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The Impact of Cloud Radiative Feedback, Remote ENSO Forcing, and Entrainment on
the Persistence of North Pacific Sea Surface Temperature Anomalies
SUNGSU PARK
Advanced Study Program, National Center for Atmospheric Research, Boulder, Colorado

MICHAEL A. ALEXANDERNOAA/Earth System Research Laboratory, Boulder, Colorado

CLARA DESER
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado(Manuscript received 16 March 2005, in final form 24 January 2006)
ABSTRACTThe influence of cloud radiative feedback, remote ENSO heat flux forcing, and oceanic entrainment on
persisting North Pacific sea surface temperature (SST) anomalies is investigated using a stochasticallyforced ocean mixed layer model. The stochastic heat flux is estimated from an atmospheric general circulation model, the seasonally varying radiative feedback parameter and remote ENSO forcing are obtained
from observations, and entrainment is derived from the observed mean seasonal cycle of ocean mixed layer
depth. Persistence is examined via SST autocorrelations in the western, central, and subtropical eastern
North Pacific and for the leading pattern of variability across the basin. The contribution of clouds, ENSO,
and entrainment to SST persistence is evaluated by comparing simulations with and without each term.
The SST autocorrelation structure in the model closely resembles nature: the pattern correlation between
the two is 0.87–0.9 in the three regions and for the basinwide analyses, and 0.35–0.66 after subtracting an
exponential function representing the background damping resulting from air–sea heat fluxes. Positive
radiative feedback enhances SST autocorrelations (⬃0.1–0.3) from late spring to summer in the central andwestern Pacific and from late summer to fall in the subtropical eastern Pacific. The influence of the remote
ENSO forcing on SST autocorrelation varies with season and location with a maximum impact on thecorrelation magnitude of 0.2–0.3. The winter-to-winter recurrence of higher autocorrelations is caused byentrainment, which generally suppresses SST variability but returns thermal anomalies sequestered beneaththe mixed layer in summer back to the surface in the following fall/winter. This reemergence mechanism
enhances SST autocorrelation by ⬃0.3 at lags of 9–12 months from the previous winter in the western andcentral Pacific, but only slightly enhances autocorrelation (⬃0.1) in the subtropical eastern Pacific.
The impact of clouds, ENSO, and entrainment on the autocorrelation structure of the basinwide SST
anomaly pattern is similar to that in the western region. ENSO’s impact on the basinwide North Pacific SST
autocorrelation in an atmospheric general circulation model coupled to an ocean mixed layer model withobserved SSTs specified in the tropical Pacific is very similar to the results from the stochastic model
developed here.

1. Introduction
Extratropical air–sea interactions have the potential
to influence many aspects of the climate system. For

Corresponding author address: Sungsu Park, Department of Atmospheric Sciences, University of Washington, ATG Building,Box 351640, Seattle, WA 98195-1640.
E-mail: sungsu@atmos.washington.edu© 2006 American Meteorological Society

JCLI3957

example, sea surface temperature (SST) anomalies in
the North Pacific are closely associated with summer
precipitation and drought over North America (Barlow
et al. 2001). In turn, the summer subtropical high over
the North Pacific, which is strongly influenced by diabatic heating over the continental United States (Hoskins 1996), may feed back on preexisting SST anomalies by modulating marine stratiform clouds and downwelling radiative fluxes at the sea surface (Klein et al.

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1995; Norris et al. 1998, Park et al. 2005). A greater
knowledge of the factors that govern the evolution of
extratropical SST anomalies may enhance our understanding and ability to predict important climate, ecosystem, and socioeconomic variability.
To first order, the persistence or decay time of midlatitude SST anomalies can be simulated by a motionless, constant-depth mixed layer ocean model that isforced by rapidly varying (stochastic) atmospheric forcing and linearly damped by heat fluxes back to theatmosphere (Frankignoul and Hasselmann 1977). In
this most basic stochastic model, the damping parameter is constant over the seasonal cycle and can be estimated by fitting an exponential function to the lagged
autocorrelation of monthly SST anomalies. Several
studies (e.g., Reynolds 1978; Frankignoul and Reynolds
1983; Frankignoul 1985; Herterich and Hasselmann
1987; Ostrovskii and Piterbarg 1995, 2000) extended the
local stochastic forcing model to include advection,
horizontal diffusion, and turbulent entrainment of water into the mixed layer. SST persistence is further influenced by the seasonal cycle of the mixed layer depth(MLD) through its impact on the ocean’s thermal inertia and on the rate and the temperature of the water
entrained into the mixed layer from below (Namias etal. 1988; Alexander and Penland 1996; Deser et al.
2003). However, the influence of several processes on
North Pacific SST persistence over the course of theannual cycle have yet to be fully explored, including
SST–radiation feedback, ENSO-induced surface flux
anomalies, and the turbulent entrainment of temperature anomalies into the mixed layer.
Over the North Pacific, the fraction of marine stratiform clouds (MSCs) is greatest during summer (Klein
and Hartmann 1993). MSC and SST variations arestrongly coupled on monthly, interannual, and interdecadal time scales (Weare 1994; Norris and Leovy 1994;
Norris 2000; Park and Leovy 2004). MSCs cool theocean mixed layer in summer by reflecting incoming
solar radiation. In turn, cold SSTs enhance the formation of MSCs by increasing the static stability, whichtraps moisture in the boundary layer and lowers thecloud base, thereby increasing the thermal and moisture coupling between the surface and the cloud layer(Bretherton and Wyant 1997; Park et al. 2004). This
positive “SST–MSC” or “radiative” feedback has been
hypothesized to be important for summer-to-winterand interannual persistence of North Pacific SST
anomalies (Zhang et al. 1998; Norris et al. 1998). Recently, Park et al. (2005) found that positive radiative
feedback can substantially enhance the persistence of
SST anomalies in portions of the North Pacific in earlysummer.

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ENSO strongly affects the atmospheric circulation
and air–sea heat exchanges over the North Pacific
Ocean during winter, when conditions are favorable forpoleward planetary wave propagation from the tropical
Pacific (Wallace and Gutzler 1981; Trenberth et al.
1998; Cayan 1992). These ENSO teleconnections drivechanges in ocean basins remote from the equatorial
Pacific; thus the atmosphere can act like a “bridge”
between the tropical and North Pacific Ocean (e.g., Alexander 1992; Lau and Nath 1996; Alexander et al.
2002; Park 2004). Alexander et al. (2004) and Park andLeovy (2004) also found remote ENSO teleconnections
over the western North Pacific during summer with meridional shifts of the jet stream, extratropical storm
track and low-level cloud decks, and large-amplitude
SST anomalies in the Kuroshio extension region. Because the persistence of ENSO varies with the seasons(Torrence and Webster 1998), ENSO is likely to have acomplex influence on the persistence of North Pacific
SSTs.
The mixed layer depth in the North Pacific Ocean
undergoes a large seasonal cycle. During fall and winter, strong winds and surface cooling increase the MLD
via the turbulent entrainment of water into the mixed
layer from below. As a result the MLD reaches a maximum depth of ⬃100 (250) m in the eastern (western)
North Pacific by February–March. The mixed layer reforms close to the surface in the following spring resulting from increased insolation and decreased wind
mixing, resulting in minimum MLDs of ⬃10–30 m inJuly–August. Some of the thermal anomalies created in
the deep winter mixed layer are sequestered in thestatically stable seasonal thermocline (25–100 m) insummer, while air–sea fluxes strongly damp the concurrent SST anomalies. The subsurface anomalies are thenreentrained into the surface layer during the following
fall and winter when the ocean mixed layer deepens
again. As a result, winter and spring SST anomalies
recur in the following fall and winter, without persisting
through the intervening summer. This processes, firstobserved by Namias and Born (1970, 1974) and termed
the “reemergence mechanism” by Alexander and Deser (1995), occurs over much of the North Pacific Ocean(Alexander et al. 1999), but its timing and strength depends on the month the anomaly was created and theannual cycle of MLD at that location.
The goal of our study is to understand how cloudradiative feedback, remote ENSO forcing, and oceanic
entrainment contribute to the persistence of monthly
SST anomalies in the North Pacific Ocean. To this end,
we employ a stochastically forced entraining ocean
mixed layer model used by Deser et al. (2003), but with

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a more realistic representation of the stochastic forcing
and atmospheric feedback parameters, and the additionof remote ENSO heat flux and Ekman forcing. The
impact of each process on the SST persistence is assessed by comparing autocorrelations of monthly SST
anomalies as a function of calendar month and lag inthe full model to simulations in which one of the processes is excluded. In addition, results from the stochastic model are compared to those from an atmosphericgeneral circulation model (AGCM) that has observed
SSTs specified in the tropical Pacific and is coupled toa mixed layer model (MLM) over the remainder of the
global oceans.
The paper is structured as follows. Section 2 provides
a detailed description of the model and the methods
used for estimating the model parameter values andforcing fields. We apply the stochastic model to thecentral western Pacific region in section 3, and to thecentral and subtropical eastern Pacific regions in section 4. The persistence of the dominant pattern of␳Cp关MLD兴

North Pacific SST anomalies from the stochastic modeland from the AGCM–MLM simulations, are examinedin section 5. The results are summarized and discussed
in section 6.

2. The stochastic ocean mixed layer model withremote ENSO forcing
A stochastically driven ocean mixed layer model,
first introduced by Frankignoul and Hasselmann(1977), simulates the basic statistical properties of midlatitude SST anomalies reasonably well (e.g., Frankignoul 1985). Following Deser et al. (2003), the stochastic model used here also contains a simplified entrainment term estimated from the seasonally varying
climatological MLD in order to include the reemergence mechanism. We further added radiative feedback
and remote ENSO heat flux and Ekman forcing terms,
resulting in the following heat budget equation for theocean mixed layer:

冉 冊dT⬘ ⫽ F⬘ ⫺ 共␭LHF⫹SHF ⫹ ␭SW⫹LW ⫹ ␭res兲T⬘ ⫹ ␳Cp关We兴共T⬘b ⫺ T⬘兲 ⫹ f⬘ENSO,
dt

where t is time, [ ] is the seasonally varying climatological mean, prime is an anomaly from the mean, T is the
SST, F is the stochastic atmospheric heat flux and Ekman transport forcing, ␭ is the feedback parameter foreach surface heat flux component [latent heat flux
(LHF), sensible heat (SHF), shortwave (SW), and longwave (LW)], We is the entrainment rate, Tb is the temperature of sub–mixed layer water, fENSO is the remote
ENSO heat flux and Ekman forcing, ␳ is the density ofseawater (⫽1025 kg m⫺3), and Cp is the specific heat ofseawater at constant pressure (⫽4000 J kg⫺1 K⫺1). We
incorporated a residual feedback parameter ␭res to approximate the contribution of other oceanic feedbacks
and to compensate for the errors in the model parameter and forcing values. One of the main simplifying
assumptions in Eq. (1) is that the MLD follows a seasonally varying climatological mean, which neglects
several additional ways by which entrainment can impact SSTs (e.g., Frankignoul 1985; Alexander et al.
2000). Temperature advection by anomalous Ekman
currents is incorporated into the stochastic and ENSO
forcing, but other advective components are not included.
A key aspect of our approach is that the heat flux
feedback parameter and remote ENSO forcing are estimated directly from the observed monthly surface
heat fluxes and SST anomalies as a function of season

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and location. In the following sections, we will discuss
how monthly values of the individual forcing and feedback terms are estimated on a 5° latitude ⫻ 5° longitudegrid. After interpolating the monthly values to daily
values, the stochastic model was integrated with a daily
time step for the nominal period of 1951–2002 (theyears with reliable SST observations) using a thirdorder Adams–Bashforth method. The model’s monthly
SST anomalies on the 5° latitude ⫻ 5° longitude gridwere analyzed and compared with observations during
1953–2002, because the entrainment forcing is activatedin the third year of the simulations.
While the remote ENSO forcing includes realistic
long-term variations, the absence of dynamic ocean
processes, such as subduction, Rossby waves, and geostrophic advection from the MLM, limits the model’sability to simulate low-frequency SST variations. Thus,
we focus our analysis on interannual variations by subtracting the 10-yr running average from the monthly
SST anomalies (equivalent to a high-pass filter with ahalf-power point at 12.5 yr).

a. Stochastic forcing
To estimate the atmospheric stochastic forcing, feedback of the ocean on the surface heat fluxes should firstbe removed. Because the observed surface heat fluxes
always contain oceanic feedback, we estimate the sto-

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FIG. 1. The total standard deviation of the daily net surface heat flux (LHF ⫹ SHF ⫹ SW ⫹ LW) ⫹ Ekman
transport anomalies during (a) January and (b) July obtained from 10 yr of a CAM3 control simulation. The
contour interval is 20 W m⫺2, and shading indicates values ⬎100 W m⫺2.

chastic forcing from a simulation of the Community
Atmospheric Model [(CAM3), the most recent versionof the National Center for Atmospheric Research(NCAR) AGCM], which is forced by seasonally varying climatological SSTs. Using the last 10 yr of a 15-yr
simulation, we calculated the total downward daily surface heat flux anomalies by summing the latent, sensible, shortwave, and longwave fluxes and the implicit
Ekman heat transport, where the latter only includes
fluctuations in the wind.
The standard deviation of the surface fluxes is shownfor January and July in Figs. 1a,b, respectively. In January, the greatest flux variability occurs along a zonal
band extending along ⬃35°N, with values exceeding
200 W m⫺2 west of the date line. Relatively weak variability occurs over the northwestern and subtropicaleastern Pacific Ocean. The flux variability is substantially weaker in summer than in winter, but it still exceeds 40 W m⫺2 over most of the basin.
The stochastic forcing fields are derived by first randomly selecting monthly values (with replacement),
where the months follow the calendar but the year is
randomly chosen from the last 10 yr of the CAM3 simulation. For example, a given reconstructed time series
could be ordered as follows: January (year 5), February(year 2), March (year 5), . . . , December (year 8). This
resampling is repeated to form a 52-yr time series. Thenthe consecutive daily fluxes over the North Pacificwithin each month of the time series is used to drive thestochastic ocean model. Because the decorrelation timescale of the surface fluxes is 1–3 days (not shown), thisresampling procedure nearly preserves the persistence,spatial continuity, and magnitude of the original flux
variability. Daily surface fluxes obtained from the National Centers for Environmental Prediction (NCEP)–
NCAR reanalysis during 1951–2002 that have been
high-pass filtered (⬍⬃30 days) to partially remove air–sea feedback and remote forcing produced similar estimates of the stochastic forcing presented in Fig. 1 (notshown).

b. Heat flux feedback parameters
The heat flux feedback parameter (␭) values used inthis study are from Park et al. (2005). They estimated ␭from the observed monthly anomalies of SST and thesurface heat fluxes over most of the world’s oceans on
a 5° latitude ⫻ 10° longitude grid, which has been interpolated to 5° ⫻ 5° grid for this study. Figure 2 shows
the turbulent (SHF ⫹ LHF; upper), radiative (SW ⫹LW; middle) and net (lower) heat flux feedback parameters during January (left) and July (right), where positive values indicate negative feedback on the SST
anomalies. The turbulent heat flux feedback damps
SST anomalies (␭SHF⫹LHF ⬎ 0) for all seasons withmaximum (minimum) damping during winter (summer) when the mean surface wind is strongest (weakest) and the moistening and warming of the nearsurface air over warm SST anomalies is relatively weak
(strong). Areas of strong damping are located over the
far western subtropical Pacific Ocean and north of the
Hawaii Islands during winter. During spring and summer the radiative feedback is generally positive(␭SW⫹LW ⬍ 0) in the midlatitude North Pacific, with amagnitude comparable to that of ␭SHF⫹LHF. The positive radiative feedback over the central and eastern
North Pacific Ocean is mainly due to strong SW feedback resulting from variations in the fraction of MSCs
and fog. The net surface heat flux feedback is generally
negative with the strongest (weakest) damping during
winter (summer), but regions of weak, positive feedback occur over the northwestern and northeastern Pacific during summer. A more detailed discussion of the
feedbacks is given in Park et al. (2005).

c. Remote ENSO forcing
The seasonally dependent remote ENSO forcing isestimated as follows. First, ENSO indices were obtained from EOF analysis of monthly SST anomalies
over the tropical Pacific and Indian Oceans during
1951–2002. The first three modes, which are well sepa-

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FIG. 2. The heat flux feedback parameters derived from observations (see Park et al. 2005). The (a), (b) turbulent(LHF ⫹ SHF), (c), (d) radiative, and (e), (f) net feedback values during January (left) and July (right). The contour
interval is 5 W m⫺2 K⫺1 with solid (dashed) lines for positive (negative) values. Shading indicates values ⬎20 W
m⫺2 K⫺1.

rated from the others by the criteria of North et al.
(1982) and have major centers of variance along theequator, were chosen to represent ENSO. The firstprincipal component (PC)—the time series giving theamplitude and polarity of the leading EOF—primarily
represents the variability associated with the maturephase of ENSO and is highly correlated with the Niño3.4 index. The second PC exhibits an increasing trendwith decadal variations, somewhat similar to the variability in Deser et al.’s (2004) tropical climate index,
while the third PC is similar to the trans-Niño index(TNI; Trenberth and Stepaniak 2001; not shown).
Then, the simultaneous seasonal regression anomaliesof surface turbulent heat fluxes and Ekman heat transport on each standardized PCs were estimated using 42
yr (1956–97) of ship-observed monthly turbulent heat
fluxes and 17 yr (1984–2000) of satellite-derived
monthly radiative fluxes as described in Park et al.
(2005). Although the PCs based on all months are orthogonal to each other, seasonal subsets of the PCs arenot. Thus, to avoid counting the ENSO-induced forcings by different PCs multiple times, seasonal flux

anomalies for a specific ENSO mode were estimated byregressing the residual monthly heat flux anomalies,
which do not contain any flux components that simultaneously vary with the other ENSO PCs. However,these regression anomalies include local feedback effects that are already parameterized in the stochastic
model [Eq. (1)]. We estimated this local feedback bymultiplying the simultaneous ENSO regression anomalies of SST by the net heat flux feedback (Fig. 2). The
direct monthly ENSO forcing at each grid point wasthen calculated as follows:
3f⬘ENSO共t兲 ⫽

兺

k⫽1

4

关Rk,Ekman ⫹兺 共R ⫹ ␭ R 兲兴E 共t兲,
k,i

i

k,T

k

i⫽1

共2兲
where R is the regression of flux anomalies on ENSO,
k and i are the indices of the ENSO modes and surface
heat flux components, respectively, and E is the standardized ENSO PCs for the period of 1951–2002.
The downward surface heat flux and Ekman trans-

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FIG. 3. Standardized ENSO regression anomalies, corrected for local air–sea feedback, of the (a), (b) total (netsurface heat flux ⫹ Ekman), (c), (d) turbulent, (e), (f) Ekman, and (g), (h) radiative heat fluxes into the ocean
during January (left; contour interval 5 W m⫺2, with shading for values ⬎|10| W m⫺2) and July (right; contour
interval 2.5 W m⫺2, with shading for values ⬎|5| W m⫺2) regressed on the first PC of SST anomalies in the tropical
Pacific–Indian Oceans. The three regions discussed in sections 3 and 4 are also indicated in (a).

port anomalies associated with the first ENSO mode,
corrected for local feedback effect, are shown in Fig. 3.
During January, the remote ENSO forcing strongly
cools the central North Pacific with maximum amplitude over 30 W m⫺2 at 35°N, 152.5°W. About 2/3 of thiscooling is from the turbulent heat fluxes, with the remainder from Ekman transport. Positive anomalies occur in the southwestern and eastern parts of the domain, where the warming in the former exceeds 20 Wm⫺2 and is largely due to the anomalous turbulent heat
fluxes. During July, the ENSO-induced heat fluxes coolthe ocean along 37.5°N to the west of the date line witha maximum magnitude of ⬃10 W m⫺2 (Fig. 3b), consistent with Alexander et al. (2004) and Park and Leovy(2004). About half of this cooling is from SHF ⫹ LHF
and the remaining half is from SW ⫹ LW, which is incontrast to January when the radiative contribution is
negligible. Although weaker than in winter, the bridgeinduced forcing still has a significant influence on the
SST tendency during summer, because the mixed layer
is very shallow (about 5%–15% of the MLD in winter)and thus has low thermal inertia.

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d. Oceanic entrainment
In our study, the entrainment rate is defined by thetendency of the seasonally varying climatological MLD,
that is, [We] ⫽ d[MLD]/dt when the mixed layer deepens and [We] ⫽ 0 when it shoals. The MLD is obtained
from Monterey and Levitus (1997) based on a potential
density criterion. Unrealistic MLD values at a few
points in the northwest Pacific were replaced using interpolation, and the MLD was constrained to begreater than 10 m to prevent the simulated SST anomalies from becoming unreasonably large.
In Deser et al. (2003), the temperature anomaly of
sub–mixed layer water (T⬘b) at a given depth within theseasonal thermocline was set to the SST anomaly when
the MLD shoaled above that depth. Here, we also allowT⬘b to evolve according toT⬘b共t兲 ⫽ T⬘共t ⫺ ⌬t兲exp共⫺k⌬t兲,

共3兲where ⌬t is the time period that seawater is sequesteredin the seasonal thermocline and k is the effective diffusivity, which roughly represents damping from several
processes, including eddy mixing and subduction. Although k is likely to be a function of location, weroughly estimated k to have a constant value of 10⫺8 s⫺1by analyzing Deser et al.’s (2003) autocorrelation mapsof subsurface temperature anomalies at several locations in the North Pacific.

e. Residual feedback parameter
Even though Eq. (1) incorporates many important
physical processes, the amplitude of the simulated SST
anomalies may be unrealistic because of neglected
terms (e.g., advection) or errors in the model parameters and forcings. The impact of all of the error sources
is crudely incorporated into the model by specifying a
residual feedback parameter ␭res. Because the goal ofthis study is to simulate the statistical properties of theobserved monthly SST anomalies, we estimated ␭ressuch that the variance of monthly SST anomalies issimilar to the observed variance at each grid point.
The residual feedback over the North Pacific isshown in Fig. 4. The magnitude of ␭res is generally lessthan 10 W m⫺2 K⫺1, except for parts of the western
North Pacific south of 35°N and the eastern subtropical
Pacific near 27.5°N, 135°W. Because ␭res is calculated asa time-independent constant, it forces the simulated
year-round variance of monthly SST anomalies to berealistic, but not the seasonal variance. The simulatedmonthly SST variance, however, is fairly realistic, because the model–observation pattern correlations range
from 0.72 (April) to 0.92 (September) and the percentage difference between the two ranges from 11.3%(February) to 19.5% (August).

FIG. 4. The residual feedback parameter. Contour interval is
5 W m⫺2 K⫺1 with solid (dashed) lines for positive (negative)
values.

3. The central western PacificIn this section we explore the mechanisms responsible for the persistence of monthly SST anomalies in
the central western Pacific region (CWP; 35°–45°N,
155°E–175°W; see Fig. 3a). This region was selected
because it is characterized by homogeneous positive
radiative feedback during summer (Fig. 2d), strong remote ENSO forcing both in January and July (Fig. 3),
and a large annual cycle in MLD that is conducive fora strong reemergence signal (Deser et al. 2003). In addition, the maximum loading of the dominant patternof North Pacific SST anomalies is located in the CWP
region (Fig. 11a).
The annual cycle of the feedback parameters (larger
positive values indicate greater damping) and MLD
values in the CWP region are shown in Fig. 5a, while
the magnitudes of stochastic and ENSO heat flux forcings are presented in Fig. 5b. The MLD is deepest during March (190 m) and shallowest during August (11m), while the maximum and minimum damping occurs
in November (26 W m⫺2 K⫺1) and June (⫺1 W m⫺2
K⫺1). Positive radiative feedback (as indicated by
␭net ⬍ ␭LHF ⫹ ␭SHF in Fig. 5a) amplifies SST anomalies
throughout the year, with the greatest effect during latespring–early summer. The stochastic forcing amplitudeis largest during winter (⬃200 W m⫺2) and smallest
during summer (⬃50 W m⫺2). Surface fluxes associatedwith the ENSO modes strongly force the ocean in the
CWP region during January–March and August–
September. We note that the ENSO-related heat flux
forcing in this region is primarily associated with thefirst mode, except during summer when the magnitudesof the first two modes are comparable (not shown).
Although the magnitude of the ENSO-related forcing is much smaller than the stochastic forcing, theformer should still have a strong influence on the SST
persistence because the remote forcing is scaled by thestandard deviation of the ENSO PC values, which canexceed 3, and more importantly, the ENSO forcing has
much greater persistence than the stochastic forcing.

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FIG. 5. (a) The observed ocean MLD (bar) and the net (␭net ⫽␭LHF ⫹ ␭SHF ⫹ ␭SW ⫹ ␭LW; dashed line) and turbulent heat flux
feedback (␭LHF ⫹ ␭SHF; solid line), and (b) the magnitudes ofstochastic heat flux forcing (bar) and remote heat flux ENSO
forcing (lines) in the CWP region (35°–45°N, 155°–185°E). The
magnitude of remote heat flux ENSO forcing is defined by
公兺3i⫽1r2i , where r is the regression of heat flux anomalies on
ENSO with local heat flux correction [see Eq. (3)] and i is the
ENSO index; ␭res ⫽ ⫺6.5 W m⫺2 K⫺1. Note that ␭ ⬎ 0 indicatesdamping and positive radiative feedback is indicated by ␭net ⬍␭LHF ⫹ ␭SHF.

a. Control simulation and observed
autocorrelations
The monthly SST autocorrelations as a function of
calendar month and lag from the control simulation,
which includes all feedbacks and forcings, and fromobservations are shown for the CWP region in Fig. 6.
The simulated autocorrelations are based on the en-

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semble average from a set of 50 simulations, where eachensemble member has the same ENSO forcing for theyears 1951–2002, but different stochastic forcings. For a
more robust estimate, the observed SST autocorrelations are based on the average of the following four
different sources: the Comprehensive Ocean–Atmosphere Data Set (COADS; Woodruff et al. 1987) for
1956–97, Met Office historical SST dataset (Folland
and Parker 1995) for 1950–99, Lamont-Doherty Earth
Observatory analysis (Kaplan et al. 1998) for 1953–
2002, and Reynolds Reconstructed SST (Reynolds andSmith 1994) for 1950–99.
Overall, the structures of the observed and simulated
autocorrelations are very similar; the pattern correlation between Figs. 6a and 6b is close to 0.9 (Table 1).
For the initial or reference months of January–June,the correlations in Figs. 6a and 6b decrease until fall,then increase, reaching a maximum in February–
March, and then decrease again to a minimum in thefollowing September–October. This late-winter peak in
autocorrelations is the signature of the reemergencemechanism.
The high pattern correlation between observations
and the model is partly due to the initial persistence of
SST anomalies resulting from the thermal inertia of theupper ocean described by Frankignoul and Hasselmann(1977). To remove this basic persistence, we first fit an
exponential function [exp (⫺␭c␶ ); where ␭c is a seasonal constant and ␶ the lag] to the SST autocorrelations for each calendar month using lags of ⬍5 months
in both the model and observations. The observed–
simulated pattern correlation is 0.66 when the exponential based on observations is subtracted from both theobservations and the model, and is 0.49 when the observed and modeled exponentials are subtracted fromtheir respective autocorrelations (Table 1). This degradation occurs because the initial persistence is toostrong over the first few months of the model simulation during winter (see Figs. 6c and 6d).
Lag autocorrelations beginning in April and September and extending over the next 24 months (extended
horizontal cross sections through Figs. 6a,b) are presented in Figs. 6c and 6d, respectively. Error bars givenby the standard deviation of the autocorrelations obtained from the 50-member ensemble and from the four
observation datasets are also shown at each lag. The
model is generally consistent with the observed autocorrelation values considering the uncertainties in both
estimates, except it underestimates the initial decay of
the SST anomalies and does not reproduce the enhanced correlations at lags of 13–20 months for thereference month of September.

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FIG. 6. Ensemble mean autocorrelation (r) of high-pass-filtered (⬍12.5 yr) monthly SST anomalies in CWP asa function of calendar month and lag from (a) observations obtained from the average of four different datasetsand (b) 50-member ensemble of the complete stochastic model. The contour/shading interval is 0.1 and the zero
contour is indicated by a red line. Lagged autocorrelations out to 24 months for the reference months of (c) Apriland (d) September, where vertical bars indicate one std dev of the autocorrelations at each lag.

b. Sensitivity of SST persistence to forcing andfeedbacks
We assess the relative contribution of radiative feedback, remote ENSO forcing, and entrainment on thepersistence of monthly SST anomalies by comparing
the control simulation to a series of sensitivity simulations where one of the processes is excluded but allother model parameters and forcings are the same as in
the control. The autocorrelations of monthly SST
anomalies from the three sensitivity simulations are indicated as color shading in Figs. 7a–c, while the anomalous autocorrelation (⌬r) values, obtained from thecontrol minus the respective sensitivity simulation, areindicated by black lines.

The radiative feedback generally enhances SST autocorrelations in CWP for all months and lags with amaximum increase of ⌬r ⬃0.15 for lags terminating inAugust (2–6-month lags for the reference months of
February–June). The change in SST persistence resulting from f⬘ENSO has a somewhat larger amplitude (⌬r⬃0.25) but a more complex structure than that resulting
from ␭SW⫹LW. ENSO forcing decreases r for lags initiated in January–July and terminating in September, but
increases r for the reference months of August–October for lags of up to 10 months. An and Wang
(2005) also found that ENSO enhanced the persistenceof North Pacific SST anomalies in summer; although
they found the maximum enhancement occurred in
June–July rather than in August–September. The in-

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JOURNAL OF CLIMATETABLE 1. The pattern correlation between the observed andsimulated autocorrelation structure of monthly SST anomalies inthe three regions and in the basinwide analyses. Values are given
for the full structure and with an exponential function [exp(⫺␭c␶)] removed; the latter represents the basic decay of SST
anomalies (e.g., see Frankignoul and Hasselmann 1977) and isestimated using lags of ⬍5 months. The observed exp (⫺␭c␶) isalways subtracted from the observed autocorrelation structure,
while the observed or simulated exp (⫺␭c␶) is subtracted from themodel’s structure.

DomainFull

Removed obs
exp(⫺␭c␶)Removed obs andmodel exp(⫺␭c␶)

CWP
CEP
SEP
Basin

0.87
0.88
0.90
0.89

0.66
0.43
0.40
0.60

0.49
0.37
0.35
0.48

crease in persistence does not necessarily imply a corresponding increase in SST variability: ENSO forcing
as well as radiative feedback enhances the interannual
variance of SST anomalies for all months (not shown).
The sensitivity of the SST persistence to the atmospheric bridge will likely depend on the temporal evolution of the remote ENSO forcing. Autocorrelationsof f⬘ENSO in the CWP region are shown in Fig. 8. Beginning in summer, the persistence of the remote
ENSO forcing is very strong, with correlations ⬎0.4 forlags of up to 8 months. The persistence gradually decreases through fall and winter until it reaches a minimum in May, when r ⬃0.1 at 1-month lag. While theshort-term (⬍⬃6 month) persistence is weak in spring,f⬘ENSO exhibits strong negative correlations at lags of⬃14–16 months. This is consistent with the evolution of
ENSO itself, which exhibits a spring persistence barrierand some tendency for ENSO to change states over 1–2
yr (e.g., Torrence and Webster 1998). Comparison ofFig. 8 with Fig. 7b indicates that the persistence off⬘ENSO and SST anomalies are related to each other,with the SST autocorrelation lagging the ENSO forcing
by 2–3 months. The main exception occurs for the reference months of May–June when the ENSO forcing isvery weak (see Fig. 5b).
The impact of entrainment on enhancing SST persistence is relatively strong with a maximum amplitude of⌬r ⬃0.25 (Fig. 7c). The largest positive anomalies occur
during the lagged month of March when the ocean
MLD is deepest, and thus the full extent of the thermal
anomalies created during the previous winter andspring can be entrained into the mixed layer via thereemergence mechanism. Entrainment also damps SST
anomalies. While entrainment reduces the initial SST
persistence for nearly all reference months, it is especially pronounced for the lagged months of July–

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August (⌬r ⬃0.45). For the reference months of September–February, the concurrent SST and Tb anomalies are uncorrelated with each other and thus
entrainment dilutes thermal anomalies in the mixed
layer and thus damps SST anomalies [Eq. (1); Frankignoul 1985]. Entrainment also decreases autocorrelations of April–June SST anomalies at lags of 1–4
months, even though the mixed layer shoals and thus
the entrainment forcing is zero by construction in themodel. This is due to a significant decrease in the interannual variance of monthly SST anomalies by entrainment (not shown); for the same random atmospheric and ENSO forcing, autocorrelation of SST
anomalies spread over a narrow range decay more rapidly than anomalies spread over a wide range.

4. The central eastern Pacific and subtropicaleastern PacificIn this section, we apply the stochastic model to regions in the central eastern Pacific (CEP; 30°–40°N,
170°–145°W, see Fig. 3a) and subtropical eastern Pacific(SEP; 25°–35°N, 140°–120°W). We chose these two regions because CEP is strongly influenced by ENSO
during winter (Fig. 3a), while SEP is characterized bystrong, positive radiative feedback during late summer
and autumn when the MSC fraction reaches a maximum (Klein and Hartmann 1993). The MLD, feedback
parameter, and forcing values are shown for the CEP(SEP) on the left (right) side of Fig. 9. Compared to the
CWP region, CEP has a reduced annual cycle of MLD
and stochastic forcing, stronger positive radiative feedback during late spring and early summer, and stronger(weaker) ENSO forcing during winter (summer). Relative to the other two regions, SEP has a weaker annual
cycle of MLD and stochastic forcing, negligible ENSO
forcing, and stronger (weaker) positive radiative feedback during August–October (March–June).
The autocorrelations for observations (top row) andfor simulations without radiative feedback (secondrow), remote ENSO forcing (third row), and entrainment (fourth row) in the CEP (left) and SEP regions(right) are shown in Figs. 10a–h. The pattern correlation between the observed and simulated autocorrelations in CEP and SEP is ⬃0.9, which decreases to ⬃0.4
when exp (⫺␭c␶) is subtracted from the original autocorrelations (Table 1). Similar to the CWP, simulations
tend to overestimate the initial persistence of the SST
anomalies and underestimate reemergence signals.
These are speculated to be associated in part with thereplacement of unresolved error sources by positive residual feedback parameters here. Positive radiative
feedback has a greater impact on SST persistence in1 DECEMBER 2006

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FIG. 8. Autocorrelation structure of the monthly forcing
resulting from ENSO in the CWP region.

CEP (Fig. 10c) than in CWP (Fig. 7a) during spring andsummer; that is, ⌬r has a maximum value of ⬃0.3 forlags that terminate in August, which is roughly double
the corresponding CWP value. Another positive
anomaly center in Fig. 10c occurs at a lag of 4 months
for the reference month of August (⌬r ⬃0.2). The radiative feedback is largest in fall to early winter in SEP,
although its impact on SSTs is modest (Fig. 10d; ⌬r⬃0.1). In general, f⬘ENSO decreases (increases) SST persistence for the reference months of February–June(July–December) for lags of up to 1 yr. In contrast,f⬘ENSO has a negligible impact on the SST persistence in
SEP. The general pattern of ⌬r resulting from entrainment in CEP and SEP are similar to the pattern in
CWP, but systematic differences exist in the timing andstrength of the reemergence mechanism.

5. Basinwide analysis
a. Stochastic model
In this section, we examine the persistence of thedominant mode of monthly SST anomalies in the North←
FIG. 7. The total (shading; interval 0.1) and anomalous (⌬r ⫽control simulation ⫺ –sensitivity simulation, black contours; interval 0.05) autocorrelations of the sensitivity simulations without(a) radiative feedback, (b) remote ENSO forcing, and (c) entrainment in the CWP region. Red lines indicate zero autocorrelation.
All analyses are based on high-pass-filtered (⬍12.5 yr) monthly
SST anomalies.

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FIG. 9. As in Fig. 5, but for the (left) CEP (30°–40°N, 170°–145°W) and (right) SEP(25°–35°N, 140°–120°W) regions. (a), (b) The
observed MLD (bars) and heat flux feedback parameters (lines), and (c), (d) the magnitude of stochastic heat flux forcing (bars) andthe remote heat flux ENSO forcing (lines) are shown; ␭res ⫽ ⫺3.1 W m⫺2 K⫺1 in the CEP and ␭res ⫽ ⫺6.5 W m⫺2 K⫺1 in the SEP.

Pacific Ocean. A 50-member ensemble of the control
and the three sensitivity simulations were performed
using the ENSO and stochastic forcing (described in
section 2) for all North Pacific grid squares during theyears 1951–2002. EOF analyses were performed on thesimulated monthly SST anomalies on the 5° latitude ⫻5° longitude grid and compared with observations. The

observed SST autocorrelations shown here are basedon the projections of the four different sources ofmonthly SST anomalies (see section 3a) on the observed dominant mode obtained from Reynolds andSmith (1994) over a 50-yr period (1950–99).
The observed and simulated dominant patterns ofmonthly SST anomalies are shown in Fig. 11. In gen-→
FIG. 10. Same as Fig. 7, but the total (shaded) and anomalous (contours) autocorrelations in the (left) CEP and (right) SEP regions,with the observed autocorrelations in the top panels. All analyses are based on high-pass-filtered (⬍12.5 yr) monthly SST anomalies
in the CEP and SEP regions.

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FIG. 11. The (a) observed and (b) simulated dominant patternof the total (unfiltered) monthly SST anomalies over the North
Pacific. Contour interval is 0.1 K with solid (dotted) lines for
positive (negative) anomalies. The observations were obtained
from Reynolds and Smith (1994) for a 50-yr period (1950–99),
while the simulated results are based on a 50-member ensemble
for a 52-yr period (nominally 1951–2002). The fraction of varianceexplained by EOF 1 is 28.8% (22.2%) for observations (themodel). The pattern correlation between (a) and (b) is 0.90.

eral, the simulation reproduces the observed leading
EOF structure (a pattern correlation of 0.9) with similar
fractions of variance explained by the leading EOFs
(28.8% for the observation, 22.2% for the simulation).

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The autocorrelations of the observed and simulatedmonthly SST anomalies projected on the observed
dominant mode are shown in Figs. 12a and 12b, respectively. (The autocorrelation pattern of the simulated
dominant PC is similar to Fig. 12b; not shown.) The
simulated autocorrelation pattern of the dominantmode, which resembles the autocorrelation pattern in
the CWP region (Fig. 6b), resembles observations(Table 1).
The total (shading) and anomalous (lines) autocorrelations of the simulated PC projected on the observed
dominant mode for the three sensitivity experiments
are shown in Fig. 13. The anomalous SST persistence
patterns are similar to those in the CWP region (Fig. 7)
in that ⌬r is (a) enhanced by positive radiative feedback
centered on the lags terminating in August (⌬r ⬃0.2);(b) reduced by remote ENSO forcing for lags terminating in September (⌬r ⬃ ⫺0.25), but enhanced in otherseasons for lags of up to 1 yr (⌬r ⬃0.25); and (c)strongly damped by entrainment in summer (⌬r ⬃⫺0.4) and enhanced afterward, reaching a maximum in
January–February resulting from the reemergencemechanism (⌬r ⬃0.3). As in the local analysis, a change
in SST persistence does not necessarily mean a similarchange in SST variability, that is, ENSO forcing enhances SST variability while entrainment reduces it(not shown).

b. AGCM–MLM simulations
To verify the results from the stochastic simulations,
we analyzed output from an AGCM coupled to an
ocean MLM, which provides a more complete repre-

FIG. 12. Persistence of the (a) observed and (b) simulated leading first PC of North Pacific SST anomalies as afunction of the reference calendar month and lag as obtained from the high-pass-filtered (⬍12.5 yr) time seriesof the projection of the (a) simulated and (b) observed monthly SST anomalies on the observed leading EOF(Fig. 11a).

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6257

sentation of the relevant physical processes, including
air–sea interaction, ENSO forcing, and entrainment.
We make use of an ensemble of 16 Geophysical Fluid
Dynamics Laboratory (GFDL) R30 AGCM–MLM integrations, described by Alexander et al. (2002). Eachsimulation spans the period of 1950–99 and consists ofobserved monthly SSTs prescribed in the eastern tropical Pacific (15°N–15°S, 172°E–South American coast)
and a grid of one-dimensional mixed layer models over
the remainder of the global oceans. This model configuration incorporates ENSO variability through theboundary forcing in the tropical Pacific, which is communicated to the global oceans via the atmosphericbridge. The ocean model simulates mixed layer temperature (⫽SST), salinity, and depth, resulting fromair–sea fluxes and entrainment. In contrast to the stochastic simulations, the mixed layer is not constrained
to follow the mean seasonal cycle in the MLM, allowing
for variability in MLD, We, and Tb to impact SST⬘ [seeEq. (1); Alexander et al. 2000]. There are no currents,
however, or any other communication between gridcells.
The leading pattern of the “total” variability in
AGCM–MLM over the North Pacific (Fig. 14a) is obtained from the first EOF of monthly SST variability
where all 16 simulations have been concatenated toform an 800-yr time series. The dominant pattern of theNorth Pacific SST variability in the AGCM–MLM issimilar to both the stochastic simulation and observations; that is, the pattern correlation between Fig. 14a
and Fig. 11a (Fig. 11b) is 0.89 (0.76). The fraction ofvariance explained by EOF 1 (17.7%), however, issmaller than in observations (28.8%). The autocorrelation structure of the first PC obtained from the total
SST time series in the AGCM–MLM experiment (color
shading in Fig. 14b) resembles the autocorrelations
from observations and the stochastic model (Fig. 13),although the reemerging anomalies occur at shorterlags and the decay of summertime SST anomalies occurs more rapidly in the MLM compared to in nature.
The ENSO-related SST signal is estimated by first
ensemble averaging the monthly anomalies, which
greatly reduces the internal variability in the atmosphere–ocean system, before computing the leading
EOF over the North Pacific, and the “residual” is theobtained from the SST anomalies with the ensemble←
FIG. 13. Same as Fig. 7, but for the high-pass-filtered (⬍12.5 yr)time series of the simulated monthly SST anomalies projected on
the observed leading EOF (Fig. 11a) for the sensitivity simulations without (a) radiative feedback, (b) ENSO forcing, and (c)entrainment.

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FIG. 14. Analysis of 16 50-yr simulations of an AGCM that has
observed SSTs specified in the tropical Pacific for the period
1950–99 and is also coupled to a mixed layer model over the restof the global oceans. (a) EOF 1 of the “total” unfiltered monthly
SST anomalies over the North Pacific, in which all 16 simulations
have been strung together to form one long time series. (b) The
autocorrelations of the corresponding PC of the total variability(shading; interval 0.1) and the variability resulting from ENSO
(contours; interval 0.05) obtained from the first PC of the 16member ensemble mean. The fraction of variance explained by
EOF 1 in (a) is 17.7%.

mean removed and includes the impact of air–sea interaction and entrainment on SSTs. ENSO (lines in Fig.
14b) enhances the persistence of North Pacific SST
anomalies, especially from late summer through the following winter, as in the stochastic simulation (Fig. 13b).
The close correspondence between ⌬r obtained fromthe stochastic model and the AGCM–MLM is indicatedby the high pattern correlation (0.75) and low mean
absolute difference (0.06) between the two.

6. Summary and discussion
We investigated how cloud radiative feedback, remote ENSO forcing, and oceanic entrainment contribute to the persistence of monthly SST anomalies in the
North Pacific using a stochastically forced ocean mixed
layer model. The radiative feedback and ENSO forcing
were directly estimated from observations, while the

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stochastic forcing was derived from an AGCM simulation with climatological SSTs as boundary conditions:
all three forcings are a function of month and location.
The heat flux through the base of the mixed layer depends on the entrainment rate and temperature anomalies within and below the mixed layer. The entrainment
rate, obtained from the observed climatological ocean
MLD, repeats the same annual cycle each year. The
anomalous temperature at a given level within the seasonal thermocline is initially set to the SST anomaly
when the MLD shoals to that level in spring. It is subsequently reduced by weak, linear damping and then isreentrained into the surface layer when the MLD deepens to that level in the following fall or winter. Finally,neglected physical processes and model biases are
crudely represented in the model by specifying a constant residual feedback parameter that forces the simulated SST variance to match the observed variance ineach 5° ⫻ 5° grid square. The contribution of the radiative feedback, ENSO forcing, and entrainment on
the high-pass-filtered (⬍12.5 yr) SST autocorrelations
is examined by comparing simulations without eachprocess to the full or control simulation.
The model simulates the observed SST autocorrelation structure in the central western Pacific (CWP) region reasonably well. Radiative feedback enhances thepersistence of winter–spring SST anomalies for lags
ending in August. ENSO-induced forcing tends to reduce the persistence of winter–spring SST anomalies
for lags terminating in September, but it enhances autocorrelations for the reference period of August–October especially for lags of 2–9 months. The sensitivity of SSTs to ENSO forcing depends on the persistence of that forcing. Entrainment reduces SST
persistence for lags ending in July–August and enhances it for lags extending to March. The latter reflects
the reemergence of SST anomalies that were stored
below the mixed layer in summer, while the former is acombined result of entrainment diluting the SST
anomalies initiated in fall and winter and suppressing
SST variability throughout the year.
Similar analyses were performed in the central eastern Pacific (CEP) and subtropical eastern Pacific (SEP)
regions. In CEP, radiative enhancement of SST persistence is generally similar to but stronger than that in
CWP, while in SEP the maximum radiative enhancement occurs during August–October at lags of 3–6
months. Remote ENSO forcing can either enhance or
reduce SST persistence in the CEP, while it has a negligible impact on persistence in the SEP. The enhancement of SST persistence by the reemergence mechanism is weakest in SEP, while damping is strongest

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(weakest) in the CWP (CEP) due partly to the strong(weak) suppression of SST variability by entrainment.
Finally, we examined the persistence of the dominantpattern of SST variability across the North Pacific. The
impact of radiative feedback, ENSO, and entrainment
on SST persistence are generally similar to those in the
CWP, because the maximum loading of the leading
EOF of SST is centered in this region. The winter-tospring persistence of the dominant mode is slightly enhanced by remote ENSO forcing but is reduced by entrainment with a negligible contribution by radiative
feedback. However, late-spring-to-summer persistence
is strongly enhanced by radiative feedback but reduced
by entrainment by a comparable amount. Zhang et al.
(1998) found that in spite of shallow MLD in summer,
the summer-to-winter persistence of the dominantmode of North Pacific SST anomalies is stronger thanwinter-to-summer persistence, which they attributed tostrong, positive radiative feedback during summer inassociation with marine stratiform clouds (Norris et al.
1998). While our results show that radiative feedback
enhances summer-to-winter SST persistence, as hypothesized by Zhang et al. (1998) and Norris et al.
(1998), this effect is modest (⌬r ⬍ 0.1), and entrainmentand remote ENSO forcing have a greater impact on thesummer-to-winter persistence (⌬r ⬃0.15–0.2) than theradiative feedback. However, it should be noted thatthe impact of these processes might not be isolated
from each other. For example, ENSO can create SST
anomalies during both winter and summer—the former
can be stored beneath the mixed layer in summer andrecur in the following winter, while the latter can beenhanced by positive MSC–SST feedback.
We also examined the autocorrelation structure of
the dominant mode of North Pacific SST anomalies
obtained from a AGCM coupled to a variable-depth
mixed layer ocean model forced by observed SSTs in
the tropical Pacific. The full autocorrelation structure
and the influence of ENSO forcing on this structure
were similar to the stochastic simulations and nature.
This suggests that both types of models can provide
insight into how various processes influence the evolution of extratropical SST anomalies.
Several processes not included in the entraining
mixed layer model may also influence extratropical SST
persistence. In addition to its impact on surface fluxes
on monthly time scales, ENSO also affects the strengthand location of the North Pacific storm track (e.g., Renwick and Wallace 1996; Compo et al. 2001), and thus
will modulate the stochastic forcing—a process that is
not included here. In addition, entrainment is treated ina highly idealized manner and does not include submonthly or interannual fluctuations in the entrainment

rate or how processes such as subduction, Rossby
waves, or Ekman pumping influence the temperature
anomalies in the seasonal thermocline (e.g., Schneider
and Miller 2001). For example, subduction and mixing
within the seasonal thermocline can remove anomalies
before they can return to the surface layer in the subsequent winter, while ocean currents can advect temperature anomalies downstream so that winter SST
anomalies reemerge at another location (de Coëtlogon
and Frankignoul 2003; Sugimoto and Hanawa 2005a,b).
Also, it is likely that fluctuations in ocean heat transport have a significant impact on SST persistence inportions of the North Pacific, such as the Kuroshio region (Qiu 2002). Investigation of the impact of these
additional oceanic processes upon SST anomaly persistence in the North Pacific, particularly on time scales
longer than a decade, is left to future work.
Acknowledgments. We thank Dr. R. Saravanan forproviding the atmospheric heat fluxes from CAM3 andfor his helpful comments. Dr. G. P. Compo provided
helpful advice on statistical analysis. We also thank theeditor and three anonymous reviewers for constructive
comments. The Reynolds Reconstructed data were obtained from the NOAA–CIRES Climate Diagnostics
Center Web site (online at http://www.cdc.noaa.gov).
Sungsu Park was supported by the Advanced Study
Program. Clara Deser and Michael Alexander gratefully acknowledge support from NOAA Office of
Global Programs CLIVAR Project.
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