Abstract

The winter extratropical teleconnection of El Niño–Southern Oscillation (ENSO) in the North Atlantic–European (NAE) sector remains controversial, concerning both the amplitude of its impacts and the underlying dynamics. However, a well-established response is a late-winter (January–March) signal in sea level pressure (SLP) consisting of a dipolar pattern that resembles the North Atlantic Oscillation (NAO). Clarifying the relationship between this “NAO-like” ENSO signal and the actual NAO is the focus of this study. The ENSO–NAE teleconnection and NAO signature are diagnosed by means of linear regression onto the sea surface temperature (SST) Niño-3.4 index and an EOF-based NAO index, respectively, using long-term reanalysis data (NOAA-20CR, ERA-20CR). While the similarity in SLP is evident, the analysis of anomalous upper-tropospheric geopotential height, zonal wind, and transient-eddy momentum flux, as well as precipitation and meridional eddy heat flux, suggests that there is no dynamical link between the phenomena. The observational results are further confirmed by analyzing two 10-member ensembles of atmosphere-only simulations (using an intermediate-complexity and a state-of-the-art model) with prescribed SSTs over the twentieth century. The SST-forced variability in the Northern Hemisphere is dominated by the extratropical ENSO teleconnection, which provides modest but significant SLP skill in the NAE midlatitudes. The regional internally generated variability, estimated from residuals around the ensemble mean, corresponds to the NAO pattern. It is concluded that distinct dynamics are at play in the ENSO–NAE teleconnection and NAO variability, and caution is advised when interpreting the former in terms of the latter.

1. Introduction

While it is no news that El Niño–Southern Oscillation (ENSO) is a primary source of global predictability, improving seasonal forecasts in the extratropics is constrained by the large internal variability and challenged by the limited understanding of the ENSO teleconnections. In this work, we clarify some aspects of the late-winter ENSO teleconnection to the North Atlantic–European (NAE) region by investigating its relationship with the North Atlantic Oscillation (NAO).

In the North Pacific–American (NPA) sector, the wintertime ENSO teleconnection shows a well-known surface response: a reinforcement of the climatological Aleutian low is observed for positive ENSO events (El Niño, i.e., warm SST anomalies in the central-eastern tropical Pacific), while a signal of opposite sign is expected for negative ones [see Trenberth et al. (1998) and Alexander et al. (2002) for reviews]. This feature is part of the surface signature of a tropospheric large-scale Rossby wave train that propagates from the tropical Pacific toward high latitudes with a distinctive eastward-arching shape (Hoskins and Karoly 1981; Horel and Wallace 1981). This wave train is not fully established in observations until January (e.g., Wang and Fu 2000; Bladé et al. 2008), although the standard winter season used to analyze ENSO teleconnections is December–February (DJF). Furthermore, this ENSO-forced pattern over the NPA region is different from the internal Pacific–North America (PNA) mode: the two patterns are in fact almost in spatial quadrature (Nigam 2003) and have distinct time scales [see Nigam and Baxter (2014) for a review]. In the NPA sector, the description in terms of tropospheric Rossby wave propagation accounts for most of the observed ENSO impacts in several fields, such as precipitation and temperature [see Trenberth et al. (1998) for a review].

In the NAE sector the situation is more intricate, as the amplitude of the impacts is weaker and less statistically significant, due to the dominant internal variability, and the underlying dynamics driving the ENSO teleconnection are still unsettled. However, a modest but systematic ENSO signal, robust and stationary over the last 300 years, has been identified in late winter [January–March (JFM)] for surface temperature, precipitation, and sea level pressure [see Brönnimann (2007) for a review]. A simple approach to reveal this “canonical” ENSO signal in sea level pressure (SLP) is by linear regression onto the Niño-3.4 index, as in Fig. 1c (details in section 2). The strongest extratropical signal is in the North Pacific sector, the deepened Aleutian low mentioned above, but a significant response is also present in the North Atlantic, with one positive center located at high latitudes, covering Greenland and part of Canada, and one negative center at about 40°N, extending from the eastern coast of the United States to almost the eastern boundary of the basin. This ENSO-related dipole is often termed “NAO-like,” alluding to the North Atlantic Oscillation, the dominant mode of variability in the NAE region. Indeed, the NAO spatial signature also exhibits a dipole in sea level pressure, as shown in Fig. 1d, where an NAO index was defined and used for the linear regression. The depicted pattern, well established in the literature [see Hurrell et al. (2003) and Hurrell and Deser (2009) for reviews], corresponds to the negative NAO phase and, despite the overall larger amplitude and spatial extent, a similarity to the pattern associated with ENSO is evident. This visual resemblance is confirmed by the spatial correlation between the two patterns in Figs. 1c and 1d, which is 0.87 over the NAE sector (20°–90°N, 90°W–40°E).

Fig. 1.

Regression maps of circulation anomalies in JFM using NOAA-20CR (1901–2014). (top) Z200 regressed onto the (a) N3.4 and (b) NAO index. (bottom) SLP regressed onto the (c) N3.4 and (d) NAO index. Contours indicate statistically significant areas at 95% confidence level. The symbol × indicates the approximate center of action in the regression of SLP onto N3.4; the symbol ⋆ is the same, but for Z200.

Fig. 1.

Regression maps of circulation anomalies in JFM using NOAA-20CR (1901–2014). (top) Z200 regressed onto the (a) N3.4 and (b) NAO index. (bottom) SLP regressed onto the (c) N3.4 and (d) NAO index. Contours indicate statistically significant areas at 95% confidence level. The symbol × indicates the approximate center of action in the regression of SLP onto N3.4; the symbol ⋆ is the same, but for Z200.

The main aim of this work is to understand whether the response to ENSO in the NAE sector should be interpreted as associated with NAO variability, beyond the similarity in their surface signatures. While doing so, we will also examine some features of the tropospheric pathway of the ENSO–NAE teleconnection; the more recent, widely discussed stratospheric pathway hypothesis (e.g., Cagnazzo and Manzini 2009; Polvani et al. 2017) will not be addressed in this manuscript. Additionally, we discuss the contribution of ENSO to the predictive skill of two atmospheric models.

ENSO events involve a variety of spatiotemporal patterns (Timmermann et al. 2018); along with the “conventional” eastern Pacific (EP) ENSO, several studies identified a second mode, the central Pacific (CP) ENSO, with SST anomalies peaking around the date line [see Capotondi et al. (2015) for a review]. Although not all events fit exclusively in one category, recent works pointed at different, possibly nonlinear extratropical impacts related to the two types of ENSO (e.g., Graf and Zanchettin 2012; Zhang et al. 2015, 2019). In this study, we focus on the so-called conventional ENSO, for which robust atmospheric teleconnections to the Northern Hemisphere have been established (e.g., DeWeaver and Nigam 2002; Hoerling and Kumar 2002; Kumar et al. 2005); these teleconnections are linearly related to Niño-3.4 SST variability (Zhang et al. 2016).

An added value of this work is to provide an analysis of the ENSO–NAE teleconnection from a JFM perspective: although DJF- and JFM-based analyses lead to comparable SLP patterns (not shown; cf. Deser et al. 2017), as they are both dominated by the JF response, several studies have reported intraseasonal (month to month) differences in the ENSO–NAE teleconnection, such as a shift in the SLP response in the Atlantic basin from a monopole in November–December to a dipole in January–February (see King et al. 2018; Ayarzagüena et al. 2018, and references therein). More generally, the entire ENSO-forced wave train in the Northern Hemisphere shows marked differences between December and January (when the classical wave-like response really emerges; e.g., Livezey and Mo 1987; Alexander et al. 2002) that are not well captured by models, which tend to simulate January-like patterns in both months (Bladé et al. 2008). For these reasons, many authors suggest avoiding December when studying the winter ENSO–NAE teleconnection and indicate JFM as a more suitable choice than DJF [e.g., Bladé et al. 2008; Fereday et al. 2008; see Brönnimann (2007) for a review]. With this fully JFM-based study, we follow their recommendation and hope that it will encourage other authors to adopt the same practice.

We begin by examining the ENSO and NAO signals in several atmospheric fields in observations and continue by considering model outputs from an intermediate-complexity AGCM (atmospheric general circulation model) and a state-of-the-art one. Finally, the skill of the two models is evaluated with and without the effects of ENSO.

2. Data and methods

a. Observational data

The primary dataset is the NOAA Twentieth-Century Reanalysis v2c (NOAA-20CR), a long record (1851–2014) of global atmospheric fields reconstructed by assimilating surface pressure and using observed sea surface temperature (SST) and sea ice distribution as boundary conditions. The atmospheric model has T62 horizontal spectral resolution and 28 levels in the vertical (L28), up to ~2.5 hPa (Compo et al. 2011). We repeated our analyses using the ECMWF twentieth-century reanalysis (ERA-20CR) dataset, which is another long-term reanalysis (1900–2010) with similar boundary conditions and assimilation system, but higher resolution: T159L91, with the top at 0.01 hPa (Poli et al. 2016). We found no appreciable differences in most cases (see Fig. A1 in the appendix) as the troposphere behaves similarly in the two products, but dissimilarities do emerge in the stratosphere (see Fig. A2). Other datasets used are the GPCC Full Data Reanalysis (v7) at 0.5° × 0.5° resolution for precipitation (Schneider et al. 2011) and the Met Office HadISST1.1 for SST (Rayner et al. 2003).

b. Models

We analyze integrations of the International Center for Theoretical Physics (ICTP) AGCM (v. 41), nicknamed SPEEDY (Simplified Parameterizations, Primitive-Equation Dynamics), forced with observed SST anomalies (HadISST1.1). The simulations consist of a 10-member ensemble over the period 1901–2014. SPEEDY is an intermediate-complexity AGCM with a coarse horizontal resolution (T30 in the standard configuration used here) and eight vertical levels, with a crude lower stratosphere (the top two layers are at 100 and 30 hPa). These features and the simplified parameterizations allow a low computational cost, but still the model compares reasonably well with observations in relevant climate aspects and atmospheric teleconnections (Kucharski et al. 2013, and references therein).

The ECMWF ERA-20CM dataset is a 10-member ensemble of atmosphere-only integrations forced with SST and sea ice cover from HadISST2, for the period 1899 to 2010 (Hersbach et al. 2015). The AGCM is an adaptation of the Integrated Forecasting System (IFS) version cy38r1, with the same resolution as ERA-20CR (T159L91).

c. Methods

We focus on the period from 1901 to 2014 (1901–2010 for ERA-20CM, 1901–2013 for GPCC); choosing long-term records responds to the need of working with a large set of ENSO events to avoid sampling issues (Deser et al. 2017), but our results fully agree with previous findings obtained using shorter periods [e.g., NCEP–NCAR in Brönnimann (2007); ERA-40 in García-Serrano et al. (2011); ERA-Interim in Zhang et al. (2016)]. All fields are linearly detrended after computing JFM averages.

In the reanalysis, we obtain the spatial signatures of ENSO and the NAO with linear regressions. For ENSO, we use the Niño-3.4 index (N3.4), defined as the area-averaged SST anomalies over 5°S–5°N, 170°–120°W. This index is commonly used to describe the conventional ENSO (e.g., Deser et al. 2010) and its teleconnections (e.g., Sterl et al. 2007; Yang and DelSole 2012); using the Niño-3 index (SST anomalies averaged over 5°S–5°N, 150°–90°W) provides identical patterns (not shown). We use EOF analysis to compute the NAO index as the first principal component (PC) of SLP over the NAE domain (20°–90°N, 90°W–40°E). For the sake of comparison with the ENSO regressions, we choose the NAO index associated with a negative NAO phase.

In the models, the experimental setup enables us to ideally separate the forced and internal variability, which are intrinsically mixed in the observations. The ensemble mean mostly contains the response to the prescribed forcing, while the deviations from the ensemble mean, emerging from having perturbed the initial conditions in the different members, represent the atmospheric internal variability unrelated to the boundary forcing. In section 3b, we separately study the patterns arising from the forced and internal variability using EOF analysis. An index describing the leading boundary-forced component is defined as the first PC of the ensemble-mean SLP in the Northern Hemisphere (20°–90°N); the regression maps of ensemble-mean variables onto this index represent the “forced” response. To estimate the internally generated variability in the NAE sector, we first compute the residuals around the ensemble mean for all 10 members; then, we use the concatenated residual time series as input for another EOF analysis and for linear regressions onto the corresponding leading PC of the SLP.

In the second part of section 3a, we diagnose the dynamics involved in the teleconnection patterns using transient-eddy momentum flux (uυ′) to examine synoptic-scale waves, and meridional eddy heat flux (υ*T*) for planetary-scale waves. To obtain uυ′, we apply a 24-h filter (e.g., Wallace et al. 1988; Chang and Fu 2002) to daily data of zonal and meridional wind from NOAA-20CR to retain high-frequency variability, and compute monthly means of their daily covariance. The term υ*T* is computed from the same daily data but with no time filtering; instead, we consider the daily deviations from the zonal mean for each variable and again produce monthly-mean covariances (e.g., Newman and Nash 2000; Hinssen and Ambaum 2010).

Finally, in section 3b(2) we evaluate the skill of the models in ensemble-mean fields by computing the anomaly correlation coefficient (ACC) with NOAA-20CR. As this is a point-by-point evaluation, the data are previously regridded by interpolating from higher to lower resolutions. To assess statistical significance, we use a two-tailed t test for correlation in the regressions and a one-tailed t test in the case of ACC, both at a 95% confidence level. To avoid too liberal statistical thresholds, we use an effective sample size that takes into account the autocorrelation of the time series (Bretherton et al. 1999).

3. Results

a. Observational teleconnections

1) Regression maps

Despite sharing some common features at the surface, the ENSO and NAO teleconnections show little similarity when considering their upper-level signatures. In the regression map of 200-hPa geopotential height (Z200) onto the N3.4 index, the familiar ENSO-forced Rossby wave train is evident (Fig. 1a). The upper-tropospheric counterpart of the deepened Aleutian low is prominent among the series of centers arching eastward across the Pacific and North America, with maximum amplitude exceeding 50 m; a weaker negative anomaly (maximum ~ 25 m) is centered over the eastern United States, well to the west of the corresponding feature at the surface (its approximate center is marked with a faded black cross), as further discussed in section 3a(2). There is no significant response over the eastern North Atlantic or Europe. In contrast, the negative lobe of the upper-level signature of the NAO (Fig. 1b) covers the entire NAE sector, spreading far into continental Europe. The zonally elongated, almost annular-shaped anomalies, which project on the circumglobal waveguide pattern (Branstator 2002; García-Serrano and Haarsma 2017), are weak over the North Pacific (up to −20 m, about half the values in the North Atlantic). The closest feature in the two patterns is the positive anomaly north of 60°N, but a closer inspection reveals marked differences, as the NAO-related anomalies are again more zonally symmetric and stronger, with a broader extent and centered in a different location (cf. the anomaly in Fig. 1b with the star marking the approximate location of the ENSO-related center in Fig. 1a). Note also that the temporal correlation between the N3.4 and NAO indices is only 0.24, indicating a shared variance of less than 6%.

The anomalies in the upper-level zonal circulation also differ between the two modes, as can be seen in the top panels of Fig. 2, which show the regression maps of 200-hPa zonal wind (U200) onto the N3.4 and NAO indices and the corresponding climatology (thick contours). Not surprisingly, the strongest response to ENSO occurs again in the North Pacific (Fig. 2a), where a reinforced zonal flow south of 40°N (maximum values above 5 m s−1) is found; a band of negative, weaker anomalies (−1 to −2 m s−1) is present around 50°N. The overall result is a lengthening and equatorward displacement of the North Pacific jet. A similar dipolar anomaly along latitude bands is present in the North Atlantic: however, the amplitudes are slightly weaker (the positive anomalies are now less than 4 m s−1) and the anomalies are mainly confined to the western part of the basin. In contrast, the anomalous pattern associated with the NAO (Fig. 2b) exhibits strong anomalies throughout the entire NAE sector (amplitudes above 5 m s−1 in both signs) that influence the exit of the North Atlantic jet.

Fig. 2.

As in Fig. 1, but for (top) U200, (middle) uυ′200, and (bottom) precipitation from GPCC (1901–2013). Thick contours in (a)–(d) indicate the JFM climatology of U200.

Fig. 2.

As in Fig. 1, but for (top) U200, (middle) uυ′200, and (bottom) precipitation from GPCC (1901–2013). Thick contours in (a)–(d) indicate the JFM climatology of U200.

The middle panels of Fig. 2 show the regression maps of transient-eddy momentum flux at 200 hPa (uυ′200), a diagnostic for eddy–mean flow interaction. Concerning the NAO (Fig. 2d), the anomalous momentum carried by synoptic-scale eddies appears key in shaping the associated circulation and precipitation patterns (Fig. 2f): the equatorward flux of westerly momentum (blue shading) in the exit region of the North Atlantic eddy-driven jet, with convergence of eddy momentum flux around 35°N, is consistent with the meridional displacement of the jet exit. The storm tracks are also shifted equatorward so that the synoptic disturbances tend to be diverted toward southern Europe, leading to the wet–dry dipole in precipitation typical of the NAO (Fig. 2f). In the case of ENSO, anomalous transient-eddy activity accompanies the large-scale impact on the North Pacific atmospheric circulation linked to the Rossby wave train [Fig. 2c; see Trenberth et al. (1998) for review]. In contrast to the NAO, the exit of the North Atlantic jet is not affected (Figs. 2a,c), leading to nonsignificant impacts on European precipitation (Fig. 2e; e.g., Mariotti et al. 2002).

2) Vertical cross sections

As noticed earlier, the ENSO-related negative anomaly in Z200 in the NAE is centered over the eastern United States (around 80°W; Fig. 1a). To explore the relationship between this center of action and the anomaly of the same sign at surface, centered eastward at roughly 50°W, we first examine the vertical structure of the anomalous geopotential height field (Z). A height–longitude cross section averaged over 30°–40°N is examined, consistent with the approximate location of the two centers of action; the linear regression onto the N3.4 index (Fig. 3a) shows that the surface and upper-level negative anomalies are part of the same vertical structure, with three main features: a limited longitudinal extent, as opposed to the NAO, which shows a broader structure (Fig. 3b); a well-defined maximum around 200 hPa, as expected for a forced Rossby wave train (e.g., Ambrizzi and Hoskins 1997); and a westward tilt with height. This tilt is a well-known aspect of vertically propagating large-scale Rossby wave trains (e.g., Lau 1979; Hsu and Wallace 1985), but in the context of the ENSO–NAE teleconnection it has barely been addressed (García-Serrano et al. 2011), despite being an important dynamical feature of this teleconnection. Figure 3c shows the regression of υ*T* (where the asterisks denote deviations from the zonal mean; see section 2) in the same cross section: an anomalous positive heat flux collocated with the tilted geopotential height anomaly dominates the signal in the ENSO case, consistent with the westward tilt with height (e.g., Vallis 2006). Unlike for ENSO, the NAO υ*T* anomalies are stronger close to the surface rather than at upper levels (Fig. 3d), which is consistent with an NAO-related change in the baroclinic region of eddy genesis (e.g., Vallis and Gerber 2008; Gerber and Vallis 2009). Additionally, as expected from the horizontal maps of Fig. 1, the NAO geopotential height anomalies (Fig. 3b) show a wider structure and less westward tilt with height than their ENSO counterpart (Fig. 3c). Note that the well-known equivalent barotropic structure of the NAO is not readily apparent in this cross section due to the southwest–northeast orientation of the anomalies in the North Atlantic (cf. the negative center in Fig. 1b with the parallels at 30° and 50°N), but is revealed by recomputing the cross section along the latitude of maximum Z200 anomalies (as shown in Fig. A3).

Fig. 3.

As in Fig. 1, but for height–longitude profiles of (top) geopotential height and (bottom) υ*T* averaged over 30°–40°N.

Fig. 3.

As in Fig. 1, but for height–longitude profiles of (top) geopotential height and (bottom) υ*T* averaged over 30°–40°N.

b. Simulated teleconnections

1) Forced and internal variability in the models

The SST-forced variability in the two models is examined by considering the ensemble mean of the AMIP-like 10-member simulations. Figures 4c and 5c show the leading EOFs of the ensemble-mean SLP in SPEEDY and ERA-20CM, respectively, north of 20°N; that is, the patterns maximizing the SST-forced variance of SLP in the Northern Hemisphere. The associated fraction of explained variance is 44.9% for SPEEDY, and 47.7% for ERA-20CM. The spatial patterns are, for both models, reminiscent of the canonical ENSO teleconnection in the extratropics: the black marks help the eye spot the similarity, as they indicate the approximate location of the main centers of action in the observational teleconnection (Fig. 1c). Both models show a signal in the North Pacific indicative of a strengthening of the Aleutian low, and negative anomalies in the North Atlantic approximately at the location of the midlatitude center of action that is part of the observed ENSO–NAE dipole, with amplitudes that are also comparable. There are, however, some differences in shape and extent. In particular, the negative SLP signal in SPEEDY is at a maximum farther into the eastern North Atlantic, whereas the positive anomalies at high latitudes are weaker and more confined with respect to observations; the opposite happens in the polar region in ERA-20CM (cf. Fig. 1c with Figs. 5c and 6c). These differences between models and observations are likely due to imperfect formulations and biases in the models, although it has to be acknowledged that substantial uncertainty resides in both in situ and reanalysis data. However, internal atmospheric variability may dominate any discrepancy.

Fig. 4.

Forced and internal variability in SPEEDY (JFM; 1901–2014). (a) Linear regression of ensemble-mean Z200 anomalies onto the first PC of ensemble-mean SLP north of 20°N. (b) Linear regression of residual Z200 anomalies onto the first PC of residual SLP in the NAE domain. (c) EOF1 of ensemble-mean SLP north of 20°N. (d) EOF1 of residual SLP in the NAE domain. Contours indicate statistically significant areas at 95% confidence level. The × and ⋆ symbols show the locations of the ENSO teleconnection in NOAA-20CR (see Fig. 1).

Fig. 4.

Forced and internal variability in SPEEDY (JFM; 1901–2014). (a) Linear regression of ensemble-mean Z200 anomalies onto the first PC of ensemble-mean SLP north of 20°N. (b) Linear regression of residual Z200 anomalies onto the first PC of residual SLP in the NAE domain. (c) EOF1 of ensemble-mean SLP north of 20°N. (d) EOF1 of residual SLP in the NAE domain. Contours indicate statistically significant areas at 95% confidence level. The × and ⋆ symbols show the locations of the ENSO teleconnection in NOAA-20CR (see Fig. 1).

Fig. 5.

As in Fig. 4, but in ERA-20CM (JFM; 1901–2010).

Fig. 5.

As in Fig. 4, but in ERA-20CM (JFM; 1901–2010).

Fig. 6.

Linear regression of JFM ensemble-mean SST anomalies onto the first PC of ensemble-mean SLP north of 20°N in (a) SPEEDY (1901–2014; Fig. 4c) and (b) ERA-20CM (1901–2010; Fig. 5c). Contours indicate statistically significant areas at 95% confidence level.

Fig. 6.

Linear regression of JFM ensemble-mean SST anomalies onto the first PC of ensemble-mean SLP north of 20°N in (a) SPEEDY (1901–2014; Fig. 4c) and (b) ERA-20CM (1901–2010; Fig. 5c). Contours indicate statistically significant areas at 95% confidence level.

The fact that ENSO dominates the SST-forced variability in the Northern Hemisphere is confirmed by examining the corresponding anomalies in the upper troposphere, here illustrated by regressing the ensemble-mean Z200 onto the leading principal component of ensemble-mean SLP (Figs. 4a and 5a). The resulting maps strongly resemble the ENSO response (Fig. 1a) with the highs and lows closely reproducing the location of the observed ENSO-induced wave train (cf. black marks). In in the NAE region, the negative center over the western North Atlantic is accompanied by a second one farther east, toward Europe (more evident in ERA20-CM, present but not significant in Fig. 1a); this secondary center of action has been suggested to be the result of a wave train split (García-Serrano et al. 2011) or the signature of a pathway involving the stratosphere (Cagnazzo and Manzini 2009). We can verify that ENSO is indeed responsible for this boundary-forced variability by examining the SST patterns associated with the SLP principal components. The distinctive signature of ENSO in the tropical Pacific is evident in the regression maps of Fig. 6, together with well-known ENSO signals in other basins, such as the warming of the Indian Ocean and parts of the subtropical Atlantic [e.g., see Alexander et al. (2002) for a review]. Finally, the connection between these SST-forced EOFs and ENSO is confirmed by the correlation of the SLP principal components with the N3.4 index: 0.87 for SPEEDY and 0.82 for ERA-20CM.

We now focus on the internally generated variability, as described in section 2c. The first EOF of the residual SLP over the NAE is the regional mode explaining the largest fraction of internal variance (47.8% for SPEEDY and 43.7% for ERA-20CM), and the associated patterns are shown in Figs. 4d and 5d. In the North Atlantic, the similarity with the dipolar signature of the observed NAO is clear (cf. Fig. 1d), with minor differences in location. In both models, anomalies of smaller amplitude in phase with those in the Atlantic basin, absent in the observational NAO (Fig. 1d; see also Fig. A1d), are present in the North Pacific, possibly related to model biases in the local atmospheric circulation (at least in SPEEDY, which overestimates the eddy activity there; see García-Serrano and Haarsma 2017). Similar remarks apply to Z200 (Figs. 4b and 5b), as in both cases the patterns strongly resemble the upper-level, circumglobal signature of the observed NAO (cf. Fig. 1b).

2) Skill

We complement the analysis by evaluating the predictive skill of the ensemble means in capturing observed variability, using NOAA-20CR as reference. In both models, the ACC (i.e., correlation between reanalysis and ensemble-mean anomalies; see section 2) maps of Z200 show areas of moderate to high skill (0.4–0.7) in the North Pacific and North Atlantic (Fig. 7, top panels) that can be attributed to the Rossby wave train associated with ENSO: the regions with higher values approximately correspond to the centers of action in the SST-forced patterns (Figs. 4a and 5a). The skill is more modest at the surface (bottom panels in Fig. 7); still, both models show significant SLP skill in the eastern North Pacific and western North Atlantic at midlatitudes. The SST-forced variability that is present at high latitudes is masked by the large total variability, leading to poor, not significant skill: indeed, the standard deviation of SLP has well-known maxima at high latitudes over the North Pacific, the North Atlantic, and the Siberian coast, a feature that is reproduced by the models (Figs. 8c,d). In other words, the signal-to-noise ratio is low at high latitudes, while it is high enough in midlatitudes to allow for some predictability and significant skill. To estimate how much of this midlatitude skill is ENSO-related, we remove the linear contribution of ENSO by using the residuals of the linear regression onto the N3.4 index to recompute the ACC of SLP. Without ENSO, the skill drops in most regions (Figs. 8a,b). The North Atlantic is left with no significant values, except for a small region around Newfoundland (slightly more extended in SPEEDY), approximately corresponding to the node of the ENSO-related and NAO dipolar patterns (Figs. 4c,d and 5c) and to a relative minimum in total variability (Figs. 8c,d). Some ENSO-unrelated skill is also found in the subtropical North Atlantic, which may indicate predictability originating from the tropical Atlantic. The SLP skill that is present at midlatitudes in the North Atlantic, at least in the western and central parts of the basin (Figs. 7c,d), is therefore likely related to the tail of the ENSO-forced wave train and its westward tilt with height discussed in section 3a.

Fig. 7.

Anomaly correlation coefficient (ACC) maps of (a) Z200 and (c) SLP ensemble-mean detrended anomalies in SPEEDY with respect to NOAA-20CR (JFM; 1901–2014). (b),(d) As in (a),(c), but in ERA-20CM (1901–2010). Contours indicate statistically significant areas at 95% confidence level.

Fig. 7.

Anomaly correlation coefficient (ACC) maps of (a) Z200 and (c) SLP ensemble-mean detrended anomalies in SPEEDY with respect to NOAA-20CR (JFM; 1901–2014). (b),(d) As in (a),(c), but in ERA-20CM (1901–2010). Contours indicate statistically significant areas at 95% confidence level.

Fig. 8.

(a),(b) As in Figs. 7c and 7d, respectively, but using the residual anomalies of the linear regression onto N3.4. (c),(d) Interannual standard deviation of SLP across all 10 members in SPEEDY and ERA-20CM, respectively.

Fig. 8.

(a),(b) As in Figs. 7c and 7d, respectively, but using the residual anomalies of the linear regression onto N3.4. (c),(d) Interannual standard deviation of SLP across all 10 members in SPEEDY and ERA-20CM, respectively.

It has to be acknowledged that some skill discussed here may arise from SST anomalies that are in part driven by atmospheric processes and would not necessarily be predictable in a coupled framework; hence, it may not translate into actual predictability.

4. Summary and discussion

In the first part, we compared the three-dimensional structure of the observed late-winter (JFM) ENSO and NAO atmospheric anomalies in the Northern Hemisphere, the starting point of the study being the similarity between the surface signature of ENSO and the NAO over the NAE sector (Figs. 1c,d). A linear approach with reanalysis data spanning the twentieth century reveals that this similarity is limited to the surface and does not extend to the upper troposphere: not only are the regression maps of Z200 onto the N3.4 and NAO indices distinct in their spatial structure (Figs. 1a,b), but more importantly the patterns suggest different mechanisms involved. The anomalous centers in SLP associated with ENSO are linked to the well-known Rossby wave train crossing the NPA sector. A nonlinear approach, such as the use of separate composites for El Niño and La Niña, provides similar results concerning the ENSO–NAE teleconnection (not shown), in agreement with Deser et al. (2017) and Garfinkel et al. (2019), who found no significant nonlinearities in SLP for DJF, and Ayarzagüena et al. (2018), who assessed linearity in JF. In contrast, the upper-level circulation anomalies associated with the NAO display a more zonally symmetric pattern reminiscent of the circumglobal waveguide pattern, which is linked to the zonal propagation of disturbances trapped in the westerly jet (Branstator 2002; García-Serrano and Haarsma 2017). These patterns related to ENSO and the NAO are not new, but in this context they provide clear evidence that the two teleconnections are widely different.

The use of transient-eddy momentum fluxes highlights further differences. The anomalous uυ′200 associated with the NAO (Fig. 2d) strongly affects the circulation over the North Atlantic and Europe, influencing the exit of the eddy-driven jet and displacing the preferred meridional location of the storm tracks, in line with the notion that the NAO is tightly related to the variability of the North Atlantic jet (e.g., Vallis and Gerber 2008; Gerber and Vallis 2009). The comparison with the corresponding ENSO pattern reveals weaker transient-eddy convergence acting closer to the core of the eddy-driven jet, rather than affecting the storm tracks (Fig. 2c). Thus, synoptic-scale systems and their two-way interaction with the climatological flow appear to be a fundamental aspect of the NAO, but are relatively minor actors in the ENSO–NAE teleconnection. This essential difference is also reflected in the precipitation patterns associated with the two modes (Figs. 2e,f): the shift in the North Atlantic storm track due to anomalous eddy activity accounts for the wet–dry dipole over Europe typical of the NAO [see Hurrell et al. (2003) and Hurrell and Deser (2009) for reviews]. In contrast, the lack of significant ENSO-related precipitation anomalies over Europe is consistent with the minor impact ENSO has on the regional storm track.

The notion that ENSO and the NAO are independent, with the latter largely encompassing internal variability, is supported by the results obtained with two ensembles of AGCM simulations used to separate SST-forced and internally generated variability. The main assumption is that the ensemble mean retains the forced atmospheric component arising from the imposed, interannually varying SSTs. In practice, some internal variability is probably still present, given the relatively small ensemble size (10 members) and the high level of stochastic noise in the NAE region (e.g., Deser et al. 2017). For clarity, we stress that in ERA-20CM the prescribed forcing also features sea ice concentration (unlike in SPEEDY) and that the prescribed SST field is itself an ensemble, accounting for observational uncertainty. There are no other assumptions concerning the forced component or the source of the signals: ENSO is not a priori singled out. However, the leading SST-forced component in the Northern Hemisphere circulation appears to be strongly related to ENSO: first, it shows similarities with the observed ENSO teleconnection, at the surface and particularly in the upper troposphere (cf. the left panels of Figs. 4 and 5 with Figs. 1a,c); second, its signature in the SST field projects on that of ENSO; finally, there is a strong temporal correlation with the N3.4 index. Our results are consistent with the recent work by Zhang et al. (2016), who used a similar approach to revisit and study the forced atmospheric teleconnections in an AGCM with 50 members; from their analysis, based on the 500-hPa geopotential height (DJF) for the period 1979–2014, the first boundary-forced EOF mode in the Northern Hemisphere corresponds to the linear, symmetric response to ENSO and its PC is highly correlated with the Niño-3.4 index (0.9), thus describing the same canonical teleconnection of ENSO addressed here.

On the other hand, the analysis carried out using the residuals (right panels of Figs. 4 and 5) shows that the NAO arises from internal atmospheric processes and that boundary forcing does not play a key role in driving the NAO at interannual time scales. The comparison between the SST-forced and internally generated patterns in two very different models supports the hypothesis that the atmospheric responses to ENSO and the NAO emerge from separate dynamics and are not physically linked. This idea was already suggested by several previous works: van Oldenborgh et al. (2000), Giannini et al. (2001), Czaja et al. (2002), Handoh et al. (2006), and Hu et al. (2013), among others.

Examining the forced variability in the two models allows for some interesting considerations on the canonical ENSO–NAE teleconnection. The two AGCMs are widely different in spatial resolution, parameterizations, and overall complexity, and most importantly in how they treat the stratosphere: ERA-20CM features a fully resolved stratosphere with 91 model levels, while in SPEEDY only two levels crudely represent the lower stratosphere. Despite that, they both capture the surface signature of ENSO in the Euro-Atlantic sector reasonably well (Figs. 4c and 5c). This result shows that the tropospheric pathway for the teleconnection is properly reproduced in both models (Figs. 4a and 5a) and suggests that the stratosphere may not play a major role in forcing the canonical signal, although further investigation is needed.

Finally, an assessment of the models’ skill in capturing observed variability shows results in agreement with estimates of potential predictability by Kumar et al. (2005). In addition, it emerges that the large-scale wave train associated with ENSO, and in particular its tail and vertically tilted structure, may account for the modest but significant skill in the western-central North Atlantic at midlatitudes.

5. Conclusions

The main objective of this work was to understand if the similarity between the dipolar pattern of SLP anomalies in the NAE associated with ENSO in late winter (JFM) and the surface NAO signature is indicative of other common aspects and possibly of a relationship between these two phenomena.

Our conclusion is that the late-winter ENSO–NAE teleconnection is dynamically distinct from the NAO, with differences in terms of both signatures and involved mechanisms. Considering the upper-level dynamics is crucial, since the contrast already emerges between the ENSO-induced arching wave train propagating northeastward, with no dipole over the Atlantic basin, and the circumglobal, more zonally symmetric perturbations related to the NAO. The differences are evident in other fields and diagnostics, such as transient-eddy momentum flux, meridional eddy heat flux, and precipitation, indicating the need to go beyond the SLP anomalies when characterizing the ENSO response in the NAE sector.

Thus, a more general term such as “dipole-like” or simply “dipole over the Atlantic” should be preferred to the widely used “NAO-like” when discussing the “canonical” winter surface signature of the ENSO–NAE teleconnection. We also suggest that the relative amplitudes of Aleutian low anomalies and dipolar anomalies in the North Atlantic may be used as a simple metric to interpret whether anomalous dipolar structures in the NAE region are related to the ENSO teleconnection or, instead, to the hemispheric signature of the NAO.

Finally, we highlight that ENSO, which is shown to dominate the forced variability in the Northern Hemisphere, contributes to the North Atlantic midlatitude predictability at the surface.

Acknowledgments

This work received funding from the Spanish MINECO-funded DANAE project (CGL2015-68342-R). B.M. and J.G.-S. were supported by the “Contratos Predoctorales para la Formación de Doctores” (BES-2016-076431) and “Ramón y Cajal” (RYC-2016-21181) programmes, respectively. Technical support at BSC (Computational Earth Sciences group) is sincerely acknowledged. The authors are thankful to the two anonymous reviewers for their comments, which helped to improve the clarity of the manuscript.

APPENDIX

Additional Results

As mentioned in the main text (section 2a), the analysis carried out with NOAA-20CR was repeated using ERA-20CR. Figure A1 shows the regression maps onto the N3.4 and NAO index of SLP and Z200, to be compared with Fig. 1. In Fig. A2, the corresponding patterns in the lower stratosphere (30 hPa) from the two datasets are compared. As both reanalyses only assimilate surface data, they should be used with caution above the tropopause (Fujiwara et al. 2017). NOAA-20CR, in particular, has coarser vertical resolution with respect to ERA-20CM (L28 vs L91) and is known to be affected by strong biases in the stratosphere (Compo et al. 2011).

Fig. A1.

As in Fig. 1, but using ERA-20CR (1901–2010).

Fig. A1.

As in Fig. 1, but using ERA-20CR (1901–2010).

Fig. A2.

(a),(b) Regression map of Z30 anomalies in JFM using NOAA-20CR (1901–2014) onto the N3.4 and NAO index, respectively. (c),(d) As in (a),(b), but using ERA-20CR (1901–2010). Contours indicate statistically significant areas at 95% confidence level.

Fig. A2.

(a),(b) Regression map of Z30 anomalies in JFM using NOAA-20CR (1901–2014) onto the N3.4 and NAO index, respectively. (c),(d) As in (a),(b), but using ERA-20CR (1901–2010). Contours indicate statistically significant areas at 95% confidence level.

Figure A3a shows the NAO vertical structure of geopotential height anomalies along the path of latitudes corresponding to the maximum values in the regressions of Z200 (shown in Fig. A3b), as discussed in section 3a(2).

Fig. A3.

(a) As in Fig. 3b, but instead of averaging over 30°–40°N we consider cross sections at varying latitudes along (b) the path that marks the maximum (negative) values in the regression of Z200 onto the NAO index.

Fig. A3.

(a) As in Fig. 3b, but instead of averaging over 30°–40°N we consider cross sections at varying latitudes along (b) the path that marks the maximum (negative) values in the regression of Z200 onto the NAO index.

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Footnotes

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