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JOURNAL OF CLIMATEVOLUME 25

ENSO and Pacific Decadal Variability in the Community Climate System
Model Version 4
CLARA DESER, ADAM S. PHILLIPS, ROBERT A. TOMAS, AND YUKO M. OKUMURA
Climate and Global Dynamics Division, NCAR, Boulder, Colorado

MICHAEL A. ALEXANDER, ANTONIETTA CAPOTONDI, AND JAMES D. SCOTTNOAA/Earth System Research Laboratory, Boulder, Colorado

YOUNG-OH KWON
Woods Hole Oceanographic Institution, Woods Hole, Massachusetts

MASAMICHI OHBA
Central Research Institute of Electric Power Industry, Abiko, Japan(Manuscript received 25 May 2011, in final form 16 September 2011)
ABSTRACTThis study presents an overview of the El Niño–Southern Oscillation (ENSO) phenomenon and Pacificdecadal variability (PDV) simulated in a multicentury preindustrial control integration of the NCAR
Community Climate System Model version 4 (CCSM4) at nominal 18 latitude–longitude resolution. Several
aspects of ENSO are improved in CCSM4 compared to its predecessor CCSM3, including the lengthened
period (3–6 yr), the larger range of amplitude and frequency of events, and the longer duration of La Niñacompared to El Niño. However, the overall magnitude of ENSO in CCSM4 is overestimated by ;30%. The
simulated ENSO exhibits characteristics consistent with the delayed/recharge oscillator paradigm, including
correspondence between the lengthened period and increased latitudinal width of the anomalous equatorial
zonal wind stress. Global seasonal atmospheric teleconnections with accompanying impacts on precipitationand temperature are generally well simulated, although the wintertime deepening of the Aleutian low erroneously persists into spring. The vertical structure of the upper-ocean temperature response to ENSO in thenorth and south Pacific displays a realistic seasonal evolution, with notable asymmetries between warm andcold events. The model shows evidence of atmospheric circulation precursors over the North Pacific associated with the ‘‘seasonal footprinting mechanism,’’ similar to observations. Simulated PDV exhibits a significant spectral peak around 15 yr, with generally realistic spatial pattern and magnitude. However, PDV
linkages between the tropics and extratropics are weaker than observed.

1. Introduction
The El Niño–Southern Oscillation (ENSO) phenomenon in the tropical Indo-Pacific provides an important source of predictability for interannual climate
anomalies worldwide (Ropelewski and Halpert 1987;
Philander 1990; Glantz 2000; Tippett and Barnston 2008;
and many others). Warm (El Niño) and cold [La NiñaCorresponding author address: C. Deser, Climate and Global
Dynamics Division, NCAR, P.O. Box 3000, Boulder, CO 80307.
E-mail: cdeser@ucar.edu
DOI: 10.1175/JCLI-D-11-00301.1
Ó 2012 American Meteorological Society(LN)] phases of ENSO typically last 1–2 yr and repeat
approximately every 3–8 yr, with cold events being somewhat weaker and of longer duration than their warm
counterparts (e.g., Harrison and Larkin 1998; Okumura
and Deser 2010). Driven primarily by coupled ocean–
atmosphere processes within the tropical Indo-Pacificbasin, the effects of ENSO are transmitted worldwide
via atmospheric teleconnections, affecting precipitation and temperature in many societally vulnerable areas(Trenberth et al. 1998; Alexander et al. 2002). Although
the basic physical mechanisms of thermal and dynamical
air–sea coupling that give rise to ENSO are well studied15 APRIL 2012

DESER ET AL.

(see reviews by Neelin et al. 1998; Wang and Picaut 2004;
Chang et al. 2006), other aspects remain less well understood. These include processes responsible for the spatial
and temporal asymmetries between El Niño and La Niña,the degree of regularity of the ENSO ‘‘cycle,’’ and triggering mechanisms of warm and cold events.
Coupled ocean–atmosphere models have improved
considerably in their simulation of ENSO in past years,
becoming realistic enough to be used to explore questions that would be difficult to answer from the short observational record alone. A recent example is the 2000-yr
control simulation of the Geophysics Fluid Dynamics
Laboratory (GFDL) model (Wittenberg 2009), which
exhibits large decadal-to-centennial modulation of ENSO
behavior under constant boundary conditions (e.g., fixedgreenhouse gas concentrations). Such internal variability
implies that identifying externally forced changes in
ENSO behavior from the relatively short observational
record may have limited success, although paleoclimate
proxy records may be useful in this regard (Li et al.
2011). Stevenson et al. (2010) estimate that approximately 250 yr is the minimum record length needed toassess changes in ENSO with 90% confidence.
In addition to interannual ENSO fluctuations, theIndo-Pacific exhibits decadal and multidecadal climate
variability (Mantua et al. 1997; Zhang et al. 1997; Deseret al. 2004; Guan and Nigam 2008; Alexander 2010). The
most prominent structure of low-frequency climate
variability in the North Pacific, with extensions to thetropical Indo-Pacific, has been termed the ‘‘Pacific decadal oscillation’’ (PDO) by Mantua et al. (1997). The
PDO is also known as the interdecadal Pacific oscillation(Power et al. 1999) in recognition of its extension to theSouth Pacific (see also Shakun and Shaman 2009). Given
the relatively short observational record, it is difficult toascertain whether there is a robust spectral peak in the
PDO time series. Some studies suggest nominal timescales of approximately 20 and 50 yr (Minobe 1997,
1999; Deser et al. 2004), but most of the variance does
not differ significantly from a first-order autoregressive
process (e.g., red noise). For this reason, we shall use theterms Pacific decadal variability (PDV) and PDO interchangeably in this study. Other patterns of decadal
variability have been identified, for example, the North
Pacific mode (Deser and Blackmon 1995; Nakamura
et al. 1997; Barlow et al. 2001; Guan and Nigam 2008),
the pan-Pacific mode (Guan and Nigam 2008), and theNorth Pacific gyre oscillation (Di Lorenzo et al. 2008).
Investigation of these additional patterns in the Community Climate System Model version 4 (CCSM4) is beyond
the scope of this study.
Various mechanisms have been hypothesized to accountfor PDV, including ENSO and associated atmospheric

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teleconnections to the North Pacific, forcing by random
fluctuations in the Aleutian low pressure system, oceanic
Rossby wave adjustment to wind stress curl variationsin the North Pacific, and upper-ocean mixed layer
processes (Latif and Barnett 1994; Deser et al. 1999;
Pierce 2001; Newman et al. 2003; Vimont 2005; Schneider
and Cornuelle 2005; Kwon and Deser 2007; Qiu et al.
2007; Alexander 2010; Shakun and Shaman 2009). The
latter includes the so-called ‘‘reemergence mechanism’’
whereby sea surface temperature (SST) anomalies areable to persist from one winter to the next because of
the seasonal cycle of upper-ocean mixed layer depth(Alexander and Deser 1995; Alexander et al. 2001).
This mechanism acts to impart additional memory to extratropical Pacific SST anomalies beyond that associatedwith the thermal inertia of a fixed-depth ocean mixed
layer (Deser et al. 2003). Coupled models simulate some
aspects of PDV, although linkages between the tropicaland North Pacific are weaker than observed (Alexander
et al. 2006; Kwon and Deser 2007; Zhong et al. 2008).
While the response to ENSO extends from the tropical Pacific to the extratropics, variability originating in
the North Pacific may also influence the tropical Pacificvia the atmosphere (e.g., Pierce et al. 2000) and theocean (e.g., Kleeman et al. 1999). Based on analyses ofan extended coupled model simulation, Vimont et al.
(2001, 2003a) proposed an extratropical-to-tropicalconnection termed the ‘‘seasonal footprinting mechanism’’ (SFM), where internal fluctuations in the atmospheric circulation over the North Pacific during winterimparts a ‘‘footprint’’ on the ocean through changes insurface heat fluxes. The SST footprint, which peaks inspring and persists through summer in the subtropical
Pacific, impacts the atmospheric circulation including
zonal wind stress and surface energy fluxes that extend
deep into the tropics. These anomalies excite a responsein the central and eastern equatorial Pacific Ocean in thesubsequent fall and winter that influence ENSO anddecadal tropical variability (Vimont et al. 2001, 2003a).
Aspects of the SFM and its impact on ENSO have beenverified in a number of subsequent observational (Vimont
et al. 2003b; Anderson 2003, 2004; Alexander et al. 2008)
and modeling studies (Vimont et al. 2009; Zhang et al.
2009; Alexander et al. 2010), including an analysis of
CCSM2 (Anderson and Maloney 2006).
The purpose of this study is to provide an overview of
ENSO and PDV in the CCSM4, the newest version of themodel. We make use of a 1300-yr control integration atnominal 18 resolution under 1850 atmospheric carbon dioxide concentrations; the long record enables us to computerobust statistics of the simulation. We assess the realismof CCSM4 by comparing it to a broad suite of observational atmospheric and oceanic datasets. We also compare

2624

JOURNAL OF CLIMATECCSM4 with the previous model version (CCSM3) whose
ENSO and PDV have been documented in Deser et al.
(2006) and Alexander et al. (2006), respectively.
An interim version of CCSM4, denoted CCSM3.5,
was the first version of the model to simulate a credible
ENSO period (Neale et al. 2008). This achievement was
attributed to changes in the parameterization of tropicalatmospheric deep convection as discussed in detail by
Neale et al. (2008). Although the frequency characteristics
of ENSO are similar between CCSM3.5 and CCSM4, ourinvestigation complements and extends the description of
ENSO provided by Neale et al. (2008) for CCSM3.5. In
particular, we assess aspects not considered in Neale et al.
(2008), including El Niño–La Niña asymmetries, global
atmospheric teleconnections, extratropical upper-ocean
thermal response, and atmospheric precursors from theNorth Pacific. Also, CCSM4 provides a longer (1300 yr)
control integration than the 80-yr CCSM3.5 simulation
examined in Neale et al. (2008), allowing a more robust
assessment of ENSO properties and teleconnections.
The rest of the paper is structured as follows. Section 2
provides a brief description of the model and salientchanges from CCSM3, as well as a listing of observational
datasets used in this study. Section 3 documents ENSO
and PDV, addressing the issues discussed above. Section 4summarizes the results and highlights outstanding issues.

2. Model description and observational datasets
a. CCSM4
The CCSM4 is a state-of-the art coupled general circulation model comprised of four components (atmosphere, ocean, land, and cryosphere) linked by means ofa flux coupler. In this study, we focus on a 1300-yr 1850
control integration of CCSM4 at an atmospheric resolution of 0.98 latitude and 1.258 longitude, hereafter referred to as CCSM4–18. We compare the CCSM4–18
simulation with a multicentury control integration of theprevious version of the model, CCSM3, at T85 atmospheric resolution (1.48 latitude and 1.48 longitude). We
also briefly examine a multicentury control integrationof the 1.88 latitude and 2.58 longitude (atmosphericresolution) version of CCSM4 (denoted CCSM4–28).
A brief overview of CCSM4 and salient changes to theatmospheric and oceanic model components is given
below. The reader is referred to Gent et al. (2011),
Danabasoglu et al. (2012), and R. B. Neale et al. (2011,
unpublished manuscript) for additional information.
The Community Atmosphere Model, version 4 (CAM4)
uses a finite-volume core (Lin 2004) compared to thespectral core in CAM3. The deep convection scheme in
CAM4 includes the effects of deep convection in the
momentum equation (Richter and Rasch 2008) and uses

VOLUME 25

a dilute approximation in the plume calculation (Nealeet al. 2008), aspects not included in CAM3. The ocean
model component has 60 vertical levels as opposed to 40
in CCSM3, with the number of levels in the upper 200 m
increased from 14 to 20. It uses spherical coordinates in
the Southern Hemisphere and a displaced-pole grid in
the Northern Hemisphere. The ocean model resolution,
identical for the 18 and 28 versions of CCSM4, is nearly
uniform in longitude (;1.138) and variable in latitude(0.278 at the equator, increasing to 0.658 at 608N in thewestern North Pacific). See Danabasoglu et al. (2012) for
more information on the ocean component of CCSM4.

b. Observational datasets
The following observational datasets are used: 1) sea
level pressure (SLP), surface wind components and
300-hPa streamfunction from the National Centers forEnvironmental Prediction (NCEP)–National Center
for Atmospheric Research (NCAR) reanalysis project(Kalnay et al. 1996) during 1957–2008 on a 2.58 grid;2) SST from the Hadley Centre Sea Ice and SST(HadISST) dataset (Rayner et al. 2003) during 1900–2010
on a 18 grid; 3) air temperature over land from theHadley Centre–Climatic Research Unit (CRU) surface
temperature version 3 (HadCRUT3) dataset (Brohan
et al. 2006) during 1957–2008 on a 58 grid; 4) precipitation from the Global Precipitation Climatology
Project (GPCP; Adler et al. 2003) based on a blend ofsatellite and in situ measurements during 1979–2009 on
a 2.58 grid; and 5) upper-ocean temperature from the
Simple Ocean Data Assimilation (SODA) reanalysis
version 2.1.6 for the period 1958–2007 on a 0.58 latitude–
0.58 longitude grid and 40 levels in the vertical (14 in theupper 200 m) (Carton and Giese 2008). The SODA reanalysis is driven by winds from the 40-yr European
Centre for Medium-Range Weather Forecasts (ECMWF)
Re-Analysis Project (ERA-40).

3. Resultsa. ENSO
1) NIÑO-3.4 SST VARIABILITY
The simulation of the climatological annual mean SST
distribution in the Indo-Pacific, in particular the strengthand zonal extent of the Pacific equatorial cold tongue
and western warm pool (SSTs . 298C), are improved in
CCSM4–18 compared to CCSM4–28 and CCSM3–T85
with respect to HadISST observations (Fig. 1). The
spatial pattern of the standard deviation dSST of monthly
SST anomalies (anomalies are defined as the departuresfrom the long-term monthly means) is realistically simulated in CCSM4–18, with maxima along the equatorial15 APRIL 2012

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FIG. 1. Climatological annual mean SST (contoured every 28C beginning at 58C; the thick contour is 298C) andstandard deviation of monthly SST anomalies (8C, color shading) from (top left) detrended observations (HadISST,
1900–2010) and multicentury preindustrial control integrations of (top right) CCSM4–18, (bottom left) CCSM3–T85,
and (bottom right) CCSM4–2. and.

Pacific cold tongue, along 408–508N in the western andcentral Pacific, and off the west coast of North America(Fig. 1). However, the magnitudes of õSST in CCSM4–18
are approximately 30% stronger than observed in all
3 regions. In comparison, õSST in CCSM4–28 is overestimated by at least 70% in these areas. Values of õSST
in CCSM3–T85 are comparable to those in CCSM4–18
over the North Pacific and more realistic than those in
CCSM4–18 over the equatorial cold tongue.
A commonly used index for representing SST variability associated with ENSO is the area average ofmonthly SST anomalies in the region, 58N–58S, 1708–
1208W, referred to as the ‘‘Niño-3.4 index’’ (see, e.g.,Trenberth et al. 2002). This region is located within thearea of maximum SST variance and also lies within thecenter of action of the leading empirical orthogonal function (EOF) pattern of monthly SST anomalies (shown
later in Fig. 20a). The monthly Niño-3.4 SST indices fromthe HadISST observations during 1900–2011 and fromeach 100-yr segment of the full 1300-yr CCSM4–18 control
integration are compared in Fig. 2. Visual inspection of thetime series reveals substantial multidecadal modulationof ENSO characteristics (amplitude, duration, and regularity) in CCSM4–18, reminiscent of that in the GFDL
model (Wittenberg 2009). Periods of strong, regular oscillations are apparent such as model years 350–400 (highlighted by the green shading in Fig. 2) as are periods ofweak, irregular fluctuations such as model years 450–499(highlighted by the yellow shading). There is also evidenceof irregularity in the duration of simulated warm and cold
events, with some extending over multiple years (e.g.,warm events during model years 100–130 highlighted by
the red shading and cold events during model years 1215–
1260 highlighted by the blue shading) and others limited
to approximately 1 yr (e.g., model years 360–390). Thereis also considerable range in the maximum amplitude ofsimulated ENSO events, with the strongest events exceeding 38C and the moderate events attaining values;1.58C. The variety of ENSO behavior in the model isqualitatively similar to that in nature, at least as sampled by
the short observational record (Fig. 2, upper left).
Overall, the CCSM4–18 control integration overestimates the amplitude of Niño-3.4 SST variability
compared to observations, with a standard deviation of
1.068C for the full 1300-yr model record compared to
0.758C for the detrended HadISST data during 1900–
2011. To quantitatively assess the low-frequency changes
in ENSO amplitude discussed above, we computed thestandard deviation of the Niño-3.4 index (õNiño-3.4) for
running 30-yr periods from observations and the model(Figs. 3a,b, respectively). Consistent with one’s visual
impression from Fig. 2, the HadISST õNiño-3.4 exhibitsa secular increase during the twentieth century, froma minimum of 0.68C during 1920–50 to a maximum of2626

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FIG. 2. The Niño-3.4 SST index from (top left) detrended HadISST observations during
1900–2011, and (remaining panels) the 1300-yr CCSM4–18 preindustrial control integration.
(top right) Model years 11–99 are shown in the first row on right side, and the remaining rows
show each subsequent 200-yr period as marked. Shaded boxes are discussed in the text. Tickmarks on the y axis are every 0.58C.

0.958C during 1970–2000. While some of the increase may
result from external forcing and inhomogeneities in thedata record, paleoclimate records suggest a similar rangeof natural variability (Li et al. 2011). These observed lowfrequency changes in ENSO amplitude provide a benchmark for assessing the range of ENSO modulation due tonatural variability in the model. The 1300-yr CCSM4–18
control integration exhibits a two-fold range (0.78–1.48C)in õNiño-3.4 for running 30-yr periods (Fig. 3b). Thus, themodel exhibits 30-yr periods in which õNiño-3.4 falls within
the upper range of the HadISST observations but never
below the long-term mean HadISST value.
Power spectrum analysis of the Niño-3.4 SST index isshown in Fig. 4 for a 300-yr segment of the CCSM3–T85

control integration, the full 1300-yr CCSM4–18 control
integration, and the HadISST observations. The observational results are shown for two periods, 1900–2010
and 1950–2010, to give an indication of the sensitivityof the spectra to different record lengths [similar results
are found for the extended reconstructed sea surface
temperature, version 3 (ERSSTv3); not shown]. The
CCSM4–18 Niño-3.4 record exhibits a single sharp spectral
peak in the 3–6-yr band, while observations show a doublepeak structure spanning the period range 3.5–8 yr. The
maximum power is approximately twice as large in themodel compared to HadISST (note that to quantitatively
compare the variance between the model and observations, the curves must be integrated over the frequency

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FIG. 3. Standard deviations (8C) of the Niño-3.4 SST index for running 30-yr periods: (a)detrended observations (HadISST, 1900–2010); and (b) CCSM4–18 1300-yr control integration.
The thin (thick) horizontal lines in both panels delineate the minimum and maximum (mean)values from HadISST.

band of interest). CCSM3–T85 exhibits better agreementwith HadISST in terms of maximum power, but thedominant period is too short (1.75–3 yr). The period of
ENSO in CCSM4–28 is similar to that in CCSM4–18 but
the power is considerably greater (not shown).
A well-known characteristic of observed ENSO eventsis the tendency for phase locking to the seasonal cycle,
with warm and cold events peaking near the end of thecalendar year. Indeed, the standard deviation of theobserved Niño-3.4 SST index maximizes (1.18C) in
December and reaches a minimum (0.558C) in April(Fig. 5). The seasonal cycle based on CCSM4–18 lags theobservations by 1–2 months, with a maximum (1.358C) in
January and a minimum (0.758C) in May (Fig. 5). The
timing of the seasonal cycle is similar in CCSM4–28 as in
CCSM4–18, but the values are ;0.28C larger (and ;0.58C
greater than observations). CCSM3–T85 exhibits realistic
timing of the seasonal cycle, but the range is weaker andthere is an erroneous secondary maximum during June–
July (Fig. 5).
We assess the regularity of ENSO in observations andCCSM4–18 and CCSM3–T85 using the autocorrelationof the Niño-3.4 record as a simple metric. The monthly
lead–lag correlations are computed with respect to theDecember time series (denoted Dec0 in Fig. 6). The
autocorrelation curve for CCSM4–18 is qualitatively
similar to observations, with strongest negative values
at approximately 62 yr consistent with the realistic 3–6-yr ENSO period. However, the maximum negative
autocorrelations are overestimated (;20.45 compared
to 20.25), indicative of greater ENSO regularity in themodel compared to nature. CCSM3–T85 displays aneven higher degree of ENSO regularity than CCSM4–18(maximum negative autocorrelations ;20.65), andthese occur at shorter lags (Dec21 and Dec11) reflecting

the biennial periodicity. The increased irregularity of
ENSO in CCSM4–18 compared to CCSM3–T85 constitutes a notable improvement.

2) COMPOSITE EL NIÑO AND LA NIÑA EVENTS
Following Okumura and Deser (2010), we construct
a composite El Niño (La Niña) event by selecting years

FIG. 4. Power spectrum [8C2 (cycles month)21] of the Niño-3.4
SST index from detrended observations (HadISST, dashed black
curve for 1900–2010 and solid black curve for 1950–2010), CCSM4–18(model years 1–1300; blue curve), and CCSM3–T85 (model years
100–599; green curve). The light-blue shading depicts the confidence
intervals for the CCSM4–18 spectrum based on the individual spectra
for each 100-yr segment.

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FIG. 5. Monthly standard deviation (8C) of the Niño-3.4 SST index for detrended observations(HadISST, 1900–2010), CCSM4–18, CCSM4–28, and CCSM3–T85 as indicated.

when the December Niño-3.4 SST Index exceeds 1 (isless than 21) standard deviation (õ). To reduce monthto-month noise, the Niño-3.4 record is smoothed witha 3-month binomial filter before selecting the events.
With these criteria, the observed composites for theperiod 1900–2010 (1957–2008) contain 19 (7) El Niño(EN) events and 14 (7) La Niña (LN) events. The simulated composites are based on 80 (82) EN (LN) events
for CCSM4–18 and 53 (46) EN (LN) events for CCSM3–
T85. To facilitate comparison of spatial patterns between the models and observations, we have scaled themodel composite fields by the value of the DecemberNiño-3.4 Index in the observed EN (LN) composite
divided by that in the simulated EN (LN) composite.
These scaling factors are: 0.75 (0.85) for EN (LN) in
CCSM4–18 and 1.26 (1.18) for EN (LN) in CCSM3–
T85 when comparing to observations during 1900–
2010; and 0.61 (0.76) for EN (LN) in CCSM4–18 and1.03 (1.06) for EN (LN) in CCSM3–T85 when comparing to observations during 1957–2008. We denotethe year that includes the December value of theNiño-3.4 Index used in the compositing criteria as year
0, and the previous and subsequent years as 21 and
11, respectively.

FIG. 6. Monthly autocorrelation curves of the Niño-3.4 SST index from observations (detrended HadISST, solid black curve for 1900–2010 and dashed black curve for 1950–2010),
CCSM4–18 (model years 1–1300; blue curve), and CCSM3–T85 (model years 100–599; green
curve). Autocorrelations are computed from the December Niño-3.4 index (Dec0) and span theprevious and subsequent 5 yr. The indices were smoothed with a 3-point binomial filter before
computing the autocorrelations.

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FIG. 7. Longitude–time sections of composite SST anomalies along the equator (38N–38S) for (top)El Niño and (bottom) La Niña based on (left) observations (detrended HadISST 1900–2010), (middle)
CCSM4–18 (model years 1–1300), and (right) CCSM3–T85 (model years 100–599). The model composites
have been scaled to match the observed composite amplitude of the Niño-3.4 SST index in Dec0. The
number at the top right of each panel indicates the number of events used for the composite.

Time–longitude plots of SST anomalies along theequator for the composite EN and LN events are shownin Fig. 7. Similar to observations (Ohba and Ueda 2009;
Okumura and Deser 2010), CCSM4–18 exhibits clear
asymmetries between the warm and cold phases of
ENSO. The most striking asymmetry is the extended duration of LN through Feb12, more than a year following
the peak of the event in Dec0, compared to EN whichtransitions to a weak LN event starting in Jun11. Anothernotable difference is that the eastern equatorial Pacific
SST anomalies extend approximately 208–308 of longitude
farther west during LN compared to EN. In contrast,
warm and cold events are nearly mirror images of
one another in CCSM3–T85: both last approximately
12 months and transition to the opposite sign in thefollowing year. There is a modest (;108 of longitude)
westward expansion of the SST anomalies during LN
compared to EN in CCSM3–T85. Both models exhibit
westward SST anomaly propagation from approximately

908 to 1508W during the second half of year 0. Westward
propagation is less evident in the observations; however,composites based on the first half of the twentieth century
display stronger westward propagation (not shown, but
see Fig. 3 in Okumura and Deser 2010).
The asymmetry in duration between EN and LN isconfirmed by the frequency distributions of individual
events (Fig. 8). EN (LN) event duration is defined as thenumber of consecutive months for which the Niño-3.4
SST index exceeds 0.25 standard deviations (is less than
20.25 standard deviations) after Dec0, using the criteria
above for event selection. The results show that ;80%(;70%) of observed EN (LN) events last less than(more than) 8 months after Dec0. CCSM4–18 exhibits
a similar, albeit less pronounced, shift in the frequency
distribution of event duration: ;70% (;60%) of EN(LN) events last less (more than) 8 months after Dec0. In
the model, LN event duration exhibits a double peakin the frequency distribution, with approximately equal

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Ocean during June–August0 (JJA0) through DJF11. The
Indian Ocean dipole (IOD: see Schott et al. 2009 for a review), characterized by positive (negative) SST anomalies
in the western (eastern) Indian Ocean, is present in
September–November0 (SON0), and in the model also
extends into DJF11. The warm event weakens substantially in MAM11 and transitions to a cold event starting
in JJA11 with subsequent growth through DJF12. The
demise of EN and transition to LN occur a couple ofmonths later in the model compared to nature.
SST anomaly growth during EN is accompanied bya deeper thermocline in the eastern Pacific and a shallower thermocline in the western Pacific, consistent withthe westerly surface wind anomalies over the central andwestern Pacific. As the event decays, the negative Z15
anomalies expand eastward along the equator, such that
by JJA11 when the SST anomalies transition to a LN,
the thermocline is anomalously shallow across the entire
equatorial Pacific. While the growth phase of LN is similar to that of EN but with opposite sign, the decay phasefollows a different evolution (Figs. 10a,b). Most notable isthe extended duration of LN (negative SST anomalies,
below normal precipitation, easterly wind anomalies,
enhanced thermocline tilt) through DJF12.

3) MECHANISTIC ANALYSIS OF THERMOCLINE
VARIABILITY

FIG. 8. Frequency distribution (%) of El Niño (shaded bars)
and La Niña (black outline bars) duration (in months) after the
December peak for (top) observations (detrended HadISST 1900–
2010) and (bottom) CCSM4–18 (model years 1–1300). The number
of EN (LN) events in each 3-month bin has been divided by the
total number of EN (LN) events.

numbers of events in the 4–7-month and 13–16-month
duration range.
A more complete view of the composite seasonalevolution of ocean-atmosphere anomalies during EN(LN) is given in Fig. 9 (Fig. 10) for observations andCCSM4–18. The composites span nearly two full years,
beginning in March–May0 (MAM0) at the event onset,
and ending in December–February12 (DJF12) a fullyear after the event peak. The left-hand panels show
SST and precipitation, and the right-hand panels show
the depth of the 158C isotherm (Z15; a proxy for thermocline depth) and surface wind vectors. The seasonalevolution of the composite EN is similar between themodel and observations (Figs. 9a,b). Both show thegrowth of positive SST and precipitation anomalies overthe equatorial eastern Pacific beginning in MAM0 andpeaking in DJF11. Negative SST and precipitation anomalies develop over the western Pacific and eastern IndianHere, we provide a more mechanistic analysis of ENSO
thermocline depth variations within the framework of thedelayed oscillator and recharge oscillator paradigms. We
note that such an analysis emphasizes the linear aspects ofEN and LN and does not take into account the asymmetric features of warm and cold events documented
above. The leading modes of thermocline variability in
CCSM4–18, defined through EOF analysis of Z15 anomalies (Figs. 11d,e), are very similar to those computed fromthe SODA ocean reanalysis (Figs. 11a,b), as well as thoseobtained from observations (Meinen and McPhaden
2000). The first EOF is characterized by an east–west
dipole, indicative of changes in the zonal thermocline
slope, while the second EOF describes a deepening or
shoaling of the equatorial thermocline as a whole.
Qualitatively similar patterns were found in a subset of
the Climate Model Intercomparison Project version 3(CMIP3) models (Capotondi et al. 2006), although pattern details varied from model to model. The two leading
EOFs of thermocline depth variability in CCSM3–T85
are also shown in Fig. 11, based on the period 1870–1999
from a twentieth-century integration; however, similar
results are obtained with the CCSM3–T85 control integration (not shown, but see Deser et al. 2006). The maindifference between the EOF patterns in CCSM3–T85 andCCSM4–18 is the meridional scale of the equatorial15 APRIL 2012

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2631

FIG. 9. (a) Observed El Niño seasonal composite evolution starting in MAM0 and ending in DJF12 for (left) SST and precipitation
anomalies and (right) thermocline depth (Z15) and surface wind anomalies. SST (8C) and Z15 (m) are shaded; precipitation contours(solid for positive anomalies, dashed for negative anomalies) are 69, 67, 65, 63, 61 mm day21; and the wind vector scale (m s21) isshown at the bottom right of each panel. Results are based on the period 1958–2007 except for precipitation, which is for 1979–2007.

anomalies. In CCSM3–T85, the equatorial thermocline
variations are confined to the latitude band 108S–58N,
with anomalies of opposite sign to the north and south;

whereas in CCSM4–18 they are broader in meridional
extent especially in the Southern Hemisphere, in better
agreement with the SODA reanalysis.

2632

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FIG. 9. (b) As in (a), but for CCSM4–18 using model years 800–1299. All of the model composite fields have been scaled such thatthe simulated composite amplitude of the Niño-3.4 SST index in Dec0 matches observations.

The leading patterns of thermocline variability displayed in Fig. 11 have been interpreted as consistent
with the recharge oscillator paradigm for ENSO (Jin1997a,b; Meinen and McPhaden 2000; Capotondi

et al. 2006). That is, the mature phase of a warm event ischaracterized by a deeper thermocline in the eastern Pacific and a shallower thermocline in the western Pacific(positive phase of EOF1). The anomalous zonal slope of15 APRIL 2012

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2633

FIG. 10. (a) As in Fig. 9a, but for La Niña.

the thermocline is associated with an anomalous poleward
Sverdrup transport responsible for discharging warm
water from the equatorial thermocline, resulting in ashallower-than-average thermocline across the equatorial Pacific (positive phase of EOF2). Upwelling ofcolder water in the eastern Pacific subsequently erodes
the original warm SST anomaly and leads to the opposite
phase of the cycle. In this view, the two leading EOFs are
expected to be approximately in quadrature. Indeed, lagcorrelation analysis between the two principal component

2634

JOURNAL OF CLIMATEVOLUME 25

FIG. 10. (b) As in Fig. 9b, but for La Niña.

(PC) time series confirms that PC1 (corresponding to thepeak of a warm event) leads PC2 (discharged thermocline)by ;10 months in SODA and CCSM4–18, and by 7months in CCSM3–T85, with maximum correlationsof 0.59 in SODA, 0.79 in CCSM4, and 0.76 in CCSM3(Fig. 11, right-hand panels). The difference in lag of the
maximum correlation between the two models reflects
their different ENSO periods. The approximate 10month lag in both SODA and CCSM4–18 is in good
agreement with the value of ;9 months estimated15 APRIL 2012

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2635

FIG. 11. (left),(middle) The two leading EOFs of thermocline depth (Z15) for (top) SODA, (middle) CCSM4–18, and (bottom)
CCSM3–T85. Blue shading is for negative values (thermocline shallower than average), and orange shading is for positive values(thermocline deeper than average). The percent variance explained by each mode is given in the panel title. A 5-month running mean wasapplied to the SODA data before computing the EOFs. (right) Lag-correlation curves between PC1 and PC2; negative (positive) lags arefor PC1 leading (lagging) PC2. The lag of maximum correlation, in absolute value, is approximately a quarter of a cycle. All correlations
are based on the total duration of the time series for each product, namely (top to bottom) 51 yr for SODA, 500 yr (model years 800–1300)
for CCSM4–18, and 130 yr (1870–1999) for CCSM3–T85.

from observations during 1980–2000 by Meinen and
McPhaden (2000).
The spatial structure of the anomalous wind stressassociated with ENSO is considered an important factor
in setting the ENSO time scale (Kirtman 1997; An andWang 2000; Capotondi et al. 2006; Neale et al. 2008). In
particular, the meridional scale of the zonal wind stressanomalies associated with ENSO determines the latitudes of largest wind stress curl anomalies responsible
for enhanced Rossby wave activity (Capotondi 2008).
Because of the decrease of the Rossby wave phase speedwith latitude, a meridionally broader structure of anomalous wind stress will result in a longer adjustment timescale of the equatorial thermocline and in a longer ENSO
period. Figure 12 compares the structures of the anomalous zonal wind stress regressed upon the normalizedNiño-3.4 index in the ERA-40 reanalysis, CCSM4–18,

and CCSM3–T85. While the anomalous wind stressin CCSM3–T85 is tightly confined within 58 of the equator, CCSM4–18 exhibits zonal wind stress anomalies ofconsiderably broader meridional extent, especially in
the Southern Hemisphere where the anomalous winds
extend southeastward in better agreement with the regression pattern obtained from the ERA-40 reanalysis(see also Neale et al. 2008). The broader meridional
scale for CCSM4–18 can clearly be detected in the rightpanels of Fig. 12, which shows the zonal average of thezonal wind regression patterns.
Capotondi et al. (2006) showed that the limited meridional extent of the zonal wind stress anomalies wascommon to many of the CMIP3 models and was associated with an ENSO time scale shorter than observed.
Figure 13 shows the ENSO period versus the meridional
scale of the zonal wind stress anomalies (denoted Ly) for2636

JOURNAL OF CLIMATEVOLUME 25

FIG. 12. (left) Regressions of the zonal surface wind stress component upon the normalizedNiño-3.4 index for (top) ERA-40, (middle) CCSM4–18, and (bottom) CCSM3–T85. The contour interval is 1 3 103 N m22 for values between 1 and 8, and 2 3 103 N m22 for values larger
than 8 in absolute value. (right) Zonal average of the patterns in the left panels. The latitudes ofzero crossing are used to define the meridional scale of the wind stress anomalies (Ly).

the subset of CMIP3 models analyzed by Capotondi
et al. (2006; filled circles), the GFDL Climate Modelversion 2.1 (CM2.1) (diamond), the CCSM4–18 (star), andtwo observational estimates. One observational estimate(open circle) uses Ly from the NCEP–NCAR reanalysis
and ENSO period from the Instituto Nationale di Geofisica e Vulcanologia (INGV) ocean analysis, which isforced by NCEP as described in Capotondi et al. (2006).
The other (triangle) uses Ly from the ERA-40 reanalysis
and ENSO period from the SODA ocean analysis, which

is forced by ERA-40. Characteristics of the GFDL CM2.1
ENSO simulation have been reported in many studies
including Wittenberg (2009). In all products, the period
was estimated from the frequency corresponding to the
maximum spectral power as in Capotondi et al. (2006).
The meridional scale was estimated for each model using
the distance between the zero crossing points of the zonal
average of the regression patterns. The meridional scales
for the CMIP3 models considered by Capodondi et al.
(2006) range between 108 and 168, smaller than the15 APRIL 2012

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2637

FIG. 13. ENSO period (determined from the frequency of maximum spectral power of theNiño-3.4 index) vs the meridional width of the zonal wind stress anomalies (Ly) for the nine
CMIP3 models used in Capotondi et al. (2006; filled circles identified by number), GFDL
CM2.1 (diamond), CCSM4–18 (star), ERA-40/SODA (triangle), and NCEP reanalysis–INGV(open circle). Note that CCSM4–18 lies within the range of the observational estimates.

198–248 range spanned by the two atmospheric reanalyses. In contrast, Ly for CCSM4–18 is ;228, well within
the range of the observational estimates. The GFDL
CM2.1 has an Ly ; 178, intermediate between observations and the CMIP3 subset. Collectively, the models andobservations exhibit an approximate linear relationshipbetween Ly and ENSO period.

4) GLOBAL TELECONNECTIONS
The tropical precipitation changes during ENSO initiate
global atmospheric circulation anomalies that in turn affectthe surface climate. Here we compare observed and simulated ENSO teleconnections for the peak seasons JJA0,
SON0, DJF1, and MAM1. Because of the largely symmetricnature of the global atmospheric teleconnections during EN and LN in these seasons (not shown), we summarize the results in terms of composite difference maps (ENminus LN). SLP and surface temperature (300-hPa
streamfunction and precipitation) anomalies are shown in
the left (right) panels for the NCEP–NCAR reanalyses(Fig. 14) and CCSM4–18 (Fig. 15). The model composites
have been scaled to match the observed composite amplitude of the Niño-3.4 SST index in Dec0.
In JJA0 when ENSO is underway, the main SLP response occurs over the South Pacific where it takes theform of a meridional dipole, with positive anomalies
near Antarctica and negative anomalies to the north,stretching from New Zealand to South America. The
pattern and amplitude of the SH response is well simulated by the model. This teleconnection pattern persists
during SON0 as ENSO continues to intensify; however,
the amplitude is somewhat underestimated by themodel. In DJF1, the strongest SLP response occurs in the
Northern Hemisphere, with negative anomalies over

the North Pacific and a weaker meridional dipole over
the North Atlantic. The model’s teleconnection to theNorth Pacific in this season is more zonally oriented andspatially extensive than observed. The model erroneously
persists the anomalous deepening of the Aleutian low
pressure system into MAM1, similar to that documented for
the Hadley Centre Atmospheric Model version 3 (HadAM3) by Spencer and Slingo (2003). Overall, the SLP
response over the Southern Hemisphere exhibits less seasonal dependence than that over the Northern Hemisphere,
consistent with Jin and Kirtman (2009) among others.
The tropical upper-level streamfunction anomalies
display a classical response to heating, with anticyclonic
centers on the northern and southern flanks of the equatorial Pacific precipitation anomalies. Upper-level wave
trains emanate from the tropical heat source, with largest
amplitudes generally in the winter and spring hemispheres. Less seasonality is evident in the Southern
Hemisphere compared to the Northern Hemisphere,
consistent with Jin and Kirtman (2009). The simulated
wave train response over the North and South Pacifictends to be more zonally elongated than in observations,similar to the character of the extratropical SLP response.
The model lacks a well-defined wave train response over
the North Pacific in SON0.
The precipitation response is generally well simulated
in CCSM4–18. In the tropical Pacific, the ITCZ and
SPCZ shift equatorward, inducing positive precipitation
anomalies across the equatorial Pacific and negative
anomalies along the poleward flanks of the convergencezones. Reduced precipitation occurs over the Maritime
Continent, the eastern tropical Indian Ocean, the tropical Atlantic Ocean, and Brazil, while enhanced rainfall
is found over the western portion of the Indian Ocean

2638

JOURNAL OF CLIMATE(except in JJA0). The model produces erroneous positive precipitation anomalies over the equatorial portionof the Maritime Continent and eastern Indian Ocean in
DJF11 and MAM11. The simulated rainfall anomalies
are also too narrowly confined near the equator and too
weak over the SPCZ.
The model captures the main features of the observed
terrestrial air temperature response, including the negative (positive) temperature anomalies over eastern Russia
and the southeastern United States (western Canada andAlaska) in SON0, DJF1, and MAM1. However, the amplitude of the temperature response over Alaska andwestern Canada in MAM1 (and to a lesser extent DJF1) isgreatly exaggerated, likely as a result of the erroneous
deepening of the Aleutian low SLP response. Similarly,
the model’s erroneous positive SLP response over northern Eurasia may be responsible for the incorrect sign of
the underlying temperature response. In polar regions, thesimulated surface temperature response reflects changes in
sea ice concentration, with positive temperature anomalies
indicative of negative sea ice anomalies and vice versa. The
largest sea ice changes occur near Antarctica where reduced (increased) ice cover is associated with anomalous
northerly (southerly) winds and warm (cold) air advection
as inferred from the SLP response. Similar responses arefound in observations (not shown, but see Yuan 2004).

5) EXTRATROPICAL UPPER-OCEAN THERMALRESPONSE

The ENSO composites in Figs. 14 and 15 indicate thatthe extratropical SST anomaly patterns and amplitudes
are generally well simulated in CCSM4–18. In particular,
negative SST anomalies extend eastward and poleward
across the North and South Pacific from SON0 throughMAM11, with amplitudes of 0.58–1.58C. The verticalstructure and temporal evolution of upper-ocean thermal anomalies in the central North Pacific (238–408N,
1628E–1408W) and South Pacific (238–408S, 1608–938W)
are shown in Fig. 16 for composite EN and LN eventsfrom SODA and CCSM4–18 (regions are marked on the
MAM11 panel in Figs. 14 and 15). The central North
Pacific region (Fig. 16a) shows clear asymmetries between EN and LN in both SODA and the model. During
EN, negative temperature anomalies are formed during
winter and extend through the deep mixed layer (;140 m
in the model and ;110 m in SODA). These anomalies
persist at depth beneath the mixed layer into the subsequent spring, summer, and fall, penetrating slowly
downward with time. Weak positive temperature anomalies replace the negative anomalies in the following
winter, consistent with the transition of EN to LN (recall
Figs. 7 and 9). The mixed layer deepens by 20–30 m in
February–March of year 11 compared to the following

VOLUME 25

winter in both the model and SODA. During LN, positive
temperature anomalies form during January–March ofyear 11 and persist beneath the mixed layer during thesubsequent spring, summer, and fall. However, the positive anomalies are noticeably shallower than their cold
counterparts during EN, consistent with the shallower
winter mixed layer. As a result, there is less downward
penetration of the positive anomalies with time comparedto the EN composite. In January–February of year 12,
the sequestered anomalies at depth become re-entrained
into the deepened winter mixed layer in both observations
and the model. The effectiveness of the re-entrainment
results from the extended duration of LN compared to
EN. During EN, the change in sign of the surface forcing
associated with the transition to LN in the second wintercompetes with the re-entrainment of subsurface temperature anomalies into the mixed layer. The deepening(shoaling) of the winter mixed layer in year 11 during EN(LN) is due to changes in stability associated with negative(positive) buoyancy and mechanical forcing via changes
in wind stress and surface heat fluxes (not shown).
The South Pacific region exhibits a somewhat different seasonal evolution compared to the North Pacific,
with the largest thermal anomalies occurring within theshallow summer mixed layer during both EN and LN(Fig. 16b: note that the time axis begins in May0 for theSouth Pacific compared to Oct0 for the North Pacific).
The anomalies remain largely confined to the mixed layer
as it deepens during fall and early winter, exhibiting little
downward penetration into the main thermocline and no
signal of reemergence.

6) EXTRATROPICAL AND TROPICAL PRECURSORSFOR ENSO
As a first step in identifying ENSO precursors, including the SFM, we examine the EN minus LN composite SLP and SST anomaly fields in the winter (DJF0)
and spring (MAM0) prior to the event peak using theusual compositing criteria (Fig. 17). The SLP anomalies
in DJF0 exhibit a meridional dipole with centers at 308 and
608N near 1608W, which is nearly identical to the North
Pacific oscillation (NPO), the second leading pattern ofinterannual SLP variability over the North Pacific in
winter (Walker and Bliss 1932; Rogers 1981; Linkin andNigam 2008). The NPO has been identified as the main
pattern of atmospheric variability that initiates the SFM(e.g., Vimont et al. 2003b). When anomalous low pressure
occupies the southern lobe of the NPO, the anomalous
winds are from the west opposing the trade winds overthe central and eastern subtropical Pacific, reducing thewind speed and upward latent flux (not shown), thereby
warming the underlying ocean through winter and into
spring (Fig. 17). The positive SST anomalies that extend

15 APRIL 2012

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2639

FIG. 14. Observed El Niño minus La Niña composite evolution of (left) SST and terrestrial
air temperature (8C; shaded) and SLP (contour interval of 2 hPa) and (right) precipitation(mm day21; shaded) and 300-hPa streamfunction (contour interval of 3 3 106 m2 s21) anomalies for (top to bottom) JJA0, SON0, DJF11, and MAM11 based on observations for the period
1958–2007 except for precipitation, which is for 1979–2007. The boxes in the MAM11 panel
outline the regions used for Fig. 16.

into the equatorial Pacific starting in boreal spring(MAM0) are enhanced in summer and subsequently develop into a warm event in both observations and the
CCSM4–18 (see Figs. 14 and 15), although the warming
reaches the equator and extends eastward sooner than in
the original SFM hypothesis especially in the CCSM4–18.

We note that the NPO signature in DJF0 is associatedwith negative SST anomalies in the eastern equatorial
Pacific and thus may in part reflect the ENSO cycle.
The SFM can also be evaluated directly based on
wintertime SLP anomalies over the North Pacific. Whilethe evolution of the SFM can be identified using the2640

JOURNAL OF CLIMATEVOLUME 25

FIG. 15. As in Fig. 14, but for CCSM4–18. The temperature anomalies are skin temperature
over both land and ocean and thus also reflect the surface temperature over sea ice. The modelcomposites have been scaled to match the observed composite amplitude of the Niño-3.4 SST
index in Dec0. The boxes in the MAM11 panel outline the regions used for Fig. 16.

second PC of North Pacific SLP anomalies, Anderson(2003, 2007), Alexander et al. (2008), and Zhang et al.
(2009) found that the SLP anomalies along the southeastern flank of the NPO was the key area for impacting
the tropical Pacific. Following Anderson (2003) we construct an SLP index (SLPI) given by the normalizedanomalies averaged over 108–258N, 1758–1208W (box in

top panels of Fig. 18) during Nov21–Mar0, to look for
ENSO precursors. Seasonal composites of SLP and SST
anomalies, shown in Fig. 18, are derived from the difference between fields averaged for when SLPI , 21s
and when SLPI . 11s. In both the NCEP reanalysis
and CCSM4–18, the SLP composite difference during
DJF0 (the middle 3 months of the SLP index) clearly15 APRIL 2012

DESER ET AL.

FIG. 16. (a) Time–depth sections of upper-ocean thermal anomalies (shading; 8C) and mixed
layer depth (black curves) for composite (left) El Niño and (right) La Niña events from SODA
(1958–2007) and CCSM4–18 (model years 800–1300) for the central North Pacific. (b) As in (a),but for the central South Pacific. Regions outlined in Figs. 14 and 15. Note the different timeaxes in (a),(b).

2641

2642

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FIG. 17. El Niño 2 La Niña composites of SLP (contour interval is 1.0 hPa) and SST (shading interval is 0.18C) anomalies for (top) DJF0
and (bottom) MAM0 from (left) NCEP–NCAR reanalysis (1948–2009) and (right) CCSM4–18 (model years 800–1300). Composites are
based on the Niño 3.4 SST index in Dec0, approximately a year after the values shown in the top panel. Note that the model composites
have not been scaled to match the observed composite amplitude of the Niño-3.4 SST index in Dec0. (top) Boxes indicate the Niño-3.4
index region.

depicts the NPO but with roughly twice the amplitudein CCSM4–18. The SLP field is also very similar to the
ENSO composite during DJF0 (top panels in Fig. 17).
Note the year demarcation in Fig. 18 is relative to
ENSO but the composites are only based on the SLPIand not whether an ENSO event occurs in the following
winter.
Consistent with the SFM, anomalous southwesterly
winds that oppose the climatological trade winds warm
the underlying ocean via surface heat fluxes (not shown),
creating positive SST anomalies in the index region in
winter (Fig. 18). Positive SST anomalies, however, also
develop along the equator between 1508E and 1808 during DJF0 and spread eastward and amplify during MAM0
through SON0, strongly resembling the early phases of
EN. Although the evolution of the SST field based on the
SLPI is slightly faster and stronger in CCSM4–18 than inobservations, the two are very similar and clearly related
to ENSO as the correlation between the SLPI and Niño3.4 SST index in JFM1 is 0.61 in nature (Anderson 2007)
and 0.59 in the model. The association between the NPO
and negative SST anomalies in the eastern equatorial
Pacific in DJF0 in both Figs. 17 and 18 raises the possibility that tropical forcing may contribute to the NPO
precursor to ENSO. Additional analysis and targeted

modeling experiments are needed to investigate thisissue further.
Anderson (2007) also found that the observed heat
content in the western equatorial Pacific served as a precursor to ENSO but was most effective in combination
with the subtropical SLP anomalies. Specifically, positive(negative) heat content anomalies associated with adeeper (shallower) thermocline in the western equatorial
Pacific during JJA21 and SON21, 12–15 months before
an ENSO event, followed by negative (positive) SLP
anomalies over the subtropical central North Pacificduring DJF0 favor EN (LN) events during the subsequent
winter (DJF1). When the two precursors have the same
sign, their impact on ENSO is much weaker.
We explore the joint impact of these precursors on
ENSO in CCSM4–18 through a scatterplot between the
SLPI in Nov21–Mar0 and the Niño-3.4 index in JFM1,
stratified by whether an index of thermocline depth in
the western equatorial Pacific [Z15I, the anomalous
Z15 depth averaged over 58N–58S, 1508E–1808 in June–
October21 (JJASO21)] has the opposite or the same sign
as the SLPI (Fig. 19a). We note that using Z20 in place ofZ15 for the thermocline index yields virtually identicalresults as the two time series are correlated at 0.98. Thereis much less scatter and the magnitude of the correlation

15 APRIL 2012

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2643

FIG. 18. Composites of SLP (contour interval is 1.0 hPa) and SST (shading interval is 0.18C) anomalies for (top to bottom) DJF0, MAM0,
JJA0, and SON0 based on the SLP index over the subtropical North Pacific (108–258N, 1758–1208W; see box in top panels) from (left)
NCEP–NCAR reanalysis (1948–2009) and (right) CCSM4–18 (model years 800–1300). The composites are obtained by averaging thefields when SLP Index is (,21s) 2 (.11s). For consistency with Fig. 17, the seasons are labeled with respect to the timing of the ENSO
cycle but an El Niño (La Niña) event is not required to follow a high (low) value of the SLP Index that was used to construct thecomposites.

2644

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FIG. 19. (a) Scatterplot of the seasonal mean November–March subtropical North Pacific
SLP index with the following year’s January–March Niño-3.4 index. Red (black) circles indicate the years in which the SLP index has the opposite (same) sign as thermocline depth
anomalies averaged over the western equatorial Pacific (58N–58S, 1608E–1808) during theprevious June–October (denoted Z15). (b) Scatterplot of the Z15 Index in JJASO21 with the
JFM11 Niño-3.4 index 18 months later. Red (black) circles indicate those years in which thefollowing year’s November–March SLP index has the opposite (same) sign as the Z15 Index.

between the Z15I and the Niño-3.4 index is high, 20.69,
when the SLPI is opposite to the Z15I, while the correlation is only 20.22 when the Z15I and SLPI anomalies
have the same sign. These results are very similar to theobservational results of Anderson (2007) who obtainedcorrelations of 20.72 and 20.37, respectively, in a comparable analysis. Likewise, the correlation between Z15I
and the Niño-3.4 index in JFM1 is strongly influenced by
the SLPI: it is 0.69 when the SLPI and Z15I have theopposite sign and 0.14 when they have the same sign (Fig.
19b); Anderson obtained values of 0.69 and 0.06 fromsimilar analyses of observations. These results suggestthat the processes in the subtropical North Pacific involved in initiating ENSO events in CCSM4–18 are similar to those in nature and strongly depend on the state of
the tropical Pacific.

b. PDVAs discussed in the introduction, PDV and ENSO
exhibit similar spatial patterns of SST variability but withdifferent regional emphasis (Zhang et al. 1997; Deseret al. 2010). These patterns are explicitly compared inFig. 20a, which shows regression maps of monthly SST
anomalies upon the leading PC time series of monthly
SST anomalies over (left) the full Indo-Pacific domain(608N–608S) and (right) the North Pacific domain (208–608N) for HadISST during 1900–2010 (top), CCSM4–18(middle), and CCSM3–T85 (bottom). The patterns
based on the full Indo-Pacific depict the ENSO modewith largest amplitudes over the equatorial Pacific, while
those based on the North Pacific define the PDO–PDV
with maximum anomalies over the western and central

North Pacific. CCSM4–18 exhibits generally realistic
ENSO and PDV spatial patterns, although the PDV
connection to the tropical Pacific is weaker than observed
and the maximum amplitude of the negative SST anomalies in the North Pacific occur directly east of Japanrather than in the central basin. In contrast, CCSM3–T85
exhibits negligible linkage between the tropical and extratropical Pacific for both ENSO and PDV.
Although their spatial patterns are similar, ENSO and
PDV exhibit distinctive temporal behavior as reflectedin their power spectra (Fig. 20b). ENSO is mainly aninterannual mode of variability while PDV exhibits a
predominantly red spectrum with maximum power atperiods greater than ;10 yr in both observations andCCSM4–18. It is interesting to note that the PDV mode
may also be obtained as the leading EOF of 8-yr lowpass-filtered SST anomalies over the full Indo-Pacificdomain (middle panel of Fig. 20a). The linkage between
SST variability in the tropical and North Pacific on decadal and longer time scales in CCSM4–18 constitutes anotable improvement over CCSM3–T85 (Fig. 20a).
Subsurface temperature variations in the main thermocline of the North Pacific have been shown to be
associated with PDV, both in observations (Deser et al.
1999; Schneider and Cornuelle 2005) and models (Latif
and Barnett 1994; Kwon and Deser 2007; Zhong et al.
2008). Figure 21 (left panels) compares the leading EOF
of annual (July–June) SST and 200-m-depth temperature(T200) anomalies over the North Pacific in CCSM4–18.
The EOF patterns are broadly similar, with anomalies of
one sign in the western and central North Pacific centeredalong the Kuroshio–Oyashio Extension (KOE) near 408N,

15 APRIL 2012

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2645

FIG. 20. (a) EOF1 of monthly SST anomalies for the full Indo-Pacific domain based on (left) unfiltered and (middle) 8-yr low-pass-filtereddata, and (right) for the North Pacific domain (outlined by the box) based on unfiltered data for (top) HadISST 1900–2010, (middle) CCSM4–
18 (model years 800–1300), and (bottom) CCSM3–T85 (model years 300–599). The results are shown as SST anomaly regressions upon thenormalized PC time series (8C per standard deviation). The numbers at the top right corner of each panel denote the percent varianceexplained by the EOF (e.g., 27% for unfiltered HadISST over the full Indo-Pacific domain). (b) Power spectra of the unfiltered PC1 timeseries for the full Indo-Pacific domain (black curves) and the North Pacific domain (gray curves) from (left) HadISST and (right) CCSM4–18.

surrounded by anomalies of the opposite sign to the northand east. The T200 EOF pattern in CCSM4–18 resembles
that in SODA (not shown). Power spectra of the associated PC1 time series (right panels of Fig. 21) exhibit enhanced variance at periods longer than a decade, witha distinctive spectral peak 12;18 yr (hereafter referred

to as the nominal 15-yr oscillation) that is statisticallysignificant at the 99% (;95%) confidence level relative
to a first-order autoregressive model for T200 (SST). A
secondary spectral peak of ;40 yr is evident for T200,
but it is not significant with respect to a first-order autoregressive model. The two PC time series are highly

2646

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FIG. 21. (left) EOF1 of annual (July–June) temperature anomalies at (top) the sea surface and (bottom) 200-m
depth over the North Pacific from CCSM4–18 (model years 800–1299). The EOF accounts for 40% (31%) of thevariance at 5 m (200 m). (right) Power spectra of the PC1 time series (thick black curve) and of the best-fit first-order
autoregressive process and associated 95% and 99% confidence limits (thin gray curves).

correlated (r 5 0.87 when SST leads T200 by 1 yr). It isinteresting to note that similar low-frequency peaks werealso found in CCSM2 and CCSM3 (Alexander et al. 2006;
Kwon and Deser 2007; Zhong et al. 2008).
To understand the role of the atmosphere in North
Pacific Ocean decadal variability, we regress 10-yr lowpass-filtered annual (July–June) SLP, precipitation, windstress curl, and SST anomalies upon the normalized 10-yr
low-pass-filtered T200 PC1, lagging the PC1 record by
3 yr to capture the strongest associations between theatmospheric fields and T200 PC1 as will be discussed
further below (Fig. 22). The lag regression maps indicatethat positive SLP and anticyclonic (negative) wind stresscurl anomalies over the North Pacific precede positive
200-m-depth temperature anomalies along the KOE by
3 yr. These atmospheric patterns and the 3-yr delay are
qualitatively consistent with the mechanism of oceanic
Rossby wave adjustment (e.g., Deser et al. 1999; Schneider
and Cornuelle 2005; Kwon and Deser 2007. The SST
anomaly pattern that occurs in association with T200
PC1 3 years later resembles the leading EOF of North
Pacific SST (Fig. 21). Multiple processes may be responsible for the SST regression pattern, including direct atmospheric forcing of the ocean mixed layer via
turbulent and radiative energy fluxes and Ekman advection, as well as ocean circulation effects throughlateral geostrophic heat flux convergence and entrainment of subsurface ocean temperature anomalies (e.g.,
Kwon and Deser 2007; Kwon et al. 2010). In addition tochanges over the North Pacific, both the precipitationand SST regression maps exhibit significant anomalies

within the tropics. In particular, negative precipitation
anomalies are found over the western equatorial Pacificextending eastward along the off-equatorial intertropicalconvergence zones (ITCZs). These tropical precipitation
anomalies occur in association with negative SST anomalies in the western equatorial Pacific ‘‘Niño-4’’ region(58N–58S, 1608E–1508W).
To further examine the lead–lag relationships among
the different fields, and to validate our choice of 3-yr lag
in Fig. 22, we computed the lag-correlation curves between the T200 PC1 and the ‘‘North Pacific SLP index’’(NPI; Trenberth and Hurrell 1994) defined as the average
SLP over the region (308–658N, 1608E–1408W), and between the T200 PC1 and the ‘‘Niño-4’’ SST index basedon 10-yr low-pass-filtered data (Fig. 23). The maximum
correlation coefficients (significant at the 99% leveltaking into account the autocorrelations in the two records) between T200 PC1 and the NPI occur when theatmosphere leads the ocean by 1–4 yr, with a peak valueof 0.63 at 3 yr. The approximate 15-yr periodicity is also
evident in the NPI-T200 PC1 lag-correlation curve. In
contrast, the lag-correlation curve between the inverted‘‘Niño-4’’ SST index and T200 PC1 does not show the
15-yr periodicity, but does show a maximum correlation(0.51; significant at the 99% level taking into account the
autocorrelations in the two records) when the ‘‘Niño-4’’
SST index leads the T200 PC1 by 2 yr.
To examine whether the atmospheric circulation may
respond significantly to SST anomalies associated with
PDV, we show the unfiltered regression maps of SLP,
SST, and wind stress curl over the North Pacific upon the15 APRIL 2012

DESER ET AL.

2647

FIG. 22. Lag regressions of July–June (a) sea level pressure, (b) precipitation, (c) wind stress curl, and (d) SST anomalies on the leading
PC time series of July–June 200-m temperature in the North Pacific when the PC time series lags by 3 yr. All variables are low-pass filtered
to retain periods longer than 10 yr. Contour intervals are 0.1 hPa, 0.1 mm day21, 0.5 3 1028 N m23, and 0.18C, respectively. Positive(negative) contours are in red (blue), and the 0 contour is in black. Gray shading indicates regression values significant at the 95%
confidence level.

unfiltered T200 PC1 time series at lags 11, 0, and 21 yr(Fig. 24). The reason for examining the unfiltered regressions is to separate the relatively fast (order several
weeks; Deser et al. 2007; Ferreira and Frankignoul 2005,2008) atmospheric response from the relatively slow
atmospheric forcing of the ocean (order several years
due to midlatitude Rossby wave adjustment processes;
Frankignoul et al. 1997). We further isolate the response
to North Pacific SST anomalies by removing covariability with the first 3 EOFs of tropical Indo-Pacific SST
anomalies through multiple linear regression (Frankignoul
et al. 2011). The results show that the atmospheric circulation anomalies undergo a rapid change in sign between
11- and 21-yr lag, such that the spatially extensive positive SLP and negative wind stress curl regressions when
the atmosphere leads the subsurface ocean by 1 yr are
replaced by statistically significant regressions of the opposite sign localized to the region of positive SST anomalies along the KOE when the atmosphere lags the
subsurface ocean by 1 yr (Fig. 24). The vertical structureof the atmospheric circulation also changes from equivalent barotropic at 21-yr lag to baroclinic at 11-yr lag(not shown). We note that the simultaneous regressions
contain signatures of both atmospheric forcing andresponse.
The characteristics of PDV, defined as the leading
EOF of North Pacific T200, in CCSM4–18 exhibit many
similarities with those in CCSM2 and CCSM3, including the patterns of atmospheric forcing and response (Alexander et al. 2006; Kwon and Deser 2007;
Zhong et al. 2008; Kwon et al. 2010). In these studies,
PDV was interpreted as a weakly damped coupled ocean–
atmosphere oscillation intrinsic to the North Pacific in
which basin-scale stochastic atmospheric forcing drives
a delayed oceanic response via Rossby wave adjustment
processes. The associated changes in oceanic lateralheat flux convergence along the KOE in turn force SST
anomalies that drive a weak atmospheric response needed
to sustain the oscillation. However, unlike CCSM2, PDV
in CCSM4–18 also shows significant associations with thetropics, in particular SST and precipitation anomalies in
the western equatorial Pacific. The role of the tropical
Pacific in modifying and responding to North Pacific
PDV remains to be investigated.

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FIG. 23. Lag correlation between the leading PC time series ofJuly–June 200-m temperature in the North Pacific and the July–
June (a) North Pacific SLP index (NPI; 308–658N, 1608E–1408W)
and (b) the inverted Niño-4 SST index (58S–58N, 1608E–1508W).
All time series are 10-yr low-pass filtered before computing thecorrelations. Dashed lines indicate the 99% significance leveltaking into account autocorrelation in each record.

4. Summary and outstanding issues
We have given an overview of ENSO and PDV in the
1300-yr preindustrial control integration of CCSM4 at 18
resolution. Several aspects of ENSO are improved in
CCSM4–18 compared to its predecessor CCSM3–T85,
including the lengthened period (3–6 yr), the larger rangeof amplitude and frequency of events, and the longer
duration of La Niña compared to El Niño. However, theamplitude of Niño-3.4 SST variability is overestimatedby approximately 30%, with a standard deviation of
1.068C compared to 0.758C for observations and 0.808C
for CCSM3–T85. In addition, although the degree of
ENSO irregularity is improved in CCSM4–18 comparedto CCSM3–T85, it is still smaller than in nature. LikeCCSM3–T85, ENSO in CCSM4–18 exhibits characteristics consistent with the delayed–recharge oscillator paradigm, and within this framework the lengthened period
is consistent with the increased latitudinal width of theanomalous equatorial zonal wind stress. CCSM4–18
generally simulates realistic global atmospheric circulation teleconnections to ENSO with accompanying
impacts on precipitation and temperature, although thewintertime deepening of the Aleutian low erroneously
persists into spring. The late winter extratropical SST
response to these teleconnections is substantially improved in CCSM4–18 compared to CCSM3–T85, bothin pattern and magnitude. The vertical structure and

VOLUME 25

seasonal evolution of the upper-ocean temperature response in the North and South Pacific is generally wellsimulated, including asymmetries between warm andcold events. The model’s ENSO shows evidence of atmospheric circulation precursors over the North Pacificassociated with the ‘‘seasonal footprinting mechanism’’,similar to observations. The spatial structure of PDV in
CCSM4–18, determined from EOF analysis of upperocean temperature anomalies over the North Pacific, isgenerally realistic except for the weaker amplitude within
the tropics and overly strong variability east of Japan
along the KOE and represents a substantial improvement
over CCSM3–T85. This mode exhibits a significant spectral peak at approximately 15 yr.
Numerous aspects of ENSO and PDV in CCSM4–18
remain to be investigated. In particular, the role of the
Madden–Julian oscillation (MJO) and westerly windbursts in the initiation and maintenance of ENSO eventsshould be examined as was done for CCSM3.5 (Nealeet al. 2008). We note that the simulation of the MJO issubstantially improved in CCSM4–18 when compared to
CCSM3 (Subramanian et al. 2011). Similarly, additional
analysis is needed regarding the role of the SFM in
triggering ENSO events from both the North and South
Pacific, and to what extent this mechanism reflects
stochastic atmospheric variability or is intrinsic to the
ENSO cycle. The mechanisms responsible for the large
range of ENSO behavior in CCSM4–18 remain to beunderstood, including whether it is randomly generated
as in the GFDL CM2.1 model (Wittenberg 2009), or
whether decadal variability within the tropical Pacificdue to dynamical and thermodynamic air–sea interaction(Clement et al. 2011) and the nonlinear rectification ofwarm and cold events (e.g., Rodgers et al. 2004) are also
important. Similarly, the mechanisms underlying the extended duration of cold events compared to warm eventsin CCSM4–18 need to be clarified and assessed for relevance to nature. Additional analysis of the upper-ocean
thermal response to ENSO in the extratropical oceans,including the consequences of the asymmetric temperature response beneath the mixed layer during EN compared to LN, would also be worthwhile. Finally, a morein-depth characterization and mechanistic understandingof the model’s PDV including patterns beyond theleading EOF and the role of tropical–extratropicalcoupling remain outstanding issues. To date, model
simulations have been equivocal concerning changes inamplitude or frequency of ENSO events in response toincreases in greenhouse gas concentrations (Guilyardi
et al. 2009). Given the large natural range of ENSO
variability in CCSM4–18, detection of an externallyforced change in ENSO behavior will be a challenging
endeavor (Stevenson et al. 2012).

15 APRIL 2012

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2649

FIG. 24. The (left to right) 21, 0, and 11 lag regressions of July–June (a)–(c) SST, (d)–(f) SLP, and (g)–(i) wind stress curl anomalies on
the leading PC time series of July–June 200-m temperature in the North Pacific. Negative (positive) lag indicates the PC lags (leads) the
regressed field. Note that covariability with the first three EOFs of tropical Indo-Pacific (12.58S–12.58N) SST anomalies have been removed before the regressions are calculated. Contour intervals are 0.28C, 0.1 hPa, and 0.5 3 1028 N m23, respectively. Positive (negative)
contours are in red (blue), and the 0 contour is in black. Gray shading indicates regression values significant at the 95% confidence level.
No time filtering has been applied to any of the data records.

Acknowledgments. We thank the anonymous reviewers and the editor for their constructive comments.
M. Alexander, A. Capotondi, and J. Scott’s participation was supported by a grant from the NSF Climate
and Large-scale Dynamics Program. Y.-O. Kwon gratefully acknowledges support from a WHOI Heyman
fellowship and a grant from the NSF Climate and Largescale Dynamics Program. The CESM project is supported by the National Science Foundation and the
Office of Science (BER) of the U.S. Department of Energy. Computing resources were provided by the Climate
Simulation Laboratory at NCAR’s Computational andInformation Systems Laboratory (CISL), sponsored by
the National Science Foundation and other agencies.
NCAR is sponsored by the National Science Foundation.

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