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Will There Be a Significant Change to El Niño in the Twenty-First Century?
SAMANTHA STEVENSON AND BAYLOR FOX-KEMPER
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, Colorado

MARKUS JOCHUM, RICHARD NEALE, CLARA DESER, AND GERALD MEEHLNational Center for Atmospheric Research, Boulder, Colorado(Manuscript received 10 May 2011, in final form 22 August 2011)
ABSTRACTThe El Niño–Southern Oscillation (ENSO) response to anthropogenic climate change is assessed in thefollowing 18 nominal resolution Community Climate System Model, version 4 (CCSM4) Coupled Model
Intercomparison Project phase 5 (CMIP5) simulations: twentieth-century ensemble, preindustrial control,
twenty-first-century projections, and stabilized 2100–2300 ‘‘extension runs.’’ ENSO variability weakens
slightly with CO2; however, various significance tests reveal that changes are insignificant at all but the highest
CO2 levels. Comparison with the 1850 control simulation suggests that ENSO changes may become significant
on centennial time scales; the lack of signal in the twentieth- versus twenty-first-century ensembles is due totheir limited duration. Changes to the mean state are consistent with previous studies: a weakening of thesubtropical wind stress curl, an eastward shift of the tropical convective cells, a reduction in the zonal SST
gradient, and an increase in vertical thermal stratification take place as CO2 increases. The extratropicalthermocline deepens throughout the twenty-first century, with the tropical thermocline changing slowly inresponse. The adjustment time scale is set by the relevant ocean dynamics, and the delay in its effect on ENSO
variability is not diminished by increasing ensemble size. The CCSM4 results imply that twenty-first-century
simulations may simply be too short for identification of significant tropical variability response to climatechange. An examination of atmospheric teleconnections, in contrast, shows that the remote influences of
ENSO do respond rapidly to climate change in some regions, particularly during boreal winter. This suggeststhat changes to ENSO impacts may take place well before changes to oceanic tropical variability itself become
significant.

1. Introduction
Much effort has been expended on understanding thepotential future behavior of the El Niño–Southern
Oscillation (ENSO) (Collins et al. 2010; Meehl et al. 2006;
Vecchi and Wittenberg 2010; Merryfield 2006). But anoften overlooked question underlying the entire debate
is the following: what do we really mean by an ENSO‘‘response’’ to climate change, and when do we expect
it to occur? From the perspective of resource management and human impacts, the relevant time horizon isroughly the next 10–50 years (Rajagopalan et al. 2009;
Dai et al. 1998; Dettinger et al. 2000; Seager 2007). However, impacts vary with the characteristics of individual

Corresponding author address: Samantha Stevenson, CIRES,
216 UCB, Boulder, CO 80303.
E-mail: samantha.stevenson@colorado.edu
DOI: 10.1175/JCLI-D-11-00252.1
Ó 2012 American Meteorological Society

El Niño–La Niña events (McPhaden 1999; Wang and An
2001; Lengaigne and Vecchi 2009; Jin et al. 2003). Even
the past few decades show wide variations from eventto event, which has prompted some authors to wonder
whether ENSO can truly be considered an oscillation,rather than a series of feedback-amplified stochastic
anomalies (Thompson and Battisti 2001; Gebbie et al.
2007; Jin et al. 2006).
Approaching the problem from a different direction,
there have been numerous studies of ENSO-relevant
dynamics that are likely to be affected by global warming (Bony and Dufresne 2005; Liu and Philander 1995;
McPhaden and Zhang 2002; Sun 2003). It is possible thatexternal forcing might lead to a change in the types of
El Niño–La Niña events that could be expected in the
future, and much attention has been paid recently topotential ‘‘switches’’ between modes having centers ofaction in the eastern and central Pacific (Kao and Yu 2009;

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Yeh et al. 2009). And indeed physical changes to thetropical Pacific, such as alterations of heat content or the
structure of the wind stress curl, must necessarily impact
ENSO and the tropical heat budget—eventually. But
understanding how and when that impact might be realized is not often discussed in the context of coupledmodel results. This paper will show that it may not be
possible to provide a definite answer that is relevant on
the societal time horizon.
It has been previously established (Wittenberg 2009;
Stevenson et al. 2010) that averaging over several hundred years of model output is required to obtain stable
ENSO statistics, under equilibrated background conditions. Over shorter intervals, changes due to natural
decadal variability may be substantial, with variancechanging by a factor of 2 or more (Wittenberg 2009;
Stevenson et al. 2011, manuscript submitted to J. Climate).
Here, wavelet probability analysis (WPA; Stevenson et al.
2010) is used to help address the question of statisticalreliability. By comparing variability within a wavelet
power time series to internal variability in a specified
reference (i.e., observations, or a red–white-noise distribution), the significance of spectral variations can bemeasured in the context of expected decadal changes.
Unlike previous climate change studies, WPA allows
all members of an ensemble to be considered simultaneously, so that the effects of intrinsic multidecadal
ENSO variations can be averaged out as they would bein a longer simulation. However, all of the ensemble
members experience the same rapid radiative forcingchanges, which are followed by a delay in adjustment of
the tropical thermocline. The time scale of these tropicalthermocline changes cannot be shortened by increasing
the ensemble size.
In state-of-the-art general circulation models (GCMs),
the link between ENSO and changes to the mean stateof the atmosphere and ocean is unclear (Guilyardi et al.
2009; Collins et al. 2010), possibly due to ENSO’s sensitivity to the balance between different feedbacks (Philipand van Oldenborgh 2006, 2010; Guilyardi 2006). Worryingly, even the models that most closely resemble observations do not respond more similarly to one anotherthan does the full Coupled Model Intercomparison Project phase 3 (CMIP3) ensemble (Collins et al. 2010). As
a result, two separate but inextricably linked questions
arise: what are the main dynamical controls on ENSO
amplitude in future climates? And perhaps even more
importantly, when can a change in ENSO amplitude be
distinguished from natural variability? This study isintended to provide a look at the climate change–ENSO
link from a statistical perspective, from which the reliability and relevance of dynamical changes can be understood. Here, the new version of the National Center

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for Atmospheric Research (NCAR) Community Climate
System Model (CCSM4; Gent et al. 2011; this issue), with
its improved representation of twentieth century tropicalvariability (Deser et al. 2012), provides a case study for
the ENSO response to climate change.

2. Model description
In the atmosphere component of CCSM4, the Community Atmosphere Model version 4 (CAM4; Neale et al.
2011), there have been a number of changes to the dynamics and moist physics. The finite-volume dynamicalcore (Lin and Rood 1996; Lin 2004) replaces the spectral
Eulerian core as the default dynamical core in CAM4.
The deep convection scheme has two major modifications:the calculation of convective available potential energy(CAPE) is now based on a dilute-entraining parcel (Nealeet al. 2011), and the subgrid-scale vertical transport ofmomentum by deep convection is included according toRichter and Rasch (2008). Minor modifications are also
made to the calculation of cloud fraction in very dry andcold conditions (Vavrus and Waliser 2008).
The convection scheme changes provide significant
improvements to the mean atmospheric climate in CAM4
(Neale et al. 2011), and improvements are even larger
when CAM4 is coupled within CCSM4 (Gent et al. 2011).
More striking are the improvements to transient climate
variability within the tropics, related to higher-frequency
convectively forced and dynamically coupled variability.
Strong precipitation events are more frequent, the precipitation diurnal cycle is more accurate, and the representation of the Madden–Julian oscillation (MJO)(Subramanian et al. 2011) and ENSO (Deser et al. 2012)is improved.
Numerous aspects of the CCSM4 ocean model have
improved as well. The full description of the changes andtheir impacts are detailed in Gent et al. (2011); here,
only the changes that proved important for the fidelity ofthe equatorial Pacific are listed. The horizontal resolution of the ocean model remained unchanged (18) overthe last 5 years but now includes a meridional refinement
to 0.258 at the equator. A significant reduction in viscosity, however, led to a sharpening of the equatorial currents and a realistic representation of tropical instability
waves (Jochum et al. 2008). The latter is responsible forremoving the cool bias in the equatorial cold tongue.
Below the surface in CCSM3, the maximum equatorial temperature biases at 1108W reached 2.58C, andthey were accompanied by an equatorial undercurrent(EUC) with an overly deep core (Large and Danabasoglu
2006). Compared to the in situ observations from theTropical Atmosphere Ocean (TAO) array, the CCSM4
hindcast now produces a more realistic temperature, with

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TABLE 1. Simulations used in this study. For transient simulations, the CO2 value quoted is the approximate value at the end of
the simulation period.

Simulation

Length(yr)

Ensemble
size

CO2
(ppm)

Stable/transient1850 control (PI)Twentieth century
RCP2.6
RCP4.5
RCP4.5 extension
RCP8.5

1300
156
95
95
200
95

1
6
5
5
1
1

250
350
450
550
550
1300

StableTransientTransientTransient
StableTransienta maximum temperature bias of 1.08C at 1108W at 100-m
depth. Three main changes to the ocean accounted forthis improvement, and all three contributed about equallyto the sharpening of the thermocline and the lifting of the
EUC core. The first was the inclusion of a stratificationdependent isopycnal and thickness diffusion (Danabasoglu
and Marshall 2007); the second was an increase of thevertical resolution from 40 to 60 layers (S. Yeager 2008,
personal communication); and the third was a reductionof background diapycnal diffusivity from 0.1 cm2 s21 to
the observed 0.01 cm2 s21 (Jochum 2009). The new mixed
layer eddy restratification parameterization is based ongeostrophic eddies, so it is limited near the equator. However, the parameterization nonetheless affects the subtropical cell circulation (Fox-Kemper et al. 2011, Fig. 7).
Analysis here focuses on the ensemble simulations
carried out for the CMIP5; a summary of all simulations
used in this study is provided in Table 1. Ensemble
simulations have been carried out for the twentiethcentury, as well as four different projections of the twenty
first. The so-called radiative concentration pathways(RCPs) provide a profile of radiative forcing, derived
using four different socioeconomic models (Moss et al.
2010). Hereafter, the RCP ensembles are referred to by
their top-of-atmosphere radiative imbalances (globally
averaged, W m22 ): RCP2.6, RCP4.5, RCP6.0, and
RCP8.5. The RCP6.0 simulations were not complete at
the time of writing, so they are not included in the subsequent analysis.
To understand differences between the stable andtransient mean-state simulations, output from two stable
control simulations is used. The first is the 1300-yr preindustrial control (Gent et al. 2011) carried out under1850 AD CO2 concentrations. The next is the so-called
extension run for RCP4.5, designed to describe the climate from 2100–2300 after CO2 concentrations havestabilized. Extension runs are planned for all RCPs, but
as of yet the others have not yet been completed. Notethat the extension runs are themselves not sufficiently
long to provide robust ENSO statistics; a detailed examination of changes to ENSO in a stabilized mean climate

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must wait for future work [although Stevenson et al.
(2011, manuscript submitted to J. Climate) provides
a look at this question using a slightly older version of the
CCSM at coarser resolution].

3. Statistical reliability
The fundamental question addressed in this paper is
how does ENSO respond to climate change? Here, we
define an ENSO amplitude ‘‘response’’ as a statisticallysignificant difference in the variability of Niño-3 SST(58N–58S, 908–1508W); our results do not fundamentallychange if Niño-3.4 (58N–58S, 808–1708W) or Niño-4(58N–58S, 1608E–1508W) is used.
Wavelet spectra for both Niño-3 and Niño-4 SST are
shown for all ensembles in Figs. 1b,c. In both indices, thetwentieth century and RCP ensembles overlap substantially, despite a slight tendency toward weakening
at higher CO2. The shape of the spectral envelope also
appears to change somewhat, with the RCP ensembles
having a weaker annual cycle than the twentieth century.
At periods longer than 7 years, nearly no change is seen inspectral power between any ensembles. To contrast withthe forced ensembles, spectra of consecutive 100-yr subsamples taken from the stabilized 1850–1990 controls andthe RCP 4.5 extension are shown in Fig. 1a. Once again,
there is a large degree of overlap, though the range ofvariability is noticeably smaller in the shorter RCP4.5
extension run. But how significant are the changes between ensembles?
The statistics of ENSO very rarely follow well-definedprobability distribution functions (PDFs), making parametric tests of significance such as the T, F, or x2 tests
inappropriate (Montgomery and Runger 2007). Therefore, several nonparametric test results are shown in
Fig. 2. The first is a bootstrap confidence interval on the
bandpassed 2–7-yr Niño-3 SST variance. Intraensemblechanges are assessed using the 90% confidence interval on
the 30-yr variance (Fig. 2a)—confidence intervals overlap
between all ensembles. By this measure, all ensembles
are statistically indistinguishable. Note particularly thelarge spread in variance observed during the control
simulations—these encompass all of the forced ensembles’ 90% confidence intervals, illustrating the large rangeof natural ENSO variability. Clearly, 30-yr subsamples are
too short to distinguish between climate projections.
The overall Niño-3 variance appears to weaken withincreasing CO2, but 90% confidence intervals on thevariance over the full simulation period overlap with thetwentieth century for all RCP ensembles except RCP8.5(Fig. 2a, green lines). Taken by itself, this would indicatethat climate change leads to significant ENSO weakening only for very high CO2 emissions pathways. But the2132

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FIG. 1. Spectral ‘‘envelopes’’ for each CCSM4 ensemble, calculated using a Morlet wavelet transform of the SSTtime series. (a) Niño-3.4 SST for the twentieth-century ensemble, preindustrial (1850) control, and the RCP4.5
extension. (b) Niño-3 SST for the twentieth- and twenty-first-century ensembles. (c) As in (b), but for Niño-4 SST.
Envelopes for the forced ensembles are calculated by finding the maximum and minimum at each wavelet scale fromspectra of each ensemble member. Envelopes for the 1850 control and RCP4.5 extension are calculated from spectraof 100-yr subintervals of those simulations.

results from the stable control simulations indicate thatthe situation may be more complex. These simulations
are long enough to provide reasonably stable ENSO
statistics (Stevenson et al. 2010), which is reflected intheir much smaller 90% confidence intervals. When
100-yr subsamples are used to calculate the bootstrap
confidence interval (Fig. 2a, red lines), Niño-3 variances
are still statistically identical, but the error bars haveshrunk considerably. Then for the 1850 control, evenlonger subsamples may be used (Fig. 2a, yellow lines);
300 years is sufficient to identify a significant difference
between simulations, as was the case in Stevenson et al.
(2011, manuscript submitted to J. Climate). In otherwords, significant ENSO changes can only be observed
after the climate has stabilized, by averaging a sufficient number of model years (see also section 4). The
twentieth century does not differ from either future or

past ENSO variability, and only when CO2 is held constant for several centuries can ENSO differences be reliably detected.
Another common metric for statistical significance,
the Kolmogorov–Smirnov (K–S) test on Niño-3 SST, isused to examine changes to the distribution of 2–7-yr
bandpass-filtered Niño-3 SST. The K–S test shows thatthe probability distribution functions (PDFs) of Niño-3
SST (as well as SST anomaly; not shown) are significantly
different between every possible combination of ensembles, indicating that a shift in the statistics of El Niño–La
Niña events occurs, despite the lack of a significant
overall change in variance with CO2. Natural variability
among ensembles also leads to significant changes in theNiño-3 SST PDFs as measured by the K–S test, eventhough these distributions are by definition generated
from the same physical processes.

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FIG. 2. Diagnostics used by four different statistical tests to detect changes to ENSO variability between ensembles. (a) Bandpassed(2–7-yr) Niño-3 SST variance with 90% bootstrap confidence intervals, calculated both for the full time span of each ensemble and for
30-yr subintervals. (b) Cumulative distribution functions for (top) Niño-3.4 SST and (bottom) the scale-averaged wavelet power between
2 and 7 years. (c) Probability density function for mean wavelet power. (d),(e) WPI distributions used in hypothesis testing procedure.
(d) Distributions calculated relative to the 1850 control, and (e) distributions calculated relative to an AR(1) fit. Solid lines represent the
90% confidence interval for the WPI distributions generated from model subintervals, dashed lines the same for the AR(1) fits to eachmodel. All WPI distributions use 30-yr subintervals.

The K–S test is also applied to the scale-averaged
wavelet power (SAWP; Torrence and Compo 1998) between 2–7-yr periods, which is defined according toj2

2

jWn (Sj )jdjdt,
SAWP 5
Cd j5j1Sjå(1)where Wn(s) is the wavelet spectrum, dj and dt are thediscrete steps in scale and time, and Cd is a constant appropriate to the wavelet function being used (for Morlet,
Cd 5 0.776; Torrence and Compo 1998). As with Niño-3SST, the K–S test shows that all of the SAWP distributions differ. We have here a situation where the distribution shapes of SST and SAWP differ, yet the total
variance in the time series is the same. This seems
contradictory in some sense, since the total amount ofvariability is constant yet the K–S test results would
suggest that there is a statistical difference. A less sensitive test is therefore necessary to determine when aspectral shift is truly distinct from natural variability.

The newly developed wavelet probability analysis
toolkit of Stevenson et al. (2010) provides a more appropriate comparison. This test compares the joint probability between subintervals of a wavelet spectrum (either
at a given period or averaged over a range of wavelet
scales), which is a measure of the degree to which theoverall behavior of the oscillation changes with time.
The advantage of this approach over the K–S test is thatit does not require that the two distributions being
compared be equal, only that they cover similar rangesof spectral power. The test statistic is the distribution of
wavelet probability index (WPI) the joint probability
between subintervals. WPA has proven useful at identifying known offsets in model ENSO spectra from observations, in the CCSM3.5 and other coupled models(Stevenson et al. 2010, 2011, manuscript submitted to
J. Climate).
Example PDFs of SAWP are shown in Fig. 2c, eachgenerated using the full set of members for each ensemble. During the WPI calculation, the PDFs of SAWP
for subsamples of individual ensemble members are

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calculated, and the overlap between each pair of PDFs
forms a single WPI value. Distributions of the WPI values
are then used in the testing procedure. In all cases, theensembles are considered as a group—thus the six
twentieth-century simulations are compared with the five
RCP4.5 simulations, etc. This approach creates the largest possible sample size.1
A qualitative sense of the behavior of each ensemble
may be gained from Fig. 2c: substantial overlap is seen
between the Niño-3 SAWP PDFs, but generally speaking
the lower-CO2 simulations show a larger high-power‘‘tail’’ (or conversely, fewer strong events occur at higher
CO2). This is simply another way of visualizing thespectral overlap seen in the envelopes of Fig. 1.
WPI distributions generated using the CCSM4 ensembles are shown in Figs. 2d,e. Two different null hypotheses
are adopted for the test; the first is the simple null hypothesis used by Stevenson et al. (2010) that internalvariability within a long ‘‘reference’’ time series can leadto the same range of WPI values observed when comparing subsets of the two time series of interest (i.e., thetwentieth versus twenty-first centuries). This is measured
using the 1850 control as the reference; the WPI distributions generated from the overlap between 30-yr
segments of the 1850 control and the twentieth- andtwenty-first-century ensembles, respectively, are tested
against one another. Figure 2d shows the WPI distributions with the corresponding 90% confidence interval(lines above the WPI distributions)—it is clear that allof the confidence intervals overlap. Note in particular
how much the WPI confidence intervals for the RCP4.5
and its extension differ—even though these two runs
reach the same level of top-of-atmosphere radiative
imbalance, their confidence intervals barely overlap,
making a stark contrast with the large overlap between
the various twentieth-century ensembles.
The second WPA null hypothesis is that low-frequency
noise could create the observed degree of spectral overlap. The WPI distributions derived from the CCSM are
now compared to those generated from a first-order
autoregressive [AR(1)] red-noise distribution, where thelag-1 autocorrelation is derived from each of the two input time series. WPI distributions are shown in Fig. 2e;
now a significant difference is indicated by a lack ofoverlap between the 90% confidence interval on WPIfrom the AR(1) fit (dashed line) and the corresponding
ensemble (solid line of the same color). Comparing thecolor-coordinated line pairs at the top of Fig. 2e, we see

1

Note that the first 10 years of each simulation are excluded tominimize effects from mutual information due to common initialconditions.

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that once again they overlap; there is no significant
change in any of the ensemble combinations. Despite
the significance of changes to the shape of the SST andwavelet power PDFs in Fig. 2c, these changes cannot be
distinguished from those one might expect due to the‘‘noisiness’’ of natural variability.
An interesting note is that the ENSO response to increased CO2 is entirely different in the CCSM4 andthe CCSM3.5 equilibrated simulations [this study and
Stevenson et al. (2011, manuscript submitted to J. Climate), respectively]. In the CCSM4-forced ensembles,
ENSO seems to weaken slightly with CO2, but the stable simulations of Stevenson et al. (2011, manuscript
submitted to J. Climate) show an increase in ENSO
amplitude. Intuitively, one might expect that a given
climate model would exhibit a certain pattern of ENSO
sensitivity to CO2, but it appears that even smallchanges to model physics are sufficient to lead todramatic changes in the simulated ENSO response to
climate change (Jochum et al. 2010). Model resolution
may be responsible; westerly wind bursts, for example,
are entirely absent in the coarse-resolution CCSM3.5,
and changes in stochastic forcing might potentiallydrive shifts in the ENSO sensitivity (Jochum et al.
2009). However, a detailed examination is beyond thescope of this paper; a discussion can be found in Stevenson
et al. (2011, manuscript submitted to J. Climate).

4. Changes to mean circulation
Given that overall ENSO response does not seem to besignificant for the forced ensembles, we next look forchanges to the atmosphere–ocean mean state. Basic-statevariables are closely linked to ENSO dynamics in all
theoretical models of the oscillation (e.g., Suarez and
Schopf 1988; Jin 1997; Weisberg and Wang 1997; Battisti
and Hirst 1989, and many more—see Neelin et al. 1992;
Wang and Picaut 2004 for reviews) and relationships withatmosphere–ocean mean-state variables are often used to
explain various aspects of ENSO dynamics. For example,
shifts between surface and thermocline-driven ENSO
modes may be related to zonal wind stress as well as thedepth and stratification of the mean thermocline (Fedorov
and Philander 2001), while the meridional extent of thesubtropical wind stress is related to the ENSO period in
coupled climate models (Capotondi et al. 2006). Examples of the mean state–ENSO interaction are too numerous to completely describe here, although the subject
is of considerable interest for ENSO studies using coupled
GCMs (Guilyardi 2006). Unfortunately, given the lack ofsignificance in Niño index spectra seen in section 3,
quantifying the importance of these mechanisms in the
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FIG. 3. Summary of changes to the atmosphere–ocean mean state between CCSM4 ensembles. (left) Ensemble-mean pattern of changes
between RCP8.5 and the twentieth century with rectangular boxes indicating averaging regions. (right) Distribution of regionally averaged diagnostics for each ensemble, corresponding to the quantity on the left, as a function of CO2 stabilization level. (a) SST (8C); (b)thermocline depth Z20; (c) Vertical temperature profile (8C); (d) wind stress magnitude (N m22); (e) precipitation (mm day21). Note that(h) the vertical temperature derivative dT/dz rather than the mean value of T(z) is shown; here, z is positive downward, and dT/dz hasunits of 8C m21. (right) The horizontal lines inside the boxes indicate the ensemble median, the extent of the boxes the distance between
the 25th and 75th percentiles, and the whiskers the 2.5th and 97.5th percentiles. Where present, 1 symbols indicate outliers. All box
averages on the right-hand side show the averages inside the boxes drawn on the left-hand panels: with the exception of (h), which instead
shows the vertical derivative of temperature averaged inside the box in (c).

important to note the direction of the CCSM4 meanstate response to climate change to provide a benchmark
for use in future multimodel comparison studies.
Figure 3 shows the changes to the atmosphere–ocean
mean state in five different variables: SST, thermocline
depth Z20, vertical temperature profile, precipitation,
and wind stress magnitude. In each row, the left-handpanel shows the ensemble-mean difference between
RCP8.5 and the twentieth century as a map over thetropical Pacific (or in the case of the vertical temperature
profile, as a cross section over 58S–58N; Fig. 3c). The corresponding right-hand panel shows the distribution of theensemble member means for the appropriate variable,
averaged over the box pictured on the map at left.2 This
allows changes with CO2 in regions of interest to be easily
visualized.
2

Panel (h) is an exception; this shows the vertical temperature
derivative averaged over the box in panel (c), rather than the mean
temperature.

Changes to SST are shown in Figs. 3a,f. Warming ismost pronounced along the equator, consistent withpredictions of enhanced meridional SST gradients fromprevious analyses (Gastineau et al. 2009; Liu et al. 2005;
Xie et al. 2010; Zhang et al. 2009). Stronger warming isseen in response to CO2 increases in the eastern Pacificthan in the west, which has been seen in multimodelexperiments (Collins et al. 2010) as well as stabilized
climate experiments with the CCSM3.5 (Stevenson
et al. 2011, manuscript submitted to J. Climate).
Changes to the ocean stratification are known to affect
ENSO dynamics (Jochum et al. 2010; Liu and Philander
1995; Yeh et al. 2006), and previous authors have predicted that the stratification should increase in response
to climate change (Timmermann et al. 1999; Merryfield2006). Figure 3c shows the same result, with stronger
warming observed above the thermocline than at depth.
The increase in stratification may be observed directly in
Fig. 3h, where the vertical temperature derivative in theeastern Pacific averaged over the top 100 m is plotted.

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FIG. 4. Trends in thermocline depth (measured as the depth of the 208C isotherm) for the CCSM4 ensembles. (left) (a)–(d) Difference
maps taken between the first and second halves of the run: (2051–2100) – (2005–50) for the RCPs, and (1931–2005) – (1850–1930) for thetwentieth century. (middle) (e)–(h) The vertical profiles of temperature averaged over the three boxes pictured at left: equatorial (EQ),
northern (N), and southern (S). In these panels, the solid lines show the vertical profiles for the first half of the run and the dashed lines the
profiles for the second. (right) (i)–(l) The time series of thermocline depth averaged over the same regions. Rows correspond to the four
simulation ensembles: (a),(e),(i) twentieth century, (b),(f),(j) RCP2.6, (c),(g),(k) RCP4.5, and (d),(h),(l) RCP8.5.

Thermocline depth ensemble-mean differences show
substantial effects in the extratropics (Figs. 3b,g). Poleward of 108, all of the RCP ensembles show a pronounced
deepening of the thermocline with CO2, which is especially strong in RCP8.5 (Fig. 3g, red boxes). Near theequator, the deepening is minimal, with changes tothermocline depth less than 10 m (Fig. 3g, blue boxes).
Note that the 208C isotherm has been used to define thethermocline, which is not an ideal metric given the largechanges to SST expected under global warming. However, test cases calculated using latitudinally varying
thermocline definitions show the same results, indicatingthat the overall dynamics are insensitive to the exact
definition used. Additionally, the center panels in Fig. 4
make it clear that extratropical deepening is observed
regardless of the threshold temperature used to define
the thermocline.
A weakening of the atmospheric overturning circulation is expected in response to climate change (Vecchi
and Soden 2007; Held and Soden 2006) due to an imbalance between increases in the evaporative flux andconvective precipitation, and signatures of such a weakening are seen in CCSM4. Figures 3d,i show ensemblemean precipitation differences; although the increase islikely not significant because of the substantial overlap
between ensembles (Fig. 3i), the ITCZ strengthenssomewhat in all RCPs relative to the twentieth century,
especially in the southern branch [South Pacific convergence zone (SPCZ); Fig. 3d].
Figures 3e,j show the changes to wind stress magnitude t; there is some strengthening of t at 58N and S(Fig. 3e), which comes primarily from increased meridional wind stress (not shown). Reductions in t are roughly
5%–10% of the twentieth-century trade wind strength
along the equator, though again the ranges covered by all
ensembles overlap (Fig. 3j). In zonal wind stress (notshown), there is a strong hemispheric asymmetry in the
Pacific trade wind shifts. The Northern Hemisphere trades(between 108 and 208N) are reduced by 2–3 times as muchas their Southern Hemisphere counterparts, possibly due
to the higher air–sea contrast created by the larger Northern Hemisphere landmasses. The trade wind asymmetry

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is consistent with the hemispheric asymmetry in overallwarming, which has been shown to be 2–3 times stronger
in the Northern Hemisphere in CCSM4. From geostrophy, a stronger reduction in the meridional temperature gradient in the Northern Hemisphere will in turnlead to a greater weakening of the trade winds.
A more direct visualization of the ocean stratification
changes is provided in the center panels of Fig. 4. Thisshows the vertical temperature profiles for each simulation, averaged in equatorial and off-equatorial regions
during the first and second halves of each simulation
period (1850–1930 and 1931–2000 for the twentieth century, 2005–50 and 2051–2100 for the RCPs). Differences
between the first and second half of the twentieth century
and of RCP2.6 are minimal (Figs. 4e,f), with mean temperature profiles nearly indistinguishable from one another in both the equatorial and off-equatorial regions.
At higher CO2, trends within the simulations become
more apparent, especially in the extratropics. In RCP4.5,
warming of roughly 0.58C is seen in the mixed layer from2051–2100 relative to 2005–50 (Fig. 4g); this warming islarger, on the order of 18C, in RCP8.5 (Fig. 4h). But mostinteresting is the warming at depth; offsets of 0.58C are
visible down to 200 m in RCP8.5, but the equatorial
temperature profile shows virtually no subsurface change.
The same effect is shown in the maps in Figs. 4a–d—theextratropical thermocline depth changes at high CO2 aremuch larger than the tropical. Likewise, the time series in
Figs. 4i–l clearly show that the extratropical thermocline
is deepening and that the deepening in the RCPs showsno sign of stopping at year 2100; yet the trend in thetropical thermocline is smaller, consistent with a delayed
response in the tropics. Note that the trends in thesepanels are statistically significant, even when they appear
visually quite small in Fig. 4.
Why is it that the change in tropical thermocline depthlags behind that in the subtropics? First of all, the subtropical thermocline is in the region of Ekman convergence, downwelling, and subduction, while Ekman
divergence and upwelling affect the tropical thermocline (at least within the cold tongue, where ENSO is
most active). Thus, the subtropical thermocline’s temperature and volume transport are locally determined,
while the tropical thermocline depends both on thelocal upwelling–mixing and on the density and temperature of water supplied from remote sources. The
adjustment of the tropical thermocline to extratropicalperturbations is therefore a combination of wave adjustment, advective supply, and diabatic balance (e.g.,
Cane and Sarachik 1979; Gu and Philander 1997;
Boccaletti 2005).
While estimates differ on the delay time scale because
forcing of downwelling regions (and consequently the2137

wave and advective pathways) differ substantially amongmodels, most studies arrive at a decadal or multidecadal
lag for the tropics after an impulsive extratropical perturbation. The time scale of warming the subtropics byan O(1 W m22) perturbation is long (14 years to warm
100 m of water by 1 K), and the communication timescale is also long (perhaps as long as a decade for thewaters making up the EUC; Blanke and Raynaud 1997;
Kessler 2006). Finally, as Boccaletti (2005) and Boccaletti
et al. (2004) show, the rebalancing of the heat budget ofthe system as a whole requires adjustment of the equatorial diabatic fluxes and a return of the signal to theoriginal perturbation location via Rossby and Kelvinwaves. In all, it is not surprising that the time scale foradjustment of the whole system is comparable to or longer than the length of the RCP simulations. It should be
noted that this is a direct consequence of the large-scaleocean dynamics involved and is expected to be a common
feature among all coupled models.

5. ENSO modes and teleconnections
The large interensemble mean-state changes in section 4 may reasonably be expected to translate intochanges to the teleconnections between ENSO andextratropical atmospheric circulation (Alexander et al.
2002; Diaz et al. 2001; Meehl et al. 2006; Xie et al. 2010),
as well as the overall modes of variability (Yeh et al. 2009;
Capotondi et al. 2006; Deser et al. 2006). Atmosphericteleconnections are known to experience substantial decadal modulation (McCabe and Dettinger 1999; Poweret al. 1999, 2006; Trenberth and Hurrell 1994) but may
nonetheless be expected to behave differently in futureclimates (Cane et al. 1997; Diaz et al. 2001). Of course,
the large number of ENSO teleconnections identified in
the literature makes a comprehensive examination impossible; as such, El Niño and La Niña composite atmospheric maps provide a representative example. It is
hoped that this brief overview of teleconnection changes
may lead to a more detailed investigation in futurestudies.
The composite El Niño and La Niña differences in sea
level pressure (SLP) and surface air temperature (SAT)
are shown during boreal summer and winter [June–
August (JJA) and December–February (DJF), respectively; Figs. 5–8]. These fields are chosen for their ability
to simply represent the atmospheric teleconnection patterns; SLP anomalies in particular have been shown to
be indicative of the upper-air circulation anomalies(Deser et al. 2012). In Figs. 5–8, El Niño and La Niñaphases are shown separately to avoid confusion due topossible nonlinear shifts in teleconnection dynamics(i.e., Okumura and Deser 2010).

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FIG. 5. Composite El Niño DJF anomaly map for the CCSM4 ensembles. In Figs. 5–8, El Niño and La Niña events are defined as years
where the deseasonalized DJF Niño-3.4 SST anomaly exceeds 61 standard deviation relative to a linearly detrended time-mean state. (a)Composite for twentieth-century ensemble, (b) RCP2.6–twentieth century, (c) RCP4.5–twentieth century, and (d) RCP8.5–twentiethcentury are shown. In all panels, surface air temperature (8C) is shown in color and sea level pressure (hPa) is shown in contours (contour
interval 0.25 hPa). Negative anomalies are indicated as blue colors or dashed contours. (b)–(d) SLP anomalies significant at 90% areindicated by thicker contours, and only the significant surface air temperature values are plotted.

This analysis is similar to the approach of Deser et al.
(2006); El Niño and La Niña events are defined as periods
where the three-month deseasonalized mean Niño-3.4
SST difference from the mean for the appropriate ensemble member exceeds 11 standard deviation during
DJF. Biases due to the differing mean-state trends between ensembles are corrected for by linearly detrending
prior to calculating the standard deviation. Any predictability to the changes to teleconnections with CO2
should then show up as a CO2-dependent shift in thecomposite plots in Figs. 5–8. To ensure that only significant anomalies are examined, the Wilcoxon ranksum test is applied to the set of SAT and SLP anomalies
at each grid point, and only SAT anomalies significant
above 90% are shown. Significant SLP anomalies areindicated by thickened contours, but the full SLP anomaly pattern has been retained to provide visual context.
The overall weakening of ENSO in the twenty-firstcentury ensembles is reflected in the smaller equatorial
Pacific surface temperature anomalies in the RCPs

relative to the twentieth century, which appear as negative (positive) anomalies during El Niño (La Niña).
However, ensemble-mean anomalies are very small(#0.18C). The change with the largest magnitude occurs
during La Niña in DJF (Figs. 6b–d)—a pronounced highpressure anomaly to the south of Alaska forms in thehigher-CO2 ensembles. This is essentially a strengtheningof the blocking high typically associated with La Niñaevents, which is accompanied by colder air temperatures
over Canada and the northern United States.
High pressure anomalies are observed during El Niño
DJF in the Southern Hemisphere subtropics, whichbecome statistically significant in RCP4.5 and RCP8.5.
These anomalies are strongest south of Tasmania andare accompanied by negative SLP anomalies over western Australia and Indonesia. SAT and SLP anomalies of
opposing sign occur in these regions during La Niña DJF(Figs. 6b–d), which are statistically significant over theSouthern Ocean but not over mainland Australia. Taken
together, these changes seem to indicate a weakening of

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2139

FIG. 6. As in Fig. 5, but for La Niña DJF.

the overall Australasian El Niño teleconnection, which
could have implications for modeled ENSO-relateddrought and flooding in the region.
Another change seen during El Niño DJF appears
similar to those documented in Meehl et al. (2007) andMeehl and Teng (2007): the North American teleconnection pattern shifts to the north and east with
CO2. This is evidenced by the negative SLP anomalies
over the northern United States and Canada and thepositive SLP anomalies north of Hawaii near the southern edge of the Aleutian low in the twentieth-century
El Niño DJF (Fig. 5a). Meehl and Teng (2007) attributed
such shifts to an anomalous upper-tropospheric wave-5
pattern, related to a shift in the midlatitude mean circulation. The SAT and SLP anomalies in Fig. 5 are certainly
consistent with such a hypothesis, but a more thoroughanalysis is left for future investigations.
During El Niño JJA, very few of the SLP differences
observed are statistically significant (Fig. 7). There is aweakening of the high pressure anomaly in the Southern Hemisphere off the coast of Antarctica, with anaccompanying positive SLP anomaly just to the north(near 508S); this may represent a southeastward shift ofthe overall teleconnection pattern in this region. During
La Niña JJA, a similar pattern of opposite sign emerges,

with positive SLP anomalies off the Antarctic coast andnegative anomalies in the Southern Hemisphere extratropics. Anomalies near Antarctica are likely associated with changes to sea ice cover (Deser et al. 2012).
Notably, there does not appear to be a monotonic trend
between RCP ensembles in either El Niño or La Niña
JJA teleconnection patterns; the Southern Hemispherechanges are much larger in RCP2.6 than the higher-CO2
ensembles. This may be related to the much faster radiative forcing stabilization in RCP2.6 (Moss et al. 2010).
In general, the larger responses seen in the SLP and
air temperature diagnostics of Figs. 5–8, in comparison
with the oceanic diagnostics examined in previous sections, are consistent with the faster response time of theatmosphere to a change in radiative forcing. It should be
noted that the high pressure anomalies near the AleutianIslands in La Niña DJF are substantial—up to 50% of
the anomaly seen in the twentieth-century ensemble(Fig. 6a). Thus it is possible for a significant shift in ENSO
teleconnections to take place in the atmospheric circulation well before the slower ocean circulation (andtherefore tropical variability) has had time to stabilize.
Future work on projections of atmospheric ENSO teleconnections and associated climate impacts is highly
recommended.

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FIG. 7. As in Fig. 5, but for El Niño JJA.

Oceanic modes of variability were examined as well, toassess the validity of the slow response time hypothesis.
And indeed, a lack of significant changes is seen; the twoleading modes of thermocline variability (not shown)
were computed following the approach of Capotondi
et al. (2006). These modes show the ‘‘recharge oscillator’’signals described by those authors, but no substantialchange in either the pattern of variability of the percentage of variance explained was seen with CO2. Spectraof the principal components were statistically identical,
and only minor qualitative changes to the spatial pattern were observed; this leads to the conclusion thatthe lack of significance in the ENSO amplitude signal
applies to modes of thermocline variability as well. It
is therefore difficult to conclude on the basis of thesesimulations that climate change will lead to any statistically significant alterations in oceanic modes of ENSO
variability.

6. Discussion
The results of this study are interesting in the context
of ongoing work on CMIP5, since they clearly demonstrate that the oceanic dynamics relevant for ENSO do

not stabilize on centennial time scales in the CCSM4;
even when changes to ENSO amplitude are expected toeventually become significant, more than a century ofobservations after the external forcing has stabilized
may be necessary for that change to manifest itself. The
exact length of observations necessary for robust ENSO
statistics is likely sensitive to the magnitude of the extratropical trends, which in turn will depend on the degree of imposed external forcing. For example, ENSO in
the RCP2.6 scenario likely stabilizes more quickly after
2100 than does the RCP8.5, since by then the radiative
forcing in RCP2.6 has already stabilized while it continues to ramp up in RCP8.5. Changes to ENSO may
be felt more or less quickly depending on the details of
future CO2 emissions but are always delayed in arriving at the tropics via the wave and advective pathways(Boccaletti 2005).
This result has been obtained using only a single
model, of course, and should be confirmed using other
CMIP-class models. If this behavior is found to be a robust feature of GCMs, it has important implications forunderstanding ENSO dynamics and other questions suchas assessing the frequency of event types. For example,
even if ‘‘Modoki’’-type events do become more frequent15 MARCH 2012

STEVENSON ET AL.

2141

FIG. 8. As in Fig. 5, but for La Niña JJA.

in future climates (Yeh et al. 2009), based on these results
one would not expect to detect such a change for several
centuries.
It is also important to note that the equilibrated response does not provide the answer most relevant to society; for impacts studies, what matters is not the ENSO
response in an idealized stable climate, but the change inlong-term ENSO statistics under changing CO2 forcing. It
is entirely possible that the observed slight ENSO amplitude weakening in the RCPs will become significant if
CO2 continues to increase for long enough; however, all
we currently know is that 100 years of observing thesystem is not enough to provide an answer. The ENSO
response of extremely long ramp-up simulations is another interesting avenue of investigation.
Figures 5–8 have demonstrated that the fast atmospheric response time allows ENSO teleconnections tochange before the oceanic statistics have stabilized (tropical ENSO variability has adjusted). This means that thepossibility of significant changes to societally relevant
quantities such as rainfall (Cayan et al. 2010; Mason
and Goddard 2001) in the next few decades cannot be
excluded on the basis of these results. This is especiallyrelevant when considering factors such as land-use changes
in the RCPs. The assumptions regarding projected future

agricultural activity are dramatically different between
RCPs (van Vuuren et al. 2007; Clarke et al. 2007; Fujino
et al. 2006; Riahi et al. 2007), and this has the potential toaffect ENSO-relevant atmospheric circulation patterns.
Although not considered in this study, a more detailed
examination of the impact of land-use changes on ENSO
in the CCSM4 would be a valuable next step.
The rapid response of atmospheric teleconnections to
CO2 also has important implications for paleoclimaticstudies: most investigations of paleo-ENSO explicitly or
implicitly assume that teleconnections remain stationary(Mann et al. 2008, 2009; Molnar and Cane 2002, 2007). But
these results clearly show that the oceanic and atmospheric
responses change on vastly different time scales. In otherwords, atmospheric teleconnections should not be viewed
as robust indicators of changes to past ENSO variability.
Future work will assess the details of the equilibrium
ENSO response to CO2 increases in the CCSM4. The
stabilized runs in this study only cover a small portion of
the potential range in CO2 forcing due to anthropogenic
climate change; does the system continue to respond in
the same way at higher and higher CO2? There is someevidence that ENSO behavior may radically change atvery high (but plausible) CO2 concentrations (Cherchi
et al. 2008). Once the extension runs for the other forcing

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pathways become available, it will become possible toperform a much more detailed diagnosis of the behavior
of ENSO in response to CO2.
The implications of this study for intermodel comparison are also important. In the context of the long
dynamical time scales observed in the CCSM4, the disagreements between model ENSO projections seen by
Collins et al. (2010) should be expected. If multiple
centuries at stable CO2 are required to observe significant
changes to ENSO, then it should come as no surprise thatlooking at much shorter integrations leads to conflicting
results. Additionally, the differences in ENSO amplitudechange between the stable CCSM4 simulations in thisstudy and the equilibrated runs of Stevenson et al. (2011,
manuscript submitted to J. Climate) prove that even
within a given model family, small changes to physics
can lead to diametrically opposing results. Are theintermodel differences seen in the Collins et al. (2010)
due to the short simulation length, or are there realdifferences in ENSO climate sensitivity? Answering
this question will require dedicated millennial-scale
simulations with stable mean climates using multiple
models, which is recommended for future investigations.

7. Conclusions
This study has provided a first look at the response of
ENSO to climate change in the CCSM4. Several different
metrics show that the change in ENSO amplitude fromthe twentieth-century value is statistically insignificant
between most RCP ensembles. The overall 2–7-yr Niño-3variance is indistinguishable between most ensembles,
although a K–S test shows that the PDFs of Niño-3 SST
and of scale-averaged wavelet power values do alter significantly within and between ensembles. Application of
the WPA tools of Stevenson et al. (2010) then shows that
despite the change in PDF, these shifts are consistent
with what one might expect because of natural variability alone.
Notably, the situation may be much different in stabilized climates—the shrinking of the 90% error bar on
Niño-3.4 variance in the stabilized CCSM4 1850 control
suggests that ENSO changes could become significant
on multicentury time scales, although averaging morethan 200 years of each simulation will likely be required to observe such significance (i.e., Wittenberg
2009; Stevenson et al. 2010). Note that the combined
length of the RCP ensembles (5 3 95 yr 5 475 yr total)
is much longer than the extension run, yet no signal isseen; from a societal perspective, these results indicatethat an individual ENSO event, or even a few decadesof anomalous ENSO events such as the period from theVOLUME 25

1970s to the present, cannot be attributed to forcing due
to present CO2 emissions. Instead, they can only be attributed to a combination of changes in past forcing andnatural variability. This also indicates that the true range
in model ENSO sensitivity to climate change remains an
unknown and must be determined using multicentury
control simulations at varying CO2 concentrations.
Mean-state changes in response to CO2 increases in
CCSM4 are generally similar to those seen in CCSM3.
The eastern Pacific SST warms along the equator, theequatorial trade winds weaken, the hydrological cycle
becomes more intense, and ocean stratification increases
with CO2. Thermocline anomalies in the extratropics
persist throughout the course of the twentieth–twentyfirst centuries, with the trend becoming stronger in thehigher-CO2 ensembles. The time scales for dynamical
adjustment expected in the system are on the order of
decades, meaning that the tropics may not be fully ‘‘spun
up’’ in response to CO2 changes by the end of the twentyfirst century. In the RCP4.5 extension simulation thetrends are much smaller; perhaps ENSO changes will
become detectable after CO2 stabilizes.
Changes to ENSO atmospheric teleconnections do
occur between ensembles, although the magnitude ofchanges to tropical surface air temperatures is very small.
The wide variety of possible teleconnections makes acomplete analysis beyond the scope of this paper, but
surface air temperature and sea level pressure were examined during both El Niño and La Niña in DJF and
JJA. Interensemble changes to the El Niño DJF SLP
anomalies are consistent with previous results (Meehl
et al. 2007; Meehl and Teng 2007); a northeastward shift
in the North American teleconnection pattern occurs
with increased CO2, possibly indicating a change in themean upper-air circulation. During La Niña, the DJF
SLP anomaly south of Alaska increases dramaticallywith CO2, strengthening the blocking high over the
Aleutians and creating much colder surface temperatures in Canada and the northern United States.
In the Southern Hemisphere, changes during DJF takeplace over Australia, which tend to oppose the prevailing
ENSO teleconnections. El Niño years show a positive
SLP anomaly south of Tasmania, with a negative anomaly over mainland Australia and Indonesia. During La
Niña, the opposite is true; this seems to indicate that climate change will lead to weaker ENSO-related impacts
in the region during DJF, at least in the CCSM4. Changes
during JJA are less conclusive and changes with CO2
are generally nonmonotonic—Southern Hemisphere SLPchanges appear stronger in RCP2.6 than the higher-CO2
ensembles. However, the presence of statistically significant SAT and SLP changes demonstrates that it is possible
for atmospheric teleconnections to respond to climate

15 MARCH 2012

STEVENSON ET AL.

change in the absence of statistically robust changes tooceanic–tropical variability.
The contrast between the (lack of) oceanic response
and the stronger atmospheric response is quite interesting.
When modes of oceanic variability were examined, noconsistent pattern of changes was observed with CO2. The
logical conclusion is that the diagnosis of a robust oceanic
ENSO response to climate change in the CCSM4 is simply
not possible until long after the CO2 forcing has stabilized,even using extremely large ensembles to average overnatural variability. However, changes to the impacts of theoscillation may be felt much sooner, owing to the much
shorter atmospheric response time scale. Investigationsof ENSO with other coupled models in the twenty-first
century and in stabilized climates should prove extremely
valuable as a confirmation of these results.
Acknowledgments. This research used computing
resources of the Climate Simulation Laboratory at the
National Center for Atmospheric Research (NCAR),
which is sponsored by the National Science Foundation;
the Oak Ridge Leadership Computing Facility, which is
supported by the Office of Science of the U.S. Departmentof Energy under Contract DE-AC05–00OR22725; and the
National Energy Research Scientific Computing Center,
which is supported by the Office of Science of the U.S.
Department of Energy under Contract DE-AC02–
05CH11231. We acknowledge Adrianne Middleton, Andy
Mai, and Diane Feddema at NCAR who helped performthe model runs, as well as members of the Community
Earth System Model (CESM) Software EngineeringGroup who also made important contributions. Portions of this study were supported by the Office of
Science, Biological and Environmental Research, U.S.
Department of Energy, Cooperative Agreement DEFC02–97ER62402, and the National Science Foundation.
Support for SS was provided by NASA NNX09AO20H;
BFK was supported by NSF FRG 0855010.
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