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ALEXANDER ET AL.

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The Reemergence of SST Anomalies in the North Pacific Ocean
MICHAEL A. ALEXANDER
Climate Diagnostics Center, CIRES, University of Colorado, Boulder, Colorado

CLARA DESER
National Center for Atmospheric Research, Boulder, Colorado

MICHAEL S. TIMLIN
Climate Diagnostics Center, CIRES, University of Colorado, Boulder, Colorado(Manuscript received 29 October 1997, in final form 24 July 1998)
ABSTRACTSea surface temperature (SST) data and two different upper-ocean temperature analyses are used to study thewinter-to-winter recurrence of SST anomalies in the North Pacific Ocean. The SSTs recur when temperature
anomalies that form in the deep ocean mixed layer in late winter/early spring are isolated from the atmospherein the summer seasonal thermocline and then reemerge at the surface when the mixed layer deepens during thefollowing fall/winter. This ‘‘reemergence mechanism’’ is evaluated over the basin by correlating the time seriesof the leading pattern of ocean temperature anomalies in the summer seasonal thermocline (;60–85 m in August–
September) with SST anomalies over the course of the year. The results indicate that the dominant large-scale
SST anomaly pattern that forms in the North Pacific during late winter, with anomalies of one sign in the central
Pacific and the opposite sign along the coast of North America, is sequestered in the seasonal thermocline insummer and returns to the surface in the following fall, with little persistence at the surface in summer.
Regions in the east, central, and west Pacific all show signs of the reemergence process but indicate that it
is influenced by the timing and amplitude of the mean seasonal cycle in mixed layer depth. The maximum mixed
layer depth increases from east to west across the basin: as a result, the thermal anomalies are shallower andreturn to the surface sooner in the east compared with the west Pacific. At some locations, the reemerging signal
is also influenced by when the SST anomalies are created. In the east Pacific, SST anomalies that are initiatedin February–March extend through a deeper mixed layer, persist at greater depths in summer, and are thenreentrained later in the year compared with those initiated in April–May.

1. Introduction
Recently, a host of observational and modeling studies have documented interannual through decadal variability in midlatitude atmosphere–ocean systems (Tanimoto et al. 1993; Trenberth and Hurrel 1994; Yukimoto
et al. 1996; Zhang et al. 1997; Mantua et al. 1997, etc.).
The physical mechanisms used to explain decadal variability generally involve ocean dynamics. Many studies
have found decadal oscillations associated with the thermohaline circulation in the North Atlantic (e.g., Weaver
et al. 1991; Delworth et al. 1993; Capatondi and Holland
1997). Several mechanisms have also been proposed to
explain decadal variability in the North Pacific, including oscillations in the ocean heat transport in the sub-Corresponding author address: Dr. Michael A. Alexander, University of Colorado, CDC-CIRES, Box 449, Boulder, CO 80309.
E-mail: maa@cdc.noaa.gov

q 1999 American Meteorological Society

tropical gyre that is sustained by positive atmosphere–
ocean feedback (Latif and Barnett 1994, 1996; Robertson 1996; Jin 1997; Zhang and Levitus 1997), slowly
propagating oceanic Rossby waves excited by stochastic
surface wind stress forcing (Frankignoul et al. 1997;
Zorita and Frankignoul 1997), poleward propagating
Kelvin waves associated with El Niño events (Jacobs
et al. 1994; Meyers et al. 1996), and tropical–extratropical interactions through both the atmosphere andthe ocean (Gu and Philander 1997).
In contrast, interannual variability in sea surface temperature (SST) has mainly been attributed to local thermodynamic interactions between the atmosphere andupper ocean (Gill and Niiler 1973; Frankignoul andReynolds 1983; Frankignoul 1985; Battisti et al. 1995;
Delworth 1996). Once created, ocean temperature
anomalies in the surface mixed layer (;20–500 m) canbe sustained for several months due to the large heat
capacity of sea water. Frankignoul and Hasselman(1977), Alexander and Penland (1996), and Hall and2420

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FIG. 1. Monthly temperature anomalies (8C) in the North Pacific in 1972 during the months of Apr, Aug, and Nov at the surface (toprow) and 80 m (bottom row). The temperature values are from the analyses of White (1995) with values greater than 0.6 shaded.

Manabe (1997) showed that away from regions withstrong currents much of the variability in midlatitude
SSTs on monthly and longer timescales can result fromthe ocean mixed layer being forced by surface heat fluxes associated with storms. The SST anomalies that develop are damped by a negative linear feedback that
represents the enhanced (reduced) loss of heat fromanomalous warm (cold) waters. However, much of theheat associated with anomalous sea-to-air fluxes remains
in the atmospheric boundary layer; as a result the surface air temperature adjusts to the underlying ocean,
reducing the negative feedback. This process, termed‘‘reduced thermal damping’’ by Barsugli and Battisti(1998), enables SST and near-surface air temperature
anomalies to persist longer; the latter has been demonstrated by comparing atmospheric general circulation
model (AGCM) simulations in which the atmosphere iscoupled to an ocean model to those in which the climatological SSTs are specified as boundary conditions(Blade 1997; Bhatt et al. 1998; Saravanan and McWilliams 1997).
Thermodynamic feedbacks between marine stratus
clouds and SSTs may also enhance the persistence of
midlatitude SST anomalies, especially in summer. Klein
and Hartmann (1993), Norris and Leovy (1994), Weare(1994), and Klein et al. (1995) have shown that there
is a strong positive feedback between anomalies in thelarge-scale SST pattern and stratiform clouds: an increase in stratus clouds reduces the solar radiation reaching the surface, which reduces SST and thereby increases the static stability of the boundary layer, a factor
that tends to enhance cloudiness. Zhang et al. (1998)
and Norris et al. (1998) have suggested that this positive
feedback can lead to persistence of SST anomalies fromboth summer to winter and winter to summer.
Local processes within the upper ocean, such as theseasonal variation in the depth of the surface mixed
layer, may also lead to SST variability. In response to
the seasonal cycle in wind stirring and surface buoyancy
forcing, the ocean mixed layer deepens through fall andwinter and then reforms close to the surface in spring
and remains shallow through late summer. Elsberry andGarwood (1978) and Lanzante and Harnack (1983)

found some indications that the time in spring when themixed layer shoals could influence summertime SSTs.
If, for example, the mixed layer shoaled earlier thannormal it would become anomalously warm by summer
as the net surface heating was distributed over a thinner
layer.
Namias and Born (1970, 1974) were the first to notea tendency for midlatitude SST anomalies to recur fromone winter to the next without persisting through theintervening summer. They speculated that temperature
anomalies that form at the surface and spread throughout
the deep winter mixed layer remain beneath the mixed
layer when it shoals in spring. The thermal anomalies
are then incorporated into the stable summer seasonal
thermocline, where they are insulated from surface fluxes. When the mixed layer deepens again in the following
fall, the anomalies are reentrained into the surface layer
and influence the SST. This ‘‘reemergence mechanism’’was examined in greater detail by Alexander and Deser(1995) using subsurface temperature data and one-dimensional mixed layer model simulations at a few
weather stations in the North Atlantic and North Pacific
Oceans. They found that the winter-to-winter reemergence of SST anomalies occurred at several locations
remote from strong ocean currents. Evidence for thereemergence mechanism was also found by Miller et al.
(1994) using a primitive equation ocean model forcedby observed surface flux anomalies; by Alexander andPenland (1996), where a one-dimensional ocean model
was driven by stochastic atmospheric forcing; and by
Bhatt et al. (1998) in a simulation where a mixed layer
ocean model was coupled to an AGCM.
In the present study, we expand on the work of Alexander and Deser (1995) by examining the extent towhich the reemergence mechanism occurs over the
North Pacific Ocean using observed temperature fields.
A possible occurrence of the reemergence mechanism
over the North Pacific is presented in Fig. 1, whichshows the monthly temperature anomalies in April, August, and November at the surface and at 80-m depth
during 1972. The temperature anomaly pattern at thesurface in August is markedly different from all of theothers, which each have warm water near 408N, 1508W

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ALEXANDER ET AL.
TABLE 1. Characteristics of the three ocean datasets used in this study.
Level depths(m) in theupper 200 m

Datasets

Interpolation
methodPeriod ofrecord used

NCEP

GCM-based assimilation system

1/1980–6/1995

5 15 25 35 45
55 65 75 85
95 106 120
136 155 177

White

Optimum interpolation

1969–94

Smith

EOF projections

1950–96

Original
resolution

Final
resolution

358S–458N in
the Pacific

18 lat 3 1.58
long

48 lat 3
48 long● enhanced by modeland other data
● short record● domain ends at 458N

0 20 40 60 80
100 120 160
200

608S–608N
all oceans

28 lat 3 58 long48 lat 3
48 long● fairly long record● spans North Pacific● smoothed in space
and time

surface

global28 lat 3 28 long(1950–92)
18 lat 3 18 long
(1993–96)

48 lat 3
48 long● long record● spans North Pacific● surface only

surrounded by cold water. While Fig. 1 suggests a link
between SST anomalies in the spring and fall via thesummer thermocline, several key questions about thereemergence process remain. Is the reemergence mechanism widespread or is its just found at a few locations?
Are the anomalies that partake in the reemergence mechanism related to the dominant patterns of SST variability, either in the winter when they are initiated orwhen they return to the surface in the following fall/
winter? Does the timing and strength of the reemerging
signal depend on when or where the SST anomaly was
initially created? We will seek to address these questions
by applying several statistical methods to a combinationof ocean datasets. The datasets used here are describedin section 2, the results are presented in section 3 andthen summarized and discussed in section 4.
2. Datasets
In order to resolve the reemerging signal across theNorth Pacific we require basinwide temperature fields
on a monthly basis. However, this resolution is not possible with existing archives of raw data, given that there
is an order of magnitude fewer upper-ocean temperature
profiles than SST observations. One way to enhance thedata coverage and obtain the necessary spatial and temporal resolution is to combine the ocean data with adynamical ocean model forced by observed atmosphericconditions, while a second is to apply a statistical interpolation method to fill data voids. In this study, we
use ocean temperature analyses produced by each method, the ocean data assimilation system at the National
Centers for Environmental Prediction (NCEP) and the
optimum interpolation scheme devised by W. White at
Scripps Institution of Oceanography. We use both ofthese datasets as they have different strengths: the datafrom NCEP analyses use models and other datasets toaugment the subsurface temperature information, while
White’s analyses incorporates only subsurface temperature data but covers more of the North Pacific for alonger period of record. We will also use the SST data

Original
domainComments

from Smith et al. (1996) in conjunction with the subsurface datasets to document the reemergence mechanism in the North Pacific. The characteristics of the three
analyses, referred to here as the NCEP, White, and Smithdatasets, are shown in Table 1.
The NCEP assimilation system consists of a modified
version of the ocean general circulation model (GCM)
developed at the Geophysical Fluid Dynamics Laboratory, which incorporates observations of SST takenfrom satellites and ships, plus subsurface thermal profiles obtained from expendable bathythermographs(BTs). Model fields are stored on a 1.08 lat 3 1.58 long
grid in the Pacific between 358S and 458N, and the upper
ocean is well resolved with 10 (15) levels in the upper
100 (200) m. We assume that the temperature obtained
from the top level, located 5 m below the surface, isrepresentative of the SST. We use monthly mean temperatures from the assimilation system from its start inJanuary of 1980 through June of 1995. A more complete
description of the data assimilation system is given in
Derber and Rosati (1989) and Ji et al. (1995).
White (1995) uses optimum interpolation, a statisticalmethod, to obtain gridded temperature analyses from aweighted average of the in situ measurements. Given
that nearby observations do not provide independent
information, the weighting functions seek to minimize
the least squares estimate of the correlation error, where
the correlation structure is fit using an autoregressivemodel that decreases exponentially in both space andtime from a given grid point. The observations include
mechanical and expendable BTs and station data thathave been vertically interpolated to 5 (8) standard levels
between the surface and 100 (200) m. The optimal interpolation produces a field of temperature anomalies
on a 28 lat 3 58 long grid from 608S–608N for the years
1955–94. We have used this analysis starting in 1969,
when the amount of data appeared to be sufficient toadequately define temperature anomaly patterns in the
North Pacific Ocean.
The SST dataset from Smith et al. (1996) is based ona set of spatial patterns defined by empirical orthogonal

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functions (EOFs), which are fit to previously grided
temperature data. This interpolation method fills data
voids and creates fields that emphasize large-scale features. The EOFs are based on the period 1982–93 whensatellite measurements of SST are available and thenapplied to a longer data record. Global monthly SST
fields are available from the Smith analyses on a 28 3
28 grid for 1950–92 and on a 18 3 18 for 1993–96.
Each dataset was placed on a 48 3 48 grid by weighting the original grid square values by the fraction thatfell within a given 48 3 48 box. We focus on the Pacificfrom 208N to the northern edge of the domain, which
is 448N in the NCEP analyses and the coastal boundaries
in the other two datasets. All of our analyses have been
calculating using monthly anomalies, defined as the departure of the mean value for a given month from thelong-term monthly mean of that dataset for the NCEP
and Smith datasets. The monthly anomalies in the White
data are relative to the long-term annual mean and a
Fourier fit to the annual cycle for the period 1980–89;
the anomalies are then adjusted to have a zero mean.
Several different statistical analyses including lead–lag correlations and regressions, EOFs, and extended
EOFs (EEOFs) are used to examine the reemergencemechanism in the North Pacific Ocean. The results fromthe EOF and EEOF analyses are presented as the correlation between the principal component (PC), the timeseries associated with the EOF, with the values at theindividual grid points. We also use correlation analyses
to characterize the temperature variations as a functionof depth and season in selected regions of the North
Pacific. The statistical significance of the magnitude ofcorrelation coefficients is assessed using a two-tailed ttest taking into account the autocorrelation in the dataaccording to Quenouille (1954). The 95% significancelevel is roughly 0.6, 0.5, and 0.4 for the NCEP, White,
and Smith data, respectively. However, these levels areapproximate given that (i) the autocorrelation varies
with location, (ii) in some cases we are calculating thecorrelation of the time series of a pattern with that ofan individual grid point, and (iii) the actual data has
been interpolated to grid points.
3. Resultsa. Basinwide analyses
As a first step in evaluating the reemergence mechanism over the North Pacific we analyze the evolutionof SST anomalies using extended empirical orthogonal
functions. EEOFS, an extension of conventional EOF
analysis but with time lags included in the covariancematrix, have been used by Weare and Nasstrom (1982),
Lau and Chan (1985), and Lau et al. (1992) to study
how patterns evolve with time. Here, the EEOF analysis
has been conducted using the monthly SST anomalies
between February and the following January, lags of
0–11 months, from the Smith dataset. The leading EEOF

VOLUME 12

is computed using the covariance matrix in which thevariance at each point in a month has been normalized
by the average standard deviation of SST at all points
in the domain during that month. Given that the basin
average standard deviation varies only slightly from aminimum 0.548C in February to a maximum of 0.708C
in July, the normalized and nonnormalized EEOF 1 (notshown) are very similar. The patterns associated with
EEOF 1 are displayed in Fig. 2a as the correlations
between the time series of EEOF 1 and time series of
SST anomalies at individual grid points for the years
1950–96. The results are presented for every othermonth beginning in March and indicate the temporal
evolution of SST anomalies over the course of the seasonal cycle; the alternate months (not shown) indicate
a similar evolution of the SST anomaly field. The dominant pattern in all months has anomalies of one signthat extend from Japan to about 1408W between approximately 308 and 508N, ringed by anomalies of the
opposite sign. However, the location and magnitude of
the anomaly centers change with time. In winter andlate spring the largest positive correlations are found in
the central Pacific while the magnitude of negative correlations are greatest along the coast of North America.
Through the summer the magnitude of the correlations
decrease in both locations, and by September they only
exceed |0.4| (shaded areas) west of 1708W between 328
and 428N, in a very small region near 388N, 1458W, andin the very southeast corner of the domain. By November and through January higher correlation values arefound over most of the area where they occurred in theprevious May.
The leading EEOF explains 19.4% of the SST variance for all months. The percent variance of the SST
anomalies over the North Pacific explained by thisEEOF in each calendar month1 is shown in Fig. 2b.
EEOF 1 explains roughly 25% of the variance fromMarch through May. The variance explained decreases
over the next several months to a minimum of ;11%
in September and then increases to about 22% from
December to January.
Figure 2 indicates that the SST anomalies in March–
May are more strongly related to those in the following
November–January than to the SST anomalies in theintervening summer months, especially in the easternpart of the basin. We have repeated the EEOF analyses
using SST anomalies east of 1608E (not shown). The
leading EEOF in this domain explains more of the total
variance (22.4%), while the percent variance explainedis enhanced in March–April (;34%) and November–
January (;22%) and slightly diminished in September(10%), compared with the basinwide analyses.

1
The percent variance explained is calculated using S Ni51 ri2 s i2/
S Ni51 s i2, where r is the correlation between SST and the timeseriesof EEOF 1, s is the standard deviation of SST, i indicates an individual
grid point, and N is the total number of grid points.

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FIG. 2. (a) Correlations between the time series of EEOF 1 of monthly SST anomalies from Feb through the following Jan, lags of 0–11
months, with the SST anomalies at individual grid points. The results are calculated using a normalized covariance matrix and presented
for every other month beginning in Mar. EEOF 1 explains 19.4% of the total variance; the percent variance explained by this EEOF in eachmonth is shown in (b). The EEOF is derived from the Smith dataset for the years (1950–96). The contour interval is 0.2, the zero contour
is thick, negative contours are dashed, and values greater (less) than 0.4 (20.4) are shaded light (dark).

We next use the temperature fields from the NCEP
ocean data analyses system for the period 1980–95, to
examine the relationship between temperature anomalies (T9) at the surface and those in the summer seasonal
thermocline east of 1608E in the North Pacific. The
leading EOF of T9 during August–September between
65- and 85-m depth is used to identify the dominantpattern of variability in the center of the summer seasonal thermocline. The EOF is presented in Fig. 3a as
the correlation between the leading principal component
in the NCEP data (NPC1), the time series associatedwith EOF 1, and the values of T9 at the individual grid
points. EOF 1 explains 21% of the variance and has adipole pattern with anomalies of one sign in the east–

central Pacific and the opposite sign along the coast of
North America. The magnitude of the correlation coefficients exceed 0.4 in much of the central and east
Pacific with maxima of more than 0.5 in the dipole
centers. NPC1 (Fig. 3b) shows interannual variability
over the 15-yr period but no clear trend.
Correlations between NPC1 with SST anomalies atindividual grid points over the North Pacific during theprevious April, concurrent September, and following
November are shown in Fig. 4. Regions of relatively
strong correlations (.|0.4|) are shaded and used to assess the strength of the relationship between the largescale pattern of T9 in the summer thermocline and SST
anomalies in spring, summer, and fall. The correlations

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FIG. 3. (a) The first EOF of the anomalous temperature field during
Aug–Sep between 65–85-m depth and (b) its associated principal
component in the NCEP data (NPC1) for the years 1980–95. EOF
1, based on the covariance matrix, is displayed as the correlation
between NPC1 and the original data. The EOF domain is 208–448N
and east of 1608E in the North Pacific. The EOF values have beensmoothed with a 1–2–1 filter in both the zonal and meridional direction. The contour interval is 0.1 and shading and contour options
are the same as in Fig. 2.

between NPC1 and SSTs in April have a dipole patternwith values that exceed 0.4 west of 1408W between 258
and 408N and are less than 20.4 along the west coastof the North America. The correlation values exceed 0.8
in the vicinity of 358N, 1658W indicating a very strong
connection between SST9 in spring and the T9 patternin the summer seasonal thermocline. Indeed, these correlations in the central Pacific are stronger than thosebetween NPC1 and the T9 at 65–85 m in August–September (Fig. 3a) on which this PC was originally based.
The magnitude of the correlations between NPC1 andSSTs are small over most of the domain in September(Fig. 4b) but increase by November (Fig. 4c), exceeding
0.4 over portions of the west, central, and eastern partof the domain. These results suggest that the SST anomalies in spring descend into the seasonal thermocline
and reemerge at the surface in the following fall without
persisting through summer; however, the reemerging
signal is weaker and has a slightly different pattern thanthe one that descends in spring. Several other processes
may influence the seasonal evolution of T9, including
diffusion into the deeper ocean, redistribution via horizontal advection and eddy mixing, and anomalous surface energy fluxes in the second half of the year thatcreate independent SST anomalies.
The first EOF of SST9 computed separately for themonths of April, September, and November using the
NCEP analyses (Fig. 5) explain 38.7%, 24.8%, and28.4% of the variance, respectively. Comparing thethree EOF patterns in Fig. 5 with the corresponding
NPC1 2 SST correlation fields in Fig. 4 indicates theextent to which the SST9 pattern associated with T9 in
the summer thermocline resembles the dominant pattern

FIG. 4. Correlations between NPC1, the time series of dominanttemperature anomaly pattern in the summer seasonal thermocline,
and gridded SST anomalies from the NCEP analyses in (a) Apr, (b)Sep, and (c) Nov of the same year. Contours and shading are thesame as in Fig. 2.

FIG. 5. EOF 1 of SST anomalies during (a) Apr, (b) Sep, and (c)Nov from the NCEP analyses, which explain 39%, 25%, and 29%of the variance in their respective months, are shown in correlation
form. Contours and shading are the same as in Fig. 2.

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FIG. 6. The percent variance of the NCEP SST anomalies between
208 and 448N and east of 1608E in the Pacific explained by NPC1,
as a function of calendar month, from the previous Jan to the following Feb for the period 1980–94.

of SST variability in spring, summer, and fall: EOF 1
bears a strong resemblance to the corresponding NPC1
2 SST correlation map in April and November but not
September. The similarity between the EOFs and thecorrelation maps for the corresponding month is quantified using pattern correlations; the two fields have apattern correlation of 0.94, 0.45, and 0.91 in April, September, and November. The map of correlations between
NPC1 and SSTs in September (Fig. 4b) does not beara strong resemblance to any of the five leading EOFs
in September: the greatest pattern correlation, 0.54, isobtained with the fourth EOF.
The percent variance of the SST anomalies explainedby NPC1 between 208 and 448N and east of 1608E in
the North Pacific as a function of calendar month isshown in Fig. 6. The percent of the SST variance explained by NPC1 increases from about 17% in January
to 30% in March and then decreases in each of thesubsequent months, reaching a minimum of 6% in September (Fig. 6). It rebounds to 17% by November andthen decreases through the following February. A similar representation of the timing of the reemergencemechanism is obtained from correlations between NPC1
and the first PC of SST for each calendar month (Timlin
et al. 1997), although the correlation in November,;0.75, is nearly as large as those from February throughMay.
We have repeated the analyses shown in Figs. 4–6
using the White (1995) dataset to estimate T9 in thesummer thermocline in order to expand the domain in
both space and time and to confirm the results obtainedusing the NCEP analyses. However, White’s optimum
interpolation scheme tends to smooth out monthly features, since it was designed to resolve gyre-scale temperature anomalies on seasonal or longer timescales.
Temperature anomalies in the summer seasonal thermocline generally persist for at least 3 months, while
the temperature anomalies at the surface can change
fairly rapidly, especially in fall. Thus, we compare T9
in the seasonal thermocline from the White data withmonthly SST anomalies from the Smith dataset to better
resolve the reemergence mechanism.
The leading EOF and PC of temperature anomalies
averaged over 60–80 m during August–September north

FIG. 7. (a) The first EOF of the anomalous temperature field during
Aug–Sep between 60- and 80-m depth and (b) its associated principal
component from the White data (WPC1) for the years 1969–94. The
EOF domain is 208–568N and east of 1608E in the North Pacific.
Contours, smoothing, and shading are the same as in Fig. 3.

of 208N and east of 1608E in the Pacific are computedfrom the White data for the period 1969–94 and shownin Fig. 7. The EOF domain extends 128 farther northand the time record begins 11 yr earlier than the NCEP
analyses. The first EOF, which explains 33% of the variance, has one sign in the central Pacific, ringed byvalues of the opposite sign. The magnitude of the EOF
correlation values exceed 0.4 west of 1458W near 408N
and along the North American coast, and exceed 0.8 in
the vicinity of 358N, 1608W and just west of British
Columbia. In addition to interannual variability, the firstprincipal component at depth in summer from White(WPC1) exhibits a low-frequency component with all
positive values from 1969–77 and primarily negativevalues from 1978–88. This ‘‘transition’’ in the climate
state of the North Pacific in 1977 has been documented
in many other studies (e.g., Trenberth and Hurrel 1994;
Deser et al. 1996; Cayan et al. 1996).
The correlations between WPC1 with the North Pacific SST anomalies from Smith’s dataset during April,
September, and November are shown in Fig. 8. The threecorrelation maps resemble their counterparts from the
NCEP analyses (Fig. 4) both in pattern and in the relative strengths of the correlations where the two overlap.
The absolute values of the correlations in the main centers of action are very strong in April, weak in September, and moderately strong in November. The correlation
maps in Fig. 8 resemble the leading EOFs of monthly
SST9 obtained from Smith (not shown but calculated
for the same period as the White data, 1969–94) in April
and November but not September. The pattern correlations between the correlation maps and the corresponding EOFs are 0.99 in April, 0.59 in September,and 0.79 in November.

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FIG. 9. The percent variance of the Reynolds SST anomalies between 208 and 568N and east of 1608E in the Pacific explained by
WPC1, as a function of calendar month, from the previous Jan to thefollowing Feb for the period (a) 1969–94 and (b) 1980–94.

b. Local and regional analyses

FIG. 8. Correlations between WPC1, and gridded SST anomalies
from the Smith analyses in (a) Apr, (b) Sep, and (c) Nov for the years
1969–94. Contours and shading are the same as in Fig. 2.

The percent variance of monthly SST anomalies explained by WPC1 from January through February of
the following year for the period 1969–94 (Fig. 9a)emphasize the asymmetric nature of the reemergencemechanism: it reaches a maximum of ;40% in March,decreases to 5% by September, but only rebounds toabout 10%–12% from November to January. While
there are several possible reasons why the connection
between T9 in the summer thermocline and the SST
anomalies is stronger in spring than in fall; one appears
to be the period of record. When we repeated the percent
variance calculation using the Smith SST and WPC1
values from 1980–94, the same period as available from
NCEP analysis, the explained SST variance decreasesby ;¼ of its original value in February–May and nearly
doubles in November–December, becoming more symmetric about the summertime minimum (Fig. 9b), which
is very similar to the results based on the NCEP data
(Fig. 6). The fairly large values in late fall/early winterare maintained if we extend the period of record backto 1977 but not before (not shown), suggesting that thebasinwide climate transition in the winter of 1976–77
disrupted the reemergence of temperature anomalies.

The basinwide analyses suggest that the reemergencemechanism is strong across much of the North Pacificat 408N (see Figs. 1, 4, and 8). The local evolution of
the reemergence process is examined by correlating
temperature anomalies at 65–85-m depth in September–August with SST anomalies over the seasonal cycle ineach 48 3 48 grid box in the NCEP analyses along 408N
(Fig. 10). The correlations are presented as a functionof lead/lag from the previous January (SSTs lead by;7.5 months) to the following April (SSTs lag by ;7.5
months). Evidence for the reemergence mechanism isclearly seen east of ;1608E. High correlations (.0.6)
from the previous February–May decrease to a minimum in August–September (,0.4) and then increase in
the following fall and/or winter (.0.5). The temperature
anomalies appear to return to the surface 1–3 months
later between 1608E and 1608W compared with the eastern Pacific. Similar analyses at other latitudes indicatethat the reemergence process is most active east of approximately 1658E and north of 288N (not shown).
We next focus on the vertical structure of the reemergence mechanism in regions where the previous
analyses suggest that it is strong: along the North American coast in the east Pacific, north of Hawaii in thecentral Pacific, and along 408N in the west Pacific, as
indicated by the three shaded areas in Fig. 11. Formal
criteria were not used to select the exact regional boundaries; rather, rectangular areas were selected to obtaina clear depiction of the reemergence process. Following
Alexander and Deser (1995), we compute the correlation
between a base point located in summer thermoclinewith temperature anomalies from the previous January
to the following winter from the surface down to 150
m. Temperature anomalies from the NCEP analyses be-

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FIG. 10. Monthly lead–lag correlations between temperature anomalies located between 65 and 85 m in Aug–Sep, and SST anomalies
from the previous Jan through the following Apr for each grid box
along 408N. For example, the correlation between SST in the previous
May (SST leads by 3.5 months) and temperature anomalies in thesummer thermocline is ;0.9 at 1608W. The temperature anomalies
are from the NCEP analyses for the period 1980–95. The correlation
values have been smoothed longitudinally using a 1–2–1 filter, thecontour interval is 0.1, and values greater than 0.5 are shaded.

tween 65 and 85 m in August–September, the samemonths and depths used to calculate NPC1, are averaged
together to create a base point time series. The temperature anomalies have been regionally averaged oneach level and then smoothed with a 1–2–1 filter in time
before performing the correlation analyses. Note that in
the following figures we have chosen to shade values
exceeding different contour levels in order to best illustrate the reemerging signal.

FIG. 11. Shaded areas indicate the eastern (268–428N, 1328–
1168W), central (268–428N, 1648–1488W), and western (388–428N,
1608E–1808) regions that will be used to examine the reemergencemechanism.

FIG. 12. Lead–lag correlations between temperature anomalies atthe base point, located between 65 and 85 m in Aug–Sep, and temperature anomalies between the surface and 150 m from the previous
Jan through the following Apr in the (a) east, (b) central, and (c)west Pacific regions. The anomalies obtained from the NCEP analyses
for the period 1980–95 are averaged over the region at each level
and then smoothed in time using a 1–2–1 filter; the anomalies in thewestern region are also smoothed over depth since the signal is somewhat noisier in this small region. The contour interval is 0.1. Shadingis used to highlight the reemergence mechanism and so it varies
between regions: correlations in excess of (a) 0.45, (b) 0.7, and (c)0.65 are shaded.

All three regions show evidence of the reemergencemechanism as the correlations between the base point
and surface temperatures are high in the previous winter,drop in summer, and rebound in the following fall/winter(Fig. 12). However, the structure and timing of the correlation pattern is different in the three locations. For
example, in the east Pacific the correlation between the
SST9 and base point T9 goes from a maximum of morethan 0.9 in March, decreases to less then 0.3 in August,and exceeds 0.45 from November through February. In
contrast, in the central Pacific the correlations do not

2428

JOURNAL OF CLIMATE

decrease as strongly in summer but rapidly decline after
reaching more then 0.7 in November, while in the western region high correlation values persist at the surface
through much of the following winter. The correlation
values in the east and central region descend from Marchthrough the following January, suggesting that some of
the thermal anomalies move downward into the permanent pycnocline.
The regional behavior of the thermal anomalies is
explored further by regressing the temperature anomalies as a function of month and depth on anomalies atthe base point, located here at 5 m (the top level of the
NCEP analyses and taken to represent the SST) in
April–May. The regression analyses provides a linear
estimate of how an SST anomaly of 18C in spring
evolves from the previous January through the following April, allowing one to track the magnitude of ananomaly through the full reemergence process. A 18C
anomaly is fairly large, as the standard deviation of SST’in April–May is approximately 0.5, 0.6, and 0.758C in
the east, central, and west Pacific. The regressions indicate the reemergence mechanism occurs in all three
regions but with clear differences between the three(Fig. 13). In the eastern region, the SST anomalies inlate spring appear to move downward over a fairly narrow zone (30–80 m), maintaining their magnitude
through September while decreasing by more then halfin the surface mixed layer over the same time. Whilesome of the thermal anomalies continue moving down
through the following winter, a portion of the signal,
indicated by regression values of more than 0.558C, returns to the surface in November and December. Compared with the east Pacific, the reemergence signal occurs earlier in the year and extends deeper in the central
and especially the west Pacific. In the western region
large regression values (.0.758C) extend over the upper
150 m in the first winter, persist through a deep layerin summer, and then return to the surface 2–3 months
later than in the other two regions.
The differences in the timing and strength of the reemergence mechanism indicated by both the correlation
and regression analyses are partly due to regional variations in the mean seasonal cycle of mixed layer depth.
The maximum mixed layer depth in the North Pacific,
which tends to occur in March, increases from about 80
m along the west coast of North America, to 120 m inthe central Pacific and 200 m east of Japan (Deser etal. 1996). As a result the depth to which temperature
anomalies penetrate in late winter increases from east
to west, as suggested by Fig 13. The mixed layer shoals
to ;25 m during summer in all three regions and thus
the vertical extent of T9 below the mixed layer is greaterin the west than the east. When the mixed layer deepens
in the following fall, the anomalies are generally closer
to the surface and thus incorporated into the mixed layer
sooner in the east and central compared with the west
Pacific.
The timing of the reemergence mechanism differs

VOLUME 12

FIG. 13. As in Fig. 11 but for lead–lag regressions [8C (18C)21 ]between temperature anomalies at the base point, located here at 5m in Apr–May, and temperature anomalies from the previous Jan
through the following Apr in the (a) east, (b) central, and (c) west
Pacific regions. The contour interval is 0.1 and values greater than(a) 0.55, (b) 0.7, and (c) 0.75 are shaded.

slightly in the correlation and regression analyses. For
example, in the eastern region the correlation analyses
suggest that the strongest return of T9 to the surface
occurs in January, while the regression analyses indicatethat the return is strongest in November; the latter isconsistent with most of the basinwide analyses. One
reason for differences between the two analysis methods
is that correlations depend on the variance of both thebase point and the other time series, while the regressions depend only on the former. Thus, the seasonal
cycle of the background variability in the upper ocean
and the position of the base point relative to this variability will influence how the two methods portray thereemergence mechanism. A second factor relates to theposition of the base point relative to the path of theAUGUST 1999

ALEXANDER ET AL.

2429

reemergence mechanism. The correlation analyses maximize the portion of the signal that passes through 65–
85 m in August–September, while the regression analyses indicate that in the eastern region the strongest
thermal anomalies that descend from the surface inApril–May are located at ;50 m during the summer.
We explore the possibility of different paths for thereemerging anomalies by computing lead–lag temperature regressions in the eastern region, similar to Fig.
13a, but with a surface base point that progresses
through the months of February–May. Advancing thebase point from February to May progressively shortensand shallows the path of the reemergence mechanism(Fig. 14). With a February base point, the center of thereemergence signal penetrates to ;80 m by the following month and then is maintained between 70 and 100
m through summer and into early fall before returning
to the surface in January–February of the following
year. With a May base point the initial SST signal moves
slowly downward, at a rate of 5–10 m month21 , and isconcentrated near 50 m in summer before returning to
the surface in November. The SST anomalies in summer
also experience a greater decrease when the reemergence process begins earlier in the year, but the signalthat reemerges in the following fall/winter is ;0.28C
relative to the summer minimum in all four cases. Comparing the evolution of SST’ in the top and bottom
panels in Fig. 14 indicates that a 18C anomaly in February (May) decays to 0.18C (0.48C) by September but
then increases to more than 0.38C in January (0.58C in
November). Moving the surface base point from February through May also causes the reemergence signal
to return to the surface earlier in the year in the western
Pacific but does not visibly alter the reemerging mechanism in the central region (not shown).
4. Summary and discussion
Three gridded datasets, the SST analysis of Smith etal. (1996) and subsurface temperature analyses from
NCEP’s ocean data assimilation system (Derber and Rosati 1989; Ji et al. 1995) and White’s optimum interpolation scheme (1995), are used to examine the winterto-winter reemergence of SST anomalies in the North
Pacific. We evaluate the reemergence mechanism on abroad scale by correlating the first principal component(PC1), the time series of the leading pattern of ocean
temperature anomalies in the summer seasonal thermocline (;60–85 m in August–September), with SST
anomalies over the course of the year. The correlations,which are of one sign in the central Pacific and the
opposite sign along the coast of North America, haverelatively large magnitudes (.|0.4|) in April and November but not September. Furthermore, the pattern ofthe correlations closely resembles the leading EOF in
April and November but not September, suggesting thatthe dominant large-scale SST anomaly pattern that
forms in the North Pacific during late winter descends

FIG. 14. As in Fig. 12 but for lead–lag regressions [8C (18C)21 ] inthe eastern region between temperature anomalies at the base point,
located at 5 m in (a) Feb, (b) Mar, (c) Apr, and (d) May. The contour
interval is 0.1 and values greater than (a) 0.3, (b) 0.4, (c) 0.5, and(d) 0.5 are shaded.

into the seasonal thermocline in summer and returns tothe surface in the following fall, with limited persistenceat the surface in the summer.
While the broad pattern of SST anomalies that participate in the reemergence mechanism are driven by

2430

JOURNAL OF CLIMATEthe large-scale atmospheric forcing, the reemergenceprocess itself is primarily local in nature, since advection
and other horizontal processes are relatively slow in theocean and do not have sufficient time to change thethermal patterns over the course of a year. Regions in
the eastern, central, and western Pacific all show evidence of the reemergence mechanism but differences
between the three suggest that geographic variability inthe mixed layer depth and the static stability of the layers
below it influence the timing and structure of the reemerging signal. The maximum mixed layer depth increases from less than 100 m near the North American
coast to more than 200 m east of Japan, and the permanent pycnocline below the mixed layer is strongest
in the east Pacific and decreases westward. As a result,thermal anomalies are confined to a fairly narrow summer seasonal thermocline in the east compared with thewest. For SST anomalies initiated at the same time,those in the east and central Pacific tend to be reentrained into the mixed layer by November–December
compared to the west, where mixed layer deepening
continues to entrain the thermal anomalies into January–
February of the following year.
The reemerging mechanism at a given location is also
influenced by when the SST anomalies are created andhow long they persist at the surface. SST anomalies thatare initiated in February–March extend through a relatively deep mixed layer, persist at greater depths insummer, and are then reentrained later in the year compared with those initiated in April–May. The anomalies
created in late spring tend to pass through the upper
part of the seasonal thermocline in summer before returning to the surface around November. Thus, the pathof the reemerging signal may be more variable where
SST anomalies change from winter to spring compared
to regions where SST anomalies in the first half of theyear are more persistent. The greater persistence of SST
anomalies in the first half of the year in the central
Pacific might help explain why the evolution of thereemergence mechanism was less sensitive to when the
SST anomaly was initiated there compared with regions
located in the east or west Pacific.
Most of the statistical analyses used here indicate thatthe descending branch of the reemergence mechanism
is stronger than the return branch; that is, the SST anomalies in the previous winter/spring are more strongly
connected to the temperature anomalies in the summer
thermocline than the SST anomalies in the following
fall/winter. For example, PC1 in the summer seasonal
thermocline explains 30%/6%/17% of the SST variability over the North Pacific in April/September/November in the NCEP dataset for the years 1980–95. The
asymmetry in the percent variance explained is evengreater using PC1 from White’s data and the SSTs from
Smith et al. for the years 1969–94. Regional analyses
suggest that for an initial SST anomaly of 18C the temperature anomalies that return to the surface in fall/
winter range between 0.38 and 0.88C, which is 0.28–

VOLUME 12

0.48C greater than the summer minimum (Figs. 11 and
12). However, comparing the fall and summer SST
anomalies directly underestimates the impact of the reemergence mechanism, since SST anomalies decay due
to negative air–sea feedbacks. In the absence of otherprocesses, SST anomalies decay at a rate of exp(2t /l ),where t is the lag in months and l, the constant air–sea damping factor, is on the order of 3–6 months (Frankignoul and Hasselmann 1977; Alexander and Penland
1996; Lau and Nath 1996). Depending on the value ofl and the length of time between the summer minimum(September) and the fall/winter maximum (November–
February), the reemergence mechanism provides an additional 0.18–0.38C of anomalous heating or cooling to
the surface layer to compensate for the SST damping
associated with surface flux anomalies.
The relative strength of the descending and return
branches of the reemergence mechanism are likely due
to differences in the mixed layer physics over the courseof the year. In the descending branch the anomalies
created at the surface are left behind when the mixed
layer retreats and are then incorporated into the stableseasonal thermocline, a relatively passive process. Overthe next few months some of the thermal anomalies are
diffused to deeper layers or mixed by eddies before
being entrained into the mixed layer. In addition, otherprocesses active in the surface layer, such as air–sea
heat fluxes and Ekman transport, may influence SST
anomalies, diluting the reemerging signal in the following fall and winter.
The reemergence mechanism is just one of several
processes that influence SST variability on interannual
and longer timescales. Winter-to-winter persistence of
SST anomalies may also result from persistence of wintertime atmospheric circulation patterns via surface heat
fluxes. Indeed, there is some evidence from observations(Namias 1986; Namias et al. 1988) and atmospheric
GCM experiments with fixed SST boundary conditions(Ting and Lau 1993; Graham et al. 1994; Lau 1997)
that circulation anomalies recur from one winter to thenext. To examine this possibility, we correlated the surface heat fluxes in winter/spring with those in the following fall/winter at each grid point over the North
Pacific. The results (not shown) indicate that the correlations between the anomalous fluxes in March–April–
May and the following October–November–December
are negative over much of the eastern half of the basin
and less than 0.3 over almost all of the North Pacificin the NCEP reanalysis (described by Kalnay et al.
1996) for the years 1969–94. In places where there is
winter-to-winter forcing of SST anomalies by the atmosphere, the reemergence mechanism would likely act
to amplify and lengthen the period of the SST anomalies.
In an apparent contradiction to our findings and thoseof Namias et al. (1988), Zhang et al. (1998) have suggested that SST anomalies in the North Pacific persist
from winter to summer and summer to winter. Norris

AUGUST 1999

2431

ALEXANDER ET AL.

FIG. 15. Correlations between PC1 obtained from White’s analyses,
and SST anomalies during March at individual gridpoints from Smithet al. for the years 1969–94. The correlations have been smoothedwith a 1–2–1 filter in both the zonal and meridional directions. Contours and shading are the same as in Fig. 2.

et al. (1998) attribute this persistence to positive feedbacks between low-level stratiform clouds and SSTs. It
is possible that both persistence at the surface and thereemergence mechanism may be operating in the North
Pacific, but the different data and analyses methods emphasize different aspects of the SST variability. For example, while the autocorrelation of the time series ofthe leading pattern of SST in Zhang et al. (their Fig.
7a) does suggest persistence of summertime SST anomalies, it also provides evidence for the reemergencemechanism, as indicated by an increase in autocorrelation after lags of 8–10 months for SST anomalies thatexisted in January through April. Extended EOF analyses of SST anomalies during each calendar month (ourFig. 2) also suggests that both processes operate in the
North Pacific: the anomaly center located along 408N
in the central and west Pacific shows some tendency topersist throughout the year, while in other regions, especially east of the date line, SST anomalies in spring
diminish in summer and then increase again in fall andearly winter.
Many studies have shown a connection between the
El Niño–Southern Oscillation (ENSO) phenomena and
SST anomalies in the eastern half of the North Pacific(e.g., Weare et al. 1976; Pan and Oort 1990; Deser andBlackmon 1995). Figure 15 shows the correlation pattern of March SSTs with the leading PC of subsurface
temperature anomalies in summer obtained from theWhite data for the years 1969–94. The correlation pattern is consistent with SST anomalies during ENSO:
temperature anomalies in the summer thermocline in thecentral North Pacific are strongly correlated with local
SSTs, and anticorrelated with SSTs along the coast of
North America and the eastern tropical Pacific in theprevious March. The high correlations (.0.6) in theeastern tropical Pacific suggest a fairly strong connection between ENSO and the North Pacific temperature
anomaly pattern involved in the reemergence mechanism.
The SST anomalies in the tropical Pacific in winterand spring and the subsurface temperature anomalies in

the North Pacific in summer are linked via two processes. First, during El Niño events enhanced convection over the warm SST anomalies in central equatorial
Pacific leads to a change in the atmospheric circulation,
including an enhancement of the Aleutian low in winter,
which in turn forces SST anomalies to form in the North
Pacific (Alexander 1990, 1992; Luksch et al. 1990; Lau
and Nath 1996). The SST anomaly pattern in the North
Pacific, which takes one to two months to develop, thenenters the seasonal thermocline in late winter and early
spring via the second process, the descending branch of
the reemergence mechanism. The extent to which theatmosphere responds to the ocean temperature anomalies that return to the surface in the North Pacific in thefollowing fall and winter remains an open question.
Acknowledgments. We thank Thomas Smith, Richard
Reynolds, Ants Leetmaa, and Ming Ji at NCEP and
Warren White of Scripps Institute of Oceanography forproviding the ocean temperature analyses. Suggestions
by two anonymous reviewers helped to improve themanuscript. This work was supported by NSF Grant
OCE-9531870 and by an omnibus grant from the NOAA
Office of Global Programs to the Climate Diagnostics
Center.
REFERENCES
Alexander, M. A., 1990: Simulation of the response of the North
Pacific Ocean to the anomalous atmospheric circulation associated with El Niño. Climate Dyn., 5, 53–65.
, 1992: Midlatitude atmosphere–ocean interaction during El
Niño. Part I: The North Pacific Ocean. J. Climate, 5, 944–958.
, and C. Deser, 1995: A mechanism for the recurrence of wintertime midlatitude SST anomalies. J. Phys. Oceanogr., 25, 122–137.
, and C. Penland, 1996: Variability in a mixed layer model ofthe upper ocean driven by stochastic atmospheric surface fluxes.
J. Climate, 9, 2424–2442.
Barsugli, J. J., and D. S. Battisti, 1998: The basic effects of atmosphere–ocean thermal coupling on midlatitude variability. J. Atmos. Sci., 55, 477–493.
Battisti, D. S., U. S. Bhatt, and M. A. Alexander, 1995: A modelingstudy of the interannual variability in the wintertime North Atlantic Ocean. J. Climate, 8, 3067–3083.
Bhatt, U. S., M. A. Alexander, D. S. Battisti, D. D. Houghton, andL. M. Keller, 1998: Atmosphere–ocean interaction in the North
Atlantic: Near-surface climate variability. J. Climate, 11, 1615–
1632.
Blade, I., 1997: The influence of midlatitude coupling on the low
frequency variability of a GCM. Part I: No tropical SST forcing.
J. Climate, 10, 2087–2106.
Capatondi, A., and W. R. Holland, 1997: Decadal variability in an
idealized ocean model and its sensitivity to surface boundary
conditions. J. Phys. Oceanogr., 27, 1072–1093.
Cayan, D. R., A. J. Miller, T. P. Barnett, N. E. Graham, J. N. Ritchie,
and J. M. Oberhuber, 1996: Seasonal-interannual fluctuations insurface temperature over the Pacific: Effects of monthly winds
and heat fluxes. Natural Climate Variability on Decade-to-Century Timescale, D. G. Martinson et al., Eds., National Academyof Sciences Press, 133–150.
Delworth, T., 1996: North Atlantic interannual variability in a coupled
ocean–atmosphere model. J. Climate, 9, 2356–2375.
, S. Manabe, and R. J. Stouffer, 1993: Interdecadal variations of2432

JOURNAL OF CLIMATEthe thermohaline circulation in a coupled ocean–atmospheremodel. J. Climate, 6, 1993–2011.
Derber, J. D., and A. Rosati, 1989: A global oceanic data assimilation
system. J. Phys. Oceangr., 19, 1333–1347.
Deser, C., and M. L. Blackmon, 1995: On the relationship between
tropical and North Pacific sea surface temperature variations. J.
Climate, 8, 1677–1680.
, M. A. Alexander, and M. S. Timlin, 1996: Upper ocean thermalvariations in the North Pacific during 1970–1991. J. Climate, 9,
1841–1855.
Elsberry, R., and R. W. Garwood, 1978: Sea surface temperature
anomaly generation in relation to atmospheric storms. Bull.
Amer. Meteor. Soc., 59, 786–789.
Frankignoul, C., 1985: Sea surface temperature anomalies, planetary
waves, and air–sea feedback in the middle latitudes. Rev. Geophys., 23, 357–390.
, and K. Hasselmann, 1977: Stochastic climate models. Part 2.
Application to sea-surface temperature variability and thermocline variability. Tellus, 29, 284–305.
, and R. W. Reynolds, 1983: Testing a dynamical model for midlatitude sea surface temperature anomalies. J. Phys. Oceanogr.,
13, 1131–1145.
, P. Müller, and E. Zorita, 1997: A simple model of the decadal
response of the ocean to stochastic wind forcing. J. Phys. Oceanogr., 27, 1533–1546.
Gill, A. E., and P. P. Niiler, 1973: The theory of the seasonal variability
in the ocean. Deep-Sea Res., 20, 141–177.
Graham, N. E., T. P. Barnett, R. Wilde, M. Ponater, and S. Schubert,
1994: On the roles of tropical and midlatitude SSTs in forcing
annual to interdecadal variability in the winter Northern Hemisphere circulation. J. Climate, 7, 1416–1442.
Gu, D., and S. G. H. Philander, 1997: Interdecadal climate fluctuations
that depend on exchanges between the tropics and extratropics.
Science, 275, 805–807.
Hall, A., and S. Manabe, 1997: Can local linear stochastic theory
explain sea surface temperature and salinity variability. Climate
Dyn., 13, 167–180.
Jacobs, G. A., H. E. Hurlburt, J. C. Kindle, E. J. Metzger, J. L.
Mitchell, and A. J. Wallcraft, 1994: Decade-scale trans-Pacificpropagation and warming effects of an El Niño anomaly. Nature,
370, 360–363.
Ji, M., A. Leetmaa, and J. Derber, 1995: An ocean analyses system
for seasonal to interannual climate studies. Mon. Wea. Rev., 123,
460–480.
Jin, F. F., 1997: A theory for interdecadal climate variability of theNorth Pacific ocean–atmosphere system. J. Climate, 10, 1821–
1835.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.
Klein, S. A., and D. L. Hartmann, 1993: The seasonal cycle of low
stratiform clouds. J. Climate, 6, 1587–1606.
,, and J. R. Norris, 1995: On the relationships among lowcloud structure, sea surface temperature, and atmospheric circulation in the summertime northeast Pacific. J. Climate, 8,1140–1155.
Lanzante, J. R., and R. P. Harnack, 1983: An investigation of summer
sea surface temperature anomalies in the eastern North Pacific
Ocean. Tellus, 35A, 256–268.
Latif, M., and T. P. Barnett, 1994: Causes of decadal climate variability over the North Pacific and North America. Science, 266,
634–637.
, and, 1996: Decadal climate variability over the North
Pacific and North America: Dynamics and predictability. J. Climate, 9, 2407–2423.
Lau, K. M., and P. H. Chan, 1985: Aspects of the 40–50 day oscillation during the northern winter as inferred from outgoing longwave radiation. Mon. Wea. Rev., 113, 1889–1909.
Lau, N.-C., 1997: Interactions between global SST anomalies and the
midlatitude atmospheric circulation. Bull. Amer. Meteor. Soc.,
78, 21–33.

VOLUME 12, and M. J. Nath, 1996: The role of the ‘‘atmospheric bridge’’in linking tropical Pacific ENSO events to extratropical SST
anomalies. J. Climate, 9, 2036–2057.
, S. G. H. Philander, and M. J. Nath, 1992: Simulation of ENSOlike phenomena with a low-resolution coupled GCM of the global ocean and atmosphere. J. Climate, 5, 284–307.
Luksch, U., H. von Storch, and E. Maier-Reimer, 1990: ModelingNorth Pacific SST anomalies as a response to anomalous atmospheric forcing. J. Mar. Syst., 1, 51–60.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. Francis,
1997: A Pacific interdecadal climate oscillation with impacts on
salmon production. Bull. Amer. Meteor. Soc., 78, 1069–1079.
Meyers, S. D., M. A. Johnson, M. Liu, J. J. O’Brien, and J. L. Spiesberger, 1996: Interdecadal variability in a numerical model ofthe northeast Pacific Ocean: 1970–89. J. Phys. Oceanogr., 26,2635–2652.
Miller, A. J., D. R. Cayan, and J. M. Oberhuber, 1994: On the reemergence of midlatitude SST anomalies. Proc. 18th Annual
Climate Diagnostics Workshop, Boulder, CO, NOAA, 149–152.
Namias, J., 1986: Persistence of flow patterns over North America
and adjacent oceans. Mon. Wea. Rev., 114, 1368–1383.
, and R. M. Born, 1970: Temporal coherence in North Pacificsea-surface temperature patterns. J. Geophys. Res., 75, 5952–
5955.
, and
, 1974: Further studies of temporal coherence in North
Pacific sea surface temperatures. J. Geophys. Res., 79, 797–798.
, X. Yuan, and D. R. Cayan, 1988: Persistence of North Pacificsea surface temperature and the atmospheric flow patterns. J.
Climate, 1, 682–703.
Norris, J. R., and C. Leovy, 1994: Interannual variability in stratiform
cloudiness and sea surface temperature. J. Climate, 7, 1915–
1925.
, Y. Zhang, and J. M. Wallace, 1998: Role of clouds in summertime atmosphere–ocean interactions over the North Pacific.
J. Climate, 11, 2482–2490.
Pan, Y. H., and A. H. Oort, 1990: Correlation analyses between sea
surface temperature anomalies in the eastern equatorial Pacificand the world ocean. Climate Dyn., 4, 191–205.
Quenouille, M. H., 1954: Associated Measurements. Butterworths,
242 pp.
Robertson, A. W., 1996: Interdecadal variability over the North Pacific in a multi-century climate simulation. Climate Dyn., 12,
227–241.
Saravanan, R., and J. C. McWilliams, 1997: Stochasicity and spatial
resonance in interdecadal climate fluctuations. J. Climate, 10,2299–2320.
Smith, T. M., R. W. Reynolds, R. E. Livezey, and D. C. Stokes, 1996:
Reconstruction of historical sea surface temperatures using empirical orthogonal functions. J. Climate, 9, 1403–1420.
Tanimoto, Y., N. Iwasaka, K. Hanawa, and Y. Toba, 1993: Characteristic variations of sea surface temperature with multiple timescales in the North Pacific. J. Climate, 6, 1153–1160.
Timlin, M. S., M. A. Alexander, and C. Deser, 1997: The re-emergence of midlatitude SST anomalies. Proc. 21st Annual Climate
Diagnostics and Prediction Workshop, Huntsville, AL, NOAA,
154–156.
Ting, M., and N.-C. Lau, 1993: A diagnostic and modeling study ofthe monthly mean wintertime anomalies appearing in a 100-year
GCM experiment. J. Atmos. Sci., 50, 2845–2867.
Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphere–ocean
variations in the Pacific. Climate Dyn., 9, 303–319.
Weare, B., 1994: Interrelationships between cloud properties and
SSTs on seasonal and interannual timescales. J. Climate, 7, 248–
260.
, and J. S. Nasstrom, 1982: Examples of extended empiricalorthogonal function analyses. Mon. Wea. Rev., 110, 481–485.
, A. R. Navato, and R. E. Newell, 1976: Empirical orthogonal
analysis of Pacific Ocean sea surface temperatures. J. Phys.
Oceanogr., 6, 671–678.
Weaver, A. J., E. S. Sarachik, and J. Marotzke, 1991: Internal low

AUGUST 1999

ALEXANDER ET AL.

frequency variability of the ocean’s thermohaline circulation.
Nature, 353, 836–838.
White, W. B., 1995: Design of a global observing system for gyrescale upper ocean temperature variability. Progress in Oceanography, Vol. 36, Pergamon, 169–217.
Yukimoto, S., M. Endoh, Y. Kitamura, A. Kitoh, T. Motoi, A. Noda,
and T. Tokioka, 1996: Interannual and interdecadal variabilities
in the Pacific in an MRI coupled GCM. Climate Dyn., 12, 667–
683.
Zhang, R.-H., and S. Levitus, 1997: Structure and cycle of decadal

2433

variability of upper-ocean temperature in the North Pacific. J.
Climate, 10, 710–727.
Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability. J. Climate, 10, 1004–1020.
, J. R. Norris, and J. M. Wallace, 1998: Seasonality of largescale atmosphere–ocean interaction over the North Pacific. J.
Climate, 11, 2473–2481.
Zorita, E., and C. Frankignoul, 1997: Modes of North Atlantic decadal
variability in the ECHAM1/LSG ocean–atmosphere general circulation model. J. Climate, 10, 183–200.