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On the Reemergence of North Atlantic SST Anomalies
MICHAEL S. TIMLIN AND MICHAEL A. ALEXANDER
National Oceanic and Atmospheric Administration–Cooperative Institute for Research in Environmental Sciences
Climate Diagnostics Center, Boulder, Colorado

CLARA DESER
National Center for Atmospheric Research,* Boulder, Colorado
14 November 2001 and 3 April 2002
ABSTRACTThe reemergence mechanism, whereby temperature anomalies extending over the deep winter mixed layer
are stored beneath the surface in summer and are reentrained into the mixed layer when it deepens again in thefollowing autumn and winter, is studied in the North Atlantic using approximately 40 years of surface andsubsurface data. Reemergence is found to be robust in the Sargasso Sea and the northeast Atlantic, regions
where (i) the mixed layer is much deeper in winter than in summer, (ii) currents are relatively weak, and (iii)
temperature anomalies are coherent over broad areas. The two leading empirical orthogonal functions of North
Atlantic SST anomalies also exhibit strong reemergence signatures. A novel application of empirical orthogonal
function analysis to temperature anomalies in the time–depth plane, which also incorporates information fromall grid points, is shown to be an efficient and useful approach for detecting reemergence without dependence
on specific spatial patterns or prior selection of regions.

1. Introduction
The upper ocean generally consists of a well-mixed
surface layer atop the pycnocline, in which density increases with depth. In midlatitudes, the seasonal evolution of the mixed layer and pycnocline can influence
the behavior of temperature anomalies. Temperature
anomalies that form over the deep mixed layer in winterare sequestered in the summer seasonal pycnocline
when the mixed layer shoals in spring. The anomalies
within the statically stable pycnocline are preserved
through summer while temperatures in the thin mixed
layer are altered by surface fluxes, gradually losing theirrelationship with those in the previous winter. In autumn, the mixed layer deepens by entraining water frombelow. As a consequence, a portion of the thermal anomalies created in the previous winter is returned to thesurface in the following autumn and winter.
* The National Center for Atmospheric Research is sponsored by
the National Science Foundation.
Corresponding author address: Michael S. Timlin, Dept. of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105
S. Gregory St., Urbana, IL 61801.
E-mail: timlin@cdc.noaa.gov

q 2002 American Meteorological Society

The tendency for sea surface temperature (SST)
anomalies to recur from one winter to the next without
persisting through the intervening summer was first noted by Namias and Born (1970, 1974). Based on subsurface data collected at ocean weather ships and mixed
layer model simulations, Alexander and Deser (1995)
documented that the wintertime mixed layer thermal
anomalies, stored in the summer pycnocline, did reemerge at the surface in the following autumn/winter.
Alexander et al. (1999, 2001) showed that this ‘‘reemergence mechanism’’ occurs in several regions of theNorth Pacific and across much of the basin. The latter
was suggested by the evolution of the leading patternof SST variability over the seasonal cycle and by thestrong relationship between the dominant pattern of temperature anomalies at the surface in March and November with the pattern beneath the mixed layer in September.
The aforementioned studies primarily focused on reemergence in the North Pacific Ocean. Bhatt et al.
(1998) and Watanabe and Kimoto (2000, hereinafterWK) analyzed the seasonal evolution of temperature
anomalies in the North Atlantic based on the leading
empirical orthogonal function (EOF) of air temperature,in the case of Bhatt et al., and SST, in the case of WK.
The first EOF of air temperature from the one study and2708

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that of SST from the other study in winter are largely
similar, with one center in the northeast Atlantic andanother off the east coast of the United States (the latter
is ;58 of latitude farther south in Bhatt et al. as compared with WK). Watanabe and Kimoto (2000) foundevidence for reemergence in their two midlatitude regions; Bhatt et al., studying similar areas, detected reemergence only in their northernmost one. Differencesin period of record and regional boundaries may havecontributed to the different findings in the two studies.
For example, the EOF analyses in WK extended farther
south and included a third center of variability locatedto the east of Africa between 108 and 258N. However,reemergence did not occur there, because the annual
cycle in mixed layer depth is small in the subtropics.
The aim of this study is to expand upon the findingsof WK by examining the evolution of upper-ocean temperature anomalies in the North Atlantic from both aregional and basinwide perspective. We employ traditional lag correlation analysis, EOF analysis in the time–
depth plane for selected regions, EOF analysis of basinwide SST anomalies, and a new EOF approach inwhich basinwide temperature anomaly fields are analyzed in the time–depth plane.
2. Data
Our analyses are performed on monthly fields from
two independent ocean temperature datasets. SSTs wereobtained from version 2.3b of the Global Sea Ice and
Sea Surface Temperature (GISST; Rayner et al. 1996)
dataset. The fields are on a 18 3 18 latitude–longitude
grid for the period of 1958–97. Subsurface data fromthe Joint Environmental Data Analysis Center (JEDAC)
contain temperatures at 11 levels (0, 20, 40, 60, 80, 120,
160, 200, 240, 300, and 400 m) during 1955–95. Optimal interpolation was used in the JEDAC analyses togenerate temperature values on a 28 3 58 grid based on
measurements from bathythermographs and Nansen
casts (White 1995). Although there are approximately
an order of magnitude fewer upper-ocean temperature
profiles than SST observations, a detailed examinationof the JEDAC fields indicated that the number of profiles
in the North Atlantic is sufficient to resolve the reemergence mechanism.
Prior to our computations, the GISST and JEDAC
data were interpolated to the same 58 3 58 grid. Allanalyses were performed on monthly temperature anomalies that were obtained by removing the long-termmonthly means from the original data.
Differences in the mean mixed layer depth between
winter and summer are used to identify regions in which
reemergence is likely to occur. The climatological mixed
layer depths, obtained from the World Ocean Atlas 1994
(Monterey and Levitus 1997), are based on density criteria that take both temperature and salinity into account.

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FIG. 1. Map of the North Atlantic region showing currents, mixed
layer depth difference threshold, and regions of study. Currents areshown schematically by the arrows; thicker arrows indicate stronger
currents. Shading appears in those areas in which the seasonal difference in mixed layer depth (Feb minus Sep) is less than 50 m. The
two regions in this study are the SAR region (258–358N, 658–458W)
and the NEA region (408–508N, 358–108W).

3. Results
a. Regional analyses
We use several factors to select regions in which wintertime SST anomalies are likely to recur. First, the reemergence process requires that the mixed layer is considerably deeper in winter than in summer. Second, reemergence is less likely to occur in the vicinity of strong
currents, because the temperature anomalies could be
advected away from a region over the course of theseasonal cycle. Eddies associated with currents, such as
meanders in the Gulf Stream, could also diffuse temperature anomalies that would otherwise undergo thereemergence process. Third, detection of reemergenceis favored in regions in which SST anomalies are spatially coherent. Two regions, the Sargasso Sea (SAR)
and the northeast Atlantic (NEA), were selected by considering winter minus summer mixed layer depth, current speed, and the coherence of the temperature anomalies. These regions, along with a schematic depictionof the surface currents in the North Atlantic and areasin which the February minus September mixed layer
depth exceeds 50 m, are shown in Fig. 1.
Monthly temperature anomalies from JEDAC wereaveraged over the SAR and NEA regions at each depth
and then were correlated with the temperature anomaly
at a ‘‘base point.’’ Following Alexander and Deser(1995), we chose a base point (60–80 m in June–July)in the summer pycnocline because it is the link between
the descending and return branches of the reemergenceprocess. Correlations between the base-point time series
and temperature anomalies between the surface and 160
m from the previous January to the following Marchare shown for the SAR and NEA regions in Fig. 2.
Evidence for the reemergence process is seen in bothregions where relatively high correlations extend to thesurface in the preceding winter/spring, extend throughout the pycnocline but not the mixed layer during sum-

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FIG. 2. Lead–lag correlations for (a) SAR and (b) NEA relative to a base-point time series constructed by averaging temperature anomalies
for Jun and Jul at 60–80 m depth. Correlations are from the preceding Jan to the following Mar and from the surface to 160 m. Computations
are based on 1955–95. The contour interval is 0.1. Light (dark) shading indicates correlation values greater than 0.4 (0.6) in SAR and greaterthan 0.3 (0.5) in NEA.

mer, and extend back to the surface in the following
autumn/winter. Reemergence is very strong in the SAR
region, for which the correlations exceed 0.6 throughthe mixed layer in the previous February–May and thefollowing December–January. In contrast, the correlation structure is asymmetric in the NEA region, withhigher correlations in the previous spring than in thefollowing autumn, which suggests that the descending
branch of the reemergence process is stronger than the
return branch. Oceanic processes such as subduction anddiffusion and surface fluxes in autumn that are uncorrelated with those in the previous winter can dilute thethermal anomalies that are returning to the surface. In
particular, the NEA region is located primarily within
an area of mean negative subduction (Marshall et al.
1993) such that net entrainment of fluid from the main
thermocline into the winter mixed layer and summer
pycnocline may act to dilute the local atmosphericallyforced reemergence signal.
To eliminate the subjective choice of a base point,
we applied EOF analysis to the regional temperature
anomalies as a function of time and depth. The mostcommon EOFs in geophysical research are constructed
such that each latitude–longitude grid point is a variable
and each time step (monthly value, annual value, or aparticular monthly value, e.g., May, from each year)
serves as an observation of that variable (Wilks 1995).
For reemergence, the pattern of interest is best displayed
in a time–depth plane (see Fig. 2), in which time is inmonths over approximately a year. In according with
that, the grid of seven levels (0–160 m) and 15 months(January–March of the following year) serve as the variables, with each year (1955–95) as an observation forour regional EOFs. The principal component (PC) for
each EOF is the projection of the original temperature
anomalies onto the EOF at each observation, each 15month period in this case. Hereinafter EOFs will beshown as the correlation between the PC and the monthly temperature anomalies.

The results of the EOF analyses (Fig. 3) are very
similar to the lead–lag correlations (Fig. 2) and highlightthe reemergence signal. The first EOF explains nearly
50% of the variance in the time–depth plane in bothregions. Differences between the lead–lag correlation
pattern and the leading EOF pattern in each region areof the same order as differences due to small variationsin the base point in the lead–lag correlation analyses.
The PC, also shown in Fig. 3, measures the correspondence between the EOF pattern and the temperature
anomaly pattern for a given year. The sign of the PC
indicates whether the anomaly pattern that year was
positive or negative, and the absolute value of the PC
indicates the strength of the relationship. The rms valueof the PC in the NEA region is greater during the firsthalf of the record than during the second half (2.3 and
1.5, respectively), which suggests that the reemergencemechanism was more vigorous prior to 1975. A comparison of lead–lag correlations in the NEA region for
1955–75 with those in 1975–95 (Timlin et al. 1999) also
indicates that reemergence was stronger in the earlier
period.
b. Basinwide surface analysis
Is reemergence of basinwide extent in the North Atlantic as it is in the North Pacific? One approach to
examine this question is to use a traditional EOF analysis of SST anomalies whose variables are the latitude–
longitude grid points in the North Atlantic. Watanable
and Kimoto calculated the leading EOF in the North
Atlantic in winter and then examined the lead–lag correlations between the associated PC and the projectionof monthly SST anomalies in the North Atlantic on thewinter EOF pattern. The correlations decreased fromnear 1.0 in January to ;0.1 in August and then increased
to ;0.4 in the following January.
We expand on the WK analysis by computing lag
autocorrelations of the leading PC based on SST anom-

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FIG. 3. (top) Time–depth EOF patterns and (bottom) associated PCs for the (a) SAR and (b) NEA regions. The domain of the EOF isfrom the preceding Jan to the following Mar and from the surface to 160 m. The EOF pattern is shown as the correlation between the PC
record and the temperature anomaly time series. Computations are based on 1955–95. Contour interval is 0.1. Shading is as in Fig. 2.

alies in all months from 1955 to 1998. Prior to the EOF
analysis, the standard deviation of Atlantic SST anomalies averaged over 208–708N is computed for eachmonth (ranging from 0.46 in February to 0.60 in July),
and then the anomalies at each grid point are scaled by

FIG. 4. Leading EOF of monthly SST anomalies and the associated
PC time series based upon 1958–97. The data in each calendar month
are scaled by their spatial standard deviation so that the PC is equallyweighted through the seasonal cycle. The EOF pattern is shown as
the correlation between the PC record and the temperature anomaly
time series at each grid point. The contour interval is 0.1. Light (dark)
shading indicates values .0.5 (,20.5).

the basinwide standard deviation from the corresponding month to deemphasize the seasonal cycle in thevariability while retaining the variability from one location to another. The first EOF and PC are shown in
Fig. 4. The leading pattern is similar to EOF analysesof North Atlantic SST anomalies obtained in previous
studies (e.g., Wallace et al. 1993; Cayan 1992; Deserand Blackmon 1993; WK). Figure 5 shows the correlations of the leading PC calculated separately for eachcalendar month (vertical axis) with the PC in subsequentmonths from lag 0 to lag 112 (horizontal axis). A similar analysis was shown in Kushnir et al. (2002). The
correlations decrease over lags of 3 months regardlessof the initial month, with especially strong decay startingfrom September when the mixed layer is shallowest.
This result is consistent with the stochastic theory for
SST variability (e.g., Frankignoul and Hasselmann1977), which indicates that SST anomalies driven byrandom surface forcing decay exponentially because of
damping of SST anomalies by air–sea heat fluxes at a
rate that is inversely proportional to mixed layer depth.
In Fig. 5, however, correlations lagged from wintermonths first decrease but then rise as the lag increases;
for example, correlations based on the PC value inMarch rise from less than 0.3 at a lag of 5 months(August) to greater than 0.7 at a lag of 10 months (January). Consistent with the reemergence hypothesis, theincreases at longer lags are not apparent in correlations
with late summer values. Figure 5 does suggest that SST
anomalies as late as June and July recur 5–6 months
later. Analysis of the second PC (not shown) also contains the reemergence signal, although it is weaker than

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FIG. 6. Pattern of the basinwide time–depth EOF shown as thecorrelation between the associated PC (not shown) and the temperature anomalies for each month and depth. See text for further explanation. The contour interval is 0.05. Light (dark) shading indicatescorrelations exceeding 0.5 (0.6).

FIG. 5. Lagged correlations of the PC time series from Fig. 4
calculated separately for each calendar month to show seasonal differences in evolution. The contour interval is 0.1. Shading indicatescorrelations exceeding 0.6. The black diagonal line is a visual aid
locating Dec.

in the leading PC. Thus, the reemergence signal is notnecessarily tied to a particular SST pattern, because it
appears in both of the leading PCs and because it isreadily apparent in regions that are not located in thecenters of the leading EOF (cf. Figs. 1–3 with Fig. 4a).
c. Basinwide depth-dependent analysis
Here we present a new approach for tracking reemergence over the North Atlantic by performing EOF analysis in the time–depth plane (similar to the regional
EOFs) but incorporating information from all grid points
over the domain. To accomplish this, we allow each 58
3 58 grid point in each year to represent a separate observation of the time–depth plane for the EOF analysis.
We tested this method on the North Pacific data, and theresults compared favorably with those reported in Alexander et al. (1999). For the North Atlantic, the variables
used to compute the EOF are seven depths (from 0 to
160 m) and 15 months from January to March of thefollowing year. The PC consists of 4879 observations:
119 (the number of 58 3 58 grid points in the analysis
domain) 3 41 (the number of years). The leading EOF,
which explains 34.5% of the variance, is shown in Fig.
6. It accounts for less variance than its regional counterparts (SAR 48.4%, NEA 46.8%) because of local variations in the timing and vertical structure of the reemergence process and by including some areas in which
reemergence does not occur. The reemergence patternseen in earlier time–depth plots is again evident here:

high correlations through the mixed layer of the firstwinter, low (high) correlations at (below) the surface layer in summer, and then rising correlations at the surface
through autumn and into the following winter. Surface
correlations can be seen to decay quickly from February(.0.6) through August (,0.3) when the mixed layer isshoaling, and then to rebound from August to December(.0.5) as water from the summer pycnocline is reentrained into the deepening mixed layer.
4. Summary and discussion
A variety of approaches have been used to examine
the seasonal evolution of upper-ocean temperature
anomalies in the North Atlantic during 1955–95. A regional approach based on three selection criteria identified two areas in which reemergence was expected tooccur: the Sargasso Sea and the northeast Atlantic. The
results, based on traditional lead–lag correlation analysis
from a subsurface base point in summer and from EOF
analysis in the time–depth plane, confirm strong reemergence in the Sargasso Sea region (correlations in
the mixed layer that exceed 0.6 from the previous February–May to the following December–January) andsomewhat weaker reemergence in the northeast Atlantic
region. Reemergence in the latter region was shown tobe more vigorous prior to 1975 than after. A basinwide
surface analysis showed that the leading EOF of monthly SST anomalies in the North Atlantic, a north–south
dipole pattern, exhibits strong reemergence, in agreement with previous studies (Bhatt et al. 1998; WK). We
note, however, that the second EOF also exhibits reemergence and that the SAR and NEA regions are notat the centers of action of either of the two leading EOF
patterns, indicating that reemergence is not constrained
to any particular spatial pattern. Last, a novel applicationof EOF analysis to temperature anomalies in the time–
depth plane, but one that also incorporates information

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from all grid points over the domain, was shown to bea useful approach for detecting reemergence without
being dependent upon any particular spatial pattern or
prior selection of regions.
Acknowledgments. We thank Warren White and Ted
Walker for providing the JEDAC data and the CDC datagroup for providing the GISST and WOA94 data. We
benefitted greatly from discussions with Yochanan
Kushnir regarding the SST EOF analysis. The suggestions by the anonymous reviewers were helpful and weremuch appreciated. Graphics were generated using
GrADS software developed at COLA. This work was
supported in part by a grant from the NOAA OGP Clivar
Program and by an omnibus grant from NOAA OGP
to CDC.
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