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Tropical Pacific and Atlantic Climate Variability in CCSM3
CLARA DESER
National Center for Atmospheric Research, Boulder, Colorado

ANTONIETTA CAPOTONDINOAA–CIRES Climate Diagnostics Center, Boulder, Colorado

R. SARAVANAN AND ADAM S. PHILLIPS
National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 26 January 2005, in final form 4 August 2005)
ABSTRACT
Simulations of the El Niño–Southern Oscillation (ENSO) phenomenon and tropical Atlantic climate
variability in the newest version of the Community Climate System Model [version 3 (CCSM3)] areexamined in comparison with observations and previous versions of the model. The analyses are based upon
multicentury control integrations of CCSM3 at two different horizontal resolutions (T42 and T85) underpresent-day CO2 concentrations. Complementary uncoupled integrations with the atmosphere and ocean
component models forced by observed time-varying boundary conditions allow an assessment of the impactof air–sea coupling upon the simulated characteristics of ENSO and tropical Atlantic variability.
The amplitude and zonal extent of equatorial Pacific sea surface temperature variability associated with
ENSO is well simulated in CCSM3 at both resolutions and represents an improvement relative to previous
versions of the model. However, the period of ENSO remains too short (2–2.5 yr in CCSM3 compared to
2.5–8 yr in observations), and the sea surface temperature, wind stress, precipitation, and thermocline depth
responses are too narrowly confined about the equator. The latter shortcoming is partially overcome in theatmosphere-only and ocean-only simulations, indicating that coupling between the two model components
is a contributing cause. The relationships among sea surface temperature, thermocline depth, and zonal
wind stress anomalies are consistent with the delayed/recharge oscillator paradigms for ENSO. We speculate that the overly narrow meridional scale of CCSM3’s ENSO simulation may contribute to its excessively
high frequency. The amplitude and spatial pattern of the extratropical atmospheric circulation response to
ENSO is generally well simulated in the T85 version of CCSM3, with realistic impacts upon surface airtemperature and precipitation; the simulation is not as good at T42.
CCSM3’s simulation of interannual climate variability in the tropical Atlantic sector, including variability
intrinsic to the basin and that associated with the remote influence of ENSO, exhibits similarities anddifferences with observations. Specifically, the observed counterpart of El Niño in the equatorial Atlantic
is absent from the coupled model at both horizontal resolutions (as it was in earlier versions of the coupledmodel), but there are realistic (although weaker than observed) SST anomalies in the northern and southern
tropical Atlantic that affect the position of the local intertropical convergence zone, and the remoteinfluence of ENSO is similar in strength to observations, although the spatial pattern is somewhat different.

1. Introduction
The El Niño–Southern Oscillation (ENSO) phenomenon is arguably the most important deterministic

Corresponding author address: Dr. Clara Deser, Climate andGlobal Dynamics Division, National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000.
E-mail: cdeser@ucar.edu© 2006 American Meteorological Society

JCLI3759

source of interannual climate variability worldwide(Pan and Oort 1983; Ropelewski and Halpert 1987;
Kiladis and Diaz 1989; Philander 1990; Glantz 2000;
among others). Due primarily to coupled ocean–
atmosphere interaction within the tropical Pacific,
ENSO is manifest as irregular warmings and coolings ofthe surface waters of the eastern equatorial Pacific lasting typically 1–1.5 yr and recurring approximately every
3–8 yr, accompanied by changes in atmospheric circu-

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lation, rainfall, and upper-ocean heat content (Rasmusson and Carpenter 1982; Deser and Wallace 1990; Harrison and Larkin 1998; Meinen and McPhaden 2000;
Trenberth et al. 2002; among others). The tropical precipitation anomalies force remote atmospheric circulation changes that in turn alter the climate in many partsof world (Horel and Wallace 1981; Trenberth et al.
1998; Alexander et al. 2002; among others). Coupled
air–sea interactions in the tropical Atlantic sector also
have important consequences for climate over the adjacent continents and Europe (see Xie and Carton 2004
for a recent review).
Global coupled ocean–atmosphere general circulation models simulate ENSO with varying degrees ofrealism (Latif et al. 2001; McAvaney et al. 2001; Daveyet al. 2002; AchutaRao and Sperber 2002). In general,the models’ ENSO variability tends to be more regularand at a higher frequency (2–3-yr periodicity) than nature’s, and the simulated tropical Pacific SST variability
tends to extend too far to the west and is more narrowly
confined about the equator than observed. [A recent
exception is the high-resolution (T106 atmosphericcomponent) coupled model integration reported by
Guilyardi et al. (2004).] A number of potential causes
for these model deficiencies have been identified, including insufficient high-frequency stochastic atmospheric variability, which enhances the regularity of
ENSO (Kleeman and Moore 1997; Blanke et al. 1997);
reduced meridional extent and amplitude of the zonal
wind stress anomalies in the western and central equatorial Pacific, which increases the frequency of ENSO
(Kirtman 1997; Jin 1997b; Guilyardi et al. 2003, 2004);
and a westward-extended mean equatorial cold tongue,
which would increase SST variability in the western halfof the basin (Kiehl and Gent 2004). The models’ ENSO
variability is generally consistent with the “delayed oscillator” mechanism (Schopf and Suarez 1988; Battisti
and Hirst 1989; Kirtman 1997; Jin 1997a; Fedorov andPhilander 2000), the leading dynamical paradigm for
ENSO (Latif et al. 2001; Otto-Bliesner and Brady 2001;
Davey et al. 2002). Recently, a “recharge oscillator”
paradigm has been proposed (Jin 1997a,b) that is dynamically consistent with the delayed oscillator but emphasizes meridional mass transports rather than wave
processes. Some observational studies (Meinen and
McPhaden 2000; Kessler 2002) have shown that the interannual evolution of equatorial Pacific heat content
anomalies is consistent with the recharge oscillator
mechanism, but analogous diagnostics have not beencommonly applied to coupled general circulation model
simulations of ENSO.
Atmospheric circulation changes forced by ENSOrelated tropical precipitation anomalies initiate climate

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impacts worldwide. One of the most robust extratropical atmospheric circulation teleconnections associatedwith ENSO is a deepening and eastward extension of
the wintertime Aleutian low pressure cell over the
North Pacific during warm events compared to cold
events (Horel and Wallace 1981; Trenberth 1991; Trenberth et al. 1998; Hoerling and Kumar 2002; Alexander
et al. 2002; among others). This anomalous circulation
brings enhanced precipitation to the southwestern
United States and warmer air temperatures to northwestern North America (Ropelewski and Halpert 1987;
Kiladis and Diaz 1989; Minobe 1997). An analogous
atmospheric teleconnection forced by ENSO occurs
over the South Pacific and South America during austral winter (cf., Kiladis and Mo 1998). The ability ofgeneral circulation models to correctly represent theextratropical atmospheric circulation response to
ENSO depends on a variety of factors, including the
spatial distribution of the tropical rainfall anomalies,
the strength and location of the midlatitude stationary
waves and storm tracks, and the barotropic instabilityof the background flow (DeWeaver and Nigam 2004
and references therein).
Climate variability in the tropical Atlantic sector includes that intrinsic to the basin and that associatedwith the remote influence of ENSO (Xie and Carton2004). The former has been characterized in terms of
two spatial patterns: a cross-equatorial SST anomaly
gradient that affects the position of the Atlantic intertropical convergence zone (ITCZ), and an equatorial
SST anomaly analogous to ENSO in the Pacific. Thermodynamic air–sea interaction is believed to be responsible for the cross-equatorial gradient pattern while dynamical air–sea interaction is important for the equatorial mode. The remote influence of ENSO is initiatedby surface heat flux anomalies associated with atmospheric teleconnections from the tropical Pacific. These
aspects of tropical Atlantic variability (TAV) are generally not well simulated in coupled general circulation
models (Xie and Carton 2004).
In this study, we document aspects of ENSO andTAV in the newest version of the Community Climate
System Model [version 3 (CCSM3)], a state-of-the-art
coupled general circulation model, in comparison withobservations and previous versions of CCSM. CCSM3
contains significant improvements over previous releases, including a diurnal cycle of penetrating solar
radiation in the upper ocean that results in a more realistic mean SST distribution across the tropical Pacific(Large and Danabasoglu 2006), an aspect particularly
relevant for this study. Our analyses are based upon
extended (multicentury) control integrations underpresent-day CO2 values at two different horizontal

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resolutions (T42 and T85 for the atmospheric and terrestrial components, coupled to 1° ocean and sea ice
components), allowing us to examine the impact ofresolution upon the simulated characteristics of ENSO
and TAV. We also compare CCSM3 with uncoupledintegrations of both the atmosphere and ocean component models forced by observed time-varying boundary
conditions, enabling us to assess the influence of air–sea
coupling upon the simulated behavior of ENSO andTAV.
The paper is structured as follows. Section 2 contains
brief descriptions of CCSM3 and the uncoupled atmosphere and ocean model integrations, as well as theobservational datasets and analysis procedures used inthis study. Section 3a documents the spatial and temporal characteristics of the simulated ENSO phenomenon in the Tropics in both versions (T42 and T85) of
CCSM3, including the patterns of SST, thermocline
depth, precipitation, surface air temperature, and sea
level pressure (SLP) anomalies. Section 3b documents
the global response patterns of SLP and 500-hPa geopotential height anomalies to ENSO at both model
resolutions, as well as those from a five-member ensemble of uncoupled integrations with the atmosphericmodel component forced by observed time-varying
global SSTs during 1950–2000. Section 3c presents a
more mechanistic analysis of ENSO thermocline depth
variability in the context of the delayed oscillator
mechanism. The results of this analysis suggest that thestrength and meridional scale of the surface wind response to ENSO SST anomalies may impact the frequency of ENSO in the model. The nature of the surface wind response and implications for air–sea energy
exchange are explored further in section 3d. Section 4
documents aspects of interannual climate variability inthe tropical Atlantic sector, including variability intrinsic to the basin and that associated with the remoteinfluence of ENSO.

2. Model description, observational datasets, andanalysis procedures
CCSM3 is a state-of-the art coupled general circulation model comprised of four components (atmosphere, ocean, land, and cryosphere) linked by meansof a flux coupler. The atmospheric component, theCommunity Atmosphere Model version 3 (CAM3), is a
global general circulation model with 26 vertical levels
and an Eulerian spectral dynamical core with triangulartruncation at T42 or T85 (a T31 version is also available), corresponding to a horizontal resolution of 2.8°
or 1.4°, respectively. The ocean component is based
upon the Parallel Ocean Program (POP) model version
1.4.3 from the Los Alamos National Laboratory. POP2453

has 40 vertical levels, a horizontal resolution of 1°, anda dipolar grid. The land surface model grid is identicalto that of CAM3, and the sea ice model grid is the sameas POP’s.
CCSM3 contains significant revisions over previous
releases, including new treatments of cloud processes,
aerosol radiative forcing, sea ice dynamics, and a diurnal cycle of penetrating solar radiation in the upper
ocean. Further details on model changes and configuration are provided in Collins et al. (2006). The CCSM3
simulations analyzed in this study are the extended(⬎600 yr) coupled control integrations at T42 and T85
resolution under 1990 CO2 concentrations; note thatCCSM3 does not utilize flux corrections. Unless statedotherwise, the ENSO and TAV analyses presented inthis paper are based upon model years 100–799 for
CCSM3 (T42), 100–599 for CCSM3 (T85), and 350–999
for CCSM2 (T42); the mean SST field over the tropicalAtlantic from CSM1, presented briefly in section 4, isbased upon model years 1–100. The analyses of CCSM3
begin in year 100 to minimize any potential climate
drifts that may have occurred at the beginning of thesimulations; those for CCSM2 begin in year 350 because of a change in model parameters in that year (seeKiehl and Gent 2004). The ending dates for the model
analyses reflect the latest year of simulation available tous at the time this study was undertaken. The reader isreferred to Boville and Gent (1998) and Kiehl and Gent
(2004) for descriptions of CSM1 and CCSM2, respectively.
In addition to CCSM3, this study makes use of two
five-member ensemble CAM3 integrations (one at T42
and one at T85) forced by observed global time-varying
SSTs and sea ice concentrations during 1950–2000
taken from J. W. Hurrell et al. (2004, personal communication). Different initial conditions are used for eachmember of the CAM3 Atmospheric Model Intercomparison Project (AMIP)-style integrations. We also
analyze thermocline variability in an uncoupled integration of the 1° POP ocean model forced by observed
global time-varying surface atmospheric conditions
based on National Centers for Environmental Research–National Center for Atmospheric Research(NCEP–NCAR) reanalyses (Kalnay et al. 1996) during
1958–2000. The details of the forcing fields for thissimulation, which include surface air temperature andhumidity, wind speed, and radiation, may be found in
Large and Danabasoglu (2006).
The following obervational datasets are used: 1) SLP,
surface wind and wind stress, turbulent latent and sensible heat fluxes at the sea surface, and 500-hPa geopotential heights from the NCEP–NCAR reanalyses (Kalnay et al. 1996) during 1950–2001 on a 2.5° grid (T62

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FIG. 1. SST long-term annual (left) means and (right) variances from observations, CCSM2 (T42), CCSM3 (T42),
and CCSM3 (T85). Variances are computed from monthly anomalies (departures from the long-term monthly
means). Color scales are given at the bottom of each column of maps.

Gaussian grid for heat fluxes); 2) SST from the Hadley
Centre Sea Ice and SST (HadISST) dataset (Rayner et
al. 2003) during 1900–2003 on a 1° grid; 3) air temperature over land from the Climatic Research Unit (CRU)
Surface Temperature (TS) 2.0 dataset (Mitchell et al.
2003, manuscript submitted to J. Climate); 4) precipitation from Xie and Arkin (1997) based on blended satellite and in situ measurements during 1979–2001 on a
2.5° grid; 5) SST, SLP, and surface winds from theInternational Comprehensive Ocean–Atmosphere Data
Set (ICOADS; Woodruff et al. 1987) during 1950–97 on
a 2° grid; and 6) surface shortwave and longwave radiation from the International Satellite Cloud Climatology Project (ISCCP; Zhang et al. 2004) during 1984–
2000 on a 2.5° grid.
The simple analysis methods used in this study include compositing, linear regression and correlation
analysis, and empirical orthogonal function (EOF)
analysis. Statistical significance of the composite andregression fields is assessed by means of a local Student’s t test. Monthly anomalies are defined by subtracting the long-term monthly means from the individual monthly values for a given year.

3. ENSO
a. Tropical Indo-Pacific SST and thermocline depth
variability
The long-term annual mean distributions of SST andthe variances of monthly anomalies of SST in the tropical Indo-Pacific for observations (HadISST, years
1950–2000), CCSM2 (T42), and CCSM3 (T42 and T85)
are compared in Fig. 1. The simulation of the long-termmean SST distribution is improved in CCSM3 relativeto CCSM2: in particular, the equatorial “cold tongue” isless pronounced and does not extend as far west, although it is still somewhat stronger than observed. The
improvement of the cold tongue bias in CCSM3 is primarily due to the inclusion of a diurnal cycle in penetrating solar radiation in the ocean model component(see Large and Danabasoglu 2006). The largest SST
anomaly variance occurs in the equatorial cold tongue;
however, differences in spatial extent and amplitude
are apparent and likely associated with biases in themean state. For example, the exaggerated strength ofthe cold tongue in CCSM2 (Kiehl and Gent 2004) produces variability that is too large and extends too far

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FIG. 2. Time series of monthly SST anomalies (°C) in the Niño3.4 region from observations (HadISST, 1900–2003), CCSM2(T42), CCSM3 (T42), and CCSM3 (T85). A 104-yr segment isshown for each model simulation (model years 400–503) to matchthe record length available for observations. The standard deviations of the time series are given at the upper right of each panel(note that these are based upon the full set of model years analyzed).

west. The T85 version of CCSM3 exhibits the most realistic representation of the variability, with maximum
values (16°C2) east of 130°W and a secondary maximum along the South American coast (although thelatter is underestimated).
A commonly used index for representing SST variability associated with ENSO is the area average ofmonthly SST anomalies in the region 5°N–5°S, 170°–
120°W, referred to as the “Nino-3.4 SST index” (see,e.g., Trenberth et al. 2002). This region is located within
the area of maximum SST variance and also lies within
the center of action of the leading EOF pattern ofmonthly SST anomalies (not shown): the associatedprincipal component (PC) time series exhibits a correlation coefficient with the Niño-3.4 SST index in excess
of 0.95 for each model and for observations. The Niño3.4 SST indices from observations and the models areshown in Fig. 2: 104-yr samples from each model integration are shown to facilitate comparison with the period of record available from observations (1900–2003).
A visual comparison of the Niño-3.4 SST records reveals that, in all cases, the simulated variability is too

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FIG. 3. Power spectra of the monthly Niño-3.4 SST anomaly
time series from observations (HadISST, years 1900–2000 and
1950–2000), CCSM2 (T42, model years 350–999), CCSM3 (T42,model years 100–799), and CCSM3 (T85, model years 100–599).

rapid and regular compared to observations. It can also
be seen that the amplitude of the Niño-3.4 variability isoverestimated in CCSM2 (standard deviation ⫽ 0.9 K
for model years 350–999) compared to observations(standard deviation ⫽ 0.75 K), but more realistic in
CCSM3 (standard deviation ⫽ 0.85 K for T42 based
upon model years 350–999 and 0.80 K for T85 based
upon model years 100–599), consistent with the results
shown in Fig. 1. The amplitude of the variability in
CSM1 is more than a factor of 2 smaller than in CCSM2[not shown, but see Kiehl and Gent (2004)].
The frequency and amplitude of ENSO is quantified
by performing a power spectrum analysis on the Niño3.4 time series (Fig. 3). The observational results areshown for two periods, 1900–2003 and 1950–2003, togive an indication of the sensitivity of the results todifferent record lengths. Consistent with one’s visual
impression of the Niño-3.4 time series, the dominantfrequencies in the model power spectra (⬃1.8–3 yr) are
too high relative to observations (⬃3–8 yr; there issome overlap between observations and the models
around 2.5 yr, but this is not the dominant frequency ineither nature or CCSM3). The effect of increasedmodel resolution in CCSM3 is to slightly broaden the
spectral peak toward lower frequencies, but it is very
minor and does not extend to periods longer than 3 yr.
(Note that CCSM2 at T42 resolution also exhibits enhanced variance around 2.5 yr, suggesting that model

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FIG. 4. Monthly standard deviations (°C) of the Niño-3.4 SST
anomaly time series from observations (HadISST, years 1900–
2003), CCSM2 (T42), CCSM3 (T42), and CCSM3 (T85).

resolution is not the only cause for enhancing the lowfrequency end of the dominant spectral peak.) Finally,an examination of the power spectra for individual 100yr segments of the Niño-3.4 index from CCSM3 at T85
resolution reveals no significant departures from thedominant frequencies seen in the full 700 yr of record(not shown).
The seasonal cycle of the standard deviation of the
Niño-3.4 SST anomaly time series from observations
and the models is shown in Fig. 4. The observed Niño3.4 record exhibits minimum variability during April–June and maximum variance near the end of the calendar year (November–January). Both CCSM3 records
show a seasonal cycle similar to observations except inboreal summer when they overestimate the variance byapproximately a factor of 2. Overall, CCSM3 yields a
more realistic amplitude and seasonal cycle of Niño-3.4
variability than CCSM2 (in CCSM2, the standard deviations during February–August are nearly twice aslarge as observed). It is interesting to note thatCCSM3’s reasonable simulation of the seasonal cycle ofinterannual variability occurs despite a poor simulationof the seasonal cycle of the mean SST (Large andDanabasoglu 2006).
As discussed in the introduction, equatorial ocean
dynamics play a key role in the evolution of ENSO. The
primary variable for equatorial ocean dynamical processes is thermocline depth, a quantity that reflects theamount of heat stored in the upper ocean. Figure 5ashows the seasonal evolution of simulated thermocline
depth, as given by the depth of the 15°C isotherm, andobserved SST anomalies during a composite ENSO
cycle. The simulated thermocline depth values are ob-

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tained from the ocean model component of CCSM3
forced by observed surface fields during 1958–2000 (recall section 2). The 15°C isotherm, located in the core of
the main thermocline, provides similar dynamical information as the 20°C isotherm, the usual choice for thermocline depth in observations, but is less subject toinfluence from surface processes in regions where thethermocline is close to the surface, for example, theeastern part of the equatorial Pacific. The seasonal
anomaly composites were constructed by averaging
years in which the Niño-3.4 SST index in September–
November [SON (hereafter 3-month periods are denoted by the first letter of each respective month);when the index is near its peak; recall Fig. 4] exceeds
one standard deviation and subtracting the average ofthe years in which it is less than ⫺1 standard deviation.
The composites begin in MAM of the composite year
(denoted MAM0) and go through JJA of the following
year (denoted JJA⫹1). In lieu of direct observational
estimates of the depth of the 15°C isotherm, which aresparse and limited in duration, we make use of thermocline depth anomalies from an integration of theocean component of CCSM3 at T85 resolution (thePOP model), forced by observed surface fields during
1958–2000 derived from the NCEP–NCAR reanalyses(Kalnay et al. 1996; see also Large and Danabasoglu
2006). The results compare well with the available observational and assimilated datasets of thermocline
depth anomalies during ENSO, shown, for example, by
Trenberth et al. (2002) and Meinen and McPhaden(2000). The observed SST anomaly composites are
based on the years 1958–2000 to be compatible with theperiod of record available for thermocline depth.
The observed ENSO composite SST evolution (Fig.
5a) exhibits an incipient warming along the equator andcoast of South America during MAM0 that subsequently grows in amplitude over the next several seasons, reaching its peak around SON0 and DJF0. The
warm event diminishes during MAM⫹1 and begins totransition to a weak cold event in JJA⫹1. Accompanying the warming is the simultaneous development ofnegative SST anomalies in the northwest and southwest
tropical Pacific. The Indian Ocean exhibits a delayed
warming relative to that in the Pacific, reaching peakstrength around MAM⫹1 but lingering through JJA⫹1
and beyond (not shown). Similar results are shown inHarrison and Larkin (1998), Rasmusson and Carpenter(1982), Trenberth et al. (2002), and Klein et al. (1999),
among others.
At the beginning of the incipient warm event in
MAM0, thermocline depth anomalies display a zonallysymmetric signal, with deeper-than-average values
along the equator and shallower-than-average values

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around 10°–15°N and 15°–25°S. This zonally symmetricsignal may reflect conditions at the end of the prior cold
event when there is a net import of upper-ocean heat
content into the equatorial zone (see also Trenberth etal. 2002). As the warm event develops, the pattern ofthermocline depth anomalies becomes more zonallyasymmetric, with deeper values in the east and shallower values in the west, reaching peak amplitude latein the calendar year. This configuration is consistent
with a reduction in the slope of the thermocline drivenby (and in balance with) a relaxation of the easterly
winds along the equator (not shown). At the end of thewarm event (JJA⫹1), the thermocline depth anomalies
begin to transition to a more zonally symmetric patternof shallower values (depleted heat content) in the equatorial zone and deeper values (enhanced heat content)
to the north and south, opposite to that at the beginningof the event. The temporal and spatial phase relationships between the composite ENSO SST and thermocline depth anomaly fields are qualitatively consistent with the delayed oscillator mechanism, as discussed previously by Trenberth et al. (2002) among
others.
The ENSO composite evolutions of SST and thermocline depth anomalies from CCSM3 at T85 resolution are shown in Fig. 5b (results based upon the T42
model are similar; not shown). The development of
positive SST anomalies along the equator from MAM0
through DJF⫹1 is similar to observations, but the transition to negative anomalies occurs one season ahead ofnature (MAM⫹1 compared to JJA⫹1) and the ensuing
cold event is stronger than observed, consistent withthe model’s stronger biennial tendency. It is also apparent that the simulated SST warming is more narrowly
confined about the equator compared to nature, andthat the magnitude of the cooling in the western Pacificand the delayed warming in the Indian Ocean are substantially weaker than observed.
• The model’s composite ENSO evolution of ther-

mocline depth anomalies resembles observations inmany respects. In particular, at the beginning of thewarm event MAM0, the thermocline is deeper than
average throughout the equatorial Pacific, withweaker anomalies of opposite sign to the north andsouth. While the warm event develops over the next
few seasons, the zonally symmetric pattern of thermocline depth anomalies transitions to a zonallyasymmetric pattern, with maximum anomalous east–west gradient occurring in DJF⫹1 near the peak of the
SST warming. At the end of the warm event andbeginning of the ensuing cold event (MAM⫹1 and
JJA⫹1), the pattern of thermocline depth anomalies

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exhibits a strong zonally symmetric component indicative of a depletion of equatorial upper-ocean
heat content; this pattern resembles that in MAM0
but with opposite phase, reflecting the overly biennial
nature of the simulated ENSO cycle. The main discrepancy between the spatial patterns of simulated
and observed thermocline depth anomalies is theirmeridional scale, which is considerably narrower inthe model than in nature (cf., e.g., MAM0, DJF⫹1, or
JJA⫹1), similar to the results for SST. A more mechanistic analysis of the simulated thermocline depth
anomalies within the framework of the delayed oscillator and recharge oscillator paradigms is given in
section 3c.

b. Global SST, precipitation, and atmosphericcirculation anomaly composites
In this section we describe selected aspects of the
global ENSO anomaly patterns of surface temperature,
precipitation, and atmospheric circulation in DJF and
JJA. The Web supplement to this paper (http://www.
cgd.ucar.edu/cas/cdeser/CCSM3ENSOsupfig.html)
contains a complete set of figures showing warm-minuscold ENSO anomaly composites of SST, surface airtemperature, precipitation, SLP and 500-hPa geopotential heights for all four seasons from observations,
CCSM3 at both resolutions, and the ensemble mean of
the CAM3 AMIP integrations at both resolutions.
Warm-minus-cold ENSO composite difference mapsof surface temperature (SST over oceans and air temperature over land) and precipitation during DJF are
shown in Fig. 6 for observations, CCSM3 at T85 resolution (results for T42 look similar: see Web supplement), and the ensemble mean of the five CAM3
AMIP integrations at T85 resolution (results for T42
look similar: see Web supplement). Warm (cold) years
were identified based on the criterion that the Niño-3.4
SST index exceeds one standard deviation (is less than
–1 standard deviation). A simple Student’s t test is used
to assess the 99% a priori (95% a posteriori) significance levels for the composite differences at each grid
point. The observed and CAM3 AMIP composites are
based upon the period 1950–2000 (1979–2000 for precipitation because of the limited duration of the satellite record). The years used for the warm event composite are 1958, 1966, 1973, 1983, 1987, 1992, 1995, and1998; and the years used for the cold event composite
are 1950, 1956, 1971, 1974, 1976, 1985, 1989, 1999, and2000.
Over the Tropics, the observed SST pattern consistsof positive anomalies in the eastern Pacific, with maximum values (⬃4 K) along the equator spreading with

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FIG. 5. (a) Seasonal evolution of (left) simulated thermocline depth and (right) observed SST anomalies during
a composite warm ENSO event relative to a cold one (see text for details of the compositing procedure). The
simulated thermocline depth values are obtained from the ocean model component of CCSM3 forced by observed
surface fields during 1958–2000. Positive (negative) thermocline depth values indicate a deeper (shallower) thermocline relative to the long-term average. Dashed contours indicate that the anomalies are significantly different
from zero at the 99% confidence level based upon a Student’s t test.

reduced amplitude into the subtropics of both hemispheres in a wedge-shaped pattern; positive anomalies(⬃1 K) also occur in the tropical Indian Ocean. Negative SST anomalies are found along the flanks of thewedge-shaped area to the north and south and in thewestern equatorial Pacific, with amplitudes ⬃ ⴑ1 K. In
the model, the tropical Pacific SST signal is largely confined to the equatorial zone where it extends westward
to New Guinea. Unlike observations, there is almost no
cooling on the flanks of this equatorially confined

warming. Weaker positive anomalies are found over
the tropical Indian Ocean, similar to observations.
In the extratropics, the model simulates the generalfeatures of the observed SST anomaly pattern: for example, in the Southern Hemisphere, the cooling to thesoutheast of Australia and warming in the Southern
Ocean between 180° and 90°W; and in the Northern
Hemisphere, the cooling in the central north Pacificand warming in the eastern north Pacific (albeit withconsiderably reduced amplitude). These extratropical1 JUNE 2006

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FIG. 5. (Continued) (b) Same as in (a), but for thermocline depth and SST from the CCSM3 simulation at T85
resolution based upon model years 100–599.

SST anomalies are likely driven by local atmosphericcirculation changes associated with ENSO teleconnection patterns (see Fig. 9 and Alexander et al. 2006).
Similarly, atmospheric circulation anomalies are responsible for positive surface air temperature anomalies over western North America, a feature that is wellsimulated in CCSM3. The model also exhibits realistic
positive air temperature anomalies over Australia,
western Africa and portions of southern Africa, andnorthern South America.
The distribution of tropical precipitation changes isclosely associated with the pattern of SST anomalies inboth observations and the model. In observations, enhanced precipitation is found over and near the regionof positive SST anomalies in the eastern Pacific andwestern Indian Ocean; reduced precipitation occurs
over Indonesia, the South Pacific convergence zone
(SPCZ) in the southwest Pacific, and in the Atlantic
ITCZ. CCSM3 also exhibits enhanced precipitation in
the vicinity of the equatorial Pacific SST warming, but
the compensating regions of reduced rainfall occur
preferentially to the north and south rather than to thewest over Indonesia as observed. Reduced rainfall isalso found in the Atlantic ITCZ, similar to observations.
The precipitation anomalies associated with ENSO
may also be viewed within the context of the climatological precipitation distribution (Fig. 7). In observa-

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FIG. 6. Warm-minus-cold ENSO event anomaly composites of (left) surface temperature (SST over ocean,surface air temperature over land and sea ice) and (right) precipitation during DJF from (top) observations,(middle) CCSM3 (T85), and (bottom) the ensemble mean of the five CAM3 AMIP integrations (T85). Details of
the compositing procedure are given in the text. Dashed contours indicate that the anomalies are significantly
different from zero at the 99% confidence level based upon a Student’s t test. Note that observations are missing
over sea ice.

tions, the ENSO-induced precipitation changes overthe Pacific may be characterized as equatorward shiftsof the ITCZ and SPCZ toward the anomalous warming
along the equator. This is also true in CCSM3, but because of the more zonal orientation of the model’s
SPCZ the ENSO precipitation signal takes on a more
zonally symmetric appearance. Another reason for the
more meridionally confined precipitation response in
CCSM3 compared to observations may be the overly
narrow equatorial Pacific SST warming and lack ofcooling to the north, south, and west.
To ascertain whether differences in the simulated
and observed precipitation ENSO composites are re-lated to differences in the SST anomaly distribution, it
is instructive to examine the ensemble mean precipitation response from the five CAM3 AMIP integrationsat T85 resolution (Fig. 6, bottom; results from theAMIP integrations at T42 resolution look similar: see
Web supplement). The AMIP precipitation composite
overcomes some of the unrealistic features seen in
CCSM3: in particular, the positive rainfall changes overthe Pacific are less equatorially confined (note especially the broader southward extension) and the negative anomalies in the SPCZ are stronger and also extend farther south, although not as far as in observations. In addition, the negative anomalies over the1 JUNE 2006

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FIG. 7. Climatological precipitation distributions during (left) DJF and (right) JJA from (top) observations and(bottom) CCSM3 (T85). The observed fields are based upon an updated version of the satellite-based estimatesof Xie and Arkin (1997) during 1979–2000; the simulated fields are based on model years 400–499.

Indian Ocean are more pronounced, although there arestill erroneous positive anomalies over the equatorial
portion of the Maritime Continent region. Thus, it appears that the coupled model’s tendency for an overly
confined equatorial precipitation response to ENSO,
with compensating anomalies to the north and south
rather than in the zonal direction, is in part a consequence of deficiencies in the model’s ENSO SST signal(which could in turn be due to coupled air–sea interactions) rather than an intrinsic property of the atmospheric model itself.
Warm-minus-cold ENSO composite difference mapsof surface temperature and precipitation during JJA
are shown in Fig. 8. As in DJF, the Pacific SST warming
signal in CCSM3 is too narrowly confined about theequator and extends too far to the west, reaching all theway to New Guinea. Also, the model lacks the broad
area of cooling in the southwestern Pacific. The precipitation anomalies in CCSM3 exhibit a double-ITCZlike structure over the Pacific, with reduced precipitation within the climatological locations of the two convergence zones (see Fig. 7) and enhanced precipitationon their equatorward flanks. This is in contrast to theobserved precipitation distribution, which exhibits asingle dominant ITCZ north of the equator that shifts
and expands southward during a warm ENSO event.
There is also an equatorward shift of the SPCZ, but thisfeature is confined to the far western Pacific in JJA, anda reduction of precipitation over the Maritime Continent. Both the model and observations exhibit less rainfall in the Atlantic ITCZ. As in DJF, some of the shortcomings of CCSM3’s ENSO precipitation signal in JJA
are overcome in the ensemble mean CAM3 AMIP integration (Fig. 8, bottom); however, there are still erroneous positive rainfall anomalies east of the Philippine Islands. The precipitation responses in the T42
versions of CCSM3 and CAM3 AMIP integrations are
similar to their T85 counterparts (see the Web supplement).
Warm-minus-cold ENSO composites of SLP during
DJF from observations, and CCSM3 and the ensemblemean CAM3 AMIP integrations at both T42 and T85
resolution, are shown in Fig. 9. The coupled models
capture the general features of the observed SLP
anomaly field: a tropical dipole between the eastern
Pacific and western Pacific/Indian Ocean (e.g., the“Southern Oscillation”) and teleconnections to theNorth and South Pacific. However, CCSM3’s simulation of the Southern Oscillation underestimates the amplitude of the positive anomalies over the western Pacific/Maritime Continent and Atlantic, and the maximum negative anomalies over the eastern Pacific are
centered along the equator rather than the Southern
Hemisphere. In the CAM3 AMIP integration, the am-

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FIG. 8. Same as in Fig. 6, but for JJA.

plitude of the tropical SLP response increases to more
realistic levels, particularly in the region of positive
anomalies over the western Pacific/Maritime Continent(which also now exhibit a more realistic northeastward
tilt into the North Pacific), and the negative anomalies
in the eastern Pacific are broader in latitudinal extent
more like observations; however, the largest pressure
falls are still located on the equator, and the positive
anomalies over the tropical Atlantic are still weaker
than observed (in the T42 AMIP run, the Southern
Oscillation is largely confined to the Pacific/eastern Indian Ocean sector).
CCSM3’s simulation of the extratropical SLP response to ENSO is more realistic at T85 resolution thanat T42. For example, the negative SLP anomaly center
over the North Pacific, corresponding to a deepening of
the Aleutian low during warm events, exhibits a realistic amplitude in the T85 model (maximum value of⬃9 hPa compared to ⬃11 hPa in observations) but is
too weak in the T42 model only (maximum value of⬃4 hPa). Also, the zonal wavenumber-2 aspect of theobserved SLP signal over the Southern Ocean, withnegative anomaly centers south of New Zealand andSouth Africa and a positive anomaly center west of the
Antarctic Peninsula, is apparent in the T85 model
whereas the T42 model exhibits a more zonally symmetric signal.
The magnitude of the Aleutian low SLP response isstrengthened in both CAM3 AMIP ensembles relativeto CCSM3 at the same resolution, reaching realistic
values in the T85 AMIP ensemble (maximum amplitude of ⬃11 hPa) but remaining weaker than observedin the T42 AMIP ensemble (maximum amplitude of⬃7 hPa). The SLP anomaly pattern over northern Eurasia is also improved in the CAM3 AMIP runs compared to the coupled models at both resolutions, where1 JUNE 2006

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FIG. 9. Warm-minus-cold ENSO event anomaly composites of SLP (hPa) during DJF from (top left and right)
observations, CCSM3 at (middle left) T85 and (bottom left) T42 resolutions, and the ensemble mean of the five
CAM3 AMIP integrations at (middle right) T85 and (bottom right) T42 resolutions. Details of the compositing
procedure are given in the text. Dashed contours indicate that the anomalies are significantly different from zeroat the 99% confidence level based upon a Student’s t test.

the anomalies are now negative as observed. The SLP
response over the North Atlantic is improved in the
CAM3 AMIP ensemble relative to CCSM3 at T85 resolution but not at T42. Over the Southern Ocean, thecenters of the zonal wavenumber-2 pattern in the T85
AMIP ensemble are displaced approximately 30° eastof their location in the coupled run at the same resolution (and observations) and their amplitude is too large;
a similar zonal wavenumber-2 pattern is present in the
T42 AMIP ensemble.
Warm-minus-cold ENSO composites of 500-hPa geopotential heights in DJF are shown in Fig. 10. While thespatial patterns of the geopotential height anomalies
are generally well simulated in both versions of

CCSM3, the amplitudes are smaller than observed. For
example, the band of positive anomalies over the Tropics is weaker by nearly a factor of 2, and the wave trainover the North Pacific and North America, which resembles the Pacific–North American (PNA) teleconnection pattern (Wallace and Gutzler 1981), is weakerby a factor of 2 (4) at T85 (T42). The strength of thetropical and extratropical anomalies is improved in the
AMIP ensemble compared to CCSM3 at both model
resolutions. For example, the magnitude of the tropicalresponse is realistic, and the amplitudes of the threecenters of action of the PNA are nearly identical toobservations in the T85 AMIP ensemble and ⬃75% of
the observed amplitude in the T42 AMIP ensemble.

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FIG. 10. Same as in Fig. 9, but for geopotential heights at 500 hPa. Positive contours are solid, negative contours
are dashed, and the shading indicates where the anomalies are significantly different from zero at the 99%
confidence level based upon a Student’s t test.

The teleconnections to the Southern Hemisphere,
which were underestimated in CCSM3, are improved in
the AMIP run, although they are generally weaker at
T42 than T85.
Warm-minus-cold ENSO composites of SLP in JJA
are shown in Fig. 11. In this season, the ENSO SLP
signal is located primarily in the Southern Hemisphere(compared to DJF when the signal is global). The positive lobe of the Southern Oscillation is reduced in magnitude compared to DJF in both observations and themodels. The negative lobe of the Southern Oscillationin the eastern Pacific in both versions of CCSM3 is split
into two centers of comparable strength, one locatedalong the equator directly over the narrow zone of positive SST anomalies and the other in the subtropics. This
split is not apparent in either observations or the AMIP
integrations, where the amplitude of the SLP anomaly
center over the southeast Pacific is more than twice asstrong as the equatorial center. The main extratropicalfeature during JJA is the meridionally oriented dipole
over the southeast Pacific, which is captured to someextent by all of the model configurations. There are also
weak, but statistically significant, negative SLP anomalies over the North Pacific in observations, a featurethat is simulated to some degree by all of the model
configurations.

c. Thermocline depth variability in the context ofthe delayed/recharge oscillator mechanisms
As discussed earlier, a leading dynamical paradigm
to explain the ENSO cycle is the delayed oscillator hypothesis, which relies upon oceanic wave processes. In
its original formulation (Schopf and Suarez 1988;
Suarez and Schopf 1988; Battisti and Hirst 1989) thistheory explains deepening of the thermocline in theeastern equatorial Pacific and the growth phase of the1 JUNE 2006

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FIG. 11. Same as in Fig. 9, but for JJA.

instability in terms of downwelling equatorial Kelvinwaves forced by the anomalous wind stress in the central/western Pacific. The anomalous wind stress also
forces first-mode equatorial Rossby waves that propagate westward, reflecting at the western boundary as
upwelling Kelvin waves. After reaching the easternequatorial Pacific, these upwelling waves may reverse
the sign of the thermocline depth anomaly, thus acting
as the agent responsible for the negative feedback.
The wave processes involved in the delayed oscillator
theory can be identified in the evolution of equatorial
thermocline depth anomalies in relation to zonal windstress anomalies from the CCSM3 integration (T85
resolution), as shown in Fig. 12a. For clarity of presentation, we consider monthly anomalies during a 15-yr
period (years 430–445 of the model integration). To
isolate the ENSO phenomenon, all the data have been

bandpass filtered using a Fourier filter with half-power
at 1.5 and 5.5 yr (recall that the model ENSO has adominant time scale of approximately 2–3 yr); however,similar results are obtained without filtering (notshown). From the area of maximum wind stress anomalies (180°–160°W) thermocline depth disturbances appear to propagate eastward, as indicated by the positive
slope of the phase lines. Reflection of Rossby waves atthe eastern boundary may be responsible for the thermocline depth anomalies east of ⬃100°W. In somecases (e.g., model years 436, 441.5, 442, and 443), westward-propagating Rossby waves generated around
160°W by the anomalous wind stress can also be detected. Notice that the wind stress anomalies in Fig. 12aare not in equilibrium with the thermocline depth
anomalies.
As discussed in previous studies (Schneider et al.

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FIG. 12. (a) Evolution of anomalous thermocline depth (given by the depth of the 15°C isotherm; color shading) and zonal wind stress(contours) along the equator during model years 430–445 from CCSM3 at T85 resolution. Positive (negative) values indicate deeper(shallower) thermocline depths relative to the long-term mean seasonal cycle. Solid (dashed) contours indicate positive (negative) windstress anomalies; the contour interval is 0.05 dyn cm⫺2. (b), (c) Same as in (a), but for (b) zonally averaged thermocline depth anomaliesand (c) thermocline depth anomaly deviations from the zonal mean [the wind stress anomalies remain as in (a)]. All plots are basedon monthly anomalies, bandpass filtered using a Fourier filter with half-power at 15 months and 5.5 yr to isolate the ENSO frequency
band.

1995; Kirtman 1997) it is useful to decompose the thermocline depth anomalies as h ⫽ h ⫹ h⬘, where h is thezonal average of the thermocline depth anomaly, and h⬘is the deviation from the zonal average. The evolutionof h is shown in Fig. 12b, and that of h⬘ in Fig. 12c. The
zonal anomalies describe changes in the zonal thermocline tilt, and are in phase with the anomalous windstresses along the equator west of approximately
130°W, with positive wind stress anomalies associatedwith a flatter thermocline, and vice versa. The nature of
the h⬘ signals east of ⬃120°W and their rapid decay
away from the eastern boundary require further investigation. The times of maximum wind stress anomalies
generally coincide with the transition phases of h, sothat the two fields are approximately in quadrature.
Thus, the zonally averaged thermocline depth anomaly
is the quantity that retains the ocean memory, and thedisequilibrium between h and ␶⬘, the anomalous zonal
wind stress, is responsible for the system evolution and
ENSO phase transition.
In the original formulation of the delayed oscillator
framework, the reflection of equatorial Rossby waves
at the western boundary is the process responsible forthe phase reversal. However, equatorial Rossby waves
are too fast to explain the observed ENSO time scale.
Extensions of the original delayed oscillator theory(Kirtman 1997 among others) include the effect of
Rossby waves forced by extraequatorial wind anomalies. Since the Rossby wave phase speed decreases withincreasing latitude, the westward propagation of offequatorial Rossby waves, and their reflection at thewestern boundary, will introduce a longer delay andincrease the dominant time scale of the cycle. [Other
factors such as the sensitivity of the wind stress to SST

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FIG. 13. Evolution of thermocline depth anomalies during model years 430–445 from CCSM3 at T85 resolution along a path from thenorthern Tropics to the equator. The anomalies averaged (a) over 8°–12°N, plotted from east to west; (b) along the western boundary(⬃130°W), plotted from north to south; and (c) along the equator, plotted from west to east. Positive (negative) values indicate deeper(shallower) thermocline depths relative to the long-term mean seasonal cycle. All plots are based on monthly anomalies, bandpass
filtered using a Fourier filter with half-power at 15 months and 5.5 yr.

anomalies also strongly influence the time scale of the
ENSO cycle (Cane et al. 1990; Neelin and Jin 1993).]
Evidence of off-equatorial westward-propagating
Rossby waves is found in CCSM3 (Fig. 13a). In the
8°–12°N latitude band thermocline depth anomalies
originate around 170°W and propagate westward, as
indicated by the slope of the phase lines. After reaching
the western boundary the waves continue equatorward
along the boundary as coastal Kelvin waves (Fig. 13b)and then eastward along the equator as equatorial
Kelvin waves (Fig. 13c). Comparison of Fig. 13 and Fig.
12a indicates that some of the equatorial thermocline
depth anomalies during model years 430–445 are preceded by thermocline depth anomalies originatingaround 8°–12°N, 170°W a few years earlier, in agreement with the delayed oscillator theory. Similar results
were shown for a modified version of the Climate Sys-

tem Model version 1 (CSM1) by Otto-Bliesner andBrady (2001).
A related conceptual framework to describe the evolution of the zonally averaged equatorial thermocline is
the recharge oscillator paradigm (Schneider et al. 1994;
Jin 1997a,b). In this theory, which is based on the same
physics as the delayed oscillator, the deepening andshoaling of the equatorial thermocline are described interms of warm-water discharge (recharge) to (from)higher latitudes due to anomalous meridional transport. Instead of focusing on wave propagation as in the
delayed oscillator paradigm, the recharge oscillator emphasizes the integrated effect of the waves upon themeridional mass transport. The ENSO cycle within therecharge oscillator paradigm can be schematically summarized as follows: during warm events, the equatorial
easterlies are weakened, and the equatorial ther-

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FIG. 14. EOFs (a) 1 and (b) 2 of bandpass-filtered monthly thermocline depth anomalies from CCSM3 based
upon model years 400–449, and (c) their associated PC time series. EOFs 1 and 2 account for 32% and 14% of thevariance, respectively. The contour interval is 5 m; solid (dashed) contours indicate positive (negative) thermocline
depth anomalies. The zero contour is thickened.

mocline is flatter than average. The reduced zonal gradient of thermocline depth is associated with an anomalous poleward Sverdrup transport, which determines a
gradual shoaling of the equatorial thermocline and progressively reduces the original SST anomaly in the eastern equatorial Pacific and the associated wind stressanomaly. At this point the thermocline is shallower
than average, and equatorial upwelling of colder water
will produce a negative SST anomaly in the easternequatorial Pacific, thus reversing the phase of the oscillation.
To examine the structure of thermocline variations,an EOF analysis is performed on the bandpass-filteredmonthly anomalies of thermocline depth (similar results are obtained without filtering; not shown). The
leading mode (Fig. 14a), which explains 32% of thevariance, is characterized by an east–west dipole pattern, thus capturing variations in the zonal tilt of thethermocline. The second mode (Fig. 14b), accountingfor 14% of the variance, has the same sign across thebasin with maximum amplitudes in the central and eastern equatorial Pacific, and describes the deepening(shoaling) of the thermocline due to the mass recharge(discharge). The principal components (PCs) of the twomodes are highly correlated (Fig. 14c) with a maximum
correlation coefficient of 0.8 when EOF1 leads EOF2
by 6 months. Since the dominant time scale is ⬃2 yr, thetwo EOFs are approximately in quadrature. Note thesimilarity between EOF1 and the seasonal thermocline
depth anomaly composite in DJF⫹1 (Fig. 5b), and between EOF2 and the composite in JJA⫹1, 6 months
later. PC1 is almost identical to the Niño-3.4 SST index:
their correlation coefficient is 0.97 with Niño-3.4 leading PC1 by 2 months (see Fig. 14c). This result is also
consistent with the seasonal anomaly composites shownin Fig. 5b.
How realistic are the dominant modes of thermocline
variability in CCSM3? As a term of comparison we use
thermocline depths from the ocean model forced withobserved surface fields during 1958–2000 (the POP
simulation). The leading EOFs of the bandpass-filteredthermocline depth anomalies (Fig. 15) have the samegeneral character as those from the coupled simulation(a Fourier filter with half-power at 18 months and 7.5 yr

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FIG. 15. Same as in Fig. 14, but from the ocean-only simulation forced by observed time-varying surface fields
during 1958–2000. To account for the longer ENSO time scales in the ocean-only integration, the bandpass filteringof the monthly thermocline depth anomalies is performed using a Fourier filter with half-power at 1.5 and 7.5 yr.

was used to isolate the ENSO frequency band in the
POP simulation due to the longer ENSO period in nature: recall Fig. 3). EOF1 (Fig. 15a) describes variationsin the east–west thermocline tilt, while EOF2 (Fig. 15b)
is characterized by anomalies of the same sign acrossthe equatorial Pacific and opposite sign north of 7°–
10°N. The PCs of the two leading EOF modes areshown in Fig. 15c. The maximum correlation between
PC1 and PC2 is 0.8 with PC1 leading PC2 by 9 months.
The maximum correlation between PC1 and the Niño3.4 index is 0.95 with Niño-3.4 leading PC1 by 2 months.
Both correlations and phase relationships are similar to
those found in CCSM3.
The most striking difference between the EOFs fromthe coupled model versus those from the ocean-only
simulation is the meridional scale of the variability (alsoevident in the seasonal ENSO anomaly composites
shown in Fig. 5). In particular, EOF2 from CCSM3 isbounded by anomalies of opposite sign north of ⬃5° N
and south of ⬃10°S. whereas in the ocean-only simulation, EOF2 changes sign farther poleward: ⬃7°–10°N
and ⬃15°–20°S. The ocean-only EOF patterns compare
very well in their large-scale characteristics with thosecomputed by Meinen and McPhaden (2000) from (un-filtered) subsurface observations spanning the period
1980–2000.
Why is the meridional scale of the anomalies muchnarrower in the coupled simulation? The recharge oscillator theory relates the changes of the meridional
Sverdrup transport to the thermocline tilt induced by
the anomalous zonal wind stress. Thus, the meridional
scale of the anomalous zonal wind stress may set themeridional scale of the warm water volume involved in
the recharge/discharge process. In Fig. 16 we compare
the zonal wind stress from the coupled simulation regressed upon the normalized Niño-3.4 index with theanalogous regression field from the ocean hindcast. Alltime series are bandpass filtered as described above. In
the latter case, the zonal wind stress is derived from the
NCEP–NCAR reanalyses. Regressions are computed
at lag 0, since the maximum zonal wind stress anomalies
are quasi-simultaneous with SST variations in the eastern equatorial Pacific. In the coupled case, the positive
wind stress anomalies are tightly confined within ⬃5° of
the equator, while in the POP simulation they extend to
approximately 7°N and 10°–20°S. Thus, the zonal windstress anomalies in CCSM3 exhibit a meridional scalethat is considerably narrower than observed.

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To address whether the erroneous wind stress structure in CCSM3 is due to deficiencies of the atmosphericmodel, to coupling between the ocean and atmosphere,or both, we show the regression of zonal wind stressanomalies from the first CAM3 (T85) AMIP simulation
upon the observed Niño-3.4 index (Fig. 16c). The positive zonal wind stress regressions from the AMIP run
have a slightly broader meridional scale with respect to
CCSM3 due to their expansion south of the equator but
are still considerably (⬃60%) narrower than observed.
Thus, both inaccuracies of the atmospheric model andcoupled feedbacks may be responsible for the unrealistically narrow meridional scale of the zonal windstress anomalies.

d. The meridional scale of the surface atmospheric
response and implications for SST

FIG. 16. Regression coefficients of monthly zonal wind stressanomalies upon the normalized monthly Niño-3.4 SST anomalyindex, based upon bandpass-filtered data as described in the text,for (a) CCSM3 T85 resolution during model years 350–600, (b)observations (NCEP–NCAR reanalyses during 1958–2000), and(c) the first AMIP integration of CAM3 at T85 resolution forced
with observed SSTs during 1950–2000. The contour interval is 0.02
dyn cm⫺2 per standard deviation of the Niño-3.4 index, and solid(dashed) contours indicate positive (negative) wind stress anomalies.

Several studies performed with intermediate coupledmodels have shown that the period of ENSO is sensitive to the meridional scale of the anomalous zonal
wind stress, with a shorter period associated with a narrower meridional scale (Kirtman 1997; An and Wang
2000; Wittenberg 2002; Guilyardi et al. 2003). According to these studies, a broader zonal wind stressanomaly is characterized by a wind stress curl anomaly
farther from the equator, which in turn generates offequatorial Rossby waves with slower velocities and thus
longer transit times to the western boundary than anarrower wind stress anomaly. The thermocline signals
associated with these extraequatorial Rossby waves
tend to reduce the negative feedback of the equatorial
Rossby waves, thus promoting ENSO events of longer
duration. Alternatively within the framework of the recharge oscillator paradigm, the time scale of recharge/
discharge can be expected to increase with increasing
warm water volume, which in turn is related to abroader zonal wind stress anomaly. Further mechanistic experiments are needed to test the quantitative relevancy of these ideas for CCSM3.

To gain insight into why the meridional scales of the
ENSO zonal wind stress, SST, and thermocline depth
anomalies are narrower in CCSM3 compared to observations, we examine in more detail the nature of the
SLP and surface heat flux responses. Warm-minus-cold
ENSO anomaly composites of annual mean SLP, nearsurface wind, and SST are shown in Fig. 17 for two
observational datasets (NCEP–NCAR reanalyses and
ICOADS), CCSM3 at T85 resolution, and the ensemble mean of the CAM3 AMIP simulations. The annual composites were formed by averaging the four seasonal composites. The reason for comparing NCEP–
NCAR with ICOADS is that there are known
deficiencies in the NCEP–NCAR reanalysis surface
wind and SLP fields over the eastern tropical Pacificbecause of the complex structure of the atmosphericboundary layer there (Lysne and Deser 2002; Hashizume et al. 2002). Note that ICOADS provides direct
measurements of surface winds and SLP from conventional surface-based platforms without any reliance on
a data assimilation system.
The CCSM3 fields (Fig. 17, third panel from top) areremarkably symmetric about the equator, with equatorially confined SST and surface wind responses, andmaximum negative SLP anomalies, centered directly
above the positive SST anomalies. Observational estimates, in contrast, show a stronger degree of equatorial
asymmetry, with the largest SLP anomalies (both positive and negative) located south of the equator. [Thereis some evidence for a relative SLP minimum over theequator in the eastern Pacific in ICOADS, a feature
supported by the independently measured southeasterly wind anomalies directly to the south; however, it is
weaker than the minimum centered at 25°S, 130°W.
The equatorial minimum in observations is hypothesized to result from the direct hydrostatic response of

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FIG. 17. Warm-minus-cold ENSO anomaly composites of annual mean (left) SLP and surface wind and (right)
SST from two different observational datasets (NCEP–NCAR reanalyses during 1950–2000 and ICOADS during
1950–1997), CCSM3 at T85 resolution, and the ensemble mean of the five CAM3 AMIP integrations at T85
resolution. Details of the compositing procedure are given in the text. The scales for all panels are identical (seecolor bars and reference wind vector at bottom of plot).

the atmospheric boundary layer to underlying SST
anomalies (Deser and Wallace 1990; Harrison and Larkin 1996).] The observed SLP anomaly distribution implies that the surface wind response will not be as narrowly confined about the equator as it is in CCSM3.
Indeed, southerly wind anomalies extend to at least
20°S over the western half of the basin, a feature that islacking in the model, and westerly wind anomalies extend to ⬃10°–20°S as opposed to ⬃6°S in the model(see also Fig. 16). The SLP and wind responses in the
CAM3 AMIP ensemble are more realistic than those in
CCSM3, particularly in terms of the increase in amplitude south of the equator. However, the SLP minimumat the equator is still too pronounced relative to that in
the Southern Hemisphere, an aspect that may contribute to the narrower meridional scale of the equatorial
zonal wind stress anomalies. [It may be noted that theequatorial zonal wind stress response to ENSO in Com-munity Climate Model Version 3 (CCM3), the predecessor to CAM3, was also too meridionally confined(Nigam and Chung 2000).]
Why does CCSM3 yield ENSO SST anomalies thatare too equatorially confined compared to nature (seeFig. 17)? One reason may be that deficiencies in thesurface wind response lead to errors in the surface energy flux that then affect the SST response. Of course,
errors in the ocean model may also contribute to theequatorially confined SST signal, which may in turnforce an overly narrow surface wind response that thenreinforces the narrow SST anomaly bias via surface flux
errors. To explore the notion that surface fluxes may
play a role in the pattern of SST anomalies during
ENSO, we note that the surface energy flux Q is related
to the time tendency of SST according to dSST/dt ⫽Q/␳Cph, where ␳ and Cp are the density and heat capacity of seawater, respectively, and h is the mixed

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FIG. 18. Regression coefficients of SST anomaly tendencies (contours) and surface heat flux anomalies (net andturbulent component; color shading) upon the normalized Niño-3.4 SST anomaly tendency index from (top)observations, (middle) CCSM3 at T85 resolution, and (bottom) the ensemble mean of the five CAM3 AMIP
integrations at T85 resolution. SST tendencies are defined as the difference between SST anomalies in SON minus
the previous MAM. Surface fluxes are averages from May through September. The contour interval is 0.25 K per
standard deviation of the Niño-3.4 SST tendency index, and the color bar is in units of W m⫺2 per standard
deviation of the Niño-3.4 SST tendency index. Surface heat flux regressions are defined as positive downward (e.g.,positive values would heat the ocean).

layer depth. To examine the contribution of surface
energy flux anomalies to the development of SST
anomalies during ENSO events, we examined thechange in SST from MAM to SON (denoted SST tendency) in relation to the surface energy flux averaged
from May to September (similar results are obtained
for averages from March to November and from April
to October; not shown). SST tendency and surface flux
anomalies were regressed upon the Niño-3.4 SST tendency index for observations, CCSM3 (T85), and the
CAM3 AMIP simulations; the results are shown in Fig.
18. The period 1984–2000 was used for the observations
because this is the period of record available for the
ISCCP radiative fluxes (the turbulent fluxes are fromthe NCEP–NCAR reanalyses); for compatibility, thesame time period was used for the AMIP simulations.

In observations (Fig. 18, top left), upward (negative)
surface heat flux anomalies are found over positive SST
anomaly tendencies along the equator east of 180° andover the negative SST tendencies in the western Pacific.
This indicates that surface heat flux anomalies contribute to forcing the negative SST tendencies, whereas
they respond to (and damp) the positive SST tendencies along the equator where oceanic processes such as
upwelling and entrainment are expected to play a dominant role. Downward surface heat flux anomalies arefound over positive SST tendencies south of the equator (and also a small patch west of Central America),
indicating that surface heat fluxes aid in spreading thenarrow equatorial SST anomaly tendencies poleward.
It is evident that the net surface heat flux regression
pattern is dominated by the turbulent flux component1 JUNE 2006

DESER ET AL.

(Fig. 18, top right). The downward turbulent heat flux
anomalies that contribute to a southward broadening of
the positive SST tendencies in the eastern Pacific arelikely due to a weakening of the southeast trade winds
associated with the negative SLP anomaly center overthe southeast Pacific (Fig. 17).
Like observations, surface heat flux anomalies in
CCSM3 generally contribute to forcing the SST
anomaly tendency pattern except along the upwellingzone in the eastern Pacific where they act as a damping(Fig. 18, middle). However, because the fluxes are soweak poleward of ⬃10° latitude in comparison to observations, they result in very small SST anomaly tendencies outside the equatorial band. The relative weakness of the flux anomalies outside the equatorial zone islikely a result of the overly narrow meridional scale ofthe low-level wind and precipitation responses in themodel compared to observations. Like observations,the turbulent flux component dominates the net surface
heat flux regression pattern.
The net surface heat flux regressions in the CAM3
AMIP integrations are largely similar to observations,consistent with the generally realistic simulation of theatmospheric circulation response to ENSO SST anomalies (note that both the meridional and zonal components of the wind stress anomalies contribute to theturbulent heat flux anomalies that dominate the net
surface heat flux). However, over the far western Pacific along ⬃30°S and ⬃5°N, the fluxes would damp
(rather than force as observed) the existing negative
SST tendencies.

e. ENSO summary
The amplitude and zonal extent of equatorial Pacific
SST variations associated with ENSO are well simulated in CCSM3 at both T42 and T85 resolutions andrepresent an improvement relative to previous generations of the coupled model. In particular, the standard
deviation of the monthly Niño-3.4 SST anomaly index
is 0.80°C (0.85°C) in CCSM3 T85 (T42), similar to theobserved record during the past century (0.75°C). However, the period of ENSO in CCSM3 is shorter thanobserved (2–2.5 yr compared to 2.5–8 yr), regardless ofmodel resolution (and similar to that in CCSM2). The
seasonal evolution of SST anomalies during a composite ENSO cycle exhibits many realistic features, including a tendency for maximum amplitudes in the equatorial Pacific cold tongue near the end of the calendar
year, phase transitions during boreal spring, and a delayed SST response over the Indian Ocean. The composite seasonal evolution of thermocline depth anomalies is also in general agreement with observations, withmaximum zonal gradients across the equatorial Pacific2473

near the time of peak SST anomalies, and a zonallysymmetric distribution indicative of a net discharge (recharge) of equatorial Pacific upper-ocean heat content
during the transition from a warm event to a cold one(cold event to a warm one). These characteristics are
qualitatively consistent with the delayed oscillator andrelated recharge oscillator paradigms for ENSO, as are
the phase relationships with zonal wind stress anomalies. The main deficiency of CCSM3’s ENSO simulationis the excessive equatorial confinement of the atmospheric (precipitation, SLP, and wind stress) and oceanic (SST and thermocline depth) responses relative toobservations. This shortcoming is partially overcome inthe uncoupled atmosphere and ocean model simulations forced by observed surface boundary conditions,indicating that coupling between the two model components is a contributing cause; however, the atmospheric model itself also appears to play a role. Given
the importance of the meridional scale of the surface
wind response to the period of ENSO within the simplified framework of the delayed oscillator and recharge oscillator paradigms, this may explain in part theexcessively high frequency of ENSO in CCSM3. Further mechanistic experiments are needed to test thisidea.
The extratropical SLP response to ENSO in CCSM3
at T85 resolution is generally realistic both in terms ofamplitude and spatial pattern, particularly over the
North Pacific/North American sector during boreal
winter and the South Pacific during boreal summer; the
SLP response in CCSM3 at T42 resolution is substantially weaker than observed. For example, the maximum negative SLP anomaly over the Aleutian low
pressure center in DJF is –9 hPa (–4 hPa) in CCSM3
T85 (T42) compared to –11 hPa in observations. In
agreement with observations, the extratropical atmospheric circulation response to ENSO exhibits anequivalent barotropic structure; however, the amplitude of the response in the midtroposphere is weaker
than observed by approximately a factor of 2 (4) at T85(T42) resolution. Further research is needed to diagnose the mechanisms that lead to the different amplitudes of the atmospheric circulation responses at thetwo model resolutions.

4. Tropical Atlantic variability
In this section we analyze the performance of
CCSM3 in the tropical Atlantic region. Although the
TAV does not have the same global impact as variability in the tropical Pacific, it does play an important rolein determining climate predictability in the adjoining
continental regions, such as the northern Nordeste(NNE) region of Brazil and the Sahel region of Africa

2474

JOURNAL OF CLIMATE[for a recent review of TAV, see Xie and Carton(2004)]. Coupled GCMs have typically been deficient intheir simulation of TAV. Davey et al. (2002), who carried out an intercomparison of the tropical simulationof several coupled GCMs, found that almost all coupled
GCMs without flux adjustment have great difficulty insimulating important features of the mean climate of
the tropical Atlantic region, such as the east–west gradient of SST at the equator. Simulation of TAV therefore remains an important challenge for coupled climate modeling.
Unlike the tropical Pacific, TAV is not characterized
by a single dominant mode of variability like ENSO.
This means that we need to examine several different
facets of the CCSM3 simulation of the tropical Atlantic
region. We will also compare the TAV simulation of
CCSM3 to the previous versions of the NCAR coupledmodel, CCSM2, and CSM1. [For a more detailed assessment of the TAV simulation in CCM3, the atmospheric component of CSM1, see Chang et al. (2000).]
The three coupled models, being part of the same family, share some common features. There have also beensignificant improvements in the physical parameterizations and horizontal resolution in the progression from
CSM1 to CCSM3. We would like to examine how thesemodel changes affect the simulations of TAV. We will
also consider the role of air–sea coupling, by comparing
the coupled CCSM3 simulations to the uncoupled
AMIP simulations with CAM3. Since the AMIP simulations use observed SSTs as the surface boundary conditions, any errors in those simulations can usually be
attributed to the atmospheric model (with the caveatthat AMIP simulations can overestimate surface flux
amplitudes). Such attribution is more difficult in the
coupled system, because either component could be responsible for the errors. We shall assess the impact ofincreased horizontal resolution in the atmosphericmodel by comparing the T42 and T85 AMIP ensemble
simulations.
We consider the following major components of
TAV: (i) the cross-equatorial SST gradient mode, often
simply referred to as the “gradient mode” (e.g., Hastenrath and Heller 1977; Moura and Shukla 1981; Xie
and Carton 2004); (ii) the equatorial mode, which issometimes referred to as the “Atlantic Niño” (Zebiak
1993); and (iii) the remote influence of ENSO (e.g.,
Covey and Hastenrath 1978; Enfield and Mayer 1997;
Saravanan and Chang 2000).
The gradient mode is believed to be an intrinsic
mode of variability of the tropical Atlantic region and ischaracterized by SST anomalies occurring either in thenorthern or in the southern tropical Atlantic, leading tosignificant meridional SST gradients near the equator

VOLUME 19

that affect the location of the Atlantic ITCZ (cf. Xie
and Carton 2004). This mode has also been referred toas the “Atlantic dipole mode” in the past, but the lackof strong anticorrelation between the northern andsouthern SST variability suggests that the gradientmode is a more appropriate name. The gradient modeis active primarily during the boreal spring season,
when the ITCZ reaches its southernmost location during the annual cycle (Sutton et al. 2000). The driving
mechanism for this mode is believed to be local air–sea
interaction that is dominated by latent heat flux exchange, rather than wind stress driving (Carton et al.
1996).
The equatorial mode is characterized by SST variability in the eastern equatorial Atlantic and associatedvariability in the surface wind stress, making it analogous to the ENSO phenomenon in the tropical Pacific(Zebiak 1993). The mode is believed to be driven by thesame Bjerknes feedback mechanism as ENSO
(Bjerknes 1969), with the surface wind stress and theequatorial upwelling playing an important role. Unlike
ENSO, this mode is seen primarily during the boreal
summer season (e.g., Sutton et al. 2000).
The remote influence of ENSO is initiated by surface
flux anomalies over the tropical Atlantic during the boreal winter, when the ENSO amplitude in the tropical
Pacific is at a maximum (e.g., Enfield and Mayer 1997).
However, the SST response associated with the remoteinfluence manifests itself during the boreal spring season and is strongest in the Northern Hemisphere. The
mechanism for this remote influence is believed to be a
combination of the anomalous Walker circulation andother atmospheric teleconnections (cf. Saravanan andChang 2000).

a. SSTWe first consider the mean state of the tropical Atlantic as simulated by CCSM. As the modes of TAV arestrongly seasonal, we will consider the climatology of
selected seasons, rather than the annual-mean climatology. We focus on the MAM and JJA seasons, when thedominant modes of TAV are most active. Figure 19
shows the simulated JJA mean SST for the three
coupled models as compared to observations (HadISST
during 1950–2000).
Note that the cold tongue of SST in the eastern equatorial Atlantic is not well simulated by the coupledmodels. Whereas observations show an equatorial SSTof about 25°C near the eastern boundary, the models
show SST in excess of 29°C. This model deficiency ismost likely associated with errors in the JJA precipitation as simulated by the uncoupled AMIP integrations,where the observed precipitation maximum along the1 JUNE 2006

DESER ET AL.

2475

FIG. 19. Climatological mean SST for the JJA season: (a) observations, (b) CSM1, (c) CCSM2, and (d)CCSM3. Contour interval is 1°C.

coast of the Gulf of Guinea occurs too far inland in thesimulations (not shown). Such an error would distort
coupled air–sea feedbacks associated with the summer
monsoon that is believed to be responsible for maintaining the Atlantic cold tongue (Mitchell and Wallace1992).
The “warm pool” of SST at the western equatorial
Atlantic is also not well simulated by the coupled models. In the western region, CCSM3 does perform significantly better with a bias of about –1°C as compared
to a bias of ⫺3°C for CCSM1. However, CCSM3 showsa larger cold bias between 20° and 30°N, off the coast of
Africa. Overall, it is clear that even the latest generation of the coupled model fails to simulate the sign ofthe east–west temperature gradient correctly, as was
the case with the previous generation of models (Daveyet al. 2002). We may expect this error to have a significant impact on the simulation of the equatorial mode of
TAV.
We also consider the mean SST for the MAM season,
when the gradient mode is most active. Figure 20 compares the observed SST to the simulated SST for
CCSM3. In this season, the model performs somewhat

better as compared to the JJA season, although there isa significant cold bias in the northern tropical Atlantic.
The southern tropical Atlantic simulation of SST compares quite well with observations, except near thecoast of Africa. CCSM3 does perform better during the
MAM season as compared to CCSM1, which exhibits
large cold biases in the northern and equatorial regions(not shown).
Next we consider variability of SST during the MAM
and JJA seasons. Figure 21 compares the observed
standard deviation of seasonal means to the simulated
standard deviations. For the JJA season (Figs. 21a,b),
the dominant feature seen in observations is the maximum in SST variability in the central and eastern partof the equatorial Atlantic, coincident with the cold
tongue region (Fig. 19a). This corresponds to the equatorial mode of TAV and is essentially absent in the
CCSM3 simulation (and also in earlier versions of thecoupled model). It seems clear that the simulation of
the SST cold tongue needs to be improved to provide a
better simulation of the equatorial mode. CCSM3 does
simulate the weaker maximum in SST variability seenin the northeastern part of the tropical Atlantic.

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JOURNAL OF CLIMATEVOLUME 19

FIG. 20. Climatological mean SST for the MAM season: (a) observations and (b) CCSM3. Contour interval
is 1°C.

For the MAM season (Figs. 21c,d), we see the two
maxima in variability near the eastern boundary in thetwo hemispheres, near 10°N and 20°S, with maximum
amplitudes of about 1°C. The corresponding maxima in
the CCSM3 simulation are somewhat weaker (about

0.7°C) and shifted poleward. Previous versions of thecoupled model show very similar features (not shown).
This mode of variability corresponds to the gradient ofTAV, and we see that the coupled model simulations
tend to underestimate it. We also computed EOFs of

FIG. 21. Climatological standard deviation of seasonal-mean SST: (a) observations for JJA season, (b) CCSM3
for JJA season, (c) observations for MAM season, and (d) CCSM3 for MAM season. Contour interval is 0.1°C.

1 JUNE 2006

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2477

FIG. 22. Climatological mean precipitation for the MAM season: (a) observations, (b) CAM3/T42 AMIP ensemble mean, (c) CAM3/T85 AMIP ensemble mean, and (d) CCSM3 T85 coupled integration. Contour interval
is 2 mm day⫺1.

SST during the MAM season and found that the dominant EOF in all cases displayed the dipolar meridional
structure characteristic of the gradient mode (notshown). However, the simulated EOFs had smaller amplitudes, and also failed to capture the northeast–
southwest tilt of the boundary between the northernand southern lobes of the dipole seen in the observations.

b. Precipitation
We now consider the mean precipitation simulationof CCSM3 and its atmospheric component, CAM3,
during the MAM season. During this season, the interaction between the gradient mode and the location of
the ITCZ contributes significantly to TAV. Here we
consider the impact of atmospheric model resolution,by comparing the AMIP simulation using the T42 andT85 versions of CAM3. We also consider the role ofcoupling, by comparing the uncoupled AMIP simulations to the coupled CCSM3 simulation. Figure 22
shows precipitation during the MAM season for observations (1979–2001 period; Xie and Arkin 1997), andthree model simulations: CAM3/T42, CAM3/T85, andCCSM3. The main feature over the tropical Atlantic isthe ITCZ, which is located near the equator at the western boundary, and north of the equator at the easternboundary, leading to a northeast–southwest tilt. The
uncoupled AMIP simulations overestimate the tilt, withthe eastern boundary precipitation maximum occurring
too far to the south. There is also the tendency for the
ITCZ to split into two zonally oriented parts, at theeastern and western boundaries. Increasing the horizontal resolution from T42 to T85 seems to have little
effect on these errors. The coupled CCSM3 simulation
also tends to simulate the ITCZ too far to the south,
which is consistent with the cold equatorial SST bias
near the western boundary seen in Fig. 20b. The
coupled model also significantly overestimates theSouthern Hemisphere precipitation near the easternboundary, consistent with the warm SST bias in theregion. Overall, we see that the simulated precipitation
reproduces the qualitative features seen in observations, such as the tilted ITCZ, but there are significant
quantitative errors in the spatial patterns of the precipitation. The precipitation amplitudes in all the simula-

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FIG. 23. Area-averaged precipitation over northern Nordeste
Brazil (11°–3°S, 46°–38°W) during the MAM season, from 1950 to1997. The observed values (solid) were obtained from the precipitation dataset of Hulme et al. (1998). The simulated values (dotted) are ensemble averages from the T85 AMIP simulation using
CAM3, but renormalized to have the same mean and standard
deviation as the observations. (The simulated ensemble-mean
precipitation has a mean value about 36% larger than observed,
and the standard deviation is about 50% of the observed value.)

VOLUME 19

tion of the mean precipitation. Figure 23 shows theobserved MAM precipitation in the northern Nordeste(NNE) region (11°–3°S, 46°–38°W) of Brazil for the
2.5° ⫻ 3.75° global precipitation dataset of Hulme et al.
(1998). Also shown in the figure is the ensemble-mean
simulated precipitation from the T85 AMIP integration
using CAM3. (The simulated precipitation time series
has been renormalized to have the same mean and standard deviation as the observed precipitation time series.) The correlation between the simulated and observed time series is approximately 0.5, indicating that
CAM3 is capable of predicting precipitation over NNE
Brazil, provided there is prior knowledge of SST variability. It appears that this predictive skill is not very
sensitive to model resolution, with the T42 andT85
AMIP integrations showing similar correlation values.
However, other models, including previous versions ofCAM, show correlation values of 0.6 or greater (e.g.,
Chang et al. 2003; Giannini et al. 2004). At this time, we
do not understand the reasons for this degradation ofpredictive skill in CAM3.

c. Remote influence of ENSO
tions are roughly consistent with the observed amplitudes.
We know that there is significant predictability of
precipitation associated with TAV, especially in the
Nordeste region of Brazil during the MAM season(e.g., Chang et al. 2003; Saravanan and Chang 2004).
We therefore examine whether the AMIP integrations
using CAM3 are skillful in predicting the observed precipitation variability, despite the errors in the simula-

Our final diagnostic of TAV will be to consider the
remote influence of ENSO on the tropical Atlantic.
This remote influence has been the subject of numerous
observational and modeling studies (e.g., Enfield andMayer 1997; Saravanan and Chang 2000), and is sometimes referred to as the tropical atmospheric bridge(e.g., Klein et al. 1999). Figure 24 shows the correlation
between MAM SST in the tropical Atlantic and Niño-3
index for the preceding season (DJF). The lagged cor-

FIG. 24. Correlation between MAM average SST and the Niño-3 index for the preceding DJF season: (a)observations and (b) CCSM3 coupled simulation. Contour interval is 0.2. The shaded regions indicate statisticallysignificant correlations (or regressions) at the 95% level.

1 JUNE 2006

2479

DESER ET AL.

relation is appropriate because of the delay in the SST
response to the flux anomalies associated with the
ENSO signal. The observations show strong (⬃0.6–0.7)
positive correlations over much of the northern tropicalAtlantic, but essentially zero correlation right at theequator. The southern tropical Atlantic is characterizedby modest (⬃0.4) negative correlations in the central
and eastern parts of the basin. In particular, the band of
positive correlations just north of the equator approximately coincides with the mean position of the ITCZ
(Fig. 22a).
In CCSM3 simulations, the correlation structure isquite different, with a band of positive correlations
along the South American coast in the northern tropical Atlantic, and extending across the basin just southof the equator. Once again, the strongest positive correlations are coincident with the location of the simulated ITCZ (Fig. 22a). The simulated ITCZ is locatedtoo far south during the MAM season, and this appears
to weaken the ENSO influence just north of the equatorial Atlantic in CCSM3. This suggests that the errors
in simulated correlation structure are due to errors inthe simulation of the mean precipitation variability. If
the simulation of the mean state were to be improved,
one might expect the remote influence of ENSO to beproperly represented by CCSM3.

d. Summary
CCSM3 shows significant errors in the simulation of
TAV. Perhaps the biggest deficiency is the absence ofthe equatorial “cold tongue” during the boreal summer
season. One possible cause of this deficiency could be
the poor simulation of the West African monsoon by
the uncoupled atmospheric model, which would lead toerroneous simulation of the coupled air–sea feedbacks
needed to maintain the cold tongue. Further mechanistic experiments would be needed to confirm this idea.
This mean climate error during the boreal summer also
has a negative impact on the simulation of the equatorial mode of variability (the “Atlantic Niño”), which isessentially absent in CCSM3.
The simulated mean climate during the boreal spring
season shows smaller errors than the summer simulation, although there is a significant cold bias in thenorthern tropical Atlantic. Correspondingly, the gradient mode of TAV, although underestimated by
CCSM3, is somewhat better simulated than the equatorial mode. Given the errors in the mean climate simulation during boreal spring, one would not expect the
remote influence of ENSO on tropical Atlantic SST to
be simulated very well, and that turns out to be thecase.
The simulated mean precipitation has roughly thesame amplitude as the observations but exhibits significant errors in the spatial structure tied to errors in the
SST simulation. The AMIP integrations exhibit significant potential predictability, with simulated precipitation in the northern Nordeste Brazil region correlating
at a value of 0.5 with the observed precipitation. Improved horizontal resolution does not seem to significantly affect the simulated precipitation patterns, ortheir potential predictability.
Acknowledgments. The model integrations were performed by the National Center for Atmospheric Research (NCAR) and CRIEPI with support and facilities
provided by NSF and ESC/JAMSTEC. We thank Dr.
Michael Alexander for helpful comments on an earlier
draft, and the two anonymous reviewers for their constructive comments and suggestions.
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