Æ Decadal interactions between the western tropical Paciﬁc ... · decadal signals). Contour...

Fred Kucharski Æ Franco Molteni Æ Annalisa Bracco

Decadal interactions between the western tropical Pacific and the NorthAtlantic Oscillation

Received: 21 January 2005 / Accepted: 11 October 2005� Springer-Verlag 2005

Abstract The relationship between interdecadal varia-tions of tropical sea surface temperature (SST) in the last120 years and circulation anomalies related to the NorthAtlantic Oscillation (NAO) is investigated in this study.Using an atmospheric general circulation model(AGCM), we confirm observational evidence that vari-ations in the SST gradient in the western tropical Pacificare related to the NAO anomalies on decadal timescale,and may be contributing to the shift towards the positiveNAO phase observed in the late 20th century. The roleplayed by the Indian Ocean-NAO teleconnection,advocated in recent studies focused on the last 50 years,is also assessed in the context of the 120-year long re-cord. It is suggested that a positive feedback between thePacific SST and the hemispheric circulation patternembedding the decadal NAO signal may act to enhancethe internal variability of the coupled ocean–atmospheresystem, and justify the stronger teleconnection found inobservational data than in SST-forced AGCM experi-ments.

1 Introduction

In the northern hemisphere, a major part of the changesin the atmospheric flow in the second half of the 20thcentury projects onto the North Atlantic Oscillation(NAO). The NAO, which modulates the pressure gra-dient over the North Atlantic, has a strong influence onEuropean climate, and the shift towards its positivephase in the last 30 years contributed to the observedwintertime warming over Europe. A crucial question is

to what extent such changes are due to natural oscilla-tions of the climate system.

The NAO is usually measured by an index defined asthe normalized sea-level pressure difference betweenLisbon, Portugal and Reykjavik, Iceland (e.g. Hurrell1995). Positive values of this index imply stronger-than-normal westerly winds over the North Atlantic, and areassociated with warm and wet winters over northernEurope and west Siberia.

Presently, there is no consensus in the scientificliterature on the causes of the observed positive trend inthe NAO in the recent decades. Current theories com-prise that the observed NAO trend is due to:

1. The residual of internal atmospheric variability orclimate noise (e.g. Wunsch 1999; Stephenson et al.2000).

2. External forcing due to stratospheric ozone reductionand increases in greenhouse gas concentrations (e.g.Shindell et al. 1999; Fyfe et al. 1999).

3. Interactions between the oceans and atmosphere (e.g.Marshall et al. 2001; Czaja and Frankignol 2002;Hoerling et al. 2001). Note, however, that parts ofthe sea surface temperature (SST) signal may beinfluenced by point 2.

In reality all the three mechanisms mentioned abovemay contribute to the full observed NAO trend. Here,we investigate the decadal NAO development in the last120 years focusing on the impact of ocean surface tem-peratures on the atmospheric circulation.

On interannual timescales SST feedbacks on NAOvariations are of secondary importance (Marshall et al.2001), whereas on decadal timescales tropical andextratropical SST variability plays a crucial role in theNAO evolution (Rodwell et al. 1999; Hoerling et al.2001, 2004; Hurrell 1995; Mehta et al. 2000; Marshallet al. 2001; Sutton and Hodson 2003; Hurrell et al.2004). The question about which parts of the oceanscontribute most to the NAO variability is controversial.Whereas, e.g. Hoerling et al. (2004), Hurrell et al. (2004)

F. Kucharski (&) Æ F. Molteni Æ A. BraccoPhysics of Weather and Climate Section,The Abdus Salam International Centre for Theoretical Physics,Strada Costiera 11, 34014 Trieste, ItalyE-mail: [email protected]

Climate Dynamics (2005)DOI 10.1007/s00382-005-0085-5

and Bader and Latif (2003) advocate a fundamental roleof the Indian Ocean in the NAO trend observed in thelast 50 years, Rodwell et al. (1999) and Sutton andHodson (2003) find that the North Atlantic SSTanomalies are most important for causing decadalNAO-like variability in the North Atlantic. Sutton andHudson (2003) consider, as in this work, the extended

130-year period. However, they detrend their databefore the analysis and their results cannot be directlycompared to those of Hoerling et al. (2004) and Hurrellet al. (2004).

The aim of this study is to further analyse the influ-ence of the decadal variations in the SSTs on the NAOdevelopment over the last 120 years. The paper is

a b

c

d

Fig. 1 Regression onto theNAO index of a re-analysissurface pressure, b re-analysis500 hPa height and c globalSST (in K). The regression ofglobal SST onto the WTP SSTgradient index is shown in paneld. The NAO index and WTPindex are filtered by an 11-yearrunning average (to retaindecadal signals). Contourintervals are 0.5 hPa for (a) and5 m for (b). In a, b and c thescaling is such that anomaliescorrespond to one unit of thefiltered NAO index, in d thescaling corresponds to onestandard deviation of the WTPSST gradient index

Kucharski et al.: Decadal interactions between the western tropical Pacific and the North Atlantic Oscillation

organized as follows: The data and methodology arebriefly introduced in Sect. 2. Section 3 presents obser-vational evidence of a special role played by the westerntropical Pacific (WTP) in setting the climate variabilityover the Atlantic sector. In Sect. 4 we analyse the pos-sible WTP–NAO link, and we compare the influence ofthe WTP and of the Indian Ocean SSTs on the atmo-spheric circulation in the northern hemisphere. Theseinvestigations are carried out using ensemble simulationsperformed in the context of the CLIVAR International‘‘Climate of the 20th Century’’ project (Folland et al.2002), using an eight-level atmospheric general circula-tion model (AGCM) (Molteni 2003; Bracco et al. 2004)forced with observed SST (Rayner et al. 2003) from1870 to 2002. Results are summarized and discussed inSect. 5.

2 Data and methodology

The paper is divided into two parts: Firstly, observa-tional evidence of a WTP–NAO link will be presented;secondly, this evidence will be confirmed using numeri-cal modelling.

2.1 Observational data

As observational data we use surface pressure, 500 hPaheight and net surface heat fluxes from theNCEP/NCARre-analysis (Kalnay et al. 1996; available from 1948 topresent) and the HadISST data set (Rayner et al. 2003;available from 1870 to present). The HadISST data set isalso used to force the AGCM used in this study. Fur-thermore, the extended winter (December to March)

NAO index as defined by Hurrell is considered (http://www.cgd.ucar.edu/cas/jhurrell/nao.stat.winter.html).

2.2 Model and experimental set-up

The numerical experiments in this work are performedwith the International Center for Theoretical PhysicsAGCM (SPEEDY for Simplified Parametrizations,primitivE-Equation DYnamics), described in detail inMolteni (2003). The model is based on a hydrostaticspectral dynamical core (see Held and Suarez 1994), andit uses the vorticity-divergence form described byBourke (1974). The parameterized processes includeshort- and long-wave radiation, large-scale condensa-tion, convection, surface fluxes of momentum, heat andmoisture, and vertical diffusion. Convection is repre-sented by a mass-flux scheme that is activated whereconditional instability is present, and boundary layerfluxes are obtained by stability-dependent bulk formu-lae. In this study, the AGCM is configured with eightvertical (sigma) levels and with a spectral truncation attotal wavenumber 30. Furthermore, land and ice tem-perature anomalies are determined by a simple one-layerthermodynamic model.

Three sets of experiments have been performed toinvestigate the connection between decadal SST varia-tions and the atmospheric response in the model.

– EXP1: 20-member ensemble with globally prescribedobserved SSTs from 1870 to 2002.

– EXP2: 18-member ensemble with prescribed observedSSTs from 1870 to 2002 only in the WTP region(namely, 140�E to 170�W and 20�S to 20�N). Else-where climatological SSTs are imposed, except for the

Fig. 2 Time series of NAOindex (black) versus WTP SSTgradient index (red); correlationcoefficient (c.c.) = 0.82, averageSST in the southern WTP box(orange); c.c. with the NAOindex = 0.52, and average SSTsin the Indian Ocean (blue; box50�E to 120�E and 20�S to20�N); c.c. with the NAO index= 0.43. All indices have beenfiltered by an 11-year runningaverage and standardized. Seetext for details


North Atlantic basin, where SST anomalies arecalculated using a thermodynamic mixed-layer model.

– EXP3: 10-member ensemble with prescribed observedSSTs from 1870 to 2002 in the tropical Indian Oceanonly (30�E to 120�E, 30�S to 30�N). As in EXP2,climatological SSTs are imposed elsewhere, except forthe North Atlantic basin, where SST anomalies arecalculated using a thermodynamic mixed-layer model.

For each ensemble, different members are created byrandomly perturbing the initial conditions and by per-forming a 1 year spin-up integration. The ensemblemean output is presented, while the inter-ensemble var-iability is used to estimate the statistical significance ofthe responses.

The thermodynamic mixed-layer has been used inEXP2 and EXP3 to include the effect of local atmo-sphere–ocean feedback in the North Atlantic region,which may increase the atmospheric response to theremote SST anomalies.

2.3 Analytical methods

The focus of this paper is the decadal interaction betweenSSTs and the atmosphere in the northern hemisphericalwinter. Therefore, all data and model output are con-sidered for the extended winter period December toMarch (DJFM). An 11-year running mean is applied tothese seasonal means to retain only the decadal signals.In the remaining we usually dispense with the notion‘filtered’ data, because it is implicit in our analysis. Theinvestigation of the connection between SSTs andatmospheric observed and modelled data involves linearregression techniques as well as principal componentanalysis (PCA) and canonical correlation analysis (CCA)based on principal components as described in Brether-ton et al. (1992). The PCA of the 500 hPa height modelfields is performed in the North Atlantic region (90�W to60�E, 20�N to 90�N). A statistical significance test hasbeen applied to all modelled regression maps using thestandard deviation of regressions from individualensemble members and applying a two-tailed t test.

3 Observational evidence of a WTP–NAO link

The regression of the surface pressure and 500 hPaheight fields from the re-analysis onto the NAO timeseries (black curve in Fig. 2) are plotted in Fig. 1a andb. Over the North Atlantic, the pattern resembles theregression with the unfiltered NAO index (not shown)with about 30% smaller amplitude. However, theteleconnections with the Pacific region, reminiscent ofthe cold-ocean–warm-land (COWL) pattern (Wallaceet al. 1996) are absent if an unfiltered index is used.

a b

c d

e

f

Fig. 3 Winter mean (DJF) 500 hPa height climatology, stationaryeddy field and precipitation climatology: a NCEP re-analysis500 hPa height, b SPEEDY 500 hPa height, c NCEP re-analysiseddy height, d SPEEDY eddy height, e CMAP (Xie and Arkin1997) precipitation, and f SPEEDY precipitation (mm/day).Contour interval (CI): 10 dam for (a) and (b); 3 dam for (c)and (d)


Figure 1c shows the regression of global SSTs onto theNAO index. The resulting distribution of anomalieshighlights an overall SST increase in the tropics associatedwith the positive NAO phase. Although such SST anom-aliesmaybeeither thecauseor theeffectof theatmosphericflowpatterns,experimentswithAGCMssuggestedthattheSSTs in the tropical Indo-Pacific basin are responsible forthe decadal NAO variation in the period 1950 to 1999(Hoerling et al. 2001, 2004; Hurrell et al. 2004).

An intriguing feature in the SST patterns of Fig. 1c isthe anomaly gradient across the 5�N latitude in thewestern and central tropical Pacific, with negative

anomalies in the northern part (at about 8�N to 20�N,150�E to 170�W) and positive anomalies to the south(12�S to 4�N, 150�E to 170�W). This feature is embed-ded in a larger scale pattern often associated with thePacific Decadal Oscillation (Zhang et al. 1997; Milleret al. 1994; Trenberth and Hurrell 1994). The locationof the SST gradient in the Pacific warmpool regionprovides a potential for a large sensitivity of atmo-spheric precipitation and heating anomalies.

To highlight the importance of the SST gradient inthe western tropical Pacific (WTP) region, we define anindex as the difference between the average SST in the

b

d

c

e

aFig. 4 Regression of SPEEDYEXP1 fields onto the WTP SSTgradient index: a precipitation,b 500 hPa height, c surfacepressure, d the linear trend(1950–1999) in 500 hPa heightin EXP1, e the NCEP re-analysis 500 hPa height trend.Contour intervals are 1 m for(b), 0.1 hPa for (c), 6 m for(d) and 20 m for (e). The scalingin plots d and e is such that themaps correspond to the linearchange in 50 years. Theregressions maps correspond toone standard deviation of theWTP SST gradient index. Fora only anomalies that aredifferent from zero at the 95%confidence level are shown,whereas red (positive) and blue(negative) shading indicate thislevel for b, c and d


box [150�E to 170�W, 12�S to 4�N] and the average SSTin the box [150�E to 170�W, 8�N to 20�N].1

Two remarks should be made regarding such a defi-nition. Firstly, although the boundaries of the two boxeshave been chosen to best match the resolution of modeldata analysed in the following sections, the results dis-cussed below are relatively insensitive to small changesin the size and location of the boxes. Secondly, we arenot proposing the existence of an ever-present SST di-pole, which would involve anti-correlated SST anoma-lies in the northern and southern boxes. Rather, wethink of WTP variability as the superposition of twoSST components, one symmetric and the other anti-symmetric in latitude around 5�N. The gradient index isa measure of the amplitude of the latter, which is foundto correlate with NAO variability on interdecadal scales.The regression of global SST onto the WTP SST gra-dient is shown in Fig. 1d. The structure is indeed verysimilar to the NAO regression pattern of Fig. 1c.However, it becomes clear that the WTP SST gradientcovaries with SST anomalies of global extent on the

decadal timescale, making it more difficult to isolate thesignal originating in the WTP region.

The time series of the WTP SST gradient is comparedwith the NAO index in Fig. 2, where both time series arenormalized to have zero mean and unit standard devi-ation. The curves have a similar decadal behaviour, witha correlation coefficient (c.c.) of 0.82, significant at the95% confidence level.

Extratropical flow changes on decadal scales haveoften been related to variations of El Nino SouthernOscillation (ENSO) (Zhang et al. 1997). The averageSST in the southern (cross-equatorial) WTP box is veryclose to the NINO-4 index, usually associated withENSO teleconnections. The southern-box SST is alsoplotted in Fig. 2 for comparison (orange curve); itscorrelation with the NAO index is 0.52. When theNINO-3.4 index (defined as the average SST anomaliesin the region 170�W to 120�W, 5�S to 5�N) is considered,the correlation with the NAO index drops further to0.36.

Numerical studies by Hurrell et al. (2004) andHoerling et al. (2004) suggest an important role of theIndian Ocean warming trend in forcing the NAO in thesecond part of the 20th century. Indeed, the NAO-SSTregression in Fig. 1c shows an overall warming in theIndian Ocean. However, if we define an Indian Oceanindex as the average SST anomalies in the box 50�E to120�E and 20�S to 20�N (blue curve in Fig. 2), the cor-relation of this index with the NAO index is only 0.43.

a

c

b

d

Fig. 5 a EOF1 of the 500 hPaheight from the globally SST-forced experiment (EXP1),b principal component (PC)associated with EOF1 in panela (black) and globally averagedSST (red), c EOF2 of the500 hPa height from EXP1,d PC associated with EOF2 inpanel c (black) and WTP SSTgradient index (red),e Regression of SST onto PC1(in K), f regression ofprecipitation onto PC1 (in mm/day), g regression of SST ontoPC2 (in K) and h regression ofprecipitation onto PC2 (in mm/day). Contour intervals 1 m fora and c. The regressions mapscorrespond to one standarddeviation of the PCs. For f andh only anomalies that aredifferent from zero at the 95%confidence level are shown

1Alternatively, it is possible to calculate first the global meridionalSST gradient and then to regress the SST gradient onto the NAOindex. This provides a direct measure of the SST gradient fieldrelated to the NAO. An index, analogous to the one in the paper,can then be defined as the maximum gradient located at about[150�E to 170�W, 5�N to 10�N]. Results do not change signifi-cantly, and we prefer the definition in Sect. 3 because of itssimplicity and of smoother fields.


Notably, all four curves in Fig. 2 show a similar positivetrend in the last decades; therefore it is difficult to sep-arate the contributions of SST in different tropicalregions to NAO variability using observations restrictedto the late 20th century.

In a related paper (M.P. King and F. Kucharski,submitted), the observed decadal connections betweentropical SSTs and the NAO are further investigatedusing multivariate statistical methods.

4 Modelling results

4.1 Model climatology

Molteni (2003) discussed extensively the model atmo-spheric response to tropical and extratropical SSTanomalies for a configuration with five vertical layers.A stronger and more realistic tropical atmosphericresponse was obtained in a seven-layer configuration ofthe ICTP AGCM, used by Bracco et al. (2004) toinvestigate decadal climate variability in the second halfof the 20th century.

The eight-layer version adopted here is significantlyimproved with respect to the previous ones, especiallyover the Atlantic sector, as briefly discussed below.

Figure 3a–d shows the December-to-February(DJF) 500 hPa height climatology and the stationaryeddy field for the period 1950–2001, for the re-analysis(Fig. 3a, c) and the model (Fig. 3b, d), respectively.The position and strength of the stationary waves inthe Atlantic region are in much better agreement withthe NCEP/NCAR data than in the seven-layer version(for comparison see Fig. 1 in Bracco et al. 2004).

The global rainfall distribution simulated by themodel in DJF is shown in Fig. 3f, and compared withthe CMAP climatology by Xie and Arkin (1997) inFig. 3e. Most of the main features in the rainfall patternare realistically reproduced, although the model tends tooverestimate rainfall over the Indian Ocean, in theequatorial WTP region and over the tropical continents.

Fig. 6 Regression of the 500 hPa height of EXP2 onto the WTPSST gradient index. Contour intervals 1 m. Anomalies that aredifferent from zero at the 95% confidence level are indicated by red(positive) and blue (negative) shading

e

f

g

h

Fig. 5 (Contd.)


Also, the South Pacific Convergence Zone tends to bemore zonally confined in the model than in observations.This should be kept in mind when interpreting some ofthe results presented in the following section.

4.2 Atmospheric response to the SST forcing

4.2.1 Global SST ensemble (EXP1)

A first indication on the role played by the WTP SSTgradient in EXP1 is given by the regression of the

ensemble mean precipitation, 500 hPa height and sur-face pressure (Fig. 4a–c) onto the SST gradient. For theheight and pressure regression red and blue shadingindicate areas of anomalies that are different from zeroat the 95% confidence level, for the precipitationregression all contours shown are statistically significant.The precipitation regression (Fig. 4a) clearly shows astrong dipolar structure in the WTP region, but as wellother regions contribute significantly, such as the easternPacific and northern Brazil. Interestingly, the 500 hPaheight regression pattern has a large correlation (0.79)

ab

c d

fe

Fig. 7 Pattern of CCA appliedto EXP2 (forced by SSTanomalies only in the WTPregion) and EXP3 (forced bySST anomalies only in theIndian Ocean): a mode-1 of500 hPa height from EXP2,b mode-1 of SSTs (in K) fromEXP2, c mode-2 of 500 hPaheight from EXP2, d mode-2 ofSSTs (in K) from EXP2,e mode-1 of 500 hPa heightfrom EXP3, f mode-1 of SSTs(in K) from EXP3. Contourintervals 1 m for a, c and e


over the northern hemisphere with the ensemblemean linear trend from 1950 to 1999 [Fig. 4d; presentedhere for JFM for better comparison with the resultsof Hurrell et al. (2004) and Hoerling et al. (2004)].Figure 4e shows the linear trend for the period 1950–1999 for the NCEP re-analysis. The spatial correlationcoefficient with the ensemble mean trend in the AGCMover the northern hemisphere is 0.54. Compared withthe multi-model ensemble mean results presented inHoerling et al. (2004), the amplitudes of the modelledtrends are similar, but the North Atlantic troughresponse is shifted to the south.

The regressions of 500 hPa height onto the first andsecond principal components, derived from the PCA of500 hPa height over the North Atlantic region, areshown in Fig. 5a and c, respectively. These maps areequivalent to the corresponding EOFs inside the domain(90�W to 60�E, 20�N to 90�N), while showing thecovariance pattern with the PCs over the whole northernhemisphere.

The regression map of the first PC (explaining 55.4%of the ensemble mean decadal variance) is dominated byan overall increase in height. The spatial anomalycorrelation with the NAO regression of Fig. 1b in theNorth Atlantic region is 0.35. The second EOF(explaining 19% of the ensemble mean decadal variance)has as well a positive spatial correlation with the NAOregression (0.45) and has a structure very similar to theWTP SST gradient regression of Fig. 4b. The time seriesof the first PC is plotted in Fig. 5b, together with theglobal mean SST time series. The correlation coefficientbetween PC1 and the global mean SST is 0.96, whereasthe correlation with the Hurrell NAO index is only 0.2.The second PC (black curve in Fig. 5d), on the otherhand, is strongly correlated both with the WTP SSTgradient index (0.75) and with the Hurrell NAO index(0.61).

In order to assess the likely forcing pattern for EOF1and EOF2, we compute the regression of SSTs (Fig. 5e,g) and precipitation fields (Fig. 5f, h) onto PC1 andPC2. The regression in Fig. 5g closely resembles theregression map of the Hurrell NAO index and of theWTP SST gradient index (Fig. 1c, d, respectively),whereas Fig. 5e is characterized by a general warmingpattern, particularly in the storm-track regions. Theprecipitation regression of PC2 (Fig. 5h) confirms thatthe WTP SST gradient dominates the signal, whereas thestrongest precipitation signal of the PC1 regression(Fig. 5f) is located over the Indian Ocean.

4.2.2 Western tropical Pacific ensemble (EXP2)

The existence of a dynamical link between the NAO andthe WTP SST gradient on decadal timescales, suggestedby the observations and the globally SST-forced EXP1, issupported and emphasized by the diagnostics performed

a

b

c

Fig. 8 Time series of the CCA applied to EXP2 and EXP3:a mode-1 of EXP2, 500 hPa pattern (black) and SST pattern(red), b mode-2 of EXP2, 500 hPa pattern (black) and SSTpattern (red), c mode-1 of EXP3, 500 hPa pattern (black) and SSTpattern (red). In a, b and c the Hurrell NAO index is plotted aswell for comparison (blue). All curves are normalized to havestandard deviation one


on the 18-member ensemble of EXP2. The regression ofthe 500 hPa height onto the WTP SST gradient index isshown in Fig. 6. This field has an NAO-like pattern overthe North Atlantic (the spatial correlation with the NAOregression of Fig. 1b is 0.65 over this region). Thestructure is similar to the corresponding regression from

EXP1 (Fig. 4b), but with the North Atlantic troughshifted to the north (therefore representing a clearerNAO-type response). The signal has as well comparableamplitude in the North Atlantic region, but is weaker inthe North Pacific, where the negative anomaly is confinedto the western part of the ocean.

In order to find optimally correlated pattern be-tween the North Atlantic 500 hPa height and the SSTin the WTP region of EXP2, we perform a CCA basedon principal components. Only the leading three PCs ofthe 500 hPa height and SST are retained for the CCAto avoid overfitting. With these three PCs, about 80%of variance is explained for the 500 hPa height and90% for SST. However, results are robust against thenumber of PCs used. The pattern of the first CCAmode (homogeneous maps) is shown in Fig. 7a and bfor height and SST, respectively. The time series relatedto the height and SST pattern are shown in Fig. 8a(black and red curve, respectively). The correlationbetween the time series is 0.92 (and the pattern explains44% of the combined height and SST variance). Thespatial correlation of the first height pattern with theNAO regression of Fig. 1b is 0.26 in the North Atlanticregion, and the time series of height and SST are onlyweakly (0.38 and 0.34, respectively) correlated with theNAO index (plotted as blue curve in Fig. 8a–c). Thepattern of the second CCA mode for height and SST isshown in Fig. 7c and d, respectively. The spatial cor-relation between the height pattern and the NAOregression is 0.67 and the SST pattern resembles thegradient regression of Fig. 1d. The time series relatedto the second pattern, shown in Fig. 8b (black and redcurve for height and SST, respectively; correlationcoefficient 0.77), show high correlation with the NAOindex (0.61 for height and 0.77 for SST). The combinedheight and SST variance explained by this pattern is31%.

a

b

Fig. 9 Regression of the 200 hPa velocity potential onto a mode-1SST time series from EXP2, b mode-1 SST time series from EXP3.Contour intervals 0.1 m2/s. All anomalies shown are different fromzero at the 95% confidence level

Fig. 10 500 hPa height response to idealized SST gradient forcingin the WTP region. Contour intervals 2 m. Anomalies that aredifferent from zero at the 95% confidence level are indicated by red(positive) and blue (negative) shading

Fig. 11 Regression of the heat flux into the ocean onto the WTPSST gradient index (in W/m2)


4.2.3 Indian Ocean ensemble (EXP3)

As already mentioned, recent works by Hoerling et al.(2004) and Hurrell et al. (2004) stressed the importanceof the Indian Ocean SSTs warming in determining theNAO pattern in the second half of the 20th century. Theten-member ensemble of EXP3 is designed to assess theresponse to the Indian Ocean SSTs in our AGCM, in thecontext of the longer 130-year record provided by theHadISST.

As for the EXP2, we analyse the SST-forced signal byperforming a CCA of the 500 hPa height in the NorthAtlantic region and the SSTs in the Indian Ocean. Thethree leading PCs retained for the CCA explain about85% of variance for the 500 hPa height and 92% forSST. The first pattern is shown in Fig. 7e and f forheight and SST, respectively. The height pattern has apositive spatial correlation with the observed NAOregression of Fig. 1b over the North Atlantic (c.c. 0.28),showing a larger amplitude than the first two patternfrom the ensemble mean of the western Pacific experi-ment (EXP2). However, as in the papers above, theassociated response over the North Pacific is nearlyopposite to the Pacific component of the NAO-relateddecadal signal. Figure 7f clearly shows that the SSTpattern related to Fig. 7e is an overall warming in theIndian Ocean. Indeed, the time series of the height(black curve) and SST (red curve) shown in Fig. 8c (c.c.0.84) show high correlation with the mean SST in theIndian Ocean shown in Fig. 2 (c.c. 0.84 and 0.99,respectively). The correlation of the height and SSTpattern time series with the NAO index is 0.19 and 0.37,respectively. Higher modes from EXP3 are not pre-sented because the correlation coefficient drops to 0.47already for the pattern associated to the second mode.

A controversial issue concerns the superposition ofsignals from different ocean basins in the atmosphericresponse to the full, globally defined SST anomalies. Forexample, the rainfall pattern in Fig. 5f suggests that theheight EOF1 of EXP1 is mainly forced by heatinganomalies in the Indian Ocean; in turn, these are asso-ciated with a warming pattern which resembles the firstCCA SST pattern of EXP3 (cf. Figs. 5e, 7f).

However, the first CCA height pattern of EXP3(Fig. 7e) has a larger amplitude over the North Atlanticthan EOF1 of the fully forced experiment EXP1 (Fig. 5a).This may be due to a partial cancellation, in the first EOFof the fully forced experiments, of the height responses inthe first CCA pattern induced by the overall warming oftheWTP and the Indian Ocean, which have opposite signover most of the northern hemisphere (cf. Fig. 7a, e;spatial c.c. �0.82), despite the strong temporal correla-tion of the leading SST pattern time series from the twodomains (c.c. 0.94; see Fig. 8a, c, red curves).

It is widely documented (e.g. from numerical tele-connection studies in opposite ENSO phases) that theresponse of the Walker circulation to warm, near-equatorial SST anomalies in the Indo-Pacific basin de-pends strongly on the location of the warming. To

confirm this point, we calculate the regressions of the200 hPa velocity potential onto the first CCA pattern ofSST of the WTP EXP2 and of the Indian Ocean EXP3(Fig. 9a, b). Such velocity potential anomalies provide ameasure of how the warming from the two oceans affectsthe large-scale divergent flow. Indeed, Fig. 9a and bshows extrema of velocity potential in similar regions,but with opposite sign, suggesting a partial cancellationof the Indian Ocean and WTP warming-forced re-sponses when global SST anomalies are considered.

4.2.4 Response to idealized SST anomalies

In order to confirm the robustness of the resultsregarding the NAO-type response in mode2 of the WTPCCA, we performed a further experiment using idealizedSST forcings. For this purpose, the SST pattern ofFig. 7d has been scaled to have a maximum amplitudeof about 1 K. Such an amplitude is comparable toforcings used in other studies (see e.g. Bader and Latif2003; Hoerling et al. 2004), and has the same order ofmagnitude as the SST trend that occurred in the last50 years in the tropical oceans. This composite SSTpattern is subtracted from a SST climatology based onthe period 1981–1990 (in which the SST gradient hasbeen in its extreme positive phase according to Fig. 2) inorder to simulate conditions that occurred in the 1960s.The experiment with these SSTs is compared with acontrol experiment in which the SST climatology of the1980s is imposed. The response for 250 seasons (aspreviously December to March), defined as the differ-ence of control minus mode-2 forced experiment, isshown in Fig. 10. It resembles closely the mode-2 ofFig. 7d and the WTP SST gradient regression of Fig. 6.Thus, the result that the SST gradient in the WTP regionforces an NAO-type response is a robust feature of ourAGCM, and not simply an artefact of statistical analy-sis. The issue of wave propagation, contributions ofdifferent parts of the WTP SST and linearity of the re-sponse to the SST forcing in a more idealized experi-mental set-up will be assessed in a separate paper.

5 Discussion

Summarising, we find that:

– On interdecadal time scale, the NAO index is signifi-cantly correlated with the observed SST gradient inthe WTP ocean, which shows a distinct signal bothfrom ENSO and Indian Ocean anomalies.

– In AGCM experiments with globally prescribed SSTanomalies, the second 500 hPa height EOF shows awavetrain structure in the North Atlantic which iscorrelated in spatial pattern with the observed NAOregression. The corresponding PC2 is temporallycorrelated both with the WTP SST gradient andHurrell’s NAO index.

– Experiments with prescribed SST anomalies in theIndian Ocean confirm results obtained with other


models, but do not suggest a leading role of the IndianOcean in forcing the NAO interdecadal variabilityover the whole 120-year period considered here.

– Experiments with prescribed SST anomalies in theWTP region confirm that variations of the WTP SSTgradient induce an NAO-like anomaly and part of itshemispheric teleconnections.

Two further issues need to be addressed for a properinterpretation of these results. Firstly, how can onereconcile the stronger NAO signal obtained from theIndian Ocean experiments (when compared to the WTPsignal in CCA mode-2) with the higher correlation be-tween the observed NAO index and the WTP SST gra-dient? Secondly, why is the amplitude of the AGCMinterdecadal response small, relative to the observedsignal over the NAO domain?

A partial answer to the first question has been sug-gested in the previous section, noting the near-oppositeresponses on both the Walker circulation and theextratropical flow of near-uniform warming in thetropical Indian and West Pacific Oceans. With regard tothe latter question, forcing from SST anomalies outsidethe WTP and Indian regions, as well as deficiencies ofour low-resolution AGCM might cause the discrepancyin the anomaly amplitude. However, experiments withglobal SST anomalies performed with more sophisti-cated GCMs still reproduce only half or less of the ob-served NAO trend in the last 50 years (Rodwell et al.1999; Hoerling et al. 2001; Hurrell et al. 2004; Schneideret al. 2002). Internal atmospheric variability should betaken into account (Bracco et al. 2004; Schneider et al.2002), but analysis of individual ensemble memberssuggests that atmospheric dynamics alone is unlikely toproduce the amplitude of the observed signal (e.g. seeFig. 4 in Hurrell et al. 2004).

On the other hand, a different picture could emerge ifinternal variability of the coupled ocean–atmospheresystem is considered. If the interdecadal NAO variabilitywere part of a coupled mode of variability, the observedcorrelations would reflect the atmospheric influence onthe SST as well as the SST feedback onto the extra-tropical flow. In such a hypothesis, it can be shown thatthe SST-forced response is bound to be smaller than thesignal deduced from observations. In the extratropics,where in situ SST anomalies are mainly produced byheat fluxes, ocean–atmosphere interactions may enhanceSST anomalies through a positive feedback (Marshallet al. 2001). However, SST anomalies in the tropicalIndo-Pacific ocean cannot be directly induced by theNAO pattern. We suggest that a dynamical feedbackmay be provided by the hemispheric nature of the in-terdecadal signal embedding the NAO, namely by thenegative pressure anomaly over the North Pacific whichis part of the COWL-like pattern (Wallace et al. 1996)shown in Fig. 1a and b.

As advocated in recent studies (e.g. Schneider et al.1999; G. Boccaletti, R.C. Pacanowski, S.G.H. Philan-

der, submitted) heat fluxes generated in the North Pa-cific may induce SST anomalies in the subtropical Pacificon decadal timescale. Therefore, when embedded in aCOWL-like pattern, the NAO might be sustained by apositive feedback through the cooling of the subtropicalWest Pacific and a consequent enhancement of the WTPSST gradient. No such feedback can be triggered by theIndian Ocean, which forces a positive height anomalyover the North Pacific (see Fig. 7f).

To test this hypothesis, we compute the regression ofocean-surface heat fluxes (from the NCEP/NCAR re-analysis) onto the filtered WTP SST gradient index. Theregressed heat-flux into the ocean (Fig. 11) shows thatSST and heat-flux anomalies have the same sign over theNorth Pacific, where the atmospheric anomaly cools theocean, but opposite sign over the subtropical west Pa-cific, where a cold SST anomaly forces the atmosphere(see Fig. 1c). The regression of the 500 hPa height ontothe WTP SST gradient, shown in Fig. 6, indicates thatthe hemispheric circulation pattern associated with theWTP SST gradient in our experiments is indeed able tostrengthen the advection of cold continental air on thewestern side of the North Pacific.

The hypothesis of a coupled feedback in the WestPacific may explain the discrepancy between the NAOsignal in AGCM, SST-forced experiments and the ob-served NAO correlations with Indian and WTP SSTs.Its existence needs to be tested in a coupled ocean–atmosphere model. If verified, it would imply a greaterrole of internal climate variability of the coupled systemin the recent NAO trend, although, as mentioned byHurrell et al. (2004), contributions from the enhancedgreenhouse effect (in the form of tropical SST warmingor stratospheric cooling) should not be ruled out. In fact,anthropogenic forcing might have enhanced the persis-tence of one phase of the coupled phenomenon (e.g.Corti et al. 1999), leading to an amplification of inter-decadal variations in the second half of the 20th century.

Acknowledgments The authors wish to thank Giulio Boccaletti andGeorge Philander for useful discussions during the preparation ofthis manuscript. We also thank the two anonymous reviewers fortheir constructive suggestions.

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