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North Pacific Decadal Variability in the Community Climate System Model Version 2
YOUNG-OH KWON AND CLARA DESER
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 4 May 2006, in final form 14 September 2006)
ABSTRACT
North Pacific decadal oceanic and atmospheric variability is examined from a 650-yr control integrationof the Community Climate System Model version 2. The dominant pattern of winter sea surface temperature (SST) variability is similar to the observed “Pacific decadal oscillation,” with maximum amplitude
along the Kuroshio Extension. SST anomalies in this region exhibit significant spectral peaks at approximately 16 and 40 yr. Lateral geostrophic heat flux divergence, caused by a meridional shift of theKuroshio Extension forced by basin-scale wind stress curl anomalies 3–5 yr earlier, is responsible for the
decadal SST variability; local surface heat flux and Ekman heat flux divergence act as a damping andpositive feedback, respectively. A simple linear Rossby wave model is invoked to explicitly demonstrate thelink between the wind stress curl forcing and decadal variability in the Kuroshio Extension. The Rossby
wave model not only successfully reproduces the two decadal spectral peaks, but also illustrates that only
the low-frequency (⬎10-yr period) portion of the approximately white noise wind stress curl forcing isrelevant. This model also demonstrates that the weak and insignificant decadal spectral peaks in the windstress curl forcing are necessary for producing the corresponding strong and significant oceanic peaks in the
Kuroshio Extension. The wind stress curl response to decadal SST anomalies in the Kuroshio Extension issimilar in structure but opposite in sign and somewhat weaker than the wind stress curl forcing pattern.
These results suggest that the simulated North Pacific decadal variability owes its existence to two-way
ocean–atmosphere coupling.

1. Introduction
Significant low-frequency variability of sea surface
temperature (SST) anomalies at decadal and longer
time scales has been observed in the North Pacific (e.g.,Trenberth and Hurrell 1994; Nakamura et al. 1997) andhas been termed the “Pacific decadal oscillation”(PDO) (Mantua et al. 1997). SST anomalies associatedwith the PDO exhibit a basinwide horseshoelike spatial
pattern with one sign in the central and western North
Pacific surrounded by the opposite sign to the north,east, and south (Mantua et al. 1997). The maximum
anomalies are found in the central North Pacific and aweaker secondary maximum is located along the Kuroshio Extension. Several studies have suggested thatthese two centers of action may be distinguished basedon their spectral characteristics and relative dependence on the Tropics. The center of action in the central

Corresponding author address: Young-Oh Kwon, Physical
Oceanography Department, Woods Hole Oceanographic Institution, Clark 320A, MS 21, Woods Hole, MA 02543.
E-mail: yokwon@whoi.edu
DOI: 10.1175/JCLI4103.1© 2007 American Meteorological Society

JCLI4103

North Pacific has relatively more power in the interannual frequency band with a strong connection to tropical ENSO variability, while that along the Kuroshio
Extension has more power at decadal and longer timescales with a weaker connection to the Tropics (Deserand Blackmon 1995; Nakamura et al. 1997; Schneider
and Cornuelle 2005).
Seager et al. (2001) examined in detail the differences in the evolution of SST anomalies at these twocenters of action as a response to changes in atmospheric forcing associated with the 1976/77 PDO transition, using both observations and a numerical ocean
model hindcast. After 1976 the winter Aleutian low
deepened and moved southeast of its previous position,
placing the North Pacific under anomalous cyclonicflow. In the central North Pacific stronger westerlies
cooled the ocean by increased equatorward Ekman
transport. This Ekman response was fast and local, andcooled the SST beginning in 1977. On the other hand,negative SST anomalies emerged near the Kuroshio
Extension in the early 1980s, in association with asouthward displacement of the boundary between the
subpolar and subtropical gyres caused by a southward1 JUNE 2007

KWON AND DESER

shift in the zero wind stress curl line 4 years earlier. The
delay time was attributed to the time scale for the gyreadjustment via first-mode baroclinic Rossby waves. The
SST cooling in the Kuroshio Extension was shown to bedamped by surface heat fluxes, implying an oceanic origin for the SST anomalies in the Kuroshio Extension.
Many other studies also presented consistent results(Miller et al. 1994; Deser et al. 1999; Xie et al. 2000;
Pierce et al. 2001; Schneider et al. 2002; Schneider andCornuelle 2005).
Although these hindcast modeling and observational
studies provided an understanding of the ocean’s role inlow-frequency SST variability in response to atmospheric circulation changes, they were limited with regard to the role of ocean to atmosphere feedback.
Therefore, fully coupled general circulation models orconceptual models were employed to explore the physical mechanisms of PDO-like decadal variability. In aseminal study, Latif and Barnett (1994) analyzed a 70yr integration of a fully coupled general circulation
model, ECHO1; their findings were updated with a 125yr integration of the same model in Latif and Barnett(1996). They suggested that the decadal variability in
the North Pacific can be attributed to a cycle involving
unstable ocean–atmosphere interaction between thesubtropical ocean gyre circulation and the Aleutian low
pressure system. When, for instance, the Aleutian low
is anomalously deep, the subtropical ocean gyre is spunup with a delay of about 5 years due to geostrophic
adjustment via the first baroclinic-mode Rossby wave.
They suggested that the delay time scale is determined
to first order by the basin crossing time of the Rossby
wave, but the role of advection by the mean current is
not negligible. An anomalously strong subtropical gyretransports more warm water into the Kuroshio Extension, leading to positive SST anomalies in the westernand central North Pacific. The atmospheric response to
the positive SST anomalies involves a weakened Aleutian low, which initiates the opposite phase of their
20-yr oscillation. The associated surface heat flux reinforces the initial SST anomalies so that the ocean andatmosphere act as a positive feedback system.
There are a few steps in their proposed mechanismthat may be inconsistent with or cannot be resolved by
the observations and realistic hindcast simulations. In
particular Seager et al. (2001) found that a southward
shift, rather than an in situ strengthening, of the atmospheric circulation and subsequently the ocean gyre circulation is responsible for the SST anomalies in the
Kuroshio Extension associated with the 1976/77 PDO
transition. This is similar to the finding of Marshall etal. (2001) for the North Atlantic. In addition Seager et2417

al. (2001) found that the surface heat flux damps the
SST anomalies in the Kuroshio Extension both in observations and the ocean model hindcast (see also Tanimoto et al. 2003; Kelly 2004), opposite to the results of
Latif and Barnett (1994). Finally, Latif and Barnett
used a stand-alone atmospheric general circulation
model experiment to show that SST anomalies from thecoupled integration can force the atmospheric circulation to shift to the opposite phase, leading to an oscillatory mode. However, the magnitude of their atmospheric response was much larger than that found inother studies and even the sign of the atmospheric response was not consistent with some of the previous
studies (Kushnir et al. 2002).
As a follow-up of Latif and Barnett (1994), Pierce etal. (2001) investigated decadal variability in the North
Pacific from a 137-yr integration of ECHO2 and a 270yr integration of the Climate System Model version 1(CSM1) [the predecessor of the Community Climate
System Model version 2 (CCSM2)]. They concluded
that ocean dynamics within the North Pacific are necessary for the existence of a statistically significant decadal spectral peak in SST anomalies along the Kuroshio Extension. They went on to test the role of “spatial
resonance” (Saravanan and McWilliams 1998) between
stochastic atmospheric forcing and oceanic dynamic response using the ocean component model of ECHO2
forced by time-permuted ECHO2 atmospheric variables. They found that the decadal spectral peak is not
reproduced when the ocean model is forced by a stochastic atmosphere, a result that led them to conclude
that the decadal spectral peak is maintained by a midlatitude atmosphere–ocean coupled mode such as that
proposed by Latif and Barnett (1994). However, unlike
Latif and Barnett but similar to Seager et al. (2001),
they found that meridional shifts of the boundary between the subpolar and subtropical gyres are responsible for the decadal SST anomalies in the Kuroshio
Extension and that the surface heat flux damps the SST
anomalies in the Kuroshio Extension. They also founda more reasonable magnitude for the atmospheric response to the coupled model’s SST anomalies than Latif and Barnett (1994). Their test for the role of spatial
resonance was, however, not entirely conclusive as
briefly discussed in Schneider et al. (2002) because theyprescribed stochastic wind stress curl and surface heat
flux forcing even in the Kuroshio Extension where the
SST anomalies are not forced but actually damped by
the surface heat flux in their coupled model.
Schneider et al. (2002) examined North Pacific decadal variability from a 147-yr integration of ECHO2,
the same integration used by Pierce et al. (2001), but

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arrived at a rather different conclusion regarding thedecadal time-scale selection. They investigated the roleof white noise wind stress curl forcing using a strategy
similar to that of Pierce et al. (2001), but used a linear
Rossby wave model instead of an ocean general circulation model. Consistent with Pierce et al., the oceanic
decadal spectral peak is not reproduced when the
Rossby wave model is forced by white noise wind stresscurl anomalies. Next, Schneider et al. (2002) incorporated an ocean to atmosphere feedback term in the
Rossby wave model to test the existence of a midlatitude atmosphere–ocean coupled oscillation. The modified model was integrated with both positive and negative feedback cases, but neither reproduced the decadal
peak. They therefore concluded that the results of
ECHO2 are inconsistent with a midlatitude atmosphere–ocean coupled oscillation.
Neelin and Weng (1999) examined the role of ocean
to atmosphere feedback in midlatitude decadal variability using a conceptual model. They considered alinearized perturbation system with the ocean represented by quasigeostrophic shallow-water dynamics
and a surface mixed layer, and the atmosphere represented by stochastic wind stress and heat flux forcing
comprising a noise component that depends on SST as
well as an additive component that is independent ofSST. Coupling, which allows a weak and irregular atmospheric response to decadal SST anomalies, is represented by the SST-dependent noise component.
Without coupling, their model produces a weak decadaloceanic peak due to spatial resonance (Jin 1997; Saravanan and McWilliams 1998) when a zonal wavenumber-one pattern is specified for the atmospheric forcing.
With coupling, the decadal peak originally introducedby spatial resonance becomes stronger and more distinctive. Thus, a weak and irregular ocean to atmosphere feedback is sufficient to maintain a coupledmode of decadal variability.
In this study, we examine decadal variability in the
North Pacific from a 650-yr integration of present-day
climate using the CCSM2 developed at the National
Center for Atmospheric Research (NCAR). CCSM2
exhibits decadal SST variability similar to the observed
PDO. The purpose of this study is to take advantage ofthis long CCSM2 integration to examine unresolved issues regarding North Pacific decadal variability, including the robustness of the atmospheric response to SST
anomalies in the Kuroshio Extension and its role inmaintaining decadal variability. We perform a carefulheat budget analysis in the Kuroshio Extension to understand the roles of surface heat flux, Ekman heat flux
divergence, and geostrophic heat flux divergence in thedecadal SST variability. We employ a linear Rossby

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wave model to demonstrate the importance of the atmospheric response for the decadal variability and use
three independent approaches to examine the robustness of the atmospheric response: lag-regression analysis using CCSM2 output, a stand-alone atmosphericgeneral circulation model experiment, and a coupledatmospheric general circulation–ocean mixed layer
model experiment. With this long CCSM2 integration,
we are able to perform a statistically robust analysis of
the mechanisms responsible for the PDO-like decadal
variability. Section 2 describes the model and analysis
methods. Results are presented in section 3, and discussed in section 4. Conclusions are presented in section 5.

2. Model description and analysis methods
The Community Climate System Model version 2
consists of atmosphere, ocean, sea ice, and land component models mutually linked by means of a coupler(Kiehl and Gent 2004). The atmospheric component of
CCSM2 is the Community Atmospheric Model version
2.0 (CAM2.0), which has T42 horizontal resolution and26 vertical levels. The ocean component uses the Parallel Ocean Program (POP) (Smith et al. 1992), which
has a horizontal resolution of 1.125° in the zonal direction and a meridional resolution of 0.27° at the equator
gradually increasing to a maximum of ⬃0.6° at about
40°N. There are 40 levels in the vertical, whose thickness monotonically increases from 10 m near the surface to 250 m in the deep ocean below 1500 m. The landand sea ice components of the CCSM2 are the Community Land Model version 2.0 (CLM2.0) and theCommunity Sea Ice Model version 4 (CSIM4), whichhave the same horizontal resolution as the atmosphericand ocean component models, respectively. The reader
is referred to Kiehl and Gent (2004) for further description of CCSM2.
The control integration is intended to simulate the“present day” climate, with greenhouse gases concentrations set to their 1990 levels. The ocean component
was initialized using January mean climatological temperature and salinity fields from Levitus et al. (1994)
and from Steele et al. (2001) in the Arctic Ocean. The
atmosphere, land, and sea ice components were initialized using realistic January fields obtained from standalone integrations of these three component models.
The CCSM2 control integration was run for 350 years
without any flux adjustments, whereupon three minorchanges were made to the code and the integration was
restarted to run out to year 999, again without any flux
adjustments. Kiehl and Gent (2004) document this control integration in more detail, including the compari-1 JUNE 2007

KWON AND DESER

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FIG. 1. Leading EOF patterns of winter (DJFM) (a) SST anomalies and (b) subsurface
ocean temperature anomalies at 500-m depth in the North Pacific (20°–60°N) from CCSM2.
Amplitudes correspond to a one standard deviation change of the corresponding principal
component time series. Contour interval is 0.2°C, with negative contours dashed. Thin contours denote the climatological winter (DJFM) mean SST and 500-m temperature fields.
Contour interval for the mean fields is 2°C. Gray solid boxes indicate the averaging region for
the SST and 500-m temperature index time series, referred to as the Kuroshio Extension
index.

sons of the mean climatologies with observations andthe impacts of the changes at year 350. This study analyzes results for the last 650 years of the control integration (years 350–999).
Standard methods of analysis, including empirical orthogonal functions (EOFs), lag correlation and regression, and spectral analysis are used. The mean annual
cycle is removed by subtracting the 650-yr long-termmonthly means from each month. EOFs are computedfrom the covariance matrix of input time series, and are
displayed as regressions on the corresponding normalized principal component (PC) time series, so that theamplitude of the EOFs corresponds to the one standard
deviation changes of the PC. All time series are detrended before calculating correlation, regression, or
power spectrum, although the results presented in thisstudy were not sensitive to detrending. The statisticalsignificance of the correlation or regression coefficientsis assessed according to a two-sided Student’s t test using an effective temporal degree of freedom taking into
account serial autocorrelation. Statistical significances
are indicated at the 99% confidence level for all correlation and regression plots. The statistical significancefor the power spectrum is indicated at the 95% and
99% confidence levels with respect to a red noise null

hypothesis based upon a first-order autoregressive process with the autocorrelation of the corresponding timeseries.

3. Resultsa. Decadal variability of SST and subsurface
temperature in the North Pacific
The leading EOF of winter [December–March(DJFM)] SST anomalies from CCSM2 exhibits anomalies of one sign in the western and central North Pacificcentered along the Kuroshio Extension near 40°N, surrounded by anomalies of the opposite sign along thewest coast of North America (Fig. 1a). This mode accounts for 20% of the variance of winter SST anomalies
and is nearly identical to the leading EOF of monthly
SST anomalies (not shown). The primary center of action along the Kuroshio Extension coincides with the
maximum meridional gradient of the climatologicalmean SST. The simulated EOF pattern resembles observations, although the latter has its primary maximum
in the central North Pacific with a weaker secondary
maximum along the Kuroshio Extension (Deser andBlackmon 1995; Mantua et al. 1997; Nakamura et al.
1997; Schneider and Cornuelle 2005). The leading EOF

2420

JOURNAL OF CLIMATEof subsurface temperature anomalies at 500-m depth,
which lies within the main thermocline, also exhibits amaximum in the western North Pacific along the Kuroshio Extension near 40°N, collocated with the maximum meridional gradient of the climatological mean
500-m temperature field (Fig. 1b). Note that unlike
SST, 500-m temperature anomalies associated with
EOF 1 are very weak in the central and eastern North
Pacific, similar to observations (Miller et al. 1998;
Deser et al. 1999).
The time series of winter SST anomalies averaged
over the Kuroshio Extension (35°–45°N, 140°E–180°;
the region outlined by the gray box in Fig. 1a), the SST
Kuroshio Extension index (SSTKEI), exhibits pronounced decadal variability that is apparent even in
unsmoothed time series (shaded bars in Fig. 2a). The
SSTKEI is highly correlated (r ⫽ 0.88) with the leading
SST PC time series. The power spectrum of the
SSTKEI exhibits two low-frequency peaks: one aroundthe 15–20-yr period (hereafter referred to as the 16-yrpeak) and the other around the 38–45-yr period (hereafter 40-yr peak). These 16- and 40-yr spectral peaks
are statistically significant at 99% and 95%, respectively, with respect to a first-order autoregressivemodel. Observed winter SST anomalies in the Kuroshio
Extension in the twentieth century show a weak spectral peak at 20–30-yr periods (Pierce et al. 2001).
The time series of 500-m temperature anomalies averaged over the Kuroshio Extension index region ishighly correlated (r ⫽ 0.66, significant at the 99% confidence level) with the SSTKEI (solid line in Fig. 2a),and exhibits similar spectral peaks at 16 and 40 yr(Fig. 2c). The enhanced power at periods longer thanabout 100 yr is due to a slight decreasing trend in thefirst 200 yr of the time series as the deep ocean comes
into equilibrium (Kiehl and Gent 2004). The 500-m
temperature time series has almost negligible power inthe frequency range higher than 0.1 cycles per year(cpy), which makes the time series appear smootherthan the SSTKEI.
Lag regressions of 10-yr low-pass-filtered SST and
500-m temperature anomalies upon the SSTKEI show
the evolution of the decadal temperature anomalies(Fig. 3). From lag ⫺4 to ⫹2 yr, positive anomalies expand eastward from Japan to occupy almost the half ofthe basin. At lag ⫹4 yr, a negative anomaly develops

冉

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FIG. 2. (a) Winter (DJFM) SST anomalies (shaded bars) and
500-m temperature (solid curve) Kuroshio Extension index (35°–45°N, 140°E–180°). Power spectrum of the (b) SST and (c) 500-m
temperature index time series. The best-fit first-order autoregressive model (AR1) spectrum and associated 95% and 99% significant levels are plotted with dashed lines based on the 1-yr lag
autocorrelations of the index time series.

off Japan and evolves in a similar fashion to the positive
anomalies. The positive SST anomalies dissipate rapidly from lag ⫹2 to ⫹4 yr, whereas those at 500-m depth
persist through lag ⫹14 yr as they continue to propagate eastward, eventually dissipating in the Gulf of
Alaska.

b. Local diagnosis of the decadal variability in the
Kuroshio Extension
As a first step to understand the processes responsible for driving the decadal variability of ocean temperature anomalies in the Kuroshio Extension, we examine the local heat budget for the upper 200 m in thefollowing form:

冊 冉

冊

1
⭸T
⭸T
⭸T
⭸T
⭸T
⭸T
⫽⫺
Qnet ⫺ uEk⫹ ␷Ek⫹ ␷geo⫹ residual,⫺ ugeo
⫺w
⭸t
␳0CpH
⭸x
⭸y
⭸x
⭸y
⭸z
共i兲

共ii兲

共iii兲

共iv兲

共1兲

1 JUNE 2007

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FIG. 3. Lag regressions of DJFM (left) SST and (right) 500-m temperature on the SSTKEI. Lags are notedin the center column: positive (negative) lag indicating the index time series is leading (lagging) thetemperature field. Positive (negative) regression coefficients are contoured with red (blue) lines. Zero
contours are plotted with black lines. Contour interval is 0.3°C °C⫺1, and gray shadings indicate regression
values significant at 99%. All variables are low-pass filtered to retain periods longer than 10 yr.

where T is the ocean temperature, Qnet is the net air–sea surface heat flux, H is the upper-ocean thickness(⫽200 m), uEk (␷Ek) is the zonal (meridional) Ekman
velocity, ugeo (␷geo) is the zonal (meridional) geo-

strophic velocity, and w is the total vertical velocity. All
the terms not explicitly listed, including diffusion, makeup the residual. The three leading terms on the righthand side of Eq. (1) [i.e., (i) the net surface heat flux,

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FIG. 4. Lag regressions of DJFM (left) SST and (right) net surface heat flux on the SSTKEI. No filtering is applied. Lags
are noted in the center column: positive lag indicates the index time series is leading and vice versa. Positive regression
coefficients for the net surface heat flux denote heat transferred from ocean to atmosphere. Negative values are contoured
with dashed lines. Contour interval is 0.3°C °C⫺1 and 10 W m⫺2 °C⫺1, respectively. Shadings indicate regressions
significant at 99%.

(ii) the horizontal Ekman heat flux divergence, and (iii)the horizontal geostrophic heat flux divergence] dominate the upper-ocean heat budget in the Kuroshio Extension and are examined in detail below.
First, the role of local net surface heat flux (Qnet) is
examined by means of lag regressions of the winter Qnet
anomalies on the winter SSTKEI (right column in Fig.
4): note that positive (negative) Qnet regressions denotecooling (heating) of the sea surface. To illustrate therelation with the SST anomalies, the corresponding lag
regressions of the winter SST anomalies on the SSTKEI
are also shown (left column in Fig. 4). Note that the
regressions are based on unfiltered data unlike thoseshown in Fig. 3. Here Qnet regressions at ⫺1 and 0 yr
lags illustrate the mixture of Qnet forcing and responseto the SST anomalies. The positive Qnet anomalies overthe upstream portion of the Kuroshio Extension occur
in response to the local maximum of the warm SST
anomalies, transferring excessive heat from ocean tothe atmosphere. At lags ⫺1 and 0 yr, negative Qnet
anomalies directly south and east of the Kuroshio Extension drive positive SST anomalies, thereby broadening the warm SST anomalies southward and eastward.
The Qnet also contributes to forcing the negative SST
anomalies in the eastern half of the basin at lags ⫺1 and
0. The local response of Qnet to SST along the Kuroshio
Extension is clearly evident at all lags, and the regressions at lags 2 and 3 yr even show the Qnet anomalies
propagating eastward in response to the propagating
warm SST anomalies. The strength of net surface heat
flux response to the SST anomalies implied from theregressions at 1-yr lag is 39 W m⫺2 °C⫺1 averaged over
the Kuroshio Extension. Similar values (40–50 W m⫺2°C⫺1) have been estimated from the observations(Frankignoul and Kestenare 2002; Park et al. 2005).
The time series of winter Qnet averaged over the
Kuroshio Extension illustrates the close correspondence with the SSTKEI: their maximum lagged corre-1 JUNE 2007

KWON AND DESER

FIG. 5. (a) DJFM net surface heat flux anomalies in the Kuroshio Extension (35°–45°N, 140°–170°E) (solid curve) and SSTKEI(shaded bars). Positive heat flux anomalies denote heat transferred from ocean to atmosphere. Note that 1–2–1 smoothing has
been applied to both time series just for the visualization. (b)Power spectrum of the net surface heat flux anomaly time series.
The best-fit first-order autoregressive model (AR1) spectrum andassociated 95% and 99% significant levels are plotted with dashedlines based on the 1-yr lag autocorrelations of the index timeseries.

lation (r ⫽ 0.58) occurs when the SSTKEI leads Qnet by
1 yr (Fig. 5a). The power spectrum of the Qnet timeseries exhibits two low-frequency peaks corresponding
to the 16- and 40-yr peaks in the SSTKEI (Fig. 5b).
Both peaks are significant at the 99% confidence level.
As discussed in Czaja and Frankignoul (2002), the 1-yr
lag should not be interpreted as a delay time in theatmospheric response. The simultaneous correlation or2423

regression can be understood as a mixture of Qnet forcing and response to the SST anomalies. Therefore, atzero lag the positive correlation due to Qnet responding
to SST anomalies partially offsets the negative correlation due to Qnet forcing SST. On the other hand, thecorrelation at 1-yr lag fully reflects the Qnet response,
free from interference of the Qnet forcing because of
short atmospheric persistence.
The simultaneous regression pattern of the total winter Ekman heat flux divergence onto the SSTKEI exhibits zonally elongated negative anomalies in the western half of the basin located slightly south of the maximum positive SST anomalies (Fig. 6a). Note thatnegative regression coefficients indicate heat convergence, which implies SST warming. Decomposition of
the Ekman heat flux divergence into VEk · ⵱SST⬘,V⬘Ek · ⵱SST, and V⬘Ek · ⵱SST⬘, where the overbar denotes the climatological winter mean and the prime denotes anomalies from the climatological mean, showsthat the regression of total Ekman heat flux divergenceis dominated by the contribution from the product of
the time mean Ekman transport and the gradient of theanomalous SST (Fig. 6). This implies that the Ekman
heat flux divergence acts to enhance the preexisting
SST anomalies preferentially in the southern half,rather than as a direct forcing of the SST anomalies.
Finally, the role of the horizontal geostrophic heat
flux divergence is examined. Lag correlation between
the SSTKEI and the geostrophic heat flux divergence
averaged over the Kuroshio Extension exhibits maximum correlation (r ⫽ ⫺0.76) when the geostrophic
heat flux divergence leads SSTKEI by 0–1 yr (Fig. 7a).
The negative correlation implies that positive SST
anomalies are induced by anomalous heat flux convergence. Furthermore, lag correlation between the sur-

FIG. 6. Simultaneous regression coefficients of DJFM Ekman heat flux divergence on the SSTKEI. (a) Total(VEk · ⵱SST), (b) VEk · ⵱SST⬘, (c) V⬘Ek · ⵱SST, and (d) V⬘Ek · ⵱SST⬘. Positive (negative) values indicate heat divergence(convergence). Negative values are contoured with dashed lines. Contour interval is 10 W m⫺2 °C⫺1, and the shading
indicates regression coefficients significant at 99%. All variables are low-pass filtered to retain periods longer than 10 yr.

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tension due to anomalous geostrophic currents, SST
and the surface heat flux from ocean to atmosphere
increase.

c. Basin-scale wind stress curl forcing

FIG. 7. Lag correlations of the winter (DJFM) horizontal geostrophic heat flux divergence in the upper 200 m with (a) SST and(b) net surface heat flux. All three variables are averaged over the
Kuroshio Extension (35°–45°N, 140°E–180°) and low-pass filtered
to retain periods longer than 10 yr. Dashed lines indicate correlations significant at 99%.

face heat flux and geostrophic heat flux divergence inthe Kuroshio Extension exhibits maximum correlation(r ⫽ ⫺0.86) when the geostrophic heat flux divergenceleads by 1–2 yr (Fig. 7b). Therefore, when heat converges laterally in the upper ocean in the Kuroshio Ex-

Diagnosis of the upper-ocean heat budget has demonstrated that anomalous horizontal geostrophic heat
flux divergence drives decadal SST variability in the
Kuroshio Extension. Previous observational and modeling studies have suggested that changes in horizontal
geostrophic heat flux divergence near the Kuroshio Extension are driven by changes in basin-scale wind stresscurl 3–5 yr earlier (Miller et al. 1998; Deser et al. 1999;
Pierce et al. 2001; Seager et al. 2001). These studies
have suggested that baroclinic adjustment of the ocean
circulation via westward propagation of Rossby waves
is responsible for the delay time scale.
Basin-scale wind stress curl forcing in CCSM2 is examined in terms of lag regressions of the annual mean
wind stress curl upon the negative of the winter horizontal geostrophic heat flux divergence time series averaged over the Kuroshio Extension based on 10-yr
low-pass-filtered anomalies (Fig. 8). The negative of the

FIG. 8. Lag regressions of annual mean wind stress curl on the negative of DJFM horizontal geostrophic heat flux
divergence in the upper 200 m averaged over the Kuroshio Extension (35°–45°N, 140°E–180°). Note that the negative ofgeostrophic heat flux divergence is used for the index time series to focus on the heat convergence phase (i.e., the positive
SST anomaly phase). Both variables are low-pass filtered to retain periods longer than 10 yr. Negative lag indicates thewind stress curl is leading geostrophic heat flux convergence. Negative values are contoured with dashed lines. Regression
coefficients are standardized to correspond to a one standard deviation change of the geostrophic heat flux divergencetime series. Contour interval is 0.2 ⫻ 10⫺8 N m⫺3, and the shadings indicate regressions significant at 99%. Thick dashedcontour in the top panel denotes the climatological annual mean zero wind stress curl line.

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2425

FIG. 9. Simultaneous regression of DJFM subsurface zonal current velocity on the SSTKEI along the meridional section at
150°E. Both variables are low-pass filtered to retain periods
longer than 10 yr. Contour interval is 0.2 cm s⫺1 °C⫺1, and theshading indicates regressions significant at 99%. Solid (dashed)
contours denote eastward (westward) velocity. Thin contours withboxed labels indicate the climatological winter (DJFM) mean
zonal velocity fields. Contour interval for the mean zonal velocity
is 2 cm s⫺1.

geostrophic heat flux divergence (⫽ geostrophic heat
flux convergence) is used so that the sign is consistent
with a positive SST anomaly. The annual-mean windstress curl is used instead of the winter average because
the atmosphere forces the ocean year-round; however,similar results are obtained using winter wind stress curlalthough the magnitudes are approximately twice aslarge (not shown). The lag regressions demonstratethat significant basin-scale negative wind stress curl
anomalies between 30° and 50°N precede and accompany anomalous geostrophic heat flux convergence inthe Kuroshio Extension with maximum values in thecentral basin when the wind stress curl leads by 3 yr.
The regression pattern implies a slight northward shiftof the zero contour of the total wind stress curl, andthus a northward shift of the boundary between the
subpolar and subtropical ocean gyres, which results inanomalous horizontal geostrophic heat flux convergence along the Kuroshio Extension. Simultaneous regression of the subsurface zonal velocity along 150°E
upon the SSTKEI confirms that the ocean gyre boundary shifts northward in association with positive SST
anomalies along the Kuroshio Extension (Fig. 9).
The anomalous wind stress curl forcing pattern thatprecedes anomalous geostrophic heat flux convergencealong the Kuroshio Extension resembles the leading
EOF of annual mean wind stress curl over the North
Pacific (Fig. 10a). However, the amplitude of the leading EOF pattern is approximately 4 times greater thanthat of the forcing pattern. The power spectrum of theassociated PC time series is approximately consistent

FIG. 10. (a) Leading EOF pattern of unfiltered annual mean
wind stress curl anomalies over the North Pacific (20°–60°N). The
leading EOF mode explains 19% of the total variance. Amplitude
corresponds to a one standard deviation change of the corresponding PC time series. Contour interval is 0.5 ⫻ 10⫺8 N m⫺3. (b)Same as in (a), but for low-pass-filtered (periods longer than 10yr) wind stress curl anomalies. This EOF mode explains 18% of
the total variance. (c) Power spectrum of the leading PC timeseries based on unfiltered data. The best-fit AR1 spectrum and
95% and 99% significant levels with respect to the AR1 spectrum
are shown as dashed lines. (d) Power spectrum of the monthly
forcing time series for the linear Rossby wave model. Note thatthe frequency range higher than 0.5 cpy is not shown. Horizontal
gray line at periods longer than 10 yr is the power spectrum used
for the calculation shown in Fig. 13d.

with white noise, with nearly equal power at all frequencies (Fig. 10c). It is interesting to note that thepower spectrum exhibits weak ⬃16 and ⬃40 yr peaks,
although they are not significant nor are they greatercompared to other peaks at higher frequencies. Whenthe annual mean wind stress curl anomalies are lowpass filtered to retain only periods longer than 10 yr,
the leading EOF is similar in pattern to that based on
unfiltered data but the amplitudes are reduced by a

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factor of ⬃2.5, consistent with the relative proportionof power in the unfiltered PC time series at periods
longer than 10 yr (Figs. 10b,c).

d. Linear Rossby wave model
We next examine the explicit linkage between
anomalous wind stress curl forcing and decadal ocean
current variability along the Kuroshio Extension using
a simple linear baroclinic Rossby wave model. The
large-scale, baroclinic ocean response to wind stresscurl forcing can be approximated using a 11⁄2-layer reduced gravity model. Under the long-wave approximation, the linear vorticity equation of the model is
g⬘
⭸h
⭸h
⫺ CRW
⫽⫺
k · ⵱ ⫻ ␶ ⫺ ␧h,
⭸t
⭸x
␳0gf

共2兲

where h is the sea surface height (SSH), CRW is the
speed of the long baroclinic Rossby waves, g⬘ is the
reduced gravity, ␳0 is the reference density of seawater,
f is the Coriolis parameter, k is the unit vector in thevertical direction, ⵱ ⫻ ␶ is the wind stress curl, and ␧ isthe Newtonian dissipation rate. An analytic solution asshown in Eq. (3) can be obtained by integrating Eq. (2)from the eastern boundary (xe) along the baroclinic
Rossby wave characteristics. The boundary condition at
the eastern boundary is assumed to be h(xe, t) ⫽ 0,
which is reasonable for our purposes (Fu and Qiu
2002):
h共x, t兲 ⫽g⬘
␳0gfCRW

冕

x

⫻

xe

冉

k · ⵱ ⫻ ␶ x⬘, t ⫹冊 冋册x ⫺ x⬘x ⫺ x⬘
exp ␧
dx⬘.
CRW
CRW
共3兲

SSH anomalies are hindcast for each latitude independently using Eq. (3). The wind stress curl forcing is
obtained by projecting the lag 3-yr regression patternfrom Fig. 8 upon the full CCSM2 wind stress curl
anomalies in each month. Similar results are obtained
when the leading wind stress curl EOF is used insteadof the lag 3-yr regression. The Rossby wave speeds ateach latitude are estimated from the CCSM2 SSH
anomalies using the method described in Chelton and
Schlax (1996); see Fig. 11. As in Chelton and Schlax, thetime–longitude plots of monthly SSH are examined visually to identify regions of unambiguous westward
propagation that span at least 30° of longitude. Notethat the regions of westward propagation were foundonly to the east of the date line for the latitudes withstrong eastward advection near 40°N. The estimated
CCSM2 Rossby wave speeds are generally consistent
with previous empirical estimates based on satellite al-

FIG. 11. Phase speeds of first baroclinic mode Rossby wave in
the North Pacific as a function of latitude, derived from thesources indicated in the legend.

timetry observations (Chelton and Schlax 1996; Qiu
2003), although they are larger in magnitude south of
40°N. Discrepancies between the empirical estimates
and theoretical estimates based on the density stratification are evident in CCSM2, similar to observations(Chelton and Schlax 1996). The dissipation rate (␧) is apoorly known quantity. Here ␧⫺1 ⫽ 12 yr is used basedon the hindcast skill of the SSH anomalies in the Kuroshio Extension. Qiu (2002) and Schneider et al. (2002)
estimated ␧⫺1 to be 6 and 4 yr, respectively, based on
similar strategies. We also obtained the highest hindcast skill using ␧⫺1 ⫽ 6 yr when the CCSM2 and thehindcasted SSH time series are not low-pass filtered.
However, when both SSH time series are low-pass filtered to retain periods longer than 10 yr, the highest
skill is obtained with ␧⫺1 ⫽ 12 yr. The question ofwhether the dissipation rate is frequency dependent isbeyond the scope of this paper.
Prior to an assessment of hindcasted SSH anomalies
against the CCSM2 SSH anomalies, the correlation between the horizontal geostrophic heat flux divergenceand the SSH anomalies in CCSM2 should be explicitly
demonstrated to ensure the relevance of SSH anomalies to the decadal variability of interest. Regression of
SSH anomalies upon the time series of the negative of
averaged anomalous horizontal geostrophic heat flux
divergence in the Kuroshio Extension exhibits maximum positive anomalies collocated with the maximum
meridional gradient of the climatological mean SSH in
the Kuroshio Extension (34°–43°N, 140°E–180°); see
Fig. 12. The maximum positive SSH anomalies in the
Kuroshio Extension are located in the region slightly
south of the maximum SST anomalies (recall Fig. 1a).

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KWON AND DESER

2427

FIG. 12. Simultaneous regressions of winter (DJFM) SSH on the negative of horizontal
geostrophic heat flux divergence in the upper 200 m averaged over the Kuroshio Extension(35°–45°N, 140°E–180°). Note that the negative of geostrophic heat flux divergence is used forthe index time series to focus on the heat convergence phase (i.e., positive SST anomaly
phase). Both variables are low-pass filtered to retain periods longer than 10 yr. Negativevalues are contoured with dashed lines. Regression coefficients are standardized to correspond to a one standard deviation change of the geostrophic heat flux divergence time series.
Contour interval is 0.01 m, and shading indicates regressions significant at 99%. Thin contours
denote the climatological winter SSH field. The contour interval for the mean SSH is 0.2 m.
Gray solid box (34°–43°N, 140°E–180°) denotes the averaging region for the SSH index timeseries shown in Fig. 13.

The correlation coefficient between the time series ofhindcast SSH from the Rossby wave model and CCSM2
SSH anomalies averaged over the region 34°–43°N,
140°E–180° is 0.63, which suggests a reasonable hindcast skill of the linear Rossby wave model (Fig. 13a).
The hindcast SSH anomalies also reproduce reasonably
well the power spectrum of the CCSM2 SSH anomalies
with peaks near 16 and 40 yr (Figs. 13b,c). The hindcast SSH anomalies are not unduly sensitive to the uncertainties in the estimates of the Rossby wave speeds
and dissipation rates as shown in Fig. 13c.
The hindcast skill of the linear Rossby wave model
forced with the diagnosed wind stress curl forcing pattern explicitly demonstrates that the wind stress curl
forcing pattern is indeed appropriately diagnosed andthat the decadal variability in the Kuroshio Extension isassociated with changes in the basin-scale wind stresscurl forcing via baroclinic adjustment of the ocean
gyres. Given the confidence in the hindcast skill of the
Rossby wave model, we next examine the role of thelow-frequency power and the existence of the weak
⬃16 and ⬃40 yr spectral peaks in the wind stress curl
forcing (Fig. 10d). SSH hindcast using only the 10-yr
low-pass filtered wind stress curl forcing is almost identical to that obtained using the full unfiltered monthly
forcing (Fig. 13d). This suggests that only the lowfrequency part of the wind stress curl forcing is relevantto the decadal variability in the Kuroshio Extension,even though the power in the wind stress curl forcing isalmost equally distributed throughout the whole frequency range. To examine the role of the weak ⬃16
and ⬃40 yr spectral peaks in the wind stress curl forcing, we replaced the power in the wind stress curl forc-

ing at periods longer than 10 yr to its average value
within this frequency band, thus removing the spectral
peaks but keeping the total low-frequency variance unchanged (see Fig. 10d). The resulting SSH hindcast exhibits a canonical red noise spectrum without any lowfrequency spectral peaks (Fig. 13d). These results suggest that the weak low-frequency spectral peaks in thewind stress forcing are essential for producing the significant commensurate spectral peaks in SSH.

e. Atmospheric response to decadal SST anomalies
in the Kuroshio Extension
We have established that basin-scale anomalous windstress curl forcing causes decadal SST anomalies in the
Kuroshio Extension. Are there local and basin-scaleatmospheric responses to the SST anomalies in the
Kuroshio Extension that could establish a closed loopof atmosphere–ocean coupled decadal variability suchas that proposed by Latif and Barnett (1994) and Neelin and Weng (1999)? Figure 14 shows SST, net surface
heat flux, precipitation, and wind stress curl anomalies
along 40°N as a function of longitude and time. Notethat all variables have been low-pass filtered to retain
periods longer than 10 yr. SST anomalies clearly exhibit
16-yr variability as well as eastward propagation in thewestern half of the basin, consistent with Fig. 3. Net
surface heat flux anomalies propagate along with the
SST anomalies and respond to, rather than force, themas discussed previously. The latent heat flux components dominate the net surface heat flux response (notshown). Precipitation anomalies also exhibit eastward
propagation with enhanced (diminished) precipitation
over warm (cool) SST anomalies. Finally, wind stress2428

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FIG. 13. Winter SSH anomalies averaged over 34°–43°N,
140°E–180°. (a) Time series of the CCSM2 SSH (shading) and the
SSH hindcast using the linear Rossby wave model (solid curve)
forced by the wind stress curl forcing pattern and a damping timescale of 12 yr. The correlation coefficient between the two timeseries is 0.63. When both time series are low-pass filtered for
longer than 10 yr, the correlation coefficient is 0.84. (b) Power
spectrum of the CCSM2 SSH. The best-fit AR1 spectrum and
95% and 99% significant levels with respect to the AR1 spectrum
are plotted with dashed lines. (c) Power spectrum of the Rossby
wave model hindcast SSH (solid line). Gray shading denotes uncertainties of ␧⫺1 with a range of ⫾2 yr. Dashed lines denote
uncertainties in the empirical estimates of the Rossby wave speed
as shown with vertical bars in Fig. 11. (d) Solid black curve isrepeated from (c). The dashed gray curve is from the Rossby wave
model using low-pass-filtered forcing. The thin gray solid curve isfrom the Rossby wave model using flat low-frequency forcing (seeFig. 10d).

curl anomalies are also positively correlated with the
SST anomalies, although the relation is not as strong as
for net surface heat flux and precipitation.
Whether these apparent local atmospheric responses
to decadal SST variations can grow into basin-scale responses is crucial to the existence of a coupled atmosphere–ocean mode. An estimate of the basin-scale annual mean wind stress curl response may be obtainedby means of lag regressions on the SSTKEI when thewind stress curl lags the SSTKEI by 1 yr. Unlike thesimultaneous regression, which consists of both response and forcing, the 1-yr lag regression reflects

VOLUME 20

mainly the response since unfiltered wind stress curl
anomalies contain practically no memory from one year
to the next. Note that the response pattern is further
scaled by the ratio of the standard deviation of 10-yr
low-pass filtered SSTKEI to that of unfiltered SSTKEI
to allow a direct comparison to the forcing pattern. The
response pattern is broadly similar to the forcing pattern but of opposite sign and approximately 65% in
magnitude (Figs. 15a,b). One noticeable difference isthat the maximum anomaly of the response pattern islocated west of the date line along 40°N, farther west
and more meridionally confined than that of the forcing
pattern. Winter wind stress curl forcing and response
patterns are consistent with their annual mean counterparts but their amplitudes are approximately twice aslarge (Figs. 15c,d).
Two additional experiments are performed to examine the robustness of the wind stress curl response pattern inferred from lag regression analysis: CAM2, thestand-alone atmospheric general circulation modelcomponent of CCSM2, forced with decadal SST
anomalies in the Kuroshio Extension taken fromCCSM2, and CAM2, coupled to an entraining ocean
mixed layer model forced with decadal geostrophic
heat flux divergence anomalies in the Kuroshio Extension taken from CCSM2. The equilibrium responses inboth experiments are examined by computing the differences between the mean of 100-yr perturbation integrations with specified SST and geostrophic heat flux
divergence anomalies, respectively, and the mean of
150-yr control integrations without specified anomalies.
The ocean mixed layer model consists of a grid of independent column models aligned with the atmospheric model grid, with a uniform mixed layer atop a
multilayered model that represents conditions in thepycnocline (Alexander et al. 2000; Cassou et al. 2007).
It has 36 vertical layers within the upper 1500 m. [Thereader is referred to Cassou et al. (2007) and http://
www.cgd.ucar.edu/cas/cdeser/REMsupfig.html for a detailed description of the ocean mixed layer model andthe control integration of the CAM2–ocean mixed layer
model experiment.]
The root-mean-square of 10-yr low-pass-filteredCCSM2 SST anomalies for each month is specified onlyin the Kuroshio Extension for the perturbation integration of the stand-alone CAM2. The annual mean of thespecified SST anomalies is shown in Fig. 16a. The annual mean wind stress curl response (Fig. 16b) exhibits
a similar shape and slightly greater magnitude in thewestern half of the basin compared to the response
estimated by lag regression. The response is weaker inthe eastern half of the basin probably because the SST
anomalies are specified only in the Kuroshio Extension,

1 JUNE 2007

KWON AND DESER

2429

FIG. 14. Hovmöller plot of winter (a) SST, (b) net surface heat flux, (c) precipitation, and (d) wind stress curl anomalies along 40°N
for years 450–550. All variables are low-pass filtered to retain periods longer than 10 yr. Positive (negative) anomalies are plotted withsolid (dashed) contours. Contour intervals are (a) 0.5°C, (b) 20 W m⫺2, (c) 0.5 mm day⫺1, and (d) 3 ⫻ 10⫺8 N m⫺3.

whereas in CCSM2 they propagate eastward to expand
across the date line (Fig. 4).
The root-mean-square of 10-yr low-pass filteredCCSM2 geostrophic heat flux divergence anomalies in
the upper 1000 m for each month is specified in the
Kuroshio Extension for the perturbation integration of
the CAM2–ocean mixed layer model. The SST response is similar to the SST anomalies specified instand-alone CAM2 integration, with a slight eastward
extension due to atmospheric processes (Fig. 16c). The
annual mean wind stress curl response is similar in pattern and magnitude to both the stand-alone CAM2 in-tegration and CCSM2, giving confidence in the robustness of the results.

4. Discussion
a. Decadal time-scale selection
The basin-crossing time of first baroclinic mode
Rossby waves has often been suggested as a source ofthe decadal time scale in the North Pacific (Latif andBarnett 1994; Pierce et al. 2001). This argument may be
relevant for the time scale selection of the 16-yr variability in CCSM2. As already described, a 3–5-yr delay

FIG. 15. Regression of wind stress curl anomalies on the winter normalized SSTKEI. (a) Annual mean wind stress curl
leading SST Index by 4 yr; both variables are smoothed with a 10-yr low-pass filter. (b) Annual mean wind stress curl
lagging the SST index by 1 yr based on unfiltered data. The unfiltered regression pattern is further scaled by the ratio of
the standard deviation of 10-yr low-pass-filtered SST index to that of unfiltered SST index. (c), (d) As in (a), (b) but for
winter (DJFM). Contour intervals are 0.2 ⫻ 10⫺8 N m⫺3 for (a), (b) and 0.5 ⫻ 10⫺8 N m⫺3 for (c), (d). Negative values
are dashed and shading indicates regressions significant at 99%.

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FIG. 16. (a) Annual mean of the SST anomalies specified as forcing of stand-alone CAM2 integration. (b) Annual mean
wind stress curl response from stand-alone CAM2 integration. (c) Annual mean SST response from CAM2–ocean mixed
layer model integration with specified geostrophic heat flux divergence anomalies in the Kuroshio Extension. (d) Annual
mean wind stress curl response from CAM2–ocean mixed layer model integration. Contour intervals are 0.2°C for the SST
and 0.2 ⫻ 10⫺8 N m⫺3 for the wind stress curl. Negative values are dashed and shading indicates response significant at 99%.

is found between the wind stress curl forcing and theanomalous geostrophic heat flux divergence in the
Kuroshio Extension via first baroclinic mode Rossby
waves. An additional delay of 1–2 yr is found between
the anomalous geostrophic heat flux divergence and the
SST and net surface heat flux response in the Kuroshio
Extension. Finally, another 1–2 yr is needed for the SST
and net surface heat flux anomalies to expand eastward
where they may more effectively induce a basin-scalewind stress curl response pattern that is similar in pattern but opposite in sign to the wind stress curl forcing.
The sum of these three delay factors could result in an
8-yr half-cycle.
Spatial resonance between stochastic atmosphericforcing and the oceanic response has also been suggested as a possible source of the decadal time scale(Jin 1997; Saravanan and McWilliams 1998; Neelin andWeng 1999). These theories require a zonal wavenumber-one atmospheric forcing pattern across the basin.
However, the wind stress curl forcing pattern in
CCSM2 exhibits one sign across the basin (e.g., zonal
wavenumber zero). Further, the Rossby wave model
hindcast forced by the flat low-frequency wind stresscurl forcing did not reproduce the decadal spectral
peaks, confirming that a monopolar forcing patterndoes not produce decadal resonance. Pierce et al.
(2001) and Schneider et al. (2002) also reported that the
spatial resonance mechanism could not reproduce decadal spectral peaks in the Kuroshio Extension in the
ECHO2 and CSM1 integrations.
Recently, Qiu (2003) proposed a resonance mechanism due to the difference in Rossby wave speeds between the southern and northern flanks of the Kuroshio
Extension, a distance of about ⬃4°–8° in latitude. Qiu’s
analytical solution, when calculated using the estimated

Rossby wave speeds and other parameters appropriate
for the CCSM2, reproduces a decadal peak around16 yr for the meridional SSH difference (⌬SSH) acrossthe Kuroshio Extension with an acceptable range ofsensitivity to the choice of parameters (not shown).
However, the Qiu mechanism is unlikely for CCSM2
since ⌬SSH across the Kuroshio Extension in CCSM2
does not exhibit a distinct 16-yr spectral peak and thevariability of ⌬SSH in the Qiu model represents astrengthening/weakening of the gyre rather than a meridional shift of gyre boundaries.
It is therefore likely that the decadal time scale in
CCSM2 is selected by coupled atmosphere–ocean processes within the North Pacific. Two key results support
the existence of a coupled atmosphere–ocean oscillation within the North Pacific in CCSM2: the linear
Rossby wave model results and the wind stress curl
response to SST anomalies in the Kuroshio Extension.
The linear Rossby wave model results illustrate that the
weak decadal spectral peaks in the wind stress curl forcing are necessary for producing the corresponding spectral peaks in SSH (and by inference, SST). The windstress curl response pattern inferred from lagregression analysis is similar in structure but opposite insign and about 35% weaker than the forcing pattern,consistent with the responses obtained from the standalone CAM2 and CAM2–ocean mixed layer model experiments. A deterministic coupled atmosphere–oceanmode as suggested by Latif and Barnett (1994) is unlikely the case for CCSM2 because the wind stress curl
response is weaker than the forcing and the decadal
variability in the wind stress curl is not as regular as it
is in SST. However, according to the theory of Neelin
and Weng (1999), even a weak and irregular atmospheric response to decadal SST anomalies enhances

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KWON AND DESER

the power of decadal peaks in SST originally introduced by ocean dynamics, and thus is sufficient to maintain a coupled mode of decadal variability.
It is plausible that the identical sets of processes described above to explain the 16-yr time scale may be
applied to explain the 40-yr time scale if the first baroclinic mode Rossby waves are replaced by slower second baroclinic mode Rossby waves. Theory suggeststhat the speed of the nth baroclinic mode Rossby wave
decreases by a factor of n⫺2 (Chelton et al. 1998), which
implies a basin-crossing time of 12–20 yr for the second
baroclinic mode Rossby waves. However, as pointed
out by Liu (1999), second- (and higher) mode Rossby
waves generally propagate southwestward along themean subduction pathway rather than directly westward because of the Doppler effect. However, since theempirically estimated speed of first baroclinic Rossby
waves in CCSM2 is faster than observational estimatesfor most latitudes, the second baroclinic Rossby waves
in the CCSM2 could still be faster than the mean advection in the Kuroshio Extension, negating the Doppler effect. Further work is needed to investigate whether second baroclinic mode Rossby waves are responsible for the 40-yr variability in CCSM2.
Remote forcing from the tropical Pacific via atmospheric teleconnections has been suggested as a sourceof North Pacific decadal variability in nature (Newman
et al. 2003; Deser et al. 2004; Schneider and Cornuelle
2005). In CCSM2, however, regressions of 10-yr lowpass filtered winter precipitation and SST anomalies
over the tropical Indo–Pacific on the SSTKEI are weak
and insignificant (not shown). Regressions of unfiltereddata are also weak but statistically significant (correlation coefficients ⬃0.2), with negative precipitation
anomalies in the Pacific and positive anomalies in theeastern Indian Ocean along the equator with magnitudes ⬃0.2 mm day⫺1 per standard deviation change in
the SSTKEI (not shown). The pattern broadly resembles that associated with cold ENSO events in themodel; however, the precipitation magnitudes are only
about 15% of those associated with ENSO, while thecorresponding North Pacific SLP response is about
70% of that associated with ENSO. These results suggest that tropical forcing of North Pacific decadal variability in CCSM2 is not likely to be important. Pierce etal. (2001) also suggested the tropical remote forcing
mainly projects onto interannual variability rather thandecadal and lower frequency variability in the ECHO2
and CSM1.

b. 1976/77 shift of the Pacific decadal oscillation
The 1976/77 regime shift is probably the most studied
aspect of the observed PDO (Miller et al. 1994; Deser

2431

et al. 1999; Xie et al. 2000; Seager et al. 2001). We
attempt to compare elements of the observed andmodel hindcast features of the 1976/77 PDO shift withthe decadal variability in CCSM2 in order to assess therealism of the coupled model and to shed light on themechanisms responsible for the observed PDO, an aspect hindered by the limited length of available observations. Several aspects of CCSM2’s decadal variability
are consistent with the observed 1976/77 PDO shift,including: the basin-scale structure of the wind stresscurl forcing centered along 30°–50°N with maximum
amplitude near 40°N, 170°W, which represents a meridional shift of the zero wind stress curl position(Miller et al. 1998; Deser et al. 1999; Xie et al. 2000;
Seager et al. 2001); the 3–5-yr delay between the windstress curl forcing and the SST response in the Kuroshio
Extension caused by a meridional shift of the boundary
between the subpolar and subtropical gyres and associated anomalous divergence of the horizontal geostrophic heat flux (Miller et al. 1998; Deser et al. 1999;
Xie et al. 2000; Seager et al. 2001); and surface heat flux
damping of the SST anomalies in the Kuroshio Extension (Seager et al. 2001; Tanimoto et al. 2003; Kelly
2004).
Other aspects of CCSM2’s decadal variability, in particular the nature of the wind stress curl response to
SST anomalies in the Kuroshio Extension, are morechallenging to confirm against the observed 1976/77
PDO shift, primarily because of the difficulty of isolating an atmospheric response in observations and theinconsistent results among atmospheric models forced
with prescribed SST anomalies (Kushnir et al. 2002).
Epoch difference maps of the wind stress curl between
the periods before and after the 1976/77 shift, whichprimarily present the wind stress curl forcing pattern,
contain a hint of a local wind stress curl response in the
Kuroshio Extension in the form of a patch of negative
anomalies embedded within the pattern of basinwide
positive anomalies, similar to our CCSM2 results(Deser et al. 1999; Seager et al. 2001). Whether thisinferred wind stress curl response to SST anomalies
along the Kuroshio Extension associated with the 1976/
77 shift is strong enough to sustain a coupled ocean–
atmosphere mode of decadal variability similar to thatin CCSM2 requires further investigation. We note thatmany studies have suggested that the tropical Indo–
Pacific also contributed to the 1976/77 shift over the
North Pacific via an atmospheric bridge (Newman et al.
2003; Deser et al. 2004; Schneider and Cornuelle 2005).
The resolution of CCSM2 is not high enough to fully
resolve the narrow and complex SST frontal structuresin the Kuroshio Extension. Recent high-resolution satellite observations and eddy-resolving ocean general

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JOURNAL OF CLIMATE

circulation models indicate two separate frontal zones;
the stronger Oyashio Front near 40°N and weaker
Kuroshio Front near 35°N (Nakamura and Kazmin
2003; Taguchi et al. 2005; Nonaka et al. 2006). The
high-resolution ocean models also suggest that oceanic
adjustment to wind stress curl variability deviates fromlinear Rossby wave dynamics and the role of mesoscaleeddies becomes increasingly important near these narrow frontal zones. However, these high-resolution
ocean models and observations agree on that the SST
anomalies in the Kuroshio Extension related to 1976/77
shift is due to meridional displacement of the fronts andthe maximum SST anomalies are located along 40°N as
simulated in the CCSM2. The meridional shift of a narrower frontal zone would produce more localized andlarger amplitude SST anomalies than a broader frontal
zone, which in turn could affect the intensity of theatmospheric response.

5. Conclusions
North Pacific decadal variability in CCSM2 is examined from a 650-yr segment of the 1000-yr control integration of present-day climate. The leading EOF of
winter SST anomalies exhibits a PDO-like horseshoe
pattern with maximum variance concentrated along theKuroshio Extension. SST anomalies averaged over the
Kuroshio Extension region exhibit significant lowfrequency spectral peaks at approximately 16 and 40 yr.
Subsurface temperature anomalies within the upper
1000 m also exhibit decadal variability coherent with
the SST anomalies. Upper-ocean lateral geostrophic
heat flux divergence caused by a meridional shift ofKuroshio Extension is responsible for the decadal SST
variability in the Kuroshio Extension. Local surface
heat flux and Ekman heat flux divergence anomalies
act as damping and positive feedback, respectively, in
response to the SST anomalies.
The meridional shift of the Kuroshio Extension isforced by basin-scale wind stress curl anomalies 3–5 yrearlier. The wind stress curl forcing has one sign between 30° and 50°N centered near 170°W, which is similar to the leading EOF pattern of the wind stress curl.
This forcing pattern represents a meridional shift of thezero wind stress curl line rather than an in situ change
in amplitude of the Aleutian low. A simple linear
Rossby wave model was employed to illustrate that
only the low-frequency (⬎10-yr period) portion of thewind stress curl forcing is relevant to the oceanic decadal variability in the Kuroshio Extension, eventhough the overall shape of the spectrum of wind stresscurl forcing is similar to white noise. Furthermore, insignificant low-frequency spectral peaks at around 16 yr

VOLUME 20

and 40 yr in the wind stress curl forcing turned out to benecessary to reproduce the significant decadal oceanic(SSH) peaks in the Kuroshio Extension. The windstress curl response pattern to the SST anomalies in the
Kuroshio Extension, inferred from lag regression
analysis, exhibits a similar spatial structure to the windstress curl forcing pattern with opposite sign and ⬃35%
decrease in amplitude. This wind stress curl response
pattern was consistent with the response patterns obtained from two additional experiments, that is, thestand-alone CAM2 and the CAM2–ocean mixed layer
model experiments. Collectively, these results suggestthat the North Pacific decadal variability in CCSM2 is a
midlatitude coupled ocean–atmosphere mode with aweak ocean to atmosphere feedback as proposed by thetheory of Neelin and Weng (1999).
The oceanic processes involved in CCSM2’s North
Pacific decadal variability, from the wind stress curl
forcing to the decadal SST anomalies in the Kuroshio
Extension and even the damping by the surface heat
flux, are consistent with the processes involved in the
1976/77 PDO shift as found by observational and numerical modeling studies. The atmospheric response to
the decadal SST anomalies still requires further investigation and better understanding, partly because of diverse results from previous atmospheric general circulation model studies and also because of the challenging
task of examining the atmospheric response from observations or fully coupled model integrations.
Acknowledgments. We are very grateful to Dr. C.
Cassou for the CAM2–ocean mixed layer model and its
control integration output. We also wish to acknowledge helpful discussions and technical support frommany colleagues including M. Alexander, A. Capotondi, B. Qiu, A. Phillips, S. Yeager, and D. Shea andconstructive reviews by Dr. A. Miller and two anonymous reviewers. The first author gratefully acknowledges financial support from NOAA’s Office of Global
Programs (grant to C. Deser) and the CCSM Project
Office.
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