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North Atlantic Winter Climate Regimes: Spatial Asymmetry, Stationarity with Time,
and Oceanic Forcing
CHRISTOPHE CASSOUCERFACS-SUC, Climate Modelling and Global Change Team, Toulouse, France, and NCAR, Climate Global Dynamics Division,
Boulder, Colorado

LAURENT TERRAYCERFACS-SUC, Climate Modelling and Global Change Team, Toulouse, France

JAMES W. HURRELL AND CLARA DESER
NCAR, Climate Global Dynamics Division, Boulder, Colorado(Manuscript received 25 October 2002, in final form 26 May 2003)
ABSTRACTThe observed low-frequency winter atmospheric variability of the North Atlantic–European region and its
relationship with global surface oceanic conditions is investigated based on the climate and weather regimes
paradigm.
Asymmetries between the two phases of the North Atlantic Oscillation (NAO) are found in the position of
the Azores high and, to a weaker extent, the Icelandic low. There is a significant eastward displacement or
expansion toward Europe for the NAO1 climate regime compared to the NAO2 regime. This barotropic signal
is found in different datasets and for two quasi-independent periods of record (1900–60 and 1950–2001); hence,
it appears to be intrinsic to the NAO1 phase. Strong spatial similarities between weather and climate regimes
suggest that the latter, representing long time scale variability, can be interpreted as the time-averaging signatureof much shorter time scale processes. Model results from the ARPEGE atmospheric general circulation model
are used to validate observed findings. They confirm in particular the eastward shift of the Atlantic centers ofaction for the NAO1 phase and strongly suggest a synoptic origin as it can be extracted from daily analyses.
These results bring together present-day climate variability and scenario studies where such an NAO shift wassuggested, as it is shown that the last three decades are clearly dominated by the occurrence of NAO1 regimes
when concentrations of greenhouse gases are rapidly increasing. These findings highlight that the displacementof the North Atlantic centers of action should be treated as a dynamical property of the North Atlantic atmosphereand not as a mean longitudinal shift of climatological entities in response to anthropogenic forcings.
The nonstationarity with time of the atmospheric variability is documented. Late-century decades differ fromearly ones by the predominance of NAO climate regimes versus others. In such a context, comments on therelevance of the station-based NAO index is provided. Both tropical and extratropical sea surface temperature(SST) anomalies alter the frequency distribution of the North Atlantic regimes. Evidence is presented that theso-called ridge regime is preferably excited during La Niña events, while the NAO regimes are associated with
the North Atlantic SST tripole. The ARPEGE model results indicate that the tropical branch of the SST tripole
affects the NAO regimes occurrence. Warm tropical SST anomalies are more efficient at exciting NAO2 regimes
than cold anomalies are at forcing NAO1 regimes. The extratropical portion of the North Atlantic SST tripole
also seems to play a significant role in the model, tending to counteract the dominant influence of the tropicalAtlantic basin on NAO regimes.

1. Introduction
During the past few decades, there has been considerable effort devoted to obtaining a better understandingof natural climate variability over the North Atlantic–
Corresponding author address: Dr. Christophe Cassou, NCAR,
Climate Modelling and Global Change Team, P.O. Box 3000, Boulder,
CO 80307.
E-mail: cassou@ucar.edu

q 2004 American Meteorological Society

European (NAE) sector on monthly to multidecadal timescales. The low-frequency extratropical atmosphere has
been generally described in terms of space-stationary
and time-fluctuating structures known as ‘‘teleconnection patterns’’ (Wallace and Gutzler 1981). Linear techniques such as one-point correlation, eigenmodes decomposition, etc., have been traditionally used to reveal
the teleconnection patterns at various time scales(Blackmon et al. 1984). Viewed in a multidimensional
phase space whose directions are defined by eigenvec-

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tors, the linear approach relies on the hypothesis thatthe climatological mean state is at the center of the phasespace, the anomaly patterns being equally distributed
along a given direction. However, there is no guaranteethat preferred atmospheric circulation states come inpairs in which the spatial structure of the anomalies are
the same, but of opposite polarity. Interest in this nonlinear interpretation of atmospheric variability has been
growing, finding some applications within climate
frameworks (Palmer 1999) as opposed to weather issues
where this approach has been more commonly used.
The nonlinear paradigm is based on the identificationof ‘‘attractors’’ that correspond to weather or climate
regimes (Lorenz 1963) being defined as peaks in theprobability density function of the climate phase space.
A modification of the climate mean state or low-frequency climate variability is then interpreted as a change
in the amplitude of these peaks or in the preferred transitions between them (e.g., Robertson et al. 2000) leading to changes in their frequency of occurrence rather
than changes in the modes themselves. Few statisticalmethods are able to take into consideration both asymmetry and time scale interactions in the determinationof variability patterns. Cluster analysis is one of themand will be the central tool of our study. It is a multivariate statistical technique based on the property of acollection of events to group together into a small number of representative states (or regimes) according to agiven criterion of similarity. Either raw daily maps
(Michelangeli et al. 1995) or 10-day low-pass-filteredones (e.g., Kimoto and Ghil 1993) are traditionally classified. Some recent studies have, however, focused attention on monthly means and have linked the monthto-month atmospheric variability to longer low-frequency time scales. For example, Corti et al. (1999) findthat the recent temperature trend over the Northern
Hemisphere is related to the preferred occurrence of theso-called cold ocean warm land monthly regime. Monahan et al. (2001), based on a generalized empiricalorthogonal function (EOF) technique that does not constrain modes to be linear, show that the regimes related
to the so-called Arctic Oscillation (Thompson and Wallace 1998) are favored in a warmer climate.
Observed monthly means are first used in the present
study to investigate the signature of the NAE interannual
variability in terms of climate regimes. Monthly means
cannot be interpreted as quasi-stationary regimes but
should rather be considered as the time average of rapidly fluctuating entities, known as weather regimes(Vautard 1990), with a typical 8–10-day e-folding timescale (Feldstein 2000). Therefore, our goal is not to
conduct a dynamical study of the atmospheric fluctuations but to extract the signature of potential ocean forcings that may affect the excitation of weather regimes
and have a clearer signature when averaged over amonth. Indeed time averaging is beneficial as it increases the signal-to-noise ratio in the data. On the other

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hand, it reduces the sampling size and may affect therobustness of statistical results.
The clustering methods used in this study are described in section 2. The NAE climate regimes are presented in section 3 over two different periods and fromtwo different datasets. Discussion about their spatial
asymmetry as well as their stationarity over the twentieth century are reported. Their links to oceanic conditions are described in section 4. The Action de Recherche Petit Echelle Grande Echelle (ARPEGE) atmospheric global circulation model is used to further
investigate the nature of the oceanic forcing on the North
Atlantic atmosphere. Similarities between weather andclimate regimes are discussed to illustrate the hypothesisthat oceanic forcings affect the frequency distributionof the weather regimes, the climate regimes occurrence
being explained by the temporal integration of the latter.
In this section emphasis is also put on the respective
impact of tropical versus extratropical oceanic conditions on simulated weather regimes. A summary andperspective are given in section 5.
2. Classification techniques
Clustering algorithms are usually divided into two
types of schemes, namely, hierarchical and partitional
methods (Anderberg 1973). The former organizes states
into nested sequences of clusters forming a growing
aggregation tree, the latter iteratively performs the classification from randomly predefined initial centroids according to a pregiven number k of clusters. As pointed
out in Alhamed et al. (2002) and due to the sampling
size, it is central to test the robustness of the cluster
solution with different types of algorithms since different schemes and different similarity criteria can lead todifferent conclusions. In this study, results are presented
either for the Ward’s hierarchical technique (Ward 1963;
Cheng and Wallace 1993) or for the k-means partitional
scheme (Michelangeli et al. 1995), but it has been verified for all the decompositions, that the two clustering
approaches give similar regimes for the same numberof clusters used.
At each step n (n 5 1, N 2 1 2 k) of clustering, N
standing for the total number of samples and k for the
final number of clusters retained, the Ward’s scheme
retains among all possible aggregations the one thatminimizes the so-called intercluster inertia. The latter
represents the sum over N 2 n clusters of the dispersion
from their respective centroids (Martineu et al. 1999).
For the k-means algorithm, the classification is performed on the first 10 principal components of theanomalous maps by minimizing the quadratic distanceto a number of arbitrary predetermined k centroids. For
this specific number, the partition is performed 100
times with a different set of arbitrary determined centroids and the classification retained is the one that correlates best with the 99 others. This correlation value
is used to compute the so-called classificability index

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that tests, for each decomposition in k regimes, its significance against an artificial red-noise dataset with thesame statistical characteristics as the physical and original one. The so-called optimal k number is given by
the highest significant correlation value and can be interpreted as the one obtained for the most robust andphysical classification. Besides cross-validation consideration, the Ward and the k-means algorithms are thus
complementary as the latter provided the optimal k number of clusters to be retained whereas the final numberof regimes to keep at the end of the iterative process
remains somewhat subjective for hierarchical schemes.
3. Climate regimes over the North Atlantic–
European region
Cluster analyses are first applied to monthly sea levelpressure (SLP) means from the National Centers forEnvironmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis (Kalnay etal. 1996) for 1950–2001 (hereafter NCEP). The statistical decomposition is performed for individual wintermonths [December–January–February (DJF)] over theNAE domain (208–808N, 808W–308E), where eachmonth is considered an individual realization of the atmospheric states. The clustering partition yields the optimal k 5 4 number of significant winter climate regimes
subsequently represented by the cluster centroid. The
latter includes all the classified months averaged for agiven cluster and it has been verified that limited averaging to the closest states around the cluster centroid
does not significantly affect the regimes spatial characteristics but enhances their spatial significance (notshown). The first two clusters (Figs. 1a,b) capture thenegative and the positive phases of the North Atlantic
Oscillation (NAO) and are characterized by a zonallyelongated meridional pressure dipole between the Icelandic low and the Azores high. The third cluster (Fig.
1c) displays a strong anticyclonic ridge (RDG) off western Europe almost covering the entire basin. It is reminiscent of the positive phase of the east Atlantic (EA)
teleconnection pattern (Wallace and Gutzler 1981)
which features the northward extension of the Azores
high. The fourth cluster (Fig. 1d) exhibits a zonal pressure dipole between Greenland and Scandinavia (GS)with a clear southeastward extension of low pressureanomalies toward the Iberian Peninsula. The GS cluster
has been revealed as the third or fourth EOF mode over
the NAE domain (Davies et al. 1997), and it is reminiscent of the Eurasian mode of Barnston and Livezey(1987), although unlike EOF modes the regime patterns
are sign definite.
The time history of occurrence of the NAO regimes(Fig. 1e) shows strong interannual variability. Negative
NAO (NAO2) phases are predominant in the 1950s and1960s while positive NAO (NAO1) regimes are clearly
excited over the last two decades excluding NAO2
states (except for the winters of 1996 and 2001). This1057

is consistent with the observed upward NAO index trend
as described in Hurrell (1995). Although less striking,the RDG pattern seems to be favored at the beginningof the 1950s and over the 1965–76 period while no clear
low-frequency changes characterize the GS seesaw regime (Fig. 1f). Note that GS occurrence generally corresponds to extreme cold outbreaks in Europe (Walshet al. 2001) and is indicative of blocking conditions.
There is an overall good agreement between the occurrence of the NAO regimes and the DJF-averaged
NAO indices either calculated from station-based data
(Hurrell and van Loon 1997) or derived from EOF decomposition (Fig. 1e). A careful year-to-year inspection
reveals, however, some discrepancy. This is particularly
striking when the NAO regimes are not dominant, yet
the traditional NAO indices can have large values. As
RDG mostly projects onto the NAO1 cluster, its predominance over the winter season may lead to significant positive NAO indices even if the NAO regime is
not prevalent. This is the case at the beginning of the
1950s and for some isolated years such as 1981, 1994,
and 1999. EOF analyses seem to be more adept at accounting for spatial coherence of the variability. They
therefore capture the regime occurrence, but they should
not be limited to the first leading mode in order to depict
the full range of the NAE variability. Even if the NAO
captures about 50% of the variance over the 1950–2001
period, EOF2 and EOF3 appear to be very important.
It is important to mention that linearly or quadraticallydetrending the data does not significantly change theshape of the regimes or their temporal distribution.
The robustness of the classification has been verified
by applying the same algorithm to an independent andlonger SLP dataset updated from Trenberth and Paolino(1980, hereafter TP–NCAR). They combined several
SLP station-based measurements from different sourcesfrom 1899 to present to form a 58 3 58 latitude–longitude gridded dataset treated with several methods tocorrect errors and trends. The minimum pointwise spatial correlation between TP–NCAR regimes computed
over 1950–2000 and those presented in Fig. 1 for NCEP
is 0.81 (not shown). Figures 2a–2d present the four climate regimes computed from the TP–NCAR dataset but
over the 1900–60 winters. For confirmation of the stability in the cluster decomposition, we have decided tokeep in common one decade for the regime extraction.
The decomposition over the overlapped period between
NCEP and TP–NCAR leads to the same partition for
28 months out of 33 (Figs. 2e,f). The spatial characteristics of the four regimes appear to be independentof the period and dataset. The only significant differencewith NCEP clusters is found for GS. The anomalous
high pressure over Scandinavia is diminished and theGreenland low pressure anomalies are reinforced anddisplaced southeastward toward Ireland. The TP–NCAR
GS regime tends to be a more symmetrical image of
RDG compared to NCEP, and it exhibits a clear wavy
structure.

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FIG. 1. (a), (b), (c), (d) 1949–2001 DJF mean SLP regimes (hPa) over the North Atlantic sector from NCEP
reanalysis. Shaded areas exceed the 95% confidence level using T and F statistics. The percentage at the top right ofeach panel gives the global population of a given cluster over the whole period. Contour interval is 1 hPa. (e), (f )Time history of the occurrence for the NAO and the RDG–GS regimes, respectively. The vertical bars indicate thenumber of months relative to each winter where the given regime is present. Solid line stands for the standardized
expansion coefficient of the leading DJF SLP EOF. The dashed line is the NAO index based on Hurrell and van Loon
(1997). Note that because only one EOF2 and EOF3 phase is represented by the RDG and GS regimes, their associatedtime series are not shown in (f).

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FIG. 2. Same as Fig. 1 but for the 1900–60 period and TP–NCAR dataset.

Interestingly, the nonlinear approach reveals some
important spatial asymmetries between the phases of the
NAO. The main difference between the two NAO regimes is in the position of the Azores high. The latteris eastward shifted by about 308 of longitude for the
NAO1 case compared to the NAO2 case. The Icelandic
low center is more symmetric, but the low tends toextend more northeastward toward Spitzberg for the
NAO1 case (see also van Loon and Madden 1983). The
general eastward shift of the centers of action of theNAO1 regime is not dependent on the time window
considered (Figs. 1 and 2). This property has strong
implications with respect to conclusions from climate
scenario studies. Eastward displacements of the NAO

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centers are seen in some scenario model experiments inwhich enhanced greenhouse gas concentrations are prescribed. These have been attributed to the mean climate
drift due to anthropogenic external forcings (Ulbrich andChristoph 1999). Our results seem to indicate, however,that the northeastward shift of the centers of action may
be better understood in terms of more frequent NAO1
regimes instead of simple static shifts of the Atlanticpressure centers. Such a conclusion is consistent withthe tendency toward higher NAO index values simulated
in most scenario studies (Gillet et al. 2003). Our interpretation is that the longitudinal apparent shift is anindirect consequence of excited NAO1 regimes that are
intrinsically eastward displaced. It is interesting that theMétéo-France model scenarios, which do not display asignificant shift of the Atlantic centers of action, do notexhibit any trend toward positive NAO values (Drévillon 2002). Indications of a longitudinal shift for the
NAO centers are found in observations as well, in Hilmer and Jung (2000). Their findings are based on the
difference between two decadal periods (1958–77/
1978–97) of winter SLP data regressed onto an ice volume export index through the Fram Strait. Our results
go beyond their linear and epoch description however.
Evidence of displacement appears more clearly when adynamical nonlinear approach is applied and thus canbe interpreted as the result of NAO1 regime dominanceover the past 30 years.
The weak modification of the clusters over the century is consistent with the hypothesis that the centroidsof the regimes are phase space regions of relative stability, being mostly linked to the quasi-daily atmospheric variability, but their excitation is subject to interannual to decadal changes. Note, for instance, that the
NAO regimes (combined positive and negative phases)
represent only 45% of the total sample over 1900–60
compared to 57% over the second part of the century.
Figures 2e,f highlight that NAO dynamics are almost
completely nonexistent over the 1900–25 period during
which RDG and GS regimes were more dominant. For
these two decades, the strongly positive traditional NAO
indices may therefore be artificial and misleading. Results from this study are complementary to the onespresented in van Loon and Madden (1983). They showthat the SLP variance is far from stationary and is affected by both the length and the time period considered.
They highlight the singular behavior of the early twentieth century variability with a significant displacementof the maximum SLP variance from the Irminger Sea
to Ireland (see their Figs. 1 and 3). The present study
explains this shift as the excitation of RDG–GS regimes
over this period. Such a conclusion can be partiallyassessed within the linear paradigm by comparing, as afunction of time, values of explained variance for thetwo leading SLP EOF modes over the same domain.
The NAO mode (52% of explained variance) clearly
dominates the EA mode (17%) over the past 50 years

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whereas they have almost the same weight (32% versus
28%) over 1900–25.
Several studies have emphasized that the short timescale dynamics of the NAO arises from the driving ofsynoptic-scale eddies, which is most prominent in theupper troposphere (e.g., Hoskins et al. 1983). To illustrate this hypothesis and to further confirm that the SLP
decomposition presented so far is robust, Ward’s algorithm is applied to the geopotential height field at 500hPa (Z500) from NCEP. The cluster decomposition is
performed over the same NAE domain and over the
1950–2001 period. The four Z500 regimes (Fig. 3) arevery similar to their SLP counterparts and clearly illustrate the equivalent barotropic characteristics of theatmospheric patterns. Although structures are slightly
displaced westward, the spatial asymmetry related to
the NAO phase is still evident. Note that the Icelandic
low displacement is enhanced for Z500. The wavy shapeof the RDG regime is better extracted as well as for the
GS regime, although the Scandinavian core has lost
some significance. The temporal distribution of the four
Z500 regimes is similar to the SLP classifications.
NAO2 and RDG regimes dominate, and 78% of themonths are similarly classified among the four states in
the SLP and Z500 cluster decompositions.
4. Sea surface temperature links
As mentioned earlier, the stability of the atmosphericregime centroids over time suggests that their existence
is set by stationary and transient wave activity. Their
excitation and associated transitions are mainly controlled by internal atmospheric processes, but external
atmospheric forcings are expected to influence the frequency of transition into one weather regime or another,leading to changes in the climate regime occurrence.
The latter could therefore be considered as the atmospheric time-averaged response of that particular timeaveraged forcing (Feldstein 2000). In the rest of thepaper, our goal will be to investigate the potential roleof ocean anomalies in preferentially exciting the four
natural atmospheric regimes extracted above. Results
will be presented for both observations and model experiments.
a. Observations
The time history of the occurrence of the four NCEP
regimes is used to build SST composites (Reynolds andSmith 1995). The events or years selected for the composite construction are those for which a given regime
is dominant for at least two months out of three for agiven winter (but only one month out of three for GS
due to its weak occurrence over the 1950–2001 period).
Due to the importance of the internal extratropical atmospheric variability, this two-month restriction reduces the likelihood that a regime can simply occur bychance without oceanic forcings.

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FIG. 3. Same as Figs. 1a–d but for Z500 from NCEP reanalysis.

The local Atlantic SST patterns first appear to beconsistent with the atmospheric forcing of the ocean.
The simultaneous winter SST anomaly patterns related
to the NAO regimes display rather symmetrical featuresstrongly projecting onto the North Atlantic tripole (Figs.
4a,b). Weak, insignificant equatorial warming occurs in
the Pacific associated with the NAO1 regime. The RDG
regime mainly captures a La Niña signal concomitantwith a positive SST lobe off Newfoundland and cold
subtropical North Atlantic anomalies (Fig. 4c). The GS
regime is linked to weak cooling off Newfoundland andsignificant warming in the Norwegian Sea (Fig. 4d).
Detrending the SST data does not change the composites, except for the NAO1 case for which the strengthof the North Atlantic tripole is slightly weaker and theequatorial Pacific warming completely disappears. Notethe asymmetries in the spatial structure of the SST tripole, especially its tropical branch. Maximum warming
occurs off the Sahara coast in relation with NAO2 regimes while the strongest cooling is located north of theSouth American continent for the NAO1 case. Such a
discrepancy is consistent with the atmospheric spatial
asymmetry between NAO phases and its forcing signature on the surface ocean. It is worth noting that the
spatial displacements for both the NAO and SST modes
can be indirectly found by compositing fields based on
their respective expansion coefficients given by EOF
techniques (not shown).
Simultaneous relationships do not give any infor-

mation on the possible feedback of the ocean on theatmospheric variability especially at midlatitudes. For
this purpose, atmospheric models forced by observed
oceanic conditions varying with time or by prescribed
SST anomaly patterns are needed. In such a context, the
ENSO impact onto the simulated North Atlantic dynamics has been recently investigated. Cassou and Terray (2001), for instance, show that the mean DJF ENSO
signature simulated in the ARPEGE model (also foundusing signal-to-noise statistical techniques in different
models; e.g., Venzke et al. 1999) projects well onto the
RDG regime (see their Fig. 8). We have verified that itcorresponds in fact to the averaged preferred excitationof the RDG regime in the model, which seems to belinked in that case to the Pacific–North America (PNA)arching pattern extending toward the western Atlantic.
Note that the asymmetry between the ENSO phases(predominant La Niña influence on European climate
compared to El Niño) appears to be consistent withnumerous studies such as Chen (1982), Halpert and Ropelewski (1992), and Hannachi (2001) among others.
To further test its relevance, we projected the raw SLP
monthly maps onto RDG and took the 21 std dev
threshold of the normalized time series to rebuild SST
composites. No clear El Niño signature appeared in thatcase (not shown) reinforcing our confidence in theasymmetrical RDG–La Niña relationship. Differentiating between winter months, the Pacific–RDG connection appears to be strongest for December and dimin-

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FIG. 4. Simultaneous DJF NCEP SST composites (8C) related to the four NAE regimes. The events selected forthe composite construction correspond to the year where the regime is dominant. SST monthly means corresponding
to the particular months of these years where the regime is excited are averaged, the number of freedom of thecomposite being set to its most restrictive value given by the number of included years thus taking into account themonth-to-month persistence of the SST. Shaded areas exceed the 95% confidence level using T and F statistics. Contour
interval is 0.28C.

ishes thereafter. This is consistent with the seasonal cycle of the equatorial SST variance in the Pacific yielding
a minimum at early spring (Yu and McPhaden 1999).
The potential role of the concomitant North Atlantic
anomalies off Newfoundland on RDG is also reported
in Peng and Fyfe (1996) and Watanabe and Kimoto(2000) from models. A quasi-barotropic response downstream of the SST anomaly center is described in thesestudies.
No clear indication of potential oceanic forcing upon
GS is hypothesized. Further support for a primarily atmospheric forcing can be seen when a lag is introducedin the composite construction. Previous SST conditions
do not exhibit any coherent basin-scale pattern while
the following month SST composite shows a clear reinforcement of the DJF oceanic anomalies.
Regarding the NAO regimes, model results have suggested the forcing role of the North Atlantic SST tripoleand especially the tropical part (Sutton et al. 2001).
Terray and Cassou (2002) have shown in particular that
the North Atlantic tropical basin affects the occurrenceof the NAO climate regimes via the alteration of the
Hadley cell and related modification of stationary andtransient waves activity. They also comment on the spatial asymmetry of the related tropical SST patterns.
To confirm the leading role of the North Atlantic tripole and to better illustrate that the long time scaleatmospheric variability described in the previous section
from the climate regime approach represents the timeaveraging of much shorter synoptic time scale processes,
results are now presented from integrations of the AR-

PEGE AGCM where anomalous SST patterns have been
introduced in the boundary conditions.
b. North Atlantic tripole forcing on atmosphericregimes1) EXPERIMENTAL DESIGNThe Météo-France ARPEGE AGCM is derived fromthe Integrated Forecast System model developed andmaintained by the European Centre for Medium-Range
Weather Forecasts (ECMWF) and is described in Déqué
et al. (1994). The present version uses a T63 triangularhorizontal truncation. Diabatic fluxes and nonlinear
terms are calculated on a Gaussian grid of about 2.88
3 2.88 latitude–longitude. The vertical resolution is discretized over 31 levels (20 levels in the troposphere)
using a progressive vertical hybrid coordinate extending
from the ground up to about 34 km (7.5 hPa). The reader
is invited to refer to Cassou and Terray (2001) for a
detailed description of the model physics package andfor its performance.
Six 30-yr model simulations have been carried out
forced by three different anomalous oceanic patterns(Fig. 5) and their opposite in sign. The SST anomalies
correspond to the oceanic conditions or ‘‘optimal forcing pattern’’ generating the most detectable atmospheric
response after ENSO (Terray and Cassou 2002) over the
NAE region in the ARPEGE model using maximizing
signal-to-noise techniques (Allen and Smith 1997). Values have been scaled to reach about 618C off Africa
and Newfoundland. The prescribed pattern bears a

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FIG. 5. SST anomalous pattern prescribed in (a)TROP1/2 simulations, (b) MIDLAT1/2 simulations,and (c) TRIPOL1/2 simulation. Contour interval is
0.28C.

strong similarity to the North Atlantic tripole previously
extracted by composites and linked to the NAO regimes.
Based on this resemblance, the choice has been madeto specify the optimal pattern instead of the composite
SST field in order to maximize the oceanic forcing on
the atmosphere while staying close enough to the observed signals. It has been shown indeed that details inprescribed anomalous SST patterns can be important inshaping atmospheric model responses as the latter areoften based on spatial resonance between imposedboundary conditions and simulated mean backgroundflow (Peng and Whitaker 1999). Biases in the latter
could therefore overcome the real role of the observed
SST pattern as opposed to the optimal SST structure
where those biases are indirectly taken into account. The
drawback of this choice relies on the linearity constraintof the optimal filtering method. Therefore, the asymmetries of the oceanic composites described in the previous section and their potential impact on the atmosphere cannot be analyzed in the following simulation
setup.
In the first set of experiments, the anomalous SST
pattern is restricted to the tropical North Atlantic (08–308N; Fig. 5a) and two simulations have been performed
with the positive (TROP1) and negative (TROP2) versions of the SST anomaly. In the second set of experiments, the anomalous SST pattern is restricted to theextratropical North Atlantic (above 308 of latitude; Fig.
5b) and similarly two simulations (MIDLAT1 andMIDLAT2) are conducted. Finally, the third type ofexperiment combines the tropical and extratropicalanomalous oceanic conditions (Fig. 5c) and will be referred to as TRIPOL1 and TRIPOL2. Anomalous oceanic patterns are applied from December to May.
The oceanic forcing is investigated from cluster analyses using the k-means algorithm applied on raw daily
data of SLP from the model. All the six combined experiments are concatenated together with a 30-yr dataset(for consistency with the duration of the sensitivity experiments) from a 200-yr long control experiment where
the seasonal cycle of the SST is repeated without anomaly. The partitioning has been done on the NAE domain
and only winter days are selected. A total of
18 900 maps (7 experiments 3 90 days 3 30 yr) arethus classified and we have verified that the weather
regimes are robust by subsampling and by performing
the decomposition from the control run of the model
only. The ARPEGE climate regimes are reported in Terray and Cassou (2002).
2) WEATHER–CLIMATE REGIME RELATIONSHIP
Four weather regimes have been extracted from ARPEGE. The first two regimes capture the positive andnegative phase of the NAO (Figs. 6a,b). The third andfourth (Figs. 6c,d) are rather symmetrical with a strong
SLP anomaly over the North Sea and will be respectively referred to as the northeast (NE) high and low.

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FIG. 6. SLP centroids of daily weather regimes (hPa) from ARPEGE experiments: (a) NAO1, (b) NAO2, (c) NE
high, and (d) NE low. Contour interval is 1 hPa.

We have verified that the k-means cluster decomposition
applied on NCEP winter daily data gives very similar
results (Fig. 7). The minimum spatial correlation between NCEP and ARPEGE weather regimes is 0.83 andis obtained for NAO1 over 1950–2001.
The simulated and observed NAO weather regimes
project remarkably well on the observed NAO climate
regimes. Particularly striking is the eastward shift of the
Azores high for NAO1 compared to NAO2 that themodel is able to represent, although less pronounced
than for NCEP. This again indicates that the NAO climate regimes are the time-averaged signature of NAOtype quasi-daily fluctuations and that the displacementof the Azores high is a property of the positive NAO
phase. The NE high is indicative of regional blocking.
Daily composites on surface continental temperatureand precipitation based on the days occupied by the NE
high reveal very cold and dry conditions in central andeastern Europe (Cassou 2001). For the NE low, warmand very wet conditions dominate western and central
Europe. The NE-high weather regime is reminiscent of
the GS climate regime and would suggest that the latteralso represents the time integration of shorter entities.
Note though that the Greenland core is considerably
reduced compared to the Scandinavian one for the dailycase. We have verified that the occurrence of the NCEP
NE high is maximum for months where the GS climate

regime is excited. By contrast, the RDG climate regime
does not seem to have its reciprocal weather regime;
however, the latter appears when the decomposition domain is limited to 08 of longitude instead of 308E, thus
excluding the NE-high/NE-low weight over Europe (notshown). The discrepency between RDG weather andclimate regimes decomposition could thus be explainedby the statistical sensitivity to the region selected forclustering. It can also be partly explained by the persistence of the RDG weather regime. The latter can berarely excited in total but be persistent when occurring.
Persistence is not a criterion for the k-means clustering
while it is indirectly taken into account in monthly
means.
3) OCEAN IMPACT ON THE FREQUENCYDISTRIBUTION OF THE WEATHER REGIMES

Changes in the frequency of occurrence for the four
weather regimes as a function of oceanic anomalies are
now presented (Fig. 8). Contrasting TROP1 andTROP2 shows that the tropical North Atlantic Ocean
has an influence on the excitation of the NAO regimes.
The NAO2 (NAO1) regime is favored by about 6%(3%) in warm (cold) conditions, these differences interms of percentage of occurrence being significant (6%represents a 1134-day difference) as tested in Robertson

1 MARCH 2004

CASSOU ET AL.

FIG. 7. Same as Fig. 6 but from NCEP reanalysis daily data. The percentage of occurrence are 21.5% and 24.5% for
the NAO1 and NAO2 weather regimes and 25.5% and 28.5% for the NE-high and NE-low regimes, respectively.

FIG. 8. Percentage of daily occurrences of the four regimes depending on experiments. As an estimate of thesignificance, the error bars correspond to the maximum dispersion of the decomposition estimated when the clustering
is performed on anomaly maps with respect to individual experiments means.

1065

1066

JOURNAL OF CLIMATEet al. (2000) and represented in Fig. 8 by error bars.
The latter quantify the maximum ‘‘within ensemble variability,’’ which can be interpreted as a conservative
measure of random sampling variations. In practice, a
new decomposition is done from anomaly maps basedon individual experiment means and the error bar corresponds to the maximum difference of occurrence between experiments for a given regime in that configuration. The tropical ocean–atmosphere relationship isconsistent with the observed link between the NAO
phases and the tripole providing further evidence thatits tropical branch forces the NAE variability in nature.
The NE high is insensitive to tropical SST anomalies
while the NE low is favored in cold versus warm conditions. The latter is consistent with the sign of thenorthernmost lobe of Rossby waves being excited fromthe western tropical Atlantic basin toward Scandinavia
in response to North Atlantic tropical oceanic cooling(Terray and Cassou 2002).
Contrasting MIDLAT1 and MIDLAT2 reveals an
opposite picture. The NAO2 (NAO1) regime is lessfrequent when high latitudes are warm (cold). Isolating
the extratropical part of the observed pattern in an idealized context, the simulated ocean–atmosphere relationship would appear reversed compared to nature. If
the atmospheric response of ARPEGE to midlatitude
oceanic forcing is correct, this inverse simulated ocean–
atmosphere link compared to reality suggests that theextratropical observed SST anomalies associated with
the NAO are primarily a response to the atmosphericforcing. But according to the model, when taken separately, they may have a negative feedback as theywould counteract the atmospheric pattern that initiallycreated them. Note also that the NE high is favoredwhen cold conditions (MIDLAT2) prevail at high latitude while the NE low is favored in MIDLAT1. Thisis consistent with Namias (1964) who suggested thatenhanced blocking episodes occur when anomalous cold
SST occurs off Newfoundland. Conclusions presented
for MIDLAT, however, should be viewed with caution.
Whereas signals are clear from a regime decomposition
point of view, the mean atmospheric anomalies averaged
over 30 yr for the MIDLAT experiments do not reveal
any significant patterns, as in Sutton et al. (2001). Extratropical oceanic anomalies have also been shown tohave a weak impact on the atmosphere (Kushnir et al.
2002).
Adding together tropical and extratropical oceanic
forcing (TRIPOL) reveals some interesting features. Extratropical and tropical influences cancel each other inthe model for the NAO1 regime, which then appears
insensitive to the sign of the tripole. The tropical forcing
still dominates the midlatitude one for the NAO2 regime, but is reduced. This asymmetry between the twosimulated NAO phase suggests that warm SST anomalies in the Tropics have more impact and might berelated to the nonlinearities associated with convective
activity and the Hadley cell. The overestimation of the

VOLUME 17

meridional circulation in ARPEGE calls again for caution in the interpretation of these results, which may be
dependent on the simulation of the tropical mean state.
As expected the NE high is insensitive to the tripole
forcing while the tropical forcing on the NE low seems
to be amplified by the presence of extratropical anomalies. This is consistent with Drévillon et al. (2003),
which suggests the reinforcement of tropical impacts
when a simple mixed-layer ocean model is added at
midlatitudes. Similarly to our results, they found a stronger signal in the TRIPOL2 case.
Finally, it is interesting to note that the weather regimes are not equally excited in the model. Intrinsically,
NAO1 regimes seem to occur less frequently than
NAO2 ones as well as the NE high compared to the
NE low. Similar results are obtained from NCEP daily
data (Fig. 7). Referring to the occurrence of climate
regimes, note also that NAO1 is less excited than
NAO2 during whatever period is considered. Thisasymmetry might be characteristic of the extratropicalNorth Atlantic atmosphere and the model seems to beable to capture this dynamical property.
5. Summary and perspective
A cluster decomposition has been carried out on several observed SLP datasets over the last century for the
North Atlantic–European domain. Four robust climate
regimes have been extracted corresponding to the twophases of the NAO, a ridge regime and a west–east
dipole between Greenland and Scandinavia. The asymmetrical perspective adopted in this study reveals a significant eastward shift of the NAO poles for the positive
phase compared to the negative phase. The Azores highand the Icelandic low are displaced about 308 of longitude toward Europe, and this barotropic displacement
is independent of the time period considered. It is therefore likely an intrinsic property of the NAO1 phase.
This result affects the interpretation on the direct roleof the greenhouse gases whose increased concentrations
were hypothesized to play a role in the shift of Atlanticpressure poles in scenario experiments. According toour findings, the simulated displacement might be related to the preferred excitation of the NAO1 regimes
in perturbed climate rather than to a drastic change ofthe atmospheric circulation.
The nonlinear approach also allows us to further document the nonstationarity of the North Atlantic–European variability in terms of amplitude and location. Decadal changes in SLP variance can be explained by thedominance of a given regime over a specific period.
Within such a context, differences between the earlypart of the century and the last 50 years are related to
the relative weight between NAO and RDG–GS regimes. Therefore, as the surface temperature and precipitation signatures associated with these regimes arequite different (van Loon and Madden 1983), it may
not be enough, in a seasonal forecast perspective and1 MARCH 2004

CASSOU ET AL.

for impact studies, to predict only the NAO index. The
latter may appear misleading when the NAO regimes
are not dominant. It would be more accurate to assess
the likelihood for the different regimes to occur.
The simultaneous oceanic surface anomalies related
to the four regimes project, respectively, onto the twophases of the North Atlantic tripole mode, La Niñaevents, and warmer-than-average conditions in the Norwegian Sea. The asymmetrical perspective highlightsthat El Niño events might have less impact on the North
Atlantic–European variability compared to La Niñaevents. The oceanic impact on the excitation of theNorth Atlantic–European weather regimes and their direct link to the climate regimes has been investigatedusing the ARPEGE model and a series of experiments
perturbed by SST anomalies at specific locations. We
have shown that model results tend to confirm the activerole of tropical North Atlantic oceanic conditions to
explain the North Atlantic–European observed interannual variability. Analyses on weather regimes revealthat the oceanic forcing affects the excitation mechanism of high-frequency atmospheric modes to favor one
sign over the other. Climate regimes can be thus interpreted as the time-averaged signature of this forcing.
The NAO1 shift of the Azores high is well captured
in the model as well as the uneven frequency of occurrence of the regimes. Extratropical SST anomalies
produce a significant atmospheric response in the modelbut one that is opposite to the observed ocean–atmosphere relationship. This suggests that the extratropicaloceanic conditions might counteract the tropical impact.
Our results thus illustrate that tropical and extratropicalcontribution are nonadditive and very nonlinear. It istherefore dangerous to consider them separately as they
are intrinsically linked.
Acknowledgments. We thank R. Madden and R. Sutton for stimulating discussions. The main author is
grateful to A. Phillips for help accessing and plotting
the data with the NCL software. This work is supportedby the European Community via the 5th framework program PREDICATE project under Contract EVK2-CT1999-00020 and by NOAA under Grant NA06GP0394.
REFERENCES
Alhamed, A., S. Lakshmivarahan, and D. J. Strensrud, 2002: Cluster
analysis of multimodel ensemble data from SAMEX. Mon. Wea.
Rev., 130, 226–256.
Allen, M., and L. Smith, 1997: Optimal filtering in singular spectrum
analysis. Phys. Lett., 234, 419–428.
Anderberg, M. R., 1973: Cluster Analysis for Applications. Academic
Press, 359 pp.
Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality
and persistance of low frequency atmospheric circulation patterns. Mon. Wea. Rev., 115, 1083–1126.
Blackmon, M. L., Y. H. Lee, and J. M. Wallace, 1984: Horizontal
structure of 500 mb height fluctuations with long, intermediateand short time scales. J. Atmos. Sci., 41, 961–979.
Cassou, C., 2001: Rôle de I’océan dans la variabilité basse fréquence1067

de l’atmosphère sur la région Nord Atlantique-Europe. Ph.D.
dissertation, University of Toulouse, 280 pp.
——, and L. Terray, 2001: Oceanic forcing of the wintertime low
frequency atmospheric variability in the North Atlantic European
sector: A study with the ARPEGE model. J. Climate, 14, 4266–
4291.
Chen, W., 1982: Fluctuations in the Northern Hemisphere 700 mb
height field associated with the Southern Oscillation. Mon. Wea.
Rev., 110, 808–823.
Cheng, X., and J. M. Wallace, 1993: Cluster analysis of the Northern
Hemisphere wintertime 500-hPa height field: Spatial patterns. J.
Atmos. Sci., 50, 2674–2696.
Corti, S., F. Molteni, and T. N. Palmer, 1999: Signature of recent
climate changes in frequencies of natural circulation regimes.
Nature, 398, 799–802.
Davies, R. E., B. P. Hayden, D. A. Gay, W. L. Phillips, and G. V.
Jones, 1997: The North Atlantic subtropical anticyclone. J. Climate, 10, 728–744.
Déqué, M., C. Dreveton, A. Braun, and D. Cariolle, 1994: The climate
version of ARPEGE/IFS: A contribution to the French community climate modelling. Climate Dyn., 10, 249–266.
Drévillon, M., 2002: Interaction océan–atmosphere à l’échelle saisonnière sur la région Atlantique Nord-Europe: Rôle des routes
dépressionaires et mécanismes associés sur la variabilité climatique. Ph.D. dissertation, University of Toulouse, 179 pp.
——, C. Cassou, and L. Terray, 2003: Model study of the wintertime
atmospheric response to fall tropical Atlantic SST anomalies.
Quart. J. Roy. Meteor. Soc., 129, 2591–2611.
Feldstein, S. B., 2000: The timescale, power spectra and climate noiseproperties of teleconnection patterns. J. Climate, 13, 4430–4440.
Gillett, N., H. F. Graf, and T. J. Osborn, 2003: Climate change andthe North Atlantic Oscillation. North Atlantic Oscillation: Climate Significance and Environmental Impact, Geophys. Monogr., Vol. 134, Amer. Geophys. Union, 193–209.
Halpert, M., and C. Ropelewski, 1992: Surface temperature patterns
associated with the Southern Oscillation. J. Climate, 5, 577–593.
Hannachi, A., 2001: Toward a nonlinear identification of the atmospheric response to ENSO. J. Climate, 14, 2138–2149.
Hilmer, M., and T. Jung, 2000: Evidence for a recent change in thelink between the North Atlantic Oscillation and Arctic sea ice
export. Geophys. Res. Lett., 27, 989–992.
Hoskins, B., I. James, and G. White, 1983: The shape, propagationand mean flow interaction of large scale weather systems. J.
Atmos. Sci., 40, 1595–1612.
Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation
and relationships to regional temperature and precipitation. Science, 269, 676–679.
——, and H. van Loon, 1997: Decadal variations associated with theNorth Atlantic Oscillation. Climate Change, 36, 301–326.
Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-Year Reanalysis Project. Bull. Amer. Meteor. Soc., 77, 437–471.
Kimoto, M., and M. Ghill, 1993: Multiple flow regimes in the Northern Hemisphere winter. Part I: Methodology and hemisphericregimes. J. Atmos. Sci., 50, 2625–2643.
Kushnir, Y., W. A. Robinson, I. Bladé, N. M. J. Hall, S. Peng, and
R. T. Sutton, 2002: Atmospheric GCM response to extratropical
SST anomalies: Synthesis and evaluation. J. Climate, 15, 2233–
2256.
Lorenz, E. N., 1963: Deterministic nonperiodic flow. J. Atmos. Sci.,
20, 130–148.
Martineu, C., J. Caneill, and R. Sadourny, 1999: Potential predictability of European winters from the analysis of seasonal simulations with an AGCM. J. Climate, 12, 3033–3061.
Michelangeli, P., R. Vautard, and B. Legras, 1995: Weather regime
recurrence and quasi stationarity. J. Atmos. Sci., 52, 1237–1256.
Monahan, A. H., L. Pandolfo, and J. C. Fyfe, 2001: The preferred
structure of variability of the Northern Hemisphere atmosphericcirculation. Geophys. Res. Lett., 28, 1019–1022.
Namias, J., 1964: Seasonal persistence and recurrence of European
blocking during 1958–1960. Tellus, 16, 394–407.

1068

JOURNAL OF CLIMATE

Palmer, T., 1999: A non-linear dynamical perspective on climate prediction. J. Climate, 12, 575–591.
Peng, S., and J. Fyfe, 1996: The coupled patterns between sea levelpressure and sea surface temperature in the midlatitude North
Atlantic. J. Climate, 9, 1824–1839.
——, and J. Whitaker, 1999: Mechanisms determining the atmospheric response to midlatitude SST anomalies. J. Climate, 12,1393–1408.
Reynolds, R. W., and T. M. Smith, 1995: A high resolution global
sea surface temperature climatology. J. Climate, 8, 1571–1583.
Robertson, A. W., C. Mechoso, and Y. Kim, 2000: The influence ofAtlantic sea surface temperature anomalies on the North Atlantic
Oscillation. J. Climate, 13, 122–138.
Sutton, R. T., W. A. Norton, and S. P. Jewson, 2001: The North
Atlantic Oscillation—What role for the ocean? Atmos. Sci. Lett.,
1, 89–100.
Terray, L., and C. Cassou, 2002: Tropical Atlantic sea surface temperature forcing of the quasi-decadal climate variability over the
North Atlantic–Europe region. J. Climate, 15, 3170–3187.
Thompson, D., and J. M. Wallace, 1998: The Arctic Oscillation signature in wintertime geopotential heights and temperature fields.
Geophys. Res. Lett., 25, 1297–1300.
Trenberth, K. E., and D. A. Paolino, 1980: The Northern Hemispheresea level pressure dataset: Trends, errors and discontinuity. Mon.
Wea. Rev., 108, 855–872.

VOLUME 17

Ulbrich, U., and M. Christoph, 1999: A shift of the NAO and increasing storm track activity over Europe due to anthropogenic
greenhouse gas forcing. Climate Dyn., 15, 551–559.
van Loon, H., and R. A. Madden, 1983: Interannual variations ofmean monthly sea level pressure in January. J. Climate Appl.
Meteor., 22, 687–692.
Vautard, R., 1990: Multiple weather regimes over the North Atlantic:
Analysis of precursors and successors. Mon. Wea. Rev., 118,
2056–2081.
Venzke, S., M. R. Allen, R. T. Sutton, and D. P. Rowell, 1999: The
atmospheric response over the North Atlantic to decadal changes
in sea surface temperature. J. Climate, 12, 2562–2584.
Wallace, J. M., and D. Gutzler, 1981: Teleconnections in the geopotentiel fields during the Northern Hemisphere winter. Mon.
Wea. Rev., 109, 784–812.
Walsh, J. E., A. S. Phillips, D. H. Portis, and W. L. Chapman, 2001:
Extreme cold outbreaks in the United States and Europe, 1948–
99. J. Climate, 14, 2642–2658.
Ward, J., 1963: Hierarchical grouping to optimize an objective function. J. Amer. Stat. Assoc., 58, 236–244.
Watanabe, M., and M. Kimoto, 2000: Atmosphere–ocean thermal
coupling in the North Atlantic: A positive feedback. Quart. J.
Roy. Meteor. Soc., 126, 3343–3369.
Yu, X., and M. J. McPhaden, 1999: Dynamical analysis of seasonaland interannual variability in the equatorial Pacific. J. Phys.
Oceanogr., 29, 2350–2369.