3510

JOURNAL OF CLIMATEVOLUME 20

Investigating the Impact of Reemerging Sea Surface Temperature Anomalies on theWinter Atmospheric Circulation over the North Atlantic
CHRISTOPHE CASSOU
CNRS–CERFACS, Toulouse, France, and National Center for Atmospheric Research, Boulder, Colorado

CLARA DESER
National Center for Atmospheric Research, Boulder, Colorado

MICHAEL A. ALEXANDERNOAA/Earth System Research Laboratory, and Climate Diagnostics Center, Boulder, Colorado(Manuscript submitted 30 March 2006, in final form 15 November 2006)
ABSTRACT
Extratropical SSTs can be influenced by the “reemergence mechanism,” whereby thermal anomalies inthe deep winter mixed layer persist at depth through summer and are then reentrained into the mixed layer
in the following winter. The impact of reemergence in the North Atlantic Ocean (NAO) upon the climate
system is investigated using an atmospheric general circulation model coupled to a mixed layer ocean/thermodynamic sea ice model.
The dominant pattern of thermal anomalies below the mixed layer in summer in a 150-yr control
integration is associated with the North Atlantic SST tripole forced by the NAO in the previous winter as
indicated by singular value decomposition (SVD). To isolate the reemerging signal, two additional 60member ensemble experiments were conducted in which temperature anomalies below 40 m obtained fromthe SVD analysis are added to or subtracted from the control integration. The reemerging signal, given by
the mean difference between the two 60-member ensembles, causes the SST anomaly tripole to recur,
beginning in fall, amplifying through January, and persisting through the following spring. The atmospheric
response to these SST anomalies resembles the circulation that created them the previous winter but with
reduced amplitude (10–20 m at 500 mb per °C), modestly enhancing the winter-to-winter persistence of the
NAO. Changes in the transient eddies and their interactions with the mean flow contribute to the large-scaleequivalent barotropic response throughout the troposphere. The latter can also be attributed to the change
in occurrence of intrinsic weather regimes.

1. Introduction
It is well established that the nonseasonal variabilityof the atmospheric circulation over the North Atlantic
sector is primarily governed by internal dynamical processes resulting in chaotic temporal behavior and a reduction in potential predictability on time scales longer
than a week or two (Rodwell 2003). This “climate
noise” paradigm (Madden 1976) has been shown to behighly relevant for the North Atlantic Oscillation

Corresponding author address: Christophe Cassou, CERFACS–
CNRS, 42, Avenue G. Coriolis, 31057 Toulouse, CEDEX 01,
France.
E-mail: cassou@cerfacs.fr
DOI: 10.1175/JCLI4202.1© 2007 American Meteorological Society

JCLI4202(NAO), the leading mode of atmospheric variability
over the North Atlantic (Wunsch 1999; Stephenson etal. 2000; Feldstein 2000). However, the temporal behavior of the NAO appears to be modulated slightly byexternal forcing and/or coupling to other componentsof the climate system, particularly the oceans (see Hurrell et al. 2003 for a review). This departure from random behavior is evident, for example, in the enhanced
variance and persistence of the NAO beyond that expected from climate noise alone (Feldstein 2000; Greatbatch 2000). Given the large environmental and socioeconomic impacts of the NAO over Europe and eastern
North America (Hurrell 1995), it is of interest to investigate mechanisms that contribute to the nonrandom
component of NAO variability. In the present study, we15 JULY 2007

CASSOU ET AL.

focus on understanding the role of the “oceanic reemergence mechanism” (Alexander and Deser 1995) in enhancing the year-to-year persistence of the NAO, especially during winter.
The interaction between the reemergence mechanism and the NAO can be viewed as follows. During
winter, anomalous surface winds and air temperaturesassociated with the positive or negative phase of the
NAO result in anomalous fluxes of sensible and latent
heat at the sea surface, which create ocean temperature
anomalies that extend down to the base of the deep
winter mixed layer (Cayan 1992; Deser and Timlin
1997; Seager et al. 2000). These thermal anomalies
project onto the so-called North Atlantic tripole whose
typical e-folding time is on the order of 3–5 months at
the surface (see a review in Frankignoul 1985). At
depth, the anomalies persist through spring and summer within the stably stratified seasonal thermocline,
insulated from thermal damping to the atmosphere by
the formation of a shallow mixed layer in response toincreasing solar radiation and weakening stirring due toslackened surface winds. Their sequestration ends inthe following fall or early winter when the mixed layer
deepens again due to the seasonal intensification of theextratropical atmospheric circulation, and the thermal
anomalies created the previous winter become reentrained into the mixed layer, affecting sea surface temperatures (SSTs). This reentrainment thus leads to the“reemergence” of the previous winter’s SST anomalies.
Oceanic reemergence occurs basinwide both in the Pacific (Alexander et al. 1999) and the Atlantic (Timlin etal. 2002; de Coëtlogon and Frankignoul 2003), and its
timing and intensity are functions of the depth of thewinter mixed layer and the strength of the anomalous
wintertime atmospheric forcing. The persistence of the
NAO-forced SST anomaly tripole pattern from one
winter to the next has been shown to be consistent withthe reemergence mechanism (Watanabe and Kimoto
2000; Timlin et al. 2002; Deser et al. 2003; de Coëtlogon
and Frankignoul 2003).
Here we investigate to what extent the reemergencemechanism affects the overlying atmosphere including
the winter-to-winter persistence of the NAO. While addressing this question, we have to keep in mind thoughthat the dominant source of the NAO variability is internal atmospheric dynamics. As claimed in Junge andHaine (2001) for the ocean, it is evident that even if
reemergence does play a role, contemporaneous heat
flux anomalies are much more effective at generating
wintertime SST anomalies. For the atmosphere, Kushnir et al. (2002) state that external forcings all together
could explain at most 20%–25% of the interannual
variance of the North Atlantic atmosphere. Neverthe-3511

less, as pointed out in the Kushnir et al. review, muchcan be gained in seasonal-to-interannual prediction
from better resolving and understanding all the weakly
coupled processes involved in North Atlantic climate,which could have a significant impact under particularconditions.
Isolating and quantifying the role of oceanic reemergence upon the atmospheric circulation in observations and fully coupled models is difficult because of
the dominance of atmospheric forcing of the underlying ocean. In this study, we perform experiments withan atmospheric general circulation model (AGCM)
coupled to an entraining ocean mixed layer/thermodynamic sea ice models in which oceanic thermal
anomalies are imposed beneath the shallow mixed layerin summer, and the coupled system is allowed to respond over the ensuing fall and winter. Such experiments include the reemergence mechanism as well as
air–sea ice exchanges that have been shown to influence the North Atlantic atmospheric variability. The
experiment design also allows for the simulation of areasonable mean ocean climate via a flux correction
term that mainly compensates for the absence of oceanic heat transport (and to a lesser extent for model
biases). On the other hand, we omit any nonlocality of
the reemergence mechanism due to neglected oceanic
advection that may add some persistence to the wintertime SST anomalies and associated atmospheric circulation as suggested by de Coëtlogon and Frankignoul(2003).
The paper is organized as follows. The atmosphere,
ocean, and ice components of the coupled model, themethod of coupling, and the model performance aredescribed in section 2. The experimental design chosen
to isolate, quantify, and understand the impact of thereemergence on the North Atlantic atmosphere is presented in section 3. The simulated atmospheric response to reemerging thermal ocean anomalies is examined in section 4. Processes involved in the atmospheric response, their timing, and their amplitude, arealso explored. The results are summarized and further
discussed in section 5.

2. Coupled model description and performancea. Model components
The coupled model used in this study has four components. The model atmosphere is the second versionof the National Center for Atmospheric Research(NCAR) Community Atmosphere Model (CAM2.1).
The dynamical core of this AGCM is based upon an
Eulerian spectral scheme solved on a Gaussian grid of
about 2.8° ⫻ 2.8° latitude–longitude corresponding to a

3512

JOURNAL OF CLIMATE

triangular horizontal truncation at 42 wavenumbers.
The vertical resolution is discretized over 26 levels using a progressive hybrid coordinate. Kiehl and Gent
(2004) provide a detailed description of the model andevaluate its performance. The land surface and the sea
ice components are the Community Land Model(CLM2; Oleson et al. 2004) and the Community Sea Ice
Model (CSIM; Briegleb et al. 2004), respectively. The
dynamical core of the latter has been turned off in thepresent case. The ocean component consists of independent single-column models with explicit mixed layer
physics and no horizontal advection. Land, sea ice,and ocean models are aligned with the CAM grid. Coupling between the ocean and the other components occurs daily, while the atmosphere, ice, and land modules
exchange flux and mass quantities at the CAM time
step.
The ocean mixed layer model (MLM) is based on
Gaspar (1988)’s formulation as implemented by Alexander and Deser (1995). In the present study, we use amodified version of that used in Alexander et al. (2000),
where we include additional layers, extend the ocean
bottom to 1500 m (instead of 1000 m), and implement
coupling between the thermodynamical sea ice component of CSIM and the MLM surface layer. Each ocean
point has 36 vertical levels with 15 layers in the upper
100 m and a realistic bathymetry. The mixed layer
depth (MLD) is computed as a prognostic variable
based on turbulent kinetic energy parameterization when deepening, or as a diagnostic quantity basedon the balance between wind stirring and surface
buoyancy forcing when shoaling. Very shallow areas(⬍40 m), however, are treated as a fixed 50-m depth
slab ocean. (A complete description of the model andits equations, as well as details of the computational
methods and correction terms, may be found in an electronic supplement (http://www.cgd.ucar.edu/cas/cdeser/
REMsupfig.html); only a brief overview is given here.
A surface heat flux correction term (Qcor) is applied
to account for missing physics in the ocean such as heattransport by the mean currents and diffusion, as well as
errors in the atmospheric surface fluxes to a lesser extent. The distribution of Qcor in December–February
and June–August (Figs. 1a,b) indicates that it adds heatto the oceans in the winter hemisphere and extract heatyear-round in the deep Tropics, especially in the Pacificand Atlantic. The latter compensates for missing horizontal advection along the equatorial cold tongues andassociated upwelling, while the former compensates formissing horizontal advection along the Gulf Stream andKuroshio, in the North Atlantic and North Pacific, respectively. In June–August in the Southern Hemisphere, Qcor brings heat along the storm track into the

VOLUME 20

mixed layer, which is fed in nature by intrusions ofwarm intermediate waters and the Ekman contributionthat is not simulated in MLM. A salinity flux correction
term Scor is also added in the model, but note that while
Qcor resembles the distribution of ocean heat transport
in extratropical regions, Scor does not reflect the distribution of the freshwater flux (Figs. 1c,d) and salt advection by the ocean is not a large contributor to Scor.
The term Scor mainly corrects for biases in the precipitation ⫺ evaporation budget and the absence of river
runoff in the model. In winter (Fig. 1c), it compensates
for the overestimated precipitation along the intertropical convergence zone (ITCZ) at 5°N in the Pacific, enhanced evaporation due to stronger trade winds in thesubtropics, and a northward shift in the extratropicalstorm track. In polar regions, Scor accounts for missing
physics in the ice model (dynamical processes).

b. The coupled model mean state
We performed a 150-yr control integration of thecoupled model, hereafter referred to as CTL. The control integration, with the inclusion of a flux correction,
reproduces the observed mean SST distribution, withdifferences between CTL and observations generally
less than ⫾0.3°C in the Tropics year-round and in midlatitudes during winter (Figs. 1e,f). The largest errors(positive) occur in summer in the extratropical oceans
due to shallower-than-observed MLD. A summertime
bias of around ⫹1°C is found basinwide in the Northern
Hemisphere extratropical oceans and above the subtropical highs in the Southern Hemisphere along 40°S.
Errors in sea ice extent lead to SST biases in the polar
regions.
The deepest MLDs occur in the winter hemisphere in
CTL (Figs. 1g,h), similar to observations (see, e.g., deBoyer Montegut et al. 2004). In the Northern Hemisphere, maxima are collocated with the storm tracks
both in the North Pacific and North Atlantic with realistically simulated values between 150 and 200 m.
Deeper MLDs (⬃250 m) are found between Greenlandand Great Britain as well as in the Labrador andNorwegian Seas; however, these values are still
strongly underestimated compared to observations, although a few grid points around Iceland and Spitzberg
in CTL reach 600–700 m. Alexander et al. (2000) provide a discussion of the factors leading to the model’sunderestimate of MLD in regions of deep-water formation.
There is no significant drift in MLD, temperature,and salinity away from coastal grid points over the 150yr duration of CTL. However a clear trend in salinity isfound beneath sea ice grid points.

15 JULY 2007

CASSOU ET AL.

3513

FIG. 1. (a) DJF and (b) JJA average surface heat flux correction (W m⫺2). Positive values indicate heat is added to the ocean. Shadinginterval is 25 W m⫺2. (c) DJF and (d) JJA average surface salinity flux correction (mm day⫺1). Negative values indicate freshwater isadded to the ocean. Shading interval is 2 mm day⫺1. (e) DJF and (f) JJA average SST bias (°C) given by the difference between the
150-yr mean climatology of CTL SST and the climatology of Hadley Centre Sea Ice and Sea Surface Temperature dataset (HadiSST)SST over 1950–99. Shading interval is 0.3°C. Simulated maximum MLD (m) for (g) boreal and (h) austral winter. Note that the shadinginterval changes with depth: 25 m for MLD ⬍ 100, 50 m for 100 ⬍ MLD ⬍ 300, and 200 m for deeper MLD.

3. Experimental design
The dominant pattern of reemerging SST anomalies
in CTL is identified based on singular value decompo-

sition (SVD; e.g., von Storch and Zwiers 1999) between
July and September (JAS; hereafter, 3-month periods
are denoted by the first initial of each respective
month) subsurface temperature anomalies (40–400-m

3514

JOURNAL OF CLIMATEVOLUME 20

TABLE 1. SCF in percentage and correlation coefficient (Corr.) between the SVD PC time series, from lag ⫺8 (NDJ) to 0, with ocean
temperature fixed in JAS and SLP lagged as indicated. Boldface denotes significance (95% level confidence) estimated following Czaja
and Frankignoul (2002).
Season

NDJ

DJF

JFM

FMA

MAM

AMJ

MJJ

JJA

JAS

SCF
Corr.

73
0.50

79
0.62

79
0.62

84
0.59

64
0.45

62
0.64

57
0.64

53
0.61

35
0.37

depth) in the North Atlantic north of 25°N and SLP
anomalies over the North Atlantic–European domain(20°–85°N, 90°W–30°E) at various lead times from theprevious November through the concurrent August. In
the SVD analysis, the ocean temperature is treated as avector-valued field and the covariance matrix can beviewed as a collection of submatrices, which contain thecovariances between SLP and levels of ocean temperature jointly at the same grid point. Table 1 gives SVD
statistics for the leading mode as a function of lead timefrom November to January (SLP leading ocean temperature by 8 months) to JAS (atmosphere and oceanin phase). The greatest covariances [squared covariancefraction (SCF)] are found between the summer subsurface ocean and the previous winter atmosphere,
indicative of the atmosphere forcing the ocean, while
contemporaneous or near-contemporaneous ocean–

atmosphere covariances are weak and not significant.
The heterogeneous spatial patterns of SLP anomalies
in February–April and homogeneous subsurface temperature anomalies at 50-m depth in July–September
associated with the leading SVD mode that accounts
for the largest SCF are shown in Fig. 2. The SLP pattern
resembles the NAO, while the subsurface thermal
anomaly pattern resembles the North Atlantic tripole.
The SLP and subsurface temperature principal component (PC) time series of this SVD mode are dominatedby interannual-to-decadal fluctuations and are significantly correlated, as portrayed in Fig. 2c. Our results
based on CTL are consistent with the observational
findings of Timlin et al. (2002) and de Coëtlogon andFrankignoul (2003). Although the maximum temporalcorrelation between the PC time series occurs with SLP
in May–July, the corresponding pattern of JAS subsur-

FIG. 2. Leading SVD modes calculated between (a) FMA SLP (hPa) and (b) JAS ocean temperature (T )anomalies (°C) between 40- and 400-m depth, computed from the 150-yr CTL simulation. Contour intervals are
0.3 hPa and 0.05°C, respectively. (c) Corresponding normalized principal component time series of FMA SLP(solid line) and JAS subsurface temperature (bars).

15 JULY 2007

CASSOU ET AL.

FIG. 3. Simulated CTL lead–lag correlations between the Marchocean temperature anomalies averaged over 50°–60°N, 60°–30°W
and the first upper 30 m (dashed box), and temperature anomalies
between the surface and 450 m, from the previous February
through the following February. Contour interval is 0.05 and values in excess of 0.65 are shaded to highlight the reemergencemechanism. The thick superimposed solid line represents the 150yr climatological mean of the MLD averaged over the domain.

face temperature anomalies is largely confined to thesubtropics (25°–30°N) and does not resemble the tripole (not shown). Because our focus is on the extent towhich reemergence of the SST tripole pattern influences the atmosphere, we choose the SVD patterns associated with the maximum SCF rather than the maximum PC correlation. In addition, the efficiency of thelocal and quasi-simultaneous forcing of the atmosphereupon the subsurface ocean in the subtropics is very likely
to be overestimated in the model in summer due toexaggerated mixed layer depth south of 30°N (Fig. 1h).
Figure 3 shows the signature of the reemergencemechanism in the northern center of action (50°–60°N,
60°–30°W) of the subsurface temperature SVD pattern,obtained by correlating March temperature anomalies
averaged over 0–30-m depth with temperature anomalies as a function of month and depth over the same
domain. While correlation values near the surface(0–30 m) decline from April until the following September, those at depth (50–100 m) exhibit almost no
attenuation, corroborating the SVD results. A portionof the signal (indicated by correlation values greaterthan 0.65) rebounds to the surface in October and November in phase with the seasonal deepening of themixed layer. Thereafter, the correlations decay rapidly
along the entire mixed column, similar to observations
although less pronounced. Such a decrease is consistent
with the effect of entrainment, which mixes the existing

3515

heat content anomalies with thermal anomalies newly
created by the wintertime atmospheric anomalies. In
CTL, the rapid loss of memory is amplified comparedto nature as the North Atlantic MLD is underestimatedand because the simulated NAO is characterized by a
too-strong quasi-biannual component (not shown), thus
considerably reducing its winter-to-winter persistencecompared to observations. The impact of the reemerging thermal anomalies on the atmosphere is consequently hard to extract from CTL only; however Fig. 3
shows that reemergence indeed occurs in the coupledmodel.
To isolate and determine the influence of reemergence of the SST tripole upon the atmosphere, two
60-member ensembles of coupled experiments, hereafter REM⫹ and REM⫺, are performed based on the
FMA atmosphere/JAS ocean SVD results. All members of each ensemble are integrated for a year startingon 1 August with the same perturbed initial oceanic
conditions but different initial atmospheric states. The
common ocean conditions correspond to the threedimensional thermal anomalies given by the SVD modethat are added in REM⫹ (subtracted in REM⫺) to the
150-yr averaged 1 August ocean conditions computedfrom CTL. The atmospheric conditions are taken randomly among the one hundred and fifty 1 August atmospheric conditions from CTL. The spatial shape andmagnitude of the imposed ocean perturbations are obtained by multiplying the three-dimensional SVD oceanic mode by the maximum value of the SLP PC timeseries shown in Fig. 2c to preserve linear relations between the variables. The maximum value of the SLP PC
time series (3.1) corresponds to an anomalous pressure
gradient between the Icelandic Low and Azores Highof approximately 12 hPa, similar to observations for amoderate-to-strong NAO (Hurrell et al. 2003). As illustrated in Fig. 4, maximum amplitudes of the REM⫹
perturbations are found in the western part of theNorth Atlantic basin and reach 1.2°C in the Labrador
Sea (Fig. 4a) and ⫺0.8°C off the east coast of theUnited States (Fig. 4b). Anomalies are mostly confined
between 40- and 125-m depth, below the summer mixed
layer of CTL (thick solid curve) but within the wintermixed layer (thick dashed curve). Note that the subsurface temperature anomaly patterns in REM⫹ andREM⫺ are derived from the coupled model as opposed
to observations in order to be consistent with the model
physics, thereby maximizing the model sensitivity to thereemergence process. As suggested by numerous studies (see Kushnir et al. 2002 for a review), a spatial
match between surface ocean anomalies and atmospheric entities (position of the jet, intensity of thestorm track, etc.) appears to be an important factor in

3516

JOURNAL OF CLIMATEVOLUME 20

the midlatitude atmospheric response to midlatitude
SST anomalies. However, the spatial pattern of thesimulated subsurface temperature anomalies is shiftedslightly northward compared to observations (deCoëtlogon and Frankignoul 2003) due to the northward
displacement of the NAO in the coupled model and thestand-alone atmospheric model (not shown). In the following analysis, we examine mainly the linear portionof the coupled model response by taking the ensemblemean difference between REM⫹ and REM⫺, hereafter referred to as REM.

4. Resultsa. Temporal evolution of the simulated ocean
anomalies

FIG. 4. Anomalous 3D temperature patterns (°C) added to(subtracted from) 1 Aug initial oceanic conditions for REM⫹
(REM⫺) and represented here for three ocean sections givenin the lower-right corner (drawn in Fig. 2b) as a function of
depth. The solid (dashed) thick black line stands for the climatological August (January) MLD simulated in CTL. Contour interval is 0.1°C.

The monthly evolution of the REM temperature
anomalies at 50-m depth (hereafter T50; left panels)
and at the surface (right panels) are contrasted in Fig. 5.
The midlatitude ocean pattern imposed in the MLM
initial conditions is well preserved at the subsurface in
September but has yet to appear at the surface. Weak
SST signals develop by October and then amplify overthe northernmost part of the basin in November and in
midlatitudes during in December. The latter mimic thesubsurface temperature anomalies, which are, by contrast, concomitantly damped between October and November in the Labrador Sea, and between November
and December for regions off the east coast of theUnited States. From December onward, the surface
and subsurface patterns match perfectly, and both intensify slightly in January and then decrease gradually
through the following spring.
The temporal evolution of the simulated ocean
anomalies is examined further using Fig. 6, which shows
the daily values of the vertical entrainment (Qwe) contribution and the surface heat flux (Qnet) contribution
in the mixed layer temperature change (see the abovementioned Web site for MLM equations). Over the
Labrador Sea (LAB.OCE box defined in Fig. 5), theheating rate due to entrainment rapidly increases andsignificantly contributes to the mixed layer warmingfrom mid-September to mid-November (Fig. 6a). It isparticularly active in late October where the temperature anomalies imposed below the seasonal thermocline in the sensitivity experiments are brought backinto the deepening mixed layer. Such timing is consistent with the mean seasonal evolution of the MLD presented previously in Fig. 3 for CTL and leads to a significant drop of the temperature anomalies at T50. SST
and T50 are identical when MLD exceeds 50 m, that is,around mid-December, while the Qwe contribution pro-15 JULY 2007

CASSOU ET AL.

3517

FIG. 6. Temporal evolution (from 1 Aug to 1 Apr) of the simulated REM anomalies averaged over the (a) LAB.OCE and (b)MID.OCE oceanic domains for temperature anomalies at 50-m
depth (T50; thick gray) and SST (dashed gray), and for the mixed
layer heating rate due to entrainment (Qwe; solid thin black line)
and due to surface fluxes (Qnet; dashed thin black line). A 15-day
running mean has been applied to Q quantities, and units are °C
and 1 ⫻ 10⫺7 °C day⫺1, respectively.

FIG. 5. Temporal evolution of the REM temperature anomalies(°C) at (left) 50-m depth and (right) the surface from September
to March. Contour interval is 0.2°C. LAB (50°–60°N, 60°–30°W)
and MID (30°–45°N, 75°–45°W) domains used subsequently for
oceanic averaged fields are shown on the March SST panel. The
entire reemerging SST pattern is statistically significant from November onward (95% significance level based on Student’s t statistic) for values greater than ⫾0.2° (not shown).

gressively diminishes. From January onward, the SSTchanges are mostly controlled by Qnet. Note that Qnet
tends to counteract the entrainment forcing in earlyfall, reducing the rate of SST warming, while it amplifies the SST anomalies from December onward, as suggested by Fig. 5. Similar results are found for the midAtlantic oceanic region (MID.OCE) with a one-monthdelay (Fig. 6b). Maximum cooling due to reentrainment
at the base of the mixed layer in response to the presence of negative thermal anomalies occurs in late November in phase with the seasonal deepening of themixed layer for that region (not shown). Negative SST
anomalies develop rapidly at the beginning of December, while T50 anomalies are damped. The two anomalies coincide by the end of December, and are further
reinforced by the Qnet contribution. The evolution of
Qnet is similar at mid- and high latitudes, whereas all
oceanic quantities are delayed by one month in the
mid-latitudes relative to the high latitudes. February

3518

JOURNAL OF CLIMATEVOLUME 20

FIG. 7. REM response in NDJFM for (a) SLP, (b)T850, (d) Z500, and (e) U200. Contour intervals are 0.3
hPa for SLP, 0.1°C for T850, 2 m for Z500, and 0.4 m s⫺1
for U200 anomalies. Shaded areas exceed the 95% significance level based on Student’s t statistic. In (e) themean U200 climatology given by CTL is superimposed(m s⫺1; thick solid line), and contours start at 30 m s⫺1with an interval of 10 m s⫺1. (c) REM sea ice response
for JFM (gray shading for decrease; dotting for increase
fraction) on which the REM surface wind (m s⫺1) response is superimposed.

differs from the other winter months as Qnet damps the
SST anomalies in both domains before amplifying themfrom March onward.
Collectively, the results show that the SST anomalies
developing in the REM sensitivity experiments at the
beginning of winter are forced by the reemergence process. In particular, we have shown that Qwe dominates
Qnet in their genesis. Thus model experiments can beused to further isolate and investigate the impact ofreemergence upon the atmospheric circulation.

b. Mean atmospheric model response to reemerging
SST anomalies
The ensemble mean REM atmospheric response for
November–March is shown in Fig. 7. The simulated
SLP pattern exhibits negative values over midlatitudes
and positive values over Greenland, with maximum
anomalies ⬃1.8 hPa (Fig. 7a). This response projects

strongly on the negative phase of the NAO, the same
phase that was used to construct the temperature at
depth in summer, and thus represents a positive feedback. Both centers of action are shifted northward compared to the NAO canonical structure estimated fromthe leading empirical orthogonal function (EOF) of
SLP displayed in Fig. 8a for CTL. The REM response isalso more confined to the central and western part ofthe basin and does not include the eastward extensionof the Icelandic center toward Scandinavia. As shownin Fig. 8b, the amplitude of the response to reemergence represents 20% (25%) of the interannual standard deviation of winter SLP in CTL locally within the
Icelandic (Azores) center of action.
The low-level temperature response at 850 hPa(T850) is westward shifted compared to SLP, withmaximum negative (positive) anomalies over North
America (Labrador Sea) (Fig. 7b). The positive anomalies over the Labrador Sea and negative anomalies over

15 JULY 2007

CASSOU ET AL.

FIG. 8. Leading EOF of (a) NDJFM SLP and (c) Z500 computed from the 150-yr CTL integration. Contour intervals are 0.4
hPa and 2 m, respectively. The variance explained by the EOF isgiven in the upper-right corner of the pattern (%). (b) Ratio
between the NDJFM REM/2 SLP response and the standard deviation of NDJFM SLP computed from CTL. The REM response
can be considered as a biased estimated of the reemerging SSTforced signal and the ratio can be interpreted as an averaged
measure of the reemergence contribution in the CTL NDJFM
interannual variability of SLP. Shading is the same as in Fig. 7a,and contour intervals are 4% starting from 8%.

the Greenland Sea are associated with reduced andincreased ice cover, respectively, as shown in Fig. 7c.
The changes in ice cover can be related to reduced(enhanced) northwesterly winds over the Labrador

3519(Greenland) Sea associated with the NAO-like largescale SLP response, similar to observations (Deser et al.
2000). Note that T850 signals are weak over Europe,
while they represent about 30%–35% of the total T850
standard deviation estimated from CTL over the eastern United States (not shown).
The upper-level atmospheric REM response isshown in Figs. 7d,e. Geopotential height anomalies at
500 hPa (Z500) strongly resemble the T850 pattern,with amplitudes corresponding to an ⬃10–20 m °C⫺1
of SST anomaly. The geopotential height response
projects onto the upper-level NAO pattern intrinsic to
the atmospheric model as estimated in Fig. 8c from theleading Z500 EOF of CTL. The latter is shifted westward compared to the observed NAO (not shown, but
see Hurrell et al. 2003). The CAM model tends to overestimate the westward tilt with height of the extratropical modes of variability, and such a bias may contributeto explaining the relatively strong upstream responseover the North American continent in T850. The REM
Z500 response reflects the T850 anomalies, particularly
over the continents and regions of anomalous sea ice[Greenland–Iceland–Norwegian (GIN) Seas]. The 200hPa zonal wind response (U200) is less affected than
T850 and exhibits a zonally elongated north–south dipole corresponding to a reinforcement and eastward
extension of the climatological subtropical upper-leveljet, in good agreement with the negative NAO phase(Cassou and Terray 2001). The largest wind anomalies
are found around 40°N/20°W, on the diffluence side of
the jet and are associated with a significant change instorm activity as discussed in the next section.
We have verified that SST anomalies that could potentially contribute to the atmospheric response in
REM, especially those in the Tropics, do not develop
outside the forcing domain. Similarly, tropical rainfall is
not significantly modified in REM, except for a few
isolated grid points in the far western tropical Pacific(20°N–20°S, 120°–150°E) that barely pass significancetests. The latter are not associated with any local SST
changes and their amplitude is very small (at most 0.12mm day⫺1); we thus do not expect them to significantly
alter the circulation over the North Atlantic domain.

c. Early winter response and storm-track analysis
Based on model sensitivity experiments, we haveshown that oceanic reemergence may contribute to enhancing the year-to-year persistence of the wintertime
NAO and SST tripole patterns. What are the physicalmechanisms responsible for the recurrence of the NAO
from one winter to the next in response to reemergence? Results have been presented so far in terms ofthe mean winter response during November–March; we3520

JOURNAL OF CLIMATE

next focus on the timing of the model response within
the winter season and the role of midlatitude synoptic
eddies in the evolution of the mean atmospheric response.
The evolution of the atmospheric temperature response as a function of height is shown in Fig. 9a over
the LAB.ATM domain (52°–62°N, 80°–45°W); notethat this region is slightly shifted northwestward compared to its oceanic counterpart (see Fig. 5), as motivated by Fig. 7b. Reemergence warms the low-levelatmosphere (below 700 hPa) in October–November,and the warming then rapidly amplifies and extends
into the upper troposphere during December. This vertical extension indicates that the atmospheric response
is approximately equivalent barotropic from December
to February, as confirmed by monthly geopotential
height anomaly maps (not shown). Warming then persists at low levels in the following spring consistent withthe positive feedback associated with the return of thenegative phase of the simulated NAO.
We now focus on the change of the character of themodel response between the early stage of reemergence (1 October–15 November, hereafter ES) and thelate stage (15 November–31 December, hereafter LS),
when vertical export of the atmospheric warming occurs. The precise choice for these two periods is basedon the sign of the surface heat flux (Qnet) contribution
to the mixed layer temperature change (Fig. 6), and on
the rapid amplification of the geopotential height response around 15 November at high latitudes (notshown). Damping of the developing SST anomalies occurs basinwide in ES (Fig. 9b) followed by a clear sign
reversal (positive SST anomalies collocated with positive heat flux penetrating the surface ocean) in LS,
leading to the amplification of the reemerging SST
anomalies and the development of the North Atlantic
tripole from mid-November onward (Fig. 9c).
The association between the surface fluxes and the
dynamics of the atmospheric response is explored separately for the ES and LS periods (Fig. 10). During ES,
the SLP response (Fig. 10a) is confined over Europe
but is weak and not significant. The surface heat exchange pattern resembles the reemerging anomalies
and acts to damp the SST anomalies mainly due to the
longwave radiation component that dominates sensible
and latent changes.
In contrast to ES, the SLP response in LS is large
scale and resembles the negative phase of the NAO.
The sign of Qnet (which acts to reinforce the SST
anomalies) is almost entirely controlled by associatedchanges in latent and sensible heat fluxes. The rapid
and strong development of the large-scale atmospheric
response is hypothesized to be linked to atmospheric

VOLUME 20

FIG. 9. (a) Temporal evolution as a function of height and pressure of the monthly REM response for temperature (°C) spatiallyaveraged over the LAB.ATM (52°–62°N, 80°–45°W) domain. (b),(c) REM response for total heat flux at the surface (W m⫺2; color
shading) and superimposed REM SST anomalies [°C; green(purple) contour for positive (negative) values; contour interval is
every 0.2°C] averaged (b) from 1 Oct to 15 Nov, and (c) from15 Nov to 31 Dec.

15 JULY 2007

CASSOU ET AL.

3521

3522

JOURNAL OF CLIMATEtransient eddy activity. The low-level baroclinicity (Fig.
10b), estimated from the Eady baroclinic instability
growth rate maximum parameter ␴BI (Hoskins andValdes 1990), exhibits a decrease due to a reduced SST
gradient at the intergyre boundary off Newfoundland.
The anomalous SST gradient is local and weak in ES
while it is large scale and significant in LS, coinciding
with the position of the storm track and its seasonal
development. A clear and significant southward shift ofthe storm track is found in LS as shown by the anomalous variance of the bandpass (2.2–6 day) filtered Z500
(Fig. 10c). Storminess is significantly enhanced (diminished) at midlatitudes (high latitudes) over and downstream of the cold (warm) reemerging SST anomalies(recall Fig. 5). The associated large-scale strengthening(slackening) of the westerlies is consistent with thechange in sign of the atmospheric feedback effect (seeFrankignoul 1985 and Kushnir et al. 2002 for reviews)
on the SST anomalies between ES and LS (Fig. 9). The
storm-track alterations also lead to a reduction in theanomalous poleward heat flux by the transient eddies(hereafter v⬘T⬘850; Fig. 10d), consistent with the presence of anomalously warm conditions in the Labrador
Sea that reduce the climatological gradient between thetwo oceanic gyres. Note that, although clearly amplified
in LS, the anomalous v⬘T⬘850 can also be found in ES
but is substantially weaker and does not seem to affectthe upper-level atmosphere via transient eddy dynamics.
Perturbations in the transient eddy momentum fluxes
computed from Eliassen–Palm (EP) vector diagnostics(Trenberth 1986) are shown in Fig. 10e. The divergence(convergence) of EP is indicative of mean flow acceleration (deceleration) due to the presence of stormtrack activity changes. During ES, EP fluxes are locallyconfined to the eastern side of the Atlantic basin andare barely significant. For LS, a clear divergence occurs
in the model on the southeastern side of the subtropicalclimatological jet (⬃35°N, 40°–10°W) and is compensated to the north by a strong convergence in the jetexit region off the British Isles and along the eddydriven jet off Newfoundland. Such an anomaly is
consistent with enhanced zonality of the basic flow
(Doblas-Reyes et al. 2001) as shown in Fig. 10e by the

VOLUME 20

superimposed U200 anomalies, and with the forcing
tendency due to synoptic eddies to develop a largescale negative NAO pattern.

d. Role of internal variability and nonlinearity
As noted in the introduction, there is an emerging
consensus that the internal variability in an atmosphericgeneral circulation model plays a strong role in shaping
the pattern of the forced response (Peng and Robinson
2001; Magnusdottir et al. 2004; Deser et al. 2004). To
examine this aspect and to gain insight into the nonlinearity of the response, we decompose the REM November–March mean atmospheric response into daily
circulation patterns or weather regimes, which are internal structures of variability: a modification of theclimate mean state can be interpreted as the integrationof the changes in their intrinsic frequency of occurrence. We identify the dominant weather regimes using
cluster analysis (the k-means algorithm: Michelangeli etal. 1995) applied to raw daily (instantaneous values at
0000 UTC) SLP anomalies from REM⫹, REM⫺, and60 yr (randomly selected) from the 150-yr control integration of the model for a total of 27 180 daily SLP
anomaly maps (3 ensembles ⫻ 60 members ⫻ 151 days
per extended winter). The significance of the results is
tested following Farrara et al. (2000), and the method
used to compute the regimes is described in greater
detail by Cassou et al. (2004). The regimes estimatedfrom Z500 are very similar to those obtained from SLP(not shown).
Four weather regimes have been extracted, corresponding to the optimal partition based on the so-calledclassificability index detailed in Michelangeli et al.
(1995) and applied to our dataset (Fig. 11). We verified
that these regimes are the same as those obtained using
only data from the entire 150-yr control integration orfrom stand-alone integrations of the atmospheric model
forced with a repeating seasonal cycle of SST and sea
ice conditions. The first regime, termed GE for Greenland–Europe, projects on the positive phase of theNAO, although compared to observations the two centers of action are northward shifted and the anomalously deep subpolar low is located over Greenland instead of Iceland. The second regime, termed IL⫹, is←FIG. 10. REM response averaged (left) from 1 Oct to 15 Nov, and (right) from 15 Nov to 31 Dec for (a) SLP, (b) Eady growth rateof baroclinic instabilities ␴BI, (c) storm-track activity estimated by (Z⬘2)1/2 computed from 2.2–6-day bandpass-filtered Z500, (d)meridional heat transport by the storms estimated by ␷⬘T⬘ computed at 850 hPa (v⬘T⬘850) from 2.2–6-day bandpass filter quantities, and(e) zonal wind at 200 hPa (U200) on which anomalous Eliassen–Palm vectors are superimposed. Contour intervals are 0.3 hPa for SLP,
0.7 ⫻ 10⫺2 day⫺1 for ␴BI, 1 m for storm track activity, 0.3 K m s ⫺1 for ␷⬘T⬘, and 0.4 m s⫺1 for U200 anomalies. Shaded areas exceed
the 95% significance level based on the Student’s t statistic. The mean storm-track climatology given by CTL is superimposed on theanomalous ␴BI and storm-track maps (m; thick solid line) and contours start at 50 m with a contour interval of 15 m.

15 JULY 2007

CASSOU ET AL.

3523

FIG. 11. (top) Leading four weather regimes (GE, EA⫺, IL⫹, and AR) based on daily SLP anomaly maps fromthe from REM⫹, REM⫺, and CTL experiments. Contour interval is 2 hPa. (bottom) Histograms showing thepercent daily occurrences of the four weather regimes for REM⫹, REM⫺, and CTL experiments. The error bars
correspond to the maximum dispersion of the decomposition when the clustering is performed on anomaly maps
with respect to individual experiment means (Farrara et al. 2000).

characterized by a strong and dominant positive SLP
anomaly centered over Iceland, with weaker-amplitude
negative anomalies in midlatitudes; this pattern projects somewhat onto the negative phase of the NAO.
The third regime exhibits low pressure that extends
over most of the basin and is reminiscent of the negative phase of the east Atlantic mode (Barnston andLivezey 1987) and is thus termed EA⫺. The fourth
regime strongly projects onto the Atlantic Ridge (AR)pattern despite a clear northwestward displacement of

the positive core toward Canada compared to observations (Cassou et al. 2004).
The frequencies of occurrence for the four regimes in
REM⫹, REM⫺, and CTL are shown in the bottom
panel of Fig. 11. Comparing REM⫹ and REM⫺ reveals that the reemergence mostly affects the excitationof the GE and EA⫺ regimes, while IL⫹ and AR occurrences are not significantly altered. The GE regime
is favored by about 6% in REM⫺ compared to REM⫹,
that is, when cold (warm) subsurface anomalies re-

3524

JOURNAL OF CLIMATE

emerge in the Labrador Sea (at midlatitudes). EA⫺
occurs more (less) often when the Labrador Sea iswarm (cold) and the midlatitude ocean is cold (warm).
These results help in the interpretation of the mean
SLP response (Fig. 7a) that can now be understood as
the average of changes in the excitation of two internal
regimes. In other words, the ocean forcing does not fix
the phase or amplitude of a mode of variability but
rather slightly changes the occurrence of regimes. This
explains why the mean forced response does not strictly
project onto the canonical NAO as discussed previously, but reflects a combination of the GE and the EA
patterns of internal variability
As a final step, the nonlinearity of the model response as a function of the sign of the reemerging SST
tripole may be assessed by comparing the frequenciesof occurrence for REM⫹ compared to CTL and
REM⫺ compared to CTL. REM⫹ differs from CTL interms of IL⫹, whereas REM⫺ differs from CTL in
both GE and EA⫺. The REM⫹ response is consistent
with Peng and Robinson (2001), showing the presenceof an equivalent barotropic ridge immediately downstream of positive SST anomalies located in the subpolar gyre (Fig. 2b). The nature of the REM⫺ response
can be understood following the same mechanism.
Warm midlatitude SST anomalies along 35°N (opposite
to Fig. 2b) favor the GE regime that is characterized byraising of geopotential height surfaces above and downstream of these anomalies. At the same time, the ocean
anomalies diminish the excitation of the EA⫺ regime
dominated by a trough located downstream of theanomalies, and projecting on the opposite phase of GE.
The fact that different regimes are altered between
REM⫹ and REM⫺ is expected to be controlled by how
strongly the SST-forced direct response projects on theregimes themselves, which are mostly controlled by internal eddy-driven dynamics. It is beyond the scope of
this paper to examine in further detail the nonlinearityof the model response.

5. Summary and discussion
A coupled model consisting of an atmospheric GCM,
an entraining ocean mixed layer model, and a thermodynamic sea ice model has been developed to examine
the impact of winter-to-winter reemergence of SST
anomalies in the North Atlantic upon the atmosphericcirculation. The reemergence mechanism is first evaluated in the model through a long 150-yr control integration from which we extract via SVD analysis thedominant structure of oceanic thermal variability below
the summer mixed layer that is related to the previous
late-winter atmospheric forcing. This pattern closely re-

VOLUME 20

sembles the North Atlantic SST tripole created by theprevious wintertime NAO. The associated threedimensional thermal anomalies are then imposed in theocean initial conditions of two 60-member ensemble
experiments (one with positive polarity and one withnegative polarity) of coupled simulations of 1-yr duration starting in August. These thermal anomalies areincluded only between 40 and 400 m (below the summer mixed layer) and north of 25°N, thus keeping thesurface ocean unperturbed in line with the reemergenceparadigm. We verify that the imposed thermal anomalies are reentrained into the seasonally deepening
mixed layer at the beginning of winter, and generatesignificant SST anomalies at that time.
Exposed to the reemerging oceanic thermal perturbation, the model atmosphere develops a mean quasibarotropic response that strongly projects onto the
NAO. The sign of the NAO response is the same asthat which generated the oceanic thermal anomalies in
the previous winter, thus contributing to winter-towinter persistence of the NAO. The amplitude of the
response is 10–20 m at 500 hPa per °C, representing
locally about 20%–25% of the interannual standard deviation estimated from the control simulation. The amplitude of the reemergence-induced signal is thus comparable to any other externally forced signals (cf. Kushnir et al. 2002).
Transient eddies along the North Atlantic storm
track play a significant role in shaping the structure of
the large-scale atmospheric response, as well as in controlling its timing. Classical diagnostics indicate that thetransient eddies have a positive feedback on the mean
flow from mid-November onward. The low-level atmospheric anomalies along the storm track are present at
the early stage of reemergence (October), whereas
upper-level fields are not altered except locally over
Europe (Fig. 10). This suggests that the perturbation in
the meridional SST gradient may not be exported toupper levels before mid-November because the mean
circulation is not sufficiently dynamically active (theSST gradient is not significantly enhanced in midNovember, arguing against a direct SST-gradientmechanism). This further highlights the importance ofthe mean seasonal flow, in particular the position andstrength of the upper-level jet with respect to the SST
anomalies as suggested by Peng and Robinson (2001).
The delayed atmospheric response may also be due tothe time scale for atmospheric adjustment to midlatitude SST anomalies as discussed in Ferreira andFrankignoul (2005) and Deser et al. (2007).
The mean winter atmospheric response may also beinterpreted in terms of weather regime. We showedthat the linear portion of the reemergence mechanism

15 JULY 2007

3525

CASSOU ET AL.

alters the occurrence of the positive GE regime (which
projects strongly onto the positive phase of the NAO)
and negative EA regime. The regime paradigm mayhelp to explain why the response to extratropical SST
anomalies is model dependent, that is, the difference inmodel sensitivity may result from the spatial coherence
between the direct SST-forced signal and the spatial
properties of the regimes controlled by internal dynamics, leading to a more or less pronounced reorganization of their occurrence.
Despite the fact that we chose to apply realistic amplitude subsurface thermal perturbations leading to realistic amplitude reemerging SST anomalies, themagnitude of the atmospheric response is detectable
and comparable to that due to other sources (⬃10–
20 m K⫺1 at 500 hPa) as reviewed in Kushnir et al.
(2002). It would be of great interest to reproduce similar sensitivity experiments with other coupled models
to better assess the robustness of the spatial pattern andmagnitude of the atmospheric response to reemerging
North Atlantic SST anomalies. The model response obtained here might be artificially enhanced due to biases
in the CAM2 mean state: for example, the modes ofvariability and in particular their signatures at middle
and upper levels are considerably westward shifted in
CAM2 compared to observations and are collocatedwith the regions where the imposed ocean thermal
anomalies are the strongest. On the other hand, thesensitivity of the coupled model could be considered as
too conservative due to the shallower-than-observed
simulated wintertime MLD, which reduces the amountof energy stored in the entire mixed oceanic column.
The results of this study do not preclude the impactof oceanic processes such as advection, diffusion, eddy
mixing, and subduction upon the persistence of themixed layer temperature anomalies and the NAO. Advection could shift the reemergence pattern and subduction could weaken it as discussed in de Coëtlogon
and Frankignoul (2003). The impact of advection/
mixing on reemergence has been examined in the Pacific in Sugimoto and Hanawa (2005), and more studies
would be needed for the Atlantic. In addition, the investigation of effects of thermal anomalies onto the atmosphere was restricted to those connected to the SST
tripole forced by the NAO. Even if the latter is the most
persistent and the NAO is the most effective forcing, it
is conceivable that other reemerging anomalies may influence the extratropical atmosphere. Further quantitative assessment of the effects of dynamical ocean processes versus mixed layer physics (such as the reemergence mechanism) is needed for a more complete
understanding of interannual and longer time-scale SST
variability in midlatitudes.

Acknowledgments. We thank J. W. Hurrell and L.
Terray for very stimulating discussions. We are also
very grateful to A. S. Phillips for his technical assistance. The figures were produced with the NCL software developed at NCAR, and the simulations werecarried out using the NCAR-SCD facilities under the
CSL project. This work was supported in part by
NOAA under Grant NA06GP0394 and by CNRS andby the European Community via the sixth framework
ENSEMBLES project under Contract GOCE-CT2003-505539.
REFERENCES
Alexander, M. A., and C. Deser, 1995: A mechanism for the recurrence of wintertime midlatitude SST anomalies. J. Phys.
Oceanogr., 25, 122–137.
——, ——, and M. S. Timlin, 1999: The reemergence of SST
anomalies in the North Pacific Ocean. J. Climate, 12, 2419–
2433.
——, J. D. Scott, and C. Deser, 2000: Processes that influence sea
surface temperature and ocean mixed layer depth variability
in a coupled model. J. Geophys. Res., 105, 16 823–16 842.
Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence of low-frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126.
Briegleb, B. P., C. M. Blitz, E. C. Hunke, W. H. Lipscomb, M. M.
Holland, J. L. Schramm, and R. E. Moritz, 2004: Scientificdescription of the sea ice component in the Community Climate System Model, Version 3. NCAR Tech. Note NCAR/
TN463⫹STR, 70 pp.
Cassou, C., and L. Terray, 2001: Dual influence of Atlantic andPacific SST anomalies on the North Atlantic/Europe winterclimate. Geophys. Res. Lett., 28, 3195–3198.
——, ——, J. W. Hurrell, and C. Deser, 2004: North Atlantic
winter climate regimes: Spatial asymmetry, stationarity withtime, and oceanic forcing. J. Climate, 17, 1055–1068.
Cayan, D. R., 1992: Latent and sensible heat flux anomalies overthe northern oceans: Driving the sea surface temperature.
J. Phys. Oceanogr., 22, 859–881.
Czaja, A., and C. Frankignoul, 2002: Observed impact of Atlantic
SST anomalies on the North Atlantic Oscillation. J. Climate,15, 606–623.
de Boyer Montégut, C. E., G. Madec, A. S. Fischer, A. Lazar, andD. Iudicone, 2004: Mixed layer depth over the global ocean:
An examination of profile data and a profile-based climatology. J. Geophys. Res., 109, C12003, doi:10.1029/
2004JC002378.
de Coëtlogon, G., and C. Frankignoul, 2003: On the persistence of
winter sea surface temperature in the North Atlantic. J. Climate, 16, 1364–1377.
Deser, C., and M. S. Timlin, 1997: Atmosphere–ocean interaction
on weekly timescales in the North Atlantic and Pacific.
J. Climate, 10, 393–408.
——, J. E. Walsh, and M. S. Timlin, 2000: Arctic sea ice variability
in the context of recent atmospheric circulation trends.
J. Climate, 13, 617–633.
——, M. A. Alexander, and M. S. Timlin, 2003: Understanding
the persistence of sea surface temperature anomalies in midlatitudes. J. Climate, 16, 57–72.

3526

JOURNAL OF CLIMATE——, G. Magnusdottir, R. Saravanan, and A. Phillips, 2004: The
effects of North Atlantic SST and sea ice anomalies on thewinter circulation in CCM3. Part II: Direct and indirect components of the response. J. Climate, 17, 877–889.
——, R. A. Tomas, and S. Peng, 2007: The transient atmosphericcirculation response to North Atlantic SST and sea ice
anomalies. J. Climate, in press.
Doblas-Reyes, F. J., M. A. Pastor, J. M. Casado, and M. Déqué,2001: Wintertime westward traveling planetary scale perturbations over the Euro-Atlantic region. Climate Dyn., 17, 811–
824.
Farrara, J. D., C. R. Mechoso, and A. W. Robertson, 2000: Ensembles of AGCM two-tier predictions and simulations of
the circulation anomalies during winter 1997–1998. Mon.
Wea. Rev., 128, 3589–3604.
Feldstein, S. B., 2000: The timescale, power spectra, and climate
noise properties of teleconnection patterns. J. Climate, 13,4430–4440.
Ferreira, D., and C. Frankignoul, 2005: The transient atmospheric
response to midlatitude SST anomalies. J. Climate, 18, 1049–
1067.
Frankignoul, C., 1985: Sea surface temperature anomalies, planetary waves and air–sea feedback in middle latitudes. Rev.
Geophys., 23, 357–390.
Gaspar, P., 1988: Modeling the seasonal cycle of the upper ocean.
J. Phys. Oceanogr., 18, 161–180.
Greatbatch, R. T., 2000: The North Atlantic Oscillation. Stochastic Environ. Res. Risk Assess., 14, 213–242.
Hoskins, B. J., and P. J. Valdes, 1990: On the existence of stormtracks. J. Atmos. Sci., 47, 1854–1864.
Hurrell, J. W., 1995: Decadal trends in the North Atlantic oscillation: Regional temperatures and precipitation. Science, 269,
676–679.
——, Y. Kushnir, G. Ottersen, and M. Visbeck, 2003: An overview of the North Atlantic Oscillation. North Atlantic Oscillation: Climate Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 1–22.
Junge, M. M., and T. W. N. Haine, 2001: Mechanisms of North
Atlantic wintertime sea surface temperature anomalies.
J. Climate, 14, 4560–4572.
Kiehl, J. T., and P. R. Gent, 2004: The Community Climate System Model, version 2. J. Climate, 17, 3666–3682.
Kushnir, Y., W. A. Robinson, I. Bladé, N. M. J. Hall, S. Peng, and
R. Sutton, 2002: Atmospheric GCM response to extratropical

VOLUME 20

SST anomalies: Synthesis and evaluation. J. Climate, 15,2233–2256.
Madden, R. A., 1976: Estimates of the natural variability of timeaveraged sea-level pressure. Mon. Wea. Rev., 104, 942–952.
Magnusdottir, G., C. Deser, and R. Saravanan, 2004: The effectsof North Atlantic SST and sea ice anomalies on the wintercirculation in CCM3. Part I: Main features and storm track
characteristics of the response. J. Climate, 17, 857–876.
Michelangeli, P.-A., R. Vautard, and B. Legras, 1995: Weather
regimes: Recurrence and quasi stationarity. J. Atmos. Sci., 52,
1237–1256.
Oleson, K. W., and Coauthors, 2004: Technical description of theCommunity Land Model (CLM). NCAR Tech. Note NCAR/
TN-461⫹STR, 174 pp.
Peng, S., and W. A. Robinson, 2001: Relationships between atmospheric internal variability and the responses to an extratropical SST anomaly. J. Climate, 14, 2943–2959.
Rodwell, M. J., 2003: On the predictability of North Atlantic climate. North Atlantic Oscillation: Climate Significance andEnvironmental Impact, Geophys. Monogr., Vol. 134, Amer.
Geophys. Union, 173–192.
Seager, R., Y. Kushnir, M. Visbeck, N. Naik, J. Miller, G. Krahmann, and H. Cullen, 2000: Causes of Atlantic Ocean climate
variability between 1958 and 1998. J. Climate, 13, 2845–2862.
Stephenson, D. B., V. Pavan, and R. Bojariu, 2000: Is the North
Atlantic a random walk? Int. J. Climatol., 20, 1–18.
Sugimoto, S., and K. Hanawa, 2005: Remote reemergenceareas of winter sea surface temperature anomalies in the
North Pacific. Geophys. Res. Lett., 32, L01606, doi:10.1029/
2004GL021410.
Timlin, M. S., M. A. Alexander, and C. Deser, 2002: On the reemergence of North Atlantic SST anomalies. J. Climate, 15,
2707–2712.
Trenberth, K. E., 1986: An assessment of the impact of transient
eddies on the zonal flow during a blocking episode using
localized Eliassen–Palm flux diagnostics. J. Atmos. Sci., 43,
2070–2087.
von Storch, H., and F. W. Zwiers, 1999: Statistical Analysis in
Climate Research. Cambridge University Press, 484 pp.
Watanabe, M., and M. Kimoto, 2000: On the persistence of decadal SST anomalies in the North Atlantic. J. Climate, 13,3017–3028.
Wunsch, C., 1999: The interpretation of short climate records,
with comments on the North Atlantic and Southern Oscillations. Bull. Amer. Meteor. Soc., 80, 245–255.