Manuscript received 27 September 2004, in final form 16 December 2005)
ABSTRACT
The ENSO variability in three long, stable, steady-state integrations of the Third Hadley Centre Coupled
Ocean–Atmosphere GCM (HadCM3) is analyzed, relevant to climatic conditions of the Last Glacial Maximum (LGM), of the preindustrial period [control (CTL)], and of a greenhouse stabilization scenario (GHS)
at 4 times the preindustrial CO2 concentration. It is found that progressively from LGM to CTL and GHS,
the SST variability pattern associated with ENSO is centered farther west, and the oscillation acquires a
shorter dominant period. While there are no large changes in total SST variability, very strong events
become less frequent, and El Niño events develop over a narrower period within the seasonal cycle. The
westward ENSO pattern shift is concurrent with a similar shift in the climatological wind stress distribution
and with increased convective activity over the west equatorial Pacific. The wind response to anomalous
SSTs follows this shift and increases in strength. The thermocline feedback becomes stronger in the 4 ⫻ CO2
integration, but the largest SST anomalies are associated with surface processes. The role of surface flux
damping for the decay of anomalous SSTs is reduced in LGM and increased in GHS. From the analysis, the
principal changes in mean climate that appear to affect the evolution of ENSO-related SST anomalies in
HadCM3 are thus the changes in zonal wind stress over the equator, the depth of the equatorial thermocline, and the sensitivity of atmospheric convection to equatorial SST anomalies.
1. Introduction
The study of potential changes that occur in El Niño–
Southern Oscillation (ENSO) when the mean climate
conditions are changed can be of interest both for reconstructing paleoclimate conditions (Rosenthal and
Broccoli 2004) and for impact-oriented projections of
future climate under continuing anthropogenic greenhouse warming (Houghton et al. 2001).
Although the great majority of numerical GCMs produce an ENSO-like dominant mode of tropical Pacific
interannual variability (Latif et al. 2001), the specific
properties (pattern, amplitude, and frequency) of
ENSO anomalies are model dependent. Furthermore,
the models’ sensitivity to climate change differs widely,
making projections of ENSO change quite uncertain
(Collins and CMIP Modelling Group 2005). Differences in resolution, formulation, and parameterization,
Corresponding author address: Dr. Thomas Toniazzo, Hadley
Centre for Climate Prediction and Research, Met Office, FitzRoy
Road, EX1 3PB Exeter, Devon, United Kingdom.
E-mail: [email protected]
© 2006 American Meteorological Society
JCLI3853
of both the atmosphere and the ocean components,
have been shown to be important factors (e.g., Guilyardi et al. 2004).
Here we address the question of which climate meanstate properties are most likely to significantly affect
the ENSO, and in which way. Theoretical studies of the
ENSO based on models of intermediate complexity
(henceforth ICMs; Neelin et al. 1998) suggest a basic set
of mean-state parameters (mean zonal wind stress,
depth, and strength of the equatorial thermocline) to
which ENSO is expected to be sensitive. However, such
predictions need to be compared with results from
GCMs, which have a more complex (and hopefully realistic) behavior.
We follow the approach of previous modeling studies
(Timmermann et al. 1999; Otto-Bliesner et al. 2003;
Peltier and Solheim 2004; An et al. 2004) that attempt
to relate modeled changes in the ENSO to changes in
the simulated mean climate, with the help of the insight
gained from previous analyses of the model (cf. references in section 2), ENSO theory (Philander 1990), and
ICM-based studies of ENSO (Fedorov and Philander
2001; Wang and An 2002; An et al. 2004). We describe
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TONIAZZO
the changes in the model climatologies to which the
ENSO is likely to be sensitive and attempt to relate
them with the changes in the simulated ENSO.
We have analyzed model data derived from monthly
average climate diagnostics as simulated in three integrations of the Third Hadley Centre Coupled Ocean–
Atmosphere GCM (HadCM3) model in its standard
configuration. All three setups are for steady-state conditions, and the integrations have been carried forward
for many centuries to ensure statistical sampling of
their internal variability and residual drifts. The first
integration, dubbed “CTL” hereafter, constitutes the
baseline “control” integration (Gordon et al. 2000) that
simulates an “unperturbed” nineteenth-century climate. The second integration is set up to simulate Last
Glacial Maximum (LGM) conditions at the peak of the
glaciation 21 000 yr ago (Hewitt et al. 2003). Finally, the
third integration is greenhouse forced (GHS) with a
fourfold CO2 atmospheric concentration relative to
CTL, but otherwise identical to it (Thorpe 2004).
ENSO in an early part of this integration is discussed in
Collins (2000), but the analysis is limited to the Niño-3
index and does not assess the actual SST variability in
the model integration. Immediately from an initial
EOF analysis (Toniazzo 2002b) significant differences
with CTL are seen, and they require explanation.
In the present paper, after a description of the
HadCM3 integrations and their main characteristics
(section 2), we show the principal differences in tropical
Pacific climatologies (section 3). In section 4, we discuss
the differences in the simulated ENSO. Section 5 focuses on ENSO-related oceanic and atmospheric
anomalies in the equatorial Pacific, which contribute to
anomalous SST tendencies in ways that depend on the
mean climatological conditions and are at least partly
responsible for the changes in the ENSO. In section 6,
we summarize our results, compare them with results
from other models, and draw conclusions.
2. Model integrations
HadCM3 is a first-order finite-difference numerical
model comprising an atmosphere component on a
3.75° ⫻ 2.5° latitude–longitude grid with 19 hybrid
( p ⫺ z or p ⫺ ␴) levels, and an ocean component on
*
a 1.25° ⫻ 1.25° regular latitude–longitude grid with 20
vertical (z⫺) levels. As usual, surface, boundary layer,
cloud, and convection processes, among others, are parameterized using local or single-column bulk formulations. The reader is referred to Pope et al. (2000) for
details.
We have analyzed a 100-yr section from each of the
three HadCM3 integrations mentioned in the introduc-
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tion. The CTL integration has a nineteenth-century climate setup (atmospheric CO2 concentration at 280
ppm) that serves as a baseline for most anomaly or
perturbation experiments with HadCM3. The integration is more than 3000 model years long and shows no
significant drifts. This numerical model has been shown
to be adequate in representing many properties of the
observed mean climate. Having been used for over 5 yr
in many experiments, its virtues and shortcomings are
by now well known (Slingo et al. 2003, and references
therein). Some of the biases that affect the ENSO include poor resolution of the complex island topography
and local circulation patterns in the Maritime Continent (MC; Neale and Slingo 2003), an easterly low-level
wind bias in the equatorial Pacific (especially in the
west) associated to a western extension of the cold
equatorial SST “tongue,” a split tropical convergence
zone that tends to straddle the equator, and a rather
zonal structure of the South Pacific convergence zone
(SPCZ; Davey et al. 2002). In the ocean model, the
equatorial current structure is represented rather well
considering the limited resolution used, but the undercurrent is rather weak and thermocline wave activity is
damped (Gordon et al. 1995). Nevertheless, the model
produces an ENSO with many realistic properties
(Latif et al. 2001; Collins et al. 2001; Toniazzo 2002a),
characterized by a Niño-3 index with a slightly short
main periodicity of broadly 3 yr, a rather narrow, but
not extreme, phase locking on the winter season, and a
realistic rms amplitude of 0.93°C (0.96°C for positive
anomalies) as compared to the Hadley Centre Sea Ice
and Sea Surface Temperature dataset (HadISST;
Rayner et al. 2003) value of 0.80°C (0.91°C for positive
anomalies). The model El Niño SST anomaly (SSTA)
patterns are similar to observed ones, albeit too narrow
in the meridional direction; anomalous precipitation
over the equator is produced, but overall convective
activity is less responsive than observed and tends to
stay locked over the eastern side of the SPCZ and near
the Maritime Continent (Spencer and Slingo 2003).
The setup of the LGM simulation is characterized by
representations for surface albedo, paleotopography,
and ice cover corresponding to the ICE-4G(VM2)
model of Peltier (1994), and a reduced concentration of
atmospheric CO2 at 200 ppm. Ocean topography is the
same as CTL, except for changes in the land–sea mask,
which allow for extended coastlines in Indonesia and
the North Atlantic. Its spinup has been realized with
the help of Haney forcing of SSTs (Haney 1971), but
subsequently the integration has been continued without flux correction for over 1000 model years in closeto-equilibrium top-of-the-atmosphere (TOA) balance
(⫺0.3 W m⫺2) and without important drifts. Properties
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of the mean modeled LGM climate, as well as details of
the model setup, are documented in Hewitt et al. (2001,
2003).
The GHS simulation setup is identical to CTL, except for a fourfold atmospheric concentration of CO2
(i.e., 1120 ppm). Although the spinup was spread over
70-yr model time with a CO2 increase of 2% per annum, and the simulation at fixed CO2 concentration has
been integrated for over 1000-yr model time, TOA balance is not achieved and remains at around ⫹1 W m⫺2,
with a persistent warming of the deep ocean and reduction of the polar ice caps (which eventually disappear
completely). Still, the climatological drift in the tropical
atmosphere and in the upper (ice free) oceans is negligibly small, over 100 yr, compared to the amplitudes
of annual and interannual variability. For our purposes,
this solution can also be considered to represent an
equilibrium climate.
One-hundred-year sections from the HadCM3 integrations were used for the analysis. When results were
thought to be potentially dependent on sampling (e.g.,
the Fourier spectra or EOFs), their robustness was controlled with the help of an additional 100 yr of data
from each run. Unfortunately, failures in the data storage systems of the Hadley Centre have caused “gaps”
of a few months in the available datasets. For time
series analysis, values for the anomalies of up to three
consecutive missing months were obtained by spline
interpolation. For LGM, due to missing years, the time
series from the ocean submodel had to be truncated,
leaving a maximum length of only 74 yr.
3. Climatological differences
In this section, we show the differences in the mean
and seasonal-mean climatology of the tropical Pacific,
with a focus on changes in the SSTs, in the strength,
seasonality, and patterns of tropical Pacific atmospheric
convection and atmospheric circulation, and in the
thermal and dynamical structure of the upper equatorial Pacific.
a. Mean climatology
The net global-average TOA radiation budget balances a total net incoming shortwave forcing of 235,
240, and 245 W m⫺2 in LGM, CTL, and GHS, respectively. The warmer GHS climate is due to increased
atmospheric opacity alone, while cooler LGM conditions are also affected by surface-albedo changes in the
Northern Hemisphere. The surface perturbation corresponding to the GHS climate relative to the CTL climate is larger than that between the CTL and LGM
VOLUME 19
climates, with a tropical-mean surface temperature difference of around 4.2°C in the former case and of only
1.5°C in the latter. Midtropospheric temperatures are
also affected differently (6.9° and 3.2°C at 500 mbar for
GHS–CTL and CTL–LGM, respectively). Consistently
with a “cold finger” effect (Broecker 1997), LGM has
generally drier conditions (3.37 mm day⫺1 30°S–30°N
average compared to 3.51 mm day⫺1 in CTL), while
GHS is slightly wetter (3.55 mm day⫺1), presumably
due to increased convective heating offsetting larger
midtropospheric radiative cooling.
Figure 1 summarizes the changes in climatology, with
progressively warmer and wetter conditions in CTL and
GHS. Temperature differences are characterized by a
more pronounced warming in the eastern parts of the
equatorial Pacific, with a reduction in the zonal SST
contrast. Also, in both the CTL–LGM and GHS–CTL
differences an area of reduced warming is found in the
southeastern subtropical Pacific. The western parts of
the basin also tend to warm less. Precipitation tends to
increase mainly over or near the equator.
Comparing CTL with LGM, the temperature difference pattern shows warm SSTs in the eastern equatorial
Pacific and in the tropical Atlantic. They are associated
with stronger easterlies in LGM, linked to a southward
extension of the cold anticyclone over the Laurentide
ice sheet (Hewitt et al. 2003) and probably with anomalous orographic forcing from the ice sheet over the eastern North Pacific via a mechanism described in Timmermann et al. (2004b). The increased differential SST
between the tropical Pacific and tropical Atlantic also
tends to encourage a strengthened interbasin atmospheric flow.
A reduced-average interbasin atmospheric flow implies a westerly low-level wind difference in the eastern
equatorial Pacific (Fig. 1, arrows in the upper-left
panel). Increased east equatorial SSTs in CTL relative
to LGM are associated with reduced equatorial upwelling and reduced evaporation. The cyclonic lowlevel circulation difference encourages convective activity in the Gulf of Panama (Fig. 1, bottom left), which
is associated with a Gill-type (Gill 1980) warming response in the midupper troposphere around 90°W,
15°N, and 15°S (black contours). The corresponding
upper-level warm anticyclones (arrows in the lower-left
panel) reinforce the subtropical westerlies, especially in
the Northern Hemisphere. Correspondingly, vertical
wind shear is reduced in the east and west Pacific convection areas and is stronger in the central Pacific (contours in the upper-left panel), inhibiting deep convection.
The surface warming appears to be well correlated
with the change in cloud radiative forcing (not shown).
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FIG. 1. Climatology differences (left) between CTL and LGM and (right) between GHS and CTL. Color coding
refers to (top) SST differences (°C) and (bottom) precipitation differences (mm day⫺1); black contours are for
differences in (top) 200–850-hPa wind shear (intervals of 2.6 m s⫺1, dashed lines for negative values) and (bottom)
200–500-hPa average temperature [intervals of (left) 0.15° and (right) 0.4°C]. Finally, the black arrows refer to
wind velocity (m s⫺1) at (top) 850 and (bottom) 200 hPa.
The latter quantity is mainly associated with a shortwave (SW) component. It is negatively correlated with
the changes in 500-hPa vertical pressure velocity, ␻, and
positively with relative humidity, and reflects an overall
reduction in low-cloud cover in CTL compared to
LGM. In the southeastern Pacific, however, negative
cloud forcing associated with the stratocumulus region
is stronger and more extended in CTL than in LGM.
This is associated with relatively cool SSTs and the
westward contraction of the SPCZ.
Turning to GHS, we also see strong negative cloud
forcing associated with a relatively cool Southern
Ocean and a contraction of the SPCZ relative to CTL
(Fig. 1, right). However, the warming pattern for GHS–
CTL in the tropical Pacific appears generally to be associated with changes in midtropospheric moisture (not
shown), with no clear relation with cloud forcing except
in the southeastern Pacific. The increase in humidity,
and its radiative effect, is larger in normally drier regions than in humid regions such as the main convective
areas, where instead it tends to induce increased precipitation. The warming in the northeastern Pacific is
associated with a weakened anticyclone, and weaker
northeasterly trades. Along the equator, the reduction
in zonal wind stress is accompanied by reduced upwelling and latent heat loss. This induces an eastward
displacement of Maritime Continent convection to the
western equatorial Pacific (colors, lower-right panel).
The upper-tropospheric response resembles a doubleanticyclone Gill-type pattern over the western Pacific,
associated now with reduced wind shear over the whole
area of the equatorial Pacific, which thus becomes more
prone to deep convection.
In GHS, the SPCZ is contracted west- and equatorward, while convective activity in the intertropical convergence zone (ITCZ) north of the equator is reinforced. In LGM, convection is more concentrated north
and south of the equator, split between the ITCZ and
the SPCZ, and reduced precipitation occurs over the
Maritime Continent. Both the CTL–LGM and GHS–
CTL differences show enhanced convection over the
equator, although with different zonal distributions.
Compared to the other integrations, CTL has a larger
meridional SST gradient north of the equator in the
central-western Pacific, and its ITCZ is narrower and is
associated with stronger trade winds in the western Pacific. Convection over the Maritime Continent is also
greatest in CTL, in spite of the generally larger precipi-
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FIG. 2. (left) Zonal and (right) meridional distribution of the mean zonal wind stress for the LGM (dashed
line), CTL (solid line), and GHS (dashed–dotted lines) integrations.
tation rates of GHS. This is mainly due to the eastward
displacement, in GHS, of the convective activity usually
associated with this region. GHS also shows increased
local wind stress and wind shear in the southeastern
Pacific, associated with convergence over the central
equatorial Pacific. A similar change is seen in the Gulf
of Panama, coincident with reduced convection despite
the warmer SSTs.
The changes in low-level circulation are reflected in
the annual-mean wind stress forcing of the ocean (Fig.
2). On the equator, the maximum wind stress is progressively farther west, with reduced strength in GHS,
as expected. The meridional distribution shows the reinforcement of the southern anticyclone and the weak-
ening of the northern anticyclone, which appear systematic across the three integrations, although much
more pronounced between GHS and the other two.
The zonally integrated wind stress is seen to be comparable in CTL and LGM.
Consistent with the changes in wind stress, equatorial
SSTs and depth-average ocean temperatures show reduced zonal SST contrast in GHS. The vertical thermal
structure of the equatorial Pacific (Fig. 3) indicates a
progressive shoaling of the thermocline and of the
zonal current system. In GHS, the weakened zonal
wind stress allows the equatorial thermocline to rise, in
qualitative accord with theoretical scaling laws (Pedlosky 1996, p. 341). The subtler changes between LGM
FIG. 3. Mean thermal structure of the equatorial Pacific for the LGM (dashed line), CTL (solid line), and GHS
(dashed–dotted lines). (left) The mean position of the thermocline (defined as the depth of the maximum 4°S–4°N
meridional-average vertical potential temperature gradient). (right) The 4°S–4°N, 170°–110°W area-average profiles of vertical temperature gradient.
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and CTL cannot be explained in this way. Probably, the
different zonal distribution of the wind stress plays a
role. Where the thermocline is very shallow, its position
may also be affected by stronger surface shortwave
flux, half of which is penetrative. The warmer climates
have a somewhat weaker vertical stratification (Fig. 3,
right) in the thermocline. Near the surface, GHS has
increased stratification, in part related to the reduced
depth of the thermocline. The weaker trades also affect
the South Equatorial Current, which is shallower and
slower (by about 25%) in GHS.
b. Seasonal cycle
Figure 4 shows a time–longitude plot of the seasonal
cycle along the equatorial Pacific in the monthly average SSTs, zonal wind stress, and 0–360-m depthaverage ocean temperatures for the three climatologies.
Over the equator, the wind stress becomes progressively weaker from LGM to GHS, and its seasonality
changes, with a single broad maximum in late summer
and autumn taking the place of two distinct peaks
around August and December. In terms of wind stress
work (i.e., the integral of the stress over the equatorial
area), the winter maximum found in LGM and CTL
disappears in GHS, and the corresponding spring relaxation of the trades, which tends to mark the termination of El Niño events in CTL, is replaced by a gentler transition in late January–February.
The changes in seasonal climatology of equatorial
SST trace the changes in strength, location, and seasonality of the wind stress, with cold eastern Pacific SSTs
forming in August and persisting into spring in LGM,
and a single, westward-shifted and relatively short-lived
minimum in autumn for GHS. The depth-average
ocean temperature shows a greater persistence of
warm-pool water over the equator in GHS (cf. the
17.5°, 19.5°, and 21.5°C isotherms for LGM, CTL, and
GHS, respectively, in the rightmost panel in Fig. 4).
The change in the wind stress seasonality over the
equator is related to the different patterns of convective
activity in the three climates (Fig. 5). Equatorial wind
stress is associated with the trade winds emanating from
the subtropical highs in the eastern parts of the ocean
basin, and converging on three main areas of convection, the ITCZ in a zonal band north of the equator, the
MC between New Guinea and Indonesia, and the
SPCZ across Melanesia and Polynesia. Southeasterlies
are seasonally prevalent when the ITCZ is active, and
northeasterlies are prevalent when MC and the SPCZ
convection are active. In the central and eastern Pacific,
there is an additional easterly contribution to the total
wind stress in winter from the low-level flow associated
with the southern anticyclone, which retains a cross-
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equatorial southeasterly component, and a westerly
contribution in autumn associated with convergence
over the Gulf of Panama, which is responsible for the
wind stress “dip” in October. Figure 5 shows that the
changed seasonal cycle in GHS mainly depends on the
weakening MC and SPCZ convection, along with the
weakening of the northern anticyclone, and persistent
convergence over the ITCZ in northern winter.
4. Interannual Pacific SST anomalies and ENSO
indices
Figure 6 shows the leading EOF of Pacific monthly
mean SST anomalies and its amplitude time series for
the three simulations LGM, CTL, and GHS. The leading EOF is in all cases ENSO-like in pattern and periodicity, with a center of action in the equatorial Pacific,
accounting for more than 20% of the total variance not
related to the seasonal cycle, and having an amplitude
of above 1°C and a main periodicity of 2–4 yr. The
center of action is progressively displaced toward the
west from LGM to CTL and from CTL to GHS, moving
the anomaly away from the Niño-3 region. The maximum amplitude increases, but the area affected by the
largest warm anomaly is reduced. The dominant period
in the Fourier decomposition of the EOF1 time series
appears to decrease from around 4 yr to around 2 yr.
The Niño-3 and Niño-4 index time series (Fig. 7)
support the results from EOF decomposition. The Niño
indices are in fact strongly correlated with the EOF1
time series. The standard deviation of the Niño-3 index
steadily decreases and is equal to 1.1°, 0.93°, and
0.83°C, for LGM, CTL, and GHS, respectively. The
changes in the standard deviation of the Niño-4 index,
which suggest weaker variability in CTL, are not as
significant. The Niño-3.4 index (5°S–5°N, 170°–110°W),
on the other hand, has a very similar standard deviation
in all three integrations (1.01°, 0.98°, and 1.02°C for
LGM, CTL, and GHS, respectively).
The changes in the standard deviation of ENSO indices between LGM and CTL are not statistically robust, as CTL has a significant interdecadal variability
whereby its ENSO activity increases, and the 1 ⫺ ␴
intervals of the running 30-yr standard deviation of the
respective indices overlap. The changes between LGM
and GHS, by contrast, are significant but are related to
the pattern shift, and not to the overall strength of the
ENSO.
The visual impression of an increasing frequency of
the ENSO oscillation that can be obtained from the
Niño-3 indices in Fig. 7 is confirmed by the power spectra in Fig. 8, which indicate a statistically significant
shift toward shorter periods. A similar change is also
present in the Niño-4 index power spectrum going from
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FIG. 4. The oceanic seasonal cycle of the equatorial Pacific. Hovmoeller diagrams of the 4°S–4°N meridional averages of (left) zonal
wind stress, (center) SST, and (right) 0–360-m depth-average ocean temperature are shown. Time increases upward; abscissa values are
in degrees east. The lower parts of the contour plots refer to the LGM run, the middle parts to the CTL run, and the upper parts to
the GHS run.
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FIG. 5. The atmospheric seasonal cycle over the tropical Pacific for (left) LGM, (middle) CTL, and (right) GHS. Monthly mean
pressure velocity, ␻, at hybrid level 8 (about 500 hPa over sea), averaged over the areas of the MC (thin long-dashed lines), over the
ITCZ (dashed–dotted lines) and over the SPCZ (dashed–triple-dotted lines) are shown together with 850-hPa zonal wind velocity (thick
dashed line) and with zonal surface wind stress (thick solid line). Units are 0.01 Pa for wind stress ␶x, m s⫺1 for wind speed U850, and
0.01 Pa s⫺1 for pressure velocity ␻.
LGM to CTL and to GHS. In the warmest climate,
there is no power in periods at or longer than 4 yr. The
power in the CTL and GHS indices peaks between 2
and 3 yr, while in LGM ENSO variability peaks between 3 and 4 yr. This shift appears to be robust and is
also observable if different sections of the runs are
used, or if different windows are applied in the Fourier
analysis. Moreover, and perhaps more importantly, El
Niño events, which are defined by a peak in the Niño-3
or in the Niño-3.4 index exceeding 1.5 standard deviations, tend to be more numerous over any 30-yr period
(or longer) in the warmer integrations.
These results show a continuous change in the pattern of the SST variability associated with the ENSO,
with El Niño events becoming more frequent in the
warmer climates. The changes in standard deviation
suggest weaker variability in CTL. This conclusion
however does not reflect the changes in the frequency
distribution of Niño-3.4 amplitude, and especially its
tails (Fig. 9). In GHS, there is a lack of very large warm
anomalies, while in LGM they are more frequent. This
result does not depend on the pattern change and is
also found for the Niño-3 or Niño-4 SST indices. In
GHS, the skewness and the kurtosis of all indices are
negative (except for the skewness of the Niño-4 index),
while they are positive for both indices in the other two
climates, and largest in LGM. As a result, changes in
the average strength of El Niño events depend on the
chosen threshold used to define them. With the 1.5
standard deviation criterion (which is commonly used),
they are not significant between our three integrations.
Analysis of the seasonal distribution of Niño-3/-4
variability (Fig. 10) shows that Niño-3 variability tends
to lock onto the seasonal cycle in early winter, November to January, when in fact 9 out of 13 events (defined
as anomalies greater than 1.5 standard deviations of the
index) reach their peak. While there is some variability
in spring and early summer (Fig. 10), “events” between
June and October are rare. In CTL, Niño-3 variability
follows a similar pattern, although winter events, which
are still the great majority, are more evenly spread between October and March. The termination of El Niño
events takes place around June. In contrast, in GHS
Niño-3 activity is at a maximum in August when most
of the events (8 out of 18) reach their peak. The Niño-3
variance, however, is fairly constant from August to
January, with January a second local maximum, and
seven events, like in CTL, peak between November and
February. Moreover, the August events do not terminate until March and stay above the Niño-3 standard
deviation, mostly with a secondary peak, during winter.
In summary, Niño-3 events appear to tend to develop
and terminate earlier in the warmer climate conditions,
and with a sharper onset locked on the seasonal cycle.
Niño-4 variability tends to follow a similar pattern of
change. The seasonal locking of this index is stronger
than for Niño-3 in all runs, and Niño-4 events tend to
occur later in the year than Niño-3 events in all cases. In
GHS, however, Niño-3 variability is secondary and
does not reflect the actual amount of variability in the
equatorial Pacific. All Niño-3 events in GHS are related
to a Niño-4 event that takes place in the same year.
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FIG. 6. Leading EOF of Pacific SST monthly average departures from the mean seasonal cycle for the three HadCM3 simulation runs:
(top) LGM, (middle) CTL, and (bottom) GHS. (left) The EOF fields (°C), (middle) the amplitude time series (nondimensional), and
(right) the power density of the Fourier transforms of the amplitude time series. The normalization is such that the amplitude time series
have an rms of 1. Seventy-year sections from each run are used.
The temporal relationship between Niño-4 and
Niño-3 anomalies is illustrated in Fig. 11 by the lagged
correlations of the increments of the two indices [i.e.,
the time series of the quantities Niño-4(t ⫹ 1) ⫺ Niño-
4(t) and Niño-3(t ⫹ 1) ⫺ Niño-3(t)]. Lagged correlations above the autocorrelation function of Niño-3 increments (dotted lines) can be deemed significant. In
all cases, a significant correlation at positive lags be-
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FIG. 7. Niño-3 indices for 100-yr sections of the (top) LGM, (middle) CTL, and (bottom) GHS simulations.
tween 1 and 5 months for the Niño-4 increments is seen,
suggesting either direct westward signal propagation or
a Niño-3 trigger for the Niño-4 anomaly. Significant
differences however are seen between the three climates at negative lags (i.e., Niño-4 leading Niño-3), especially for positive Niño-3 anomalies. Positive Niño-4
increments tend to lead Niño-3 progressively more in
the warmer climates, with a clean double-peak structure for the GHS case. This suggests that equatorial
SST anomalies tend to be mainly westward propagating
in LGM and CTL, while an increasing eastwardpropagating component appears in the warmer climates. Such an impression is also obtained from inspection of Fig. 12, showing the zero-level contour in a Hovmoeller plot of 5°S–5°N equatorial SST anomalies for a
20-yr section from each model simulation. Another
confirmation is found by tracking the maximum SST
anomalies in time for the composite El Niño event (Fig.
13), defined as the anomaly of the average over all
years when the Niño-3 index exceeds 1.5 standard deviations in extended winter—November to March. (We
have assured ourselves that other choices yield results
consistent with those discussed throughout this work.)
In the months leading to the peak, LGM shows a weak
westward propagation of the maximum SST anomaly.
CTL anomalies appear broadly stationary between August and November, followed by clear westward propagation well into the Niño-4 region. In GHS, the largest
SST anomaly is attained within the Niño-4 region, as
already seen. In the months around the peak, it tends to
move westward, as in the other climates. Eastward
propagation however appears before the peak of the
event (from May to August), and in the decay stage
(from February to May). Note also from Fig. 13 that the
peak in maximum amplitude is reached latest in LGM
(February) and earliest in GHS (November), as was
suggested also by the canonical Niño indices.
5. Generation of ENSO-related SST tendencies
The amplitude, pattern, propagation properties, and
frequency of ENSO SST anomalies are related to the
processes providing amplification or damping of such
anomalies. Here we discuss some of the processes envisaged by ENSO theory (Neelin et al. 1998) to produce
such mechanisms: the alteration of subsurface ocean
temperatures induced by anomalies in the depth of the
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FIG. 8. Power spectra of the (top) Niño-3 and (bottom) Niño-4
indices of the LGM, CTL, and GHS simulations. The symbols
represent the estimates for the normalized (nondimensional)
power in each bin of 1-yr width. Vertical lines indicate statistical
uncertainties. LGM (dashed line), CTL (solid line), and GHS
(dash-dotted line).
equatorial thermocline; changes in upwelling and surface advection; and anomalies in surface fluxes determined by evaporation and cloud response.
SST growth generated by surface processes (upwelling and advection) is usually associated with westward propagation in what is called the “surface” or
“SST” mode, while SST tendencies generated by thermocline depth anomalies generally cause eastward
propagation of the SST anomaly (“thermocline mode”;
Jin and Neelin 1993).
Such processes are linked in a feedback loop responsible for the growth and decay of El Niño events that
can be divided into an atmospheric component, which
creates anomalous wind stress and surface radiation
fluxes in response to SST anomalies, and an oceanic
component, which responds to the wind anomalies, in
particular the zonal component over the equator.
a. Atmospheric sensitivity and surface fluxes
Figure 14 shows diagnostics originally introduced by
Timmermann et al. (1999) as a proxy for the strength of
the atmospheric feedback responsible for growth of the
ENSO mode. The “atmospheric sensitivity” index is
defined as the covariance of equatorial wind stress and
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FIG. 9. Cumulative distribution of positive Niño-3.4 anomalies.
Niño-3.4 SST anomalies, divided by the variance of
Niño-3.4 SSTA. Between CTL and GHS, there is an
increase in atmospheric sensitivity. A steadily increasing atmospheric sensitivity index is also found in the
transient greenhouse-forced integration (orange curve
in Fig. 4) leading from CTL to GHS, which also displays
an increase in frequency and a westward displacement
of the ENSO SST pattern (Toniazzo 2002b). The difference between CTL and LGM is less clear cut, because of the large interdecadal variations of the wind
stress sensitivity in CTL.
Figure 14 (right) shows that the differences in atmospheric sensitivity index correspond to different zonal
distribution of wind stress anomalies over the equator,
with a greater response in the east for LGM and in the
west for GHS. Because the low-level wind is affected
both by direct thermal forcing of the boundary layer
and by convective activity, the pattern differences depend on the SST anomaly pattern associated with Niño3.4 anomalies as well as on the different climatologies.
To investigate the importance of the latter, we have
conducted a simple atmosphere-only experiment (using
the atmospheric component of HadCM3) forced with
SSTs obtained from the climatology of GHS, and with
the same CO2 concentration, plus El Niño anomalies
obtained from the composite El Niño of CTL (applied
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FIG. 11. Lag correlations of monthly Niño-4 anomaly increments vs Niño-3 anomaly increments, divided for (left) positive
Niño-3 anomalies and (right) negative Niño-3 anomalies. Dotted
lines are the autocorrelation functions of Niño-3 increments: (top)
LGM, (middle) CTL, and (bottom) GHS.
FIG. 10. Standard deviation of (top) Niño-3 and (bottom)
Niño-4 indices.
in the Pacific only). The result, for a single El Niño
event, is shown as the thin solid line in Fig. 14 (right).
Despite the obvious differences, the wind stress response appears also in this case locally larger, more
concentrated, and to some extent shifted westward
compared to CTL.
Figure 15, for the peak of the composite El Niño
event, shows that the spatial relation between SST
anomalies and precipitation anomalies differs in the
three climate integrations. Almost irrespective of the
shift in SST anomaly pattern, the largest convective
anomalies occur in all cases at the edges of the climatological convective regions, where convection is more
easily triggered by SST anomalies. As the main area of
tropical Pacific convection changes from a zonally extended SPCZ in LGM to a more zonally confined re-
gion in the western Pacific in GHS, anomalous convective activity moves from a zonal band south of the equator to a narrower, near-equatorial region around the
date line in GHS. As a result, the maximum SST
anomalies are widely separated from the maximum precipitation anomalies in LGM, while they are nearly collocated in GHS. This contributes to increased precipitation anomalies. The wind anomalies become more
confined, more coherently zonal in direction, and more
symmetric about the equator. The greater sensitivity of
equatorial Pacific convection to SST anomalies in GHS
is probably related to weaker climatological wind shear
and greater moisture availability. Precipitation is not as
well correlated with low-level convergence in LGM and
CTL, and anomalous moisture sources are more remote. To some extent, similar differences hold also between CTL and LGM.
The changes in strength and location of climatological convective activity, and the associated cloud cover,
also affect the response of radiative and evaporative
surface fluxes to SST anomalies over the equatorial Pacific (Fig. 16). The SW radiation flux at the surface
depends on the prevailing climatological cloud cover
conditions. In LGM and CTL, the boundary layer in the
eastern Pacific is capped by thin, warm stratocumulus.
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FIG. 12. Hovmoeller plot of 5°–5°S SST anomalies along the
equator for 20-yr sections of each model simulations (left) LGM,
(middle) CTL, and (right) GHS. Only the zero-anomaly contour
is shown. Time increases upward.
Warm SST anomalies imply reduced boundary layer
stability and destruction of some stratocumulus clouds,
resulting in a positive SW feedback. By contrast, in the
trade cumulus areas over the equatorial Pacific “cold
tongue” farther west the SW feedback is negative. In
GHS, with increased convective cloud over the equator,
the SW feedback to warm SST is generally smaller in
magnitude, and negative everywhere. This behavior is
again qualitatively confirmed in the atmosphere-only
experiment mentioned previously.
The surface longwave feedback (not shown), which
depends on the changes in cloud-top temperature, generally has the opposite sign than the SW feedback, and
is smaller in magnitude. In GHS, it is quite negligible,
probably due to both the generally higher climatological cloud tops and the greater clear-sky downward longwave. Evaporation depends nonlinearly both on the
wind speed and on the SSTs. With increasing SSTs and
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weakening winds the differences in anomalous latent
heat loss per unit Niño-3.4 SST anomaly (also not
shown) are small between the three climates. Sensible
heat fluxes are never important.
The zonal distribution of the total surface flux response to ENSO anomalies (Fig. 16, right) mainly reflects the behavior of the SW component. The implied
damping is of the order of a few tenths of degree C per
month per degree C Niño-3.4 anomaly, comparable to
the positive feedbacks dependent on upwelling and advection (see below), and therefore its timing and its
zonal distribution affect the magnitude, pattern, and
evolution of the SST anomalies.
During the growth phase of an El Niño, surface flux
damping is negligible everywhere over the equatorial
Pacific in LGM and in CTL. In the eastern side of the
basin, surface fluxes sometimes have a reinforcing effect, especially in LGM. Around the peak of the composite event, damping is still confined to the west of the
equatorial Pacific, with negligible effect elsewhere.
Damping becomes large and effective only in winter,
when SPCZ convection moves out into the central Pacific, particularly east of the date line, where SST
anomalies are largest. By contrast, with increased climatological CAPE over the equator, in GHS anomalous convection and surface flux damping are significant and approximately stationary, located around the
date line, for the whole duration of the event, competing with the reinforcing wind feedback locally. We have
already noted that the equatorial wind stress associated
with northeasterlies emanating from the northern anticyclone is weak in GHS compared with LGM and CTL.
As the seasonal cycle moves convective activity away
from the ITCZ and toward the SPCZ (Fig. 5), climatological wind stress weakens, while surface flux damping
increases, thus causing the termination of the event.
This can help explaining the earlier and sharper seasonal locking of ENSO in GHS (Fig. 10).
b. Thermocline anomalies
Climatological upwelling produces a positive
anomaly in SST tendency if the temperature contrast
between the upwelled water and the surface water is
smaller (i.e., less negative) than average, which in turn
can be associated with a deepening of the thermocline.
If the latter is due to positive wind stress anomalies
over the equator, this mechanism gives rise to an eastward-propagating “mode” of SST anomaly growth (Jin
and Neelin 1993). To assess whether it is effective in the
HadCM3 integrations we compare anomalies in subsurface-to-surface ocean temperature contrast with SST
anomalies and with anomalies in the depth of the thermocline. The latter is diagnosed for each month and
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FIG. 13. Amplitude and position of maximum and near-maximum SST anomalies for the composite El Niño
event in the three climates. All model grid points at which SST anomalies within 0.4°C of the maximum anomaly
are shown; darker shades of gray are used to indicate larger anomalies, and the lines connect the maxima. (top)
Amplitude, (middle) longitude, and (bottom) latitude of the anomalies as a function of time; (left) LGM, (center)
GHS, and (right) GHS.
FIG. 14. (left) Atmospheric sensitivity indices (after Timmermann et al. 1999) for the three HadCM3 integrations
(LGM: blue; CTL: black; GHS: red). The orange curve represents the transient HadCM3 integration that joins
CTL and GHS with a yearly 2% increase in CO2. A sliding 20-yr window is used to calculate the correlation
coefficients. (right) Regression of the 5°S–5°N wind stress response onto the 5°S–5°N, 170°–110°W average SST
anomaly in the three steady-state integrations. The thinner line represents the result from a 4xCO2 atmosphereonly integration forced with the GHS SST climatology and with El Niño anomalies from the CTL composite.
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FIG. 15. December–February (DJF) (top) SST and (bottom) precipitation fields for the three HadCM3 integrations; (left) LGM, (middle) CTL, and (right) GHS. Climatology is
shown as solid contours (drawn at 1°C intervals for SSTs and at 2 mm month⫺1 intervals for precipitation), and El Niño anomalies are shown as colors (scales on the bottom of each
panel). (top) The arrows represent 850-hPa DJF wind anomalies for each case (arrows of magnitude smaller than 1 m s⫺1 are not plotted), and (bottom) the broken contours represent
DJF 200–850-hPa thickness anomalies (drawn at 25-m intervals).
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FIG. 16. Sensitivity of (left) SW and (right) total surface fluxes to SST anomalies in the equatorial Pacific.
location as the depth at which the vertical potential
temperature gradient is largest (excluding the first two
model layers). Note that a bulk warming of the ocean
does not imply subsurface influence on the evolution of
surface anomalies, hence using the depth of a fixed
isotherm, or the temperature along a particular depth,
as proxies for the thermocline depth can be misleading.
Figure 17 shows such relations. Results from composites (graphs) and lag correlations with Niño-3 (small
Hovmoeller plots) are shown, along with representative
El Niño events plotted against the subsurface temperature contrast anomalies and thermocline depth anomalies (large Hovmoeller plots). Positive anomalies in
subsurface vertical temperature contrast are consistently associated with thermocline depth anomalies and
with positive trends in SST anomalies only in GHS.
The lack of correlation between large El Niño surface anomalies and the thermocline suggests that the
canonical thermocline mode is generally ineffective in
LGM and CTL, while more active in GHS, which is
consistent with the evidence from propagation characteristics of anomalies discussed above. This does not
mean that subsurface temperature anomalies are absent or unimportant in LGM and CTL. Because of
phase mixing, noise, and the effect of strong surface
feedbacks that generate a largely independent mode of
SST growth, composites and correlations tend to lead
to an underestimation of the importance of subsurface
anomalies for the initiation of El Niño events. Single
events also differ widely from one another, especially in
LGM where, for example, the strongest event in the
100-yr section is thermocline led and eastward propagating. The examples shown in Fig. 17 are representative of the “typical,” or most common, El Niño events.
In all integrations, warm anomalies at depth are seen
to accompany El Niño events, often propagating eastward with typical speed exceeding the advective velocity of the undercurrent. In a large fraction of all events
in LGM and CTL, these anomalies surface in the eastern equatorial Pacific and trigger or reinforce El Niño
events. In about a third of all cases, however they fail to
directly produce significant surface anomalies. The majority of El Niño events in LGM and in CTL are then
either generated at the surface, with a neutral or even
cool thermocline (when they occur the year after the
“thermocline event”), or appear to be of a mixed nature. Although there are episodes of clear eastward
propagation connected to the thermocline, SST anomalies appear to be reinforced in a stationary or westwardpropagating mode that is not coupled with the thermocline.
In GHS, by contrast, thermocline anomalies surface
much farther west and affect SSTs across the whole
basin. All warm and cold events are led by the thermocline, and they all show eastward propagation, especially for the small anomalies. Near the peak of the
event, SST growth appears to occur in a stationary or
westward-propagating mode, decoupled from the thermocline anomaly and qualitatively similar to the other
two integrations.
For LGM and CTL, more robust eastward propagation is seen in the development of cold anomalies, when
the thermocline is raised with respect to its mean position, and has a smaller zonal slope. A higher sensitivity
of the thermocline to wind anomalies may arise from
reduced stability (Fedorov and Philander 2001). However, Fourier analysis of the Hovmoeller fields of 2°S–
2°N depth anomalies (not shown) does not show an
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FIG. 17. Depth of the thermocline in the equatorial Pacific and anomalies in subsurface temperature difference in the development of an El Niño event. In the graphs on the left,
the thick solid and dashed lines represent the thermocline depth in the composite event and in the climatology, respectively, for April–June (scale on the left in each panel, m). The
thin solid lines indicate the anomalies in vertical temperature contrast between 25 and 5 m of depth (scale on the right, °C); positive values indicate elevated 25-m temperature with
respect to 5-m temperature. The small Hovmoeller contour plots to the right of the graphs show the lag correlations with the Niño-3 index of the: (left) thermocline depth (contours)
and anomaly in temperature difference between the depth of the thermocline and the surface (colors); (right) SST anomaly (contours) and of the subsurface vertical temperature
contrast (colors). Contour interval is 0.2. The three large Hovmoeller plots on the right-hand side show a representative El Niño event for each climate; SST anomalies are shown
as black contour lines (thin solid for positive, thick solid for zero, and thin dot-dashed for negative; units are °C); colors indicate thermocline depth anomalies (interval: 10 m;
yellow/red denotes positive values).
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FIG. 18. Sensitivity of ocean surface advective heating processes to wind stress anomalies in the
equatorial Pacific. (top) The graphs show regressions onto mean zonal wind stress anomalies of SST
tendencies implied by linear monthly mean anomalous upwelling terms. (bottom) Similarly, the graphs
show the regressions onto zonal-mean wind stress anomalies of the linear zonal temperature advection
anomalies. The regressions for the total anomalous vertical and zonal advective heating are very close
to the sum of the linear terms shown. Terms associated with the advection anomaly field are on the
right-hand side and those associated with the temperature anomaly field are on the left-hand side.
increase of thermocline activity, in terms of total power
in nonadvective, eastward-propagating components,
between LGM and GHS. This suggests that the main
reason for the stronger effect of thermocline anomalies
on near-surface waters in GHS is its smaller climatological depth (see Fig. 3; Fedorov and Philander 2001),
especially in the central Pacific. Increased numerical
dissipation of equatorial Kelvin waves (Gordon et al.
1995) with increasing depth and zonal slope could also
be a factor adversely affecting the thermocline feedback in LGM and CTL.
c. Upwelling and zonal advection
Given the dominance of the surface westward mode
of SST growth, the response of the surface ocean to
wind stress anomalies clearly must be very important
for the ENSO in HadCM3. Unfortunately, due to highfrequency variability and nonlinearity, monthly mean
data are not adequate to diagnose them properly. From
budget calculations for the upper-ocean layer, we find
residuals that are particularly significant, comparable in
magnitude to the total, around and after the peak of El
Niño events. Here we provide estimates for upwelling
and zonal-current feedbacks based on correlations with
monthly mean wind stress, which are not useful for
budget calculations but are better behaved, and show
systematic and interpretable differences between the
three integrations.
Figure 18 shows temperature advection anomalies associated with equatorial wind stress anomalies. In the
Niño-4 region, increased subsurface stratification is accompanied by reduced anomalies in upwelling and increased anomalies in surface current (Fig. 18, left), consistently with reduced vertical mixing. Such relation still
holds in the Niño-3 region when comparing CTL and
LGM. In the case of GHS, there is a significant thermocline feedback that implies a significant negative
correlation between anomalies in subsurface stratifica-
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tion and in wind stress, and the reduced subsurface
ocean stability with weakened easterlies encourages upwelling and vertical mixing of momentum (not shown).
In terms of implied advective SST tendencies, the
upper panels in Fig. 18 confirm a predominant thermocline effect (advection of temperature anomaly
field, W⌬T⬘, where W is the climatological upwelling) in
GHS across most of the basin, by virtue of its shallower
thermocline. In CTL and LGM, instead, the increase in
surface stability associated with warm surface anomalies in the central Pacific may be responsible for both a
greater sensitivity of the upwelling anomaly W⬘ to wind
anomalies in the east (not shown) and the confinement
of the net positive SST tendency to the Niño-3 region.
Zonal advective anomalies (Fig. 18, bottom) are always largest in the west, as may be expected by the
upwelling-dependent downstream intensification of the
South Equatorial Current and by the localization of the
largest negative zonal SST gradient in the central Pacific. While advective SST tendencies associated with
positive wind stress anomalies are positive over most of
the basin for LGM and CTL, in GHS both the reduced
climatological zonal SST gradient and the localization
of the largest SST anomalies in the Niño-4 region implies positive anomalous SST tendency contributions at
and west of the date line, and negative elsewhere.
LGM, by contrast, shows the largest, positive advective
(vertical and zonal) SST tendencies associated with
wind stress anomalies.
Positive advective and upwelling feedbacks are consistent with westward propagation of the surface
anomalies (Philander 1990, their section 6.2; Jin and
Neelin 1993). They appear to be dominant in LGM and
in CTL. In GHS, the thermocline feedback is dominant
in the eastern Pacific, while advective tendencies are
dominant in the central and western Pacific. Also in this
case, when comparing with the right-most panel in Fig.
17, consistency with theoretical expectations is found.
6. Summary and discussion
a. Summary
The ENSO that is found in the GHS integration of
the HadCM3 model is more rapid, more regular, and
more localized over the central equatorial Pacific, with
a maximum SST anomaly that is shifted westward, compared to the ENSOs in the LGM and CTL integrations
(section 4, Figs. 6–9). The phase locking of the oscillation is earlier in the seasonal cycle (section 4, Fig. 10).
Differences of the same sign, although smaller, are observed between the CTL and the LGM integrations.
Additionally, however, the ENSO in GHS differs from
the other cases in that it shows clear evidence of east-
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ward propagation of the anomalies (section 4, Fig. 12;
section 5, Fig. 17), although the maximum SST anomalies still tend to propagate westward like in the other
cases (section 4, Fig. 13; section 5, Fig. 17) The amplitude of the ENSO appears to be slightly larger in LGM
and in GHS than in CTL.
Anomalies in the depth of the thermocline of the
equatorial Pacific are always accompanying ENSO
events, and usually initiating them, but over the development of an El Niño event they appear to feed back
on SSTs, via climatological upwelling, only in the case
of GHS (section 5, Fig. 17). This is in theoretical agreement (e.g., Philander 1990, their section 6.2) with the
observed eastward propagation of SST anomalies in
GHS. Such difference with the colder climate integrations is likely to depend on the smaller climatological
depth and zonal slope of the equatorial Pacific thermocline (section 2, Fig. 3), in accord with results from
models of intermediate complexity (ICMs; Fedorov
and Philander 2001). Moreover, the reduced depth of
the thermocline in the central Pacific appears to allow
subsurface anomalies to produce SST anomalies farther
west than in the LGM and CTL integrations (section 5,
Fig. 17 and Fig. 18, top).
The sensitivity of surface winds to anomalous SST
forcing (as measured by the “atmospheric sensitivity”
index) appears to increase (section 5, Fig. 14), and is
particularly large in GHS. The wind response appears
to be increasingly stronger in the west and weaker in
the east (Fig. 14), tracing qualitatively the zonal distribution of climatological wind stress. In GHS, it is also
narrower and more symmetric about the equator, and
more confined zonally (Fig. 15). We have verified by
means of an atmosphere-only experiment that the increased atmospheric sensitivity and the changed pattern partly depend on the mean climatology with
greater propensity to convection in the central equatorial Pacific.
We have attempted an estimate of the surface ocean
response to changed wind stress (Fig. 18). There appears to be a shift toward smaller upwelling and somewhat larger zonal current anomalies, which combine
with climatological SST gradients and with SST anomalies to produce a weaker reinforcement of SST anomalies in the east in CTL compared to LGM (Fig. 18). In
GHS, they produce a strengthened reinforcement in
the west, while in the east the thermocline feedback is
counteracted by a negative advection feedback. The
different magnitude and pattern of advective SST tendencies are likely to depend on the increasing surface
stability from LGM to GHS. The upper panels in Fig.
18 highlight the negligible contribution, on average, of
thermocline anomalies compared to upwelling anoma-
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lies in LGM and CTL. Positive advective and upwelling
feedbacks are associated with westward propagation as
found in LGM and CTL (Philander 1990; Jin and Neelin 1993). The prevalence of westward-propagating SST
anomalies in LGM and CTL, and in the central and
western Pacific for GHS, is qualitatively consistent with
our estimate of the feedbacks.
Surface flux anomalies are also seen to change in
pattern and strength across the equatorial Pacific (Fig.
16). The main differences are in the SW component, the
response of which depends on climatological cloud
cover. In LGM and CTL, SW anomalies damp SST
anomalies in the western and central Pacific but reinforce them in the eastern Pacific during the growth and
peak phases of ENSO events. This appears to be related to loss/gain of stratocumulus cover for warm/cold
SST anomalies. Such effect is greater in LGM, but absent in GHS, where the equatorial Pacific is relatively
warm. Total surface flux damping of SST anomalies is
significant only where convection takes place. This results in larger and earlier damping in GHS, with a pattern that is stationary and nearly collocated with the
reinforcing wind anomalies. In CTL and LGM, reinforcing feedbacks and damping take place in different
areas until late in the development of the SST anomaly.
LGM in particular has very small surface flux damping
rates.
In summary, El Niño events in HadCM3 are generally initiated by an oscillation of the thermocline, which
appears to be an important component of the ENSO.
Eastward-propagating SST anomalies associated with
the thermocline occur when the thermocline is raised
sufficiently close to the surface to affect the subsurface
vertical temperature gradient, but generally its coupling
with the atmosphere appears to be weak, and the amplification of surface anomalies is mainly associated
with surface processes (upwelling and advection).
Strong surface flux damping occurs in areas where
there is convective activity.
In LGM, thermocline anomalies usually only surface
far in the east, where they cannot propagate much farther eastward. Occasionally, when the thermocline is
raised compared to its climatological depth farther
west, it concurs to generate larger El Niño events, but
its large climatological depth in the Niño-4 region prevents significant surface effects there. Climatological
wind stress and upwelling are large in the east, implying
stronger positive surface feedback. The general ineffectiveness of surface flux damping on the equator, related
to very cold eastern-equatorial SSTs that do not easily
allow convection to move away from the climatological
convergence zones, permits large SST anomalies, in excess of 5°C, to be sustained.
4873
In CTL, the situation is similar, but the sensitivity to
thermocline-depth anomalies is weaker, probably because of the reduced climatological wind stress and upwelling in the eastern Pacific. In the central and west
Pacific, however the wind stress is larger, allowing for a
stronger surface feedback. Surface anomalies are never
eastward propagating. With the increased proximity of
climatological convection to the equator, surface flux
damping is also larger. Strong events are not as large as
those in LGM.
In GHS, the thermocline is sufficiently close to the
surface to allow thermocline modes throughout the
equatorial Pacific. Subsurface anomalies generate significant SST anomalies in the central Pacific, which
then propagate eastward. At the same time, however,
surface amplification takes place, generating a larger,
independent, stationary or westward-propagating SST
anomaly. The surface feedbacks are stronger than in
the other integrations but also more localized in the
central/western Pacific. The same is true for surface flux
damping. Such strong, localized atmospheric response
is associated with the greater susceptibility to convective activity over the equator, which in turn is related
with smaller climatological wind shear and reduced meridional SST contrast. Increased stability of the oceanic
surface layers may also be a contributing factor for localized SST growth. Larger growth rates would favor El
Niño events of large amplitude, but surface flux damping associated with convection limits the value of equatorial SSTs (at about 35°C).
The earlier and sharper phase locking of the ENSO
in GHS relative to the LGM and CTL (Fig. 10) is related to the relative weakening of the northeasterly
trades emanating from the northern anticyclone in the
tropical east Pacific and associated with winter convection over the Maritime Continent and in the SPCZ (Fig.
5) that imply a single, summer maximum of equatorial
wind stress and hence a progressive weakening of the
positive surface SST feedbacks in autumn and winter.
Our conclusion from the present analysis is that in
HadCM3 the main climatological factors affecting the
ENSO, in strength and pattern, are the depth of the
equatorial thermocline, the distribution and the seasonal cycle of climatological convection in the tropical
Pacific, and the sensitivity of atmospheric convection to
equatorial SST anomalies.
b. Discussion and conclusions
We have presented an in-depth analysis of the ENSO
in three steady-state integrations of HadCM3, showing
significant shifts in ENSO-related variability when the
mean climatology is changed. This work also updates
the analysis of an early part of the GHS integration by
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Collins (2000), which was based on the Niño-3 index
alone and hence failed to detect the changes in ENSO
properties with respect to CTL.
The changes in the mean climatology and in the
ENSO that have been presented here are clearly model
dependent. For example, the relatively cold tropical Atlantic SST in LGM may be related to the stronger Atlantic overturning circulation (Hewitt et al. 2003),
which is not seen in other models (Peltier and Solheim
2004; Timmermann et al. 2004b). Paleoclimatological
evidence points toward a slightly reduced ENSO variability in the LGM (Tudhope et al. 2001) but is not
compelling, and consensus has still to be reached within
the paleoclimatological community (e.g., Rosenthal
and Broccoli 2004). Significant uncertainties also affect
the cloud response to warming (e.g., Williams et al.
2003). Discussing such uncertainties would exceed the
scope of the present work, however, which is concerned
primarily with the relations between changes in the
mean climatology and changes in ENSO, as represented in the HadCM3 model.
The differences we have found in some climatological aspects of the three integrations help demonstrate
the westward pattern shift seen between LGM, CTL,
and GHS as related with a predominance of surface
feedbacks reinforcing SST anomalies, and with their
shift toward the western Pacific related to the mean
zonal wind stress and to the atmospheric sensitivity to
local SST changes. In a qualitative sense, we also can
argue that the greater regularity of the ENSO in GHS
is related with the smaller climatological depth of the
thermocline, which is always active, as opposed to
LGM or CTL where it reinforces surface anomalies
occasionally, with the rapid growth of the anomaly, and
with the reduced temperature contrast between the
equatorial “cold tongue” and the surrounding convective areas, which implies increased surface flux damping that does not allow SSTs to grow much beyond a
certain threshold. We also can see a relation between
the seasonal cycle and the preferred period for El Niño
growth in the three integrations.
Our analysis does not explain, however, the small
changes in the overall amplitude of the ENSO, and the
increase in its frequency. Drawing direct inferences
from simple ENSO paradigms, like the “recharge oscillator” of Jin (1997), might help our understanding but
may equally be misleading, unless evidence can be
shown that the implied mechanisms are indeed dominant for the ENSO in the GCM (see discussion in Neelin et al. 1998). For example, we do not find support for
a simple recharge oscillator–type ENSO in HadCM3.
Moreover, the changes between integrations are many
and are complex and require careful consideration.
VOLUME 19
Clearly, more work in complementary modeling and in
sensitivity studies is necessary to assess the causes of
the changes in the model ENSO. While this exceeds the
scope of the present paper, here we briefly compare our
results with previous studies using other GCMs and
ICMs.
Timmermann et al. (1999) observe an increase in
ENSO amplitude and frequency under greenhouse
forcing in the ECHAM4/OPYC3 GCM. This GCM
predicts changes in atmospheric mean state and seasonal cycle that are consistent with ours (Timmermann
et al. 2004a). However, they do not find increased atmospheric sensitivity and attribute the changes in the
ENSO to an increased vertical temperature contrast
across the equatorial thermocline. Otto-Bliesner et al.
(2003) and Peltier and Solheim (2004) find increased
LGM ENSO variability, with no significant change in
frequency, from a simulation with the National Center
for Atmospheric Research (NCAR) Climate System
Model version 1.4 (CSM1.4) GCM. Various aspects of
the mean-state changes between the control (with trace
gas concentrations of 1990) and the LGM integration of
this model are the opposite of those seen in HadCM3
between LGM and CTL (and similar to those between
GHS and CTL): weakened trade winds, smaller zonal
and meridional SST gradients, and a raised thermocline. An et al. (2004) use each of these mean-state
changes in turn to perturb the background climatology
of an ICM of the Cane–Zebiak (CZ) type (Cane and
Zebiak 1985). They find that the raised thermocline
and the reduced meridional SST contrast both promote
amplification of the ENSO. The effect of the latter
change is attributed to reduced damping by meridional
advection.
In HadCM3, we also find a slightly reduced meridional SST gradient in LGM compared to CTL, which
may contribute to its slightly larger ENSO variability.
Moreover, between CTL and GHS, with a larger reduction in meridional SST gradient, we can see a significant decrease in heat loss from the equatorial Pacific
due to oceanic meridional advection during El Niño
events. Here we also argue however that a reduced
meridional and zonal SST gradient makes the equator
more prone to anomalous convective activity, potentially increasing the model’s atmospheric sensitivity, a
quantity related to the crucial (and poorly constrained)
“coupling strength” parameter ␮ of the Cane–Zebiak
model. Greater values of ␮ promote growth of the leading ENSO mode (Jin and Neelin 1993). At the same
time, convection is also associated with greater surface
flux damping. In general, by reducing the effective
Bjerknes feedback [parameter R in Eq. (13a) of Neelin
et al. (1998)], this would contribute to reduced ampli-
1 OCTOBER 2006
4875
TONIAZZO
tude and increased frequency, opposing the effect of
meridional SST gradients.
The effects of the raising thermocline and of weakening zonal winds are more complex. The presence of a
thermocline oscillation with a long period and of strong
surface feedbacks with dominant westward propagation
of SST anomalies suggest that the ENSO in CTL corresponds to a mixed state involving both local and remote modes as described by Fedorov and Philander
(2001), probably near the middle of the diagrams on the
right-hand side of Fig. 11 in their paper. This would be
consistent with the slightly strong winds and weak thermocline of CTL. A reduction in zonal wind stress by
25% and in mean thermocline depth by 34%, as in GHS
compared to CTL, would then imply according to Fig.
11 of Fedorov and Philander (2001) a net transition
toward a local (“SST”) mode in GHS, with larger amplitude and higher frequency. Although we do not see
such an extreme behavior, at a qualitative level our
results are not necessarily inconsistent with this and are
in line with those of An et al. (2004). Certainly, the
raised thermocline in GHS favors fast and local feedbacks over remote and delayed feedbacks.
The changes in the mean climatology between LGM
and CTL are not consistent with results from the
CSM1.4 simulations, so the results for the ENSO are
difficult to compare. There is a better agreement with
the results from the ECBILT/CLIO model (Timmermann et al. 2004b), which however does not resolve
equatorial Pacific variability. An et al. (2004) estimate
the implied changes in ENSO according to their linearized CZ model and find an increase in amplitude and
frequency (Fig. 7 in their paper). (Unfortunately, they
do not discuss the separate effect of single climatological changes, as for the CSM1.4 simulations.) With
HadCM3, we find changes in the amplitude distribution, with a possible small increase in variability. The
dominant period of the oscillation however tends to
increase. The changes in atmospheric sensitivity and
surface flux damping between LGM and CTL are similar, with opposite sign, to those between GHS and CTL,
even if smaller (and not as statistically robust, due to
the significant interdecadal variability of CTL). Moreover, the patterns of wind and SST changes are qualitatively similar to those discussed by Wang and An
(2002) in relation to the 1976 “climate shift” that appears to be associated with less frequent El Niño events
that have a stronger signature in the eastern Pacific.
Wang and An (2002) find that the driving change is in
the zonal wind stress distribution. A wind response to
SST changes shifted more to the east, as in LGM compared to CTL (cf. Fig. 14), leads to a greater delay in
the thermocline response to wind stress anomalies and
to lower frequencies.
A limitation arising in the use of ICMs for the interpretation of results from full GCMs lies in their crude
parameterization of atmospheric dynamics, particularly
convection. We have shown that the changed atmospheric response plays an important role in the ENSO
changes between CTL and GHS, in particular the westward shift in the pattern. Guilyardi et al. (2004) have
shown that the model’s atmospheric component has a
large impact on the simulated ENSO. To bridge the gap
between GCMs and ENSO theory, along with insightful modeling like that of An et al. (2004), the inclusion
of atmospheric processes beyond the Gill linear dynamical response in CZ models might be helpful.
Acknowledgments. I am grateful to Adam Scaife for
the comments and advice in the course of this work, and
to Matthew Collins for comments and suggestions. Useful comments and thorough correction of the manuscript by one of the anonymous referees were also
much appreciated. I would like to thank Chris Hewitt
for making the data from his Last Glacial Maximum
integration available to me. My thanks go to the tropical CGAM group at Reading University for much interesting and stimulating discussion. This work was supported by the U.K. Department of the Environment,
Food and Rural Affairs under Contract PECD 7/12/37
and by the Government Meteorological Research Contract.
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