a. Dipole Mode and wind variance
Analysis of climate patterns is often done using mean fields anomalies. However, here we propose a further analysis to better understand the influence of the surface wind on SSTA, which does not define a static configuration of the anomalous atmospheric circulation as the mean wind does. In this case, the anomalies of the STD of the zonal and meridional winds can provide a more dynamic approximation, one that does consider the disturbances that affect the mean wind and hence facilitates the physical interpretation of the coupling patterns. In this way, low values in the STD fields would indicate a predominance of the mean wind over the wind-SST coupling. On the contrary, higher values of the STD fields indicate that the wind speed or direction is more variable and, thus, the mean value of the wind is less representative, and its variations are the ones that exert a greater influence on the wind-SST coupling.
In accordance with the above, we conducted a MCA using the interannual anomalies of SST and the STD of both wind components. The MCA leading mode explains 31% of the covariance between SSTA and wind STD anomalies. The heterogeneous regression maps reveal that positive anomalies of the wind STD (Fig. 1a-b) are coupled with a SSTA pattern analogous to the DM (Fig. 1c), which was previously obtained through the CCA using the mean wind anomalies (RV19). Therefore, the SSTA dipole is very consistent and robust, as it emerges from both the CCA, using the mean wind anomalies, and the MCA, using the anomalies of the wind STD. The wind STD anomaly fields (Fig. 1a-b) show an increase in wind variability over almost the entire domain, in phase with years having a positive value of the expansion coefficient. In the GM, this greater wind variability is mainly related to anomalies of the zonal wind variance (Fig. 1a), while in the CS an increase in the variability of the meridional wind predominates (Fig. 1b).
The corresponding expansion coefficients for SST and wind STD of the MCA leading mode are shown in Fig. 1d. They exhibit a significant correlation of 0.80, suggesting a strong coupling. The MCA Index consists of the average between this pair of time series (like Handoh et al. 2006). The interannual variability of the MCA and Dipole indices are also comparable (Fig. 1d), with a significant correlation of 0.85. Both the MCA and the Dipole indices can be considered as a very accurate representation of the Dipole Mode. We verified that the analyzes carried out using the Dipole Index of RV19 or the MCA Index yield very similar results to each other and reflect the same mode of variability, so the interpretation and discussions apply to both cases. Hence, in the subsequent figures we show the results obtained using the Dipole Index of RV19 to facilitate comparison with the previous work.
Anomaly composites of the mean wind field have a strong signal in the CLLJ region, indicating that the DM is closely related to the interannual variability of this important wind system in the CS, where the mean wind speed reduces (intensifies) during the positive (negative) DM (Fig. 2). The MCA further indicates that this is associated with an increase (decrease) in atmospheric variability reflected mainly in the meridional wind. In other words, greater instability in years with a DM positive (negative) phase leads to a decrease (increase) of the persistence of the easterly winds and warming (cooling) in the CS. On the other hand, for the GM the increase (decrease) of zonal and meridional winds variability coincides with a colder (warmer) SST (Fig. 2). Northwesterly wind anomalies cover most of the GM in the positive phase of the DM; but, in general, the mean wind speed does not exhibit significant anomalies, except in the northeast of the GM, the Florida Peninsula and adjacent Atlantic (Fig. 2). This implies that the mechanisms involved in air-sea coupling at the CS differ from those at the GM. For the negative phase of DM, the SSTA pattern appears to be less marked and less significant, except in the northernmost Atlantic region of the domain (Fig. 2). In the following subsections, we will explore the antecedent conditions, some large-scale modulators, and the physical mechanisms involved in this ocean-atmosphere coupling.
b. Evolution of the Dipole Mode and atmospheric drivers
The mean life cycle of the DM from the previous winter to next summer is shown in Fig. 3 through the wind and SST anomalies associated with the Dipole Index. The DM seems to be strongly linked to the SSTA variability in the equatorial Pacific during the boreal winter and to subsequent warming of the TNA. This is consistent with earlier studies that pointed out the increase of the TNA SST about three to five months after a mature phase of El Niño event, in combination with a decrease of the trade winds speed and the surface heat fluxes release from the ocean (Curtis and Hastenrath 1995; Enfield and Mayer 1997; Saravanan and Chang 2000; Chiang and Sobel 2002; Wang and Enfield 2003). The weakening of the low-level winds in the TNA leads to a decrease in the cooling of the surface by evaporation and upwelling due to the advection of the surface water, resulting in positive SSTA, and conversely. The anomalous wind pattern in the North Atlantic from peak winter through spring suggests a connection with the NASH circulation. Moreover, from JFM to AMJ the cold SSTA in the GM and the subtropical North Atlantic in conjunction with the warming in the TNA and the subpolar North Atlantic (above 40°N) allude to the SST tripole associated with NAO (Desser et al. 2010). Hence, the distribution of wind-SST anomalies associated with DM, especially in CS, suggests that these are linked to a larger scale coupled pattern that comprises the entire TNA since late winter and persists for several months after the mature DM.
The large-scale nature of the DM triggers is also evident in the temporal evolution of the correlations of GPH anomalies at different pressure levels with the Dipole Index (Fig. 4). Since the peak winter, a trough-shaped pattern stands out in the negative correlations, which connects two cores of maximum correlations, on both sides of the North American continent, one in the North Pacific around 40°N and the other in the North Atlantic around 30°N approximately. This pattern reinforces, in effect, the suggestion of a link with the north belt of high subtropical pressures, especially with the NASH, and possibly with the descending branch of the Hadley cell. The negative correlation pattern is bounded by positive correlations to the north side and a wide zone in the middle and upper atmosphere around the region between 20°N and 20°S.
During DJF through FMA, in the CS region, positive correlations are observed between the Dipole Index and the GPH anomalies at the middle and upper troposphere, whereas near the surface the correlations are negative (Fig. 4). Such correlation patterns over the CS correspond to GPH anomalies that have an impact on the vertical wind shear, so that for the positive DM the wind shear decreases, which is consistent with the wind speed reduction and the westerly wind anomalies along with the surface warming in the CLLJ region (Figs. 2–3). The opposite would apply for the negative DM. Similar anomalies in the vertical GPH distribution, and therefore in the wind shear, associated with warmer/colder TNA SST zones in February, were obtained by Maldonado et al. (2017), with an impact on the tropical convection and the Central American precipitation during May-June, the early rainy season. Meanwhile, the GPH negative correlations across the vertical column over the GM, suggest that the physical mechanisms in the GM involved in the ocean-atmosphere coupling that triggers the DM are different from those in the CS (Fig. 4).
The GPH anomaly correlations described above continue during MAM (not shown), and by AMJ although the trough-shaped pattern disappears, the bands of positive correlation at the middle and upper troposphere and the negative correlation near the surface still prevail (Fig. 4). The patterns of wind and SST anomalies on the TNA also tend to remain after spring (Fig. 3). In other words, the persistence of these GPH, SST, and winds anomaly signals for several months suggests that the DM variability might be of importance to modulate climate anomalies into CS&GM and adjacent continent throughout the next summer. It has been proposed that TNA SSTA interannual variability can be used as a good predictor for the precipitation anomalies at the early rainy season in the CS and Central America (Taylor et al. 2002; Alfaro 2007; Maldonado et al. 2017). Recently, Martinez et al. (2020) pointed out that dry early rainy seasons in the CS are strongly associated with a preceding winter-spring SSTA pattern compatible with the negative phase of the DM, positive sea level pressure anomalies over the TNA, and a positive NAO. The inverse signals are expected to precede the wet early rainy seasons.
Heretofore, the results show sufficient evidence about the importance of the preceding winter atmospheric circulation for DM development. The antecedent winter conditions are explored in more detail using the GPH composites for the positive and negative phases of the DM at 500hPa (Fig. 5). In these panels, a zone of significant anomalies into the North Pacific connected with the southwest flank of the NASH highlights, which is consistent with the two cores of maximum correlations on both sides of North America previously mentioned (Fig. 4). Anomalies associated with the North Pacific core are more significant for the positive than for the DM negative phase. This denotes that the interannual variability of the Pacific side during the previous winter takes greater importance for triggering the positive DM. The significant GPH anomalies associated with the Atlantic core are opposite in sign between the extreme phases of the DM so that negative (positive) GPH anomalies over the CS&GM are expected in the winter before the positive (negative) DM (Fig. 5).
Mean conditions at 850 hPa do show a weak (strong) and less (more) expanded NASH towards GM during winter (not shown) before the positive (negative) DM, and these patterns persist into springtime (Fig. 6). Significant changes in the NASH intensity and extension involve strengthening/weakening of the meridional pressure gradient between the tropics and subtropics. This may explain the response of the North Atlantic trade winds as gleaned from Fig. 3, and the anomalies in wind speed observed over the CS during the DM extreme phases (Fig. 2). It is argued that weak (intense) trade winds are associated -through hydrostatic processes such as mixing and evaporative heat lost- with warmer (colder) SST and lower (higher) atmospheric pressures (Curtis and Gamble 2016). Figures 2, 3, and 6 corroborate that these wind-SST-pressure relations are valid for the CS domain, and could imply the deceleration (acceleration) of the warm water advection towards the GM, which also weakens (intensifies) the upwelling in the CS. In turn, the strengthening and westward extension of the NASH has an indirect impact on SSTA of the GM, via counteracting the entry of cold and dry continental air into this region (Reding 1992). This argument agrees with the changes in the wind variance estimated by the MCA such that during the DM positive phase a weakened NASH over the region coincides with greater wind STD, which could be associated with the more frequent passage of mid-latitude systems. A reverse situation occurs during the DM negative phase.
Although the NASH exerts a high control in the climate of the CS&GM, the north-south migration and frequency of cold air masses that reach such latitudes are closely modulated by the location of the subtropical jet stream (Schultz et al. 1998). Patterns of jet streams for the DM positive and negative phases show very different pictures from each other (Fig. 7). During the positive phase, there is a significant increase in the subtropical jet core frequency around 25°N above Mexico, GM and Florida, and a weakened double jet structure compared with the mean conditions (Fig. S1d). During the negative phase, the jet core is more frequently split into two branches, one over Northeast America north of 40°N and the other over the Atlantic Ocean along the 20°N vicinity.
The panels of significant anomalies in the jet core frequency (second row in Fig. 7) show a marked latitudinal shift of the jet branch that passes through North America, further south during the DM positive phase and further north during the negative phase. These changes in jet location alter prevailing wind patterns and affect the storm tracks such that they reach the southern United States and the GM more (less) frequently during the DM positive (negative) phase. The higher (lower) frequency of storm passage over the region is consistent with an increase (decrease) in wind variability and, therefore, in its STD. Also, this modification of the jet stream justifies the anomalous cooling (warming) in the GM and the smaller (greater) influence of the NASH on the CS&GM during the DM positive (negative) phase (Figs. 2 and 6).
Composites of moisture and temperature advection at 850hPa and turbulent air-sea heat fluxes are provided to support the dynamical analysis (Fig. 8). Latent heat flux is dominant compared to sensible heat flux, and both show significant changes only for the positive phase of DM. The atmosphere at low levels also presents a dipole pattern of significant anomalies in specific humidity and temperature for the DM positive phase, and an opposite pattern in humidity is reflected in the negative phase (Fig. S2). Apparently, in the negative DM, the air-sea differences are not sufficient to reflect a significant change in the turbulent fluxes. On the other hand, the radiative fluxes for the positive DM tend to oppose between them, and they have a negligible impact during the negative DM (Fig. S2). Thus, the net radiative heat flux (shortwave plus longwave radiation) plays a minor role in the SSTA associated to the DM pattern.
The bulk formula for the turbulent fluxes depends on the wind speed and the air-sea difference in temperature and specific humidity (Enfield and Mayer 1997; Alexander and Scott 2002). The nature of the air-sea coupled anomalies in the CS associated with the DM positive phase seems to be quite comparable to that of the deep tropics, where the wind forcing plays a dominant, but not exclusive, role in the generation of heat fluxes via evaporation, and consequently leading to SSTA (Seager et al. 2000; Mahajan et al. 2010). This mechanism can be explained by the wind, evaporation, and SST feedback (e.g., Xie and Philander 1994). The wind composites highlight the sensitivity of this field in the CS to the phase change of the DM (Fig. 2), and these wind anomalies can be strongly modulated by the interannual variability of the NASH (Fig. 6). In contrast, in the GM the heat loss that occurs in the positive DM seems to respond more to the influence of continental air masses that have an impact on the specific humidity and temperature difference between the atmosphere and the ocean. This is confirmed by the temperature and moisture advection anomalies over the GM (Fig. 8). During the DM positive phase, dry and cold air advection take place in the GM and southern United States; and during the negative phase the pattern is opposed, but the anomalies are significant in a smaller area.
Since the springtime SSTA dipole in the Intra-Americas Seas and the coupled DM have been associated with the variability of NAO and ENSO in previous winter (e.g., Alexander and Scott 2002; Muñoz et al. 2010; RV19), in the next section we will further analyze the relationship between both large-scale patterns and the coupled DM signal.
c. Dipole Mode teleconnections
Table 1 summarizes the maximum correlations (R) of the MCA and the Dipole indices with previous NAOI and ONI, as well as with the TNAI for the following months. As mentioned above, the results using both the MCA and the Dipole indices are very consistent with each other. The DM showed maximum correlations, in absolute values, with the previous winter NAOI, specifically for the JFM quarter. Unlike the results discussed in the study on the SSTA dipole by Muñoz et al. (2010), this work shows a stronger DM connection with NAO than with ENSO (Table 1). This may be due to the fact that the DM is an air-sea coupled pattern (it includes coupled surface wind anomalies along with the CS&GM SSTA).
Table 1
Correlations between the Dipole Index, the MCA Index, the March-April timeseries of the SSTA in the CS and GM (boxed areas defined in Fig. 1c), and the zonal wind (U) in the CS with the indices NAOI, ONI, and TNA for the specified quarters. The correlations calculated considering only the positive and negative DM years are in parentheses. Only results above 95% confidence level are shown.
|
March-April
|
JFM NAOI
|
JFM ONI
|
MJJ TNAI
|
|
Dipole Index
|
-0.73 (-0.92)
|
0.58 (0.75)
|
0.64 (0.78)
|
|
MCA Index
|
-0.67 (-0.84)
|
0.54 (0.75)
|
0.58 (0.80)
|
|
CS SSTA
|
(-0.76)
|
0.75 (0.80)
|
0.40 (0.61)
|
|
GM SSTA
|
0.65 (0.77)
|
(-0.55)
|
-0.42 (-0.61)
|
|
CS U
|
-0.47 (-0.82)
|
0.40
|
0.61 (0.77)
|
In addition to the NAO and ENSO, the PNA teleconnection pattern was considered, given the significant GPH anomalies found around its origin region, associated with the DM development (Figs. 4–5). Thereby, the correlations of the Dipole Index were found significant only with the March (R = 0.69) and April (R = 0.63) PNAI, but these do not provide much predictive skill for the DM. Although the PNA is a natural internal mode of climatic variability, it is also strongly influenced by ENSO, so that the positive phase of the PNA pattern tends to be associated with El Niño, and the negative phase tends to be associated with La Niña (Trenberth et al. 1998; Hurrell 2003). Thus, the correlation of the Dipole Index with PNAI agrees with previous knowledge since it presents the same sign as the correlation with ONI.
The positive correlation of MCA Index with ONI is consistent with the results for the coupling with the wind STD (Fig. 1), since the positive (negative) phase of ENSO has been associated with an increase (decrease) in the frequency of the mid-latitude systems that cross the CS&GM region, such as cold surges and migratory high pressures (e.g., Hernández 2002; Romero-Centeno et al. 2003). In other words, El Niño contributes to an increase in spring wind variability over the CS&GM region that favors the development of the DM positive phase. On the contrary, the significant negative correlation found with the NAOI suggests that a positive (negative) phase of NAO would be favoring a decrease (increase) in this atmospheric variability; namely, greater (less) stability and persistence in the magnitude and direction of the trade winds -highly consistent with the NASH position and intensity (Wang 2007)-, supporting the development of the negative (positive) DM.
For a deeper understanding, analogous correlations were computed for the SST and wind components anomalies, separately, and for CS and GM. Significant outcomes were obtained with the SSTA in both basins and only with the zonal wind in CS (Table 1). The correlations of GM SSTA with the NAOI, ONI, and TNAI are of opposite sign to those obtained for the CS SSTA, which denotes the tendency to a dipole structure in the spring SSTA field over the CS&GM in response to large-scale forcings. Overall, considering only the years with positive and negative DM, the correlations result in higher values (inside parentheses in Table 1), confirming the high sensitivity of the DM to remote influences.
Previously, we detected that the wind-SST anomalies associated with the DM life cycle seem to form part of a large-scale pattern and could remain on the TNA for several months after spring (Fig. 3). The highest correlations of DM with the TNAI following the DM months were found for the May-June-July quarter (MJJ, Table 1), although significant positive values persist throughout the rest of the summer (not shown). The positive correlation of the CS zonal wind with the TNAI stands out, which reinforces the well-documented close relationship between the weakening (strengthening) of the trade winds and the SST warming (cooling) in the TNA (e.g., Kushnir 1994; Enfield and Mayer 1997). Such mechanism, present over most of the deep tropics, has been referred to as Wind-Evaporation-SST (WES) feedback and it is mainly governed by the impact of winds on the latent heat flux (Xie and Philander 1994; Xie 1999; Saravanan and Chang 2004; Mahajan 2008). At mid and higher latitudes, changes in the advection of air masses, that cause changes in the air-sea temperature and humidity gradients, are important too (Seager et al. 2000).
For both extreme phases of NAO and ENSO, we performed composite analyses of the anomalous wind and SST fields (Figs. 9 and 10, respectively). Composites of wind anomalies confirm that, inside the CS, the trade winds show a significant change between opposite phases of the NAO (Fig. 9), with also a significant signal in the resulting wind speed. This result agrees with Wang (2007), but it differs from Chang and Oey (2013) who found insignificant correlations and a weak influence of NAO on the interannual changes of the CS trade winds since 1980.
However, comparing the years of El Niño versus La Niña, the wind over the CLLJ region presents significant anomalies only during the El Niño phase (Fig. 9). As mentioned at the beginning of this subsection, of the wind components for the CS and GM, only the CS zonal wind anomalies showed a significant correlation with the NAO and ENSO of the previous winter, especially with the former; and the correlation with the JFM NAOI is notably higher when only the years with extreme DM are considered (Table 1). This analysis is consistent with the GM wind anomaly patterns displayed in Fig. 9, where a sufficiently robust response to the phase change of the NAO and ENSO is not observed, like that found between opposite DM phases (Fig. 2).
By contrast, the SSTA associated with the extreme phases of the NAO are more significant in the GM (in general they are above the 95% level) than in the CS, indicating that the NAO may have a greater effect on the SSTA of the former (Fig. 10). This could be partially linked to the large-scale tripole pattern in the North Atlantic SSTA or to an indirect response associated with the CLLJ variability, both induced by the NAO. Although the physical mechanisms that justify this relationship are not clear, Wang (2007) found a significant correlation between the intensity of the CLLJ in January-February and the SSTA in the GM. Also, it is known that the northward branch of the CLLJ advects moisture from the warm CS across the GM modulating climate variability in that region (Cook and Vizy 2010; Durán-Quesada et al. 2010; Garcia-Martinez and Bollasina 2020). On the contrary, the CS SSTA are more sensitive to the ENSO variability. In this region, significant SSTA completely change the sign between the extreme phases of ENSO (Fig. 10). The GM also presents significant SSTA mainly during the El Niño phase. The apparent polarized SSTA response to these teleconnection patterns at the CS and the GM is also evident in the results provided in Table 1. Nevertheless, ENSO, like the NAO, shows a significant correlation with the SSTA of both subdomains in the DM extreme years (Table 1, values in parentheses).
As illustrated in Figs. 2, 9, and 10, the spatial patterns of the wind and SST anomalies during the DM positive phase are quite comparable to those in the springs after winters of El Niño, and somewhat similar to those springs following a negative NAO, although the anomaly amplitudes are much smaller for the last one. In contrast, the patterns of the negative DM have a higher resemblance with those following a positive NAO winter, but not much with those after a La Niña winter. Figure 11 shows the dispersion of the DM phases based on the ONI and NAOI. Here, the asymmetric relation of the DM with the extremes phases of both teleconnection events is evidenced. The scatters of the positive DM are mostly located in the upper-left quadrant; therefore, this phase tends to occur after an El Niño event during the previous winter under negative NAO conditions. On the other hand, the scatters of the negative DM are concentrated around the lower and medium right sides of the graphic. Hence, the DM negative phase seems to be more sensitive to a positive NAO during the previous winter, under La Niña or neutral conditions of ENSO.
The neutral phases of DM usually occur under neutral or negative NAO in combination with or very close to La Niña conditions (Fig. 11). That is, in the absence of both, El Niño and a positive NAO, during the antecedent winter it is unlikely that an extreme phase of the DM will develop; only 3 of the 14 DM extreme phases do not meet this criterion. The distribution of the scattered points suggests that simultaneous phases of opposite signs of NAO and ENSO during the previous winter, tend to reinforce the DM anomalies. Considering the position in the graph of 10% of the most positive and negative DM cases, the link of the positive DM with El Niño and the negative DM with a positive NAO appears more robust; in both cases, the effect is strengthened by the concomitance of a negative NAO and La Niña, respectively.
Otherwise, using the FMA quarter as representative of the persistent anomaly patterns, the GPH anomaly composites for six cases were explored and compared: for DM positive and negative years, after both extreme phases of winter NAO, and after a mature El Niño or La Niña events. Figure 12 shows these six GPH composites for 500hPa. Additionally, Figs. S3 and S4 provide the same composites for 300hPa and 850hPa levels, respectively. Overall, GPH patterns for opposite phases of the DM, NAO, and ENSO present fairly specular anomalies in each case; to a lesser extent for El Niño/La Niña, considering the smaller magnitude and spatial coverage of significant anomalies above the 95% level for La Niña composites.
The GPH composites for DM in Fig. 12 exhibit again the two cores of significant anomalies on both sides of the North American continent, that were identified in Section 3b for the preceding winter (Figs. 4–5). These GPH anomaly patterns with marked changes in each DM phase from the upper to the lower troposphere would modulate the meridional pressure gradient, particularly on the equatorial side of the NASH (Figs. 12, S3, and S4). The negative (positive) GPH anomalies are associated with the weakening (strengthening) of such gradient and a consequent decrease (increase) in the intensity of the trade winds over the TNA, all of which, as it is already explained, favors the positive (negative) phase of the DM.
The GPH composites for positive DM and El Niño display very similar distributions, suggesting the strong modulator role of the equatorial Pacific warming on the DM positive event (Figs. 12, S3, and S4). These large-scale atmospheric patterns present under an El Niño event are in good accordance with many earlier studies (e.g., Alvarado et al. 2001; Ribera and Mann 2002), particularly the great asymmetry observed, at middle and upper troposphere, between the anomalies over the North Pacific at midlatitudes and those over the equatorial and tropical North Pacific. Another of the best-defined atmospheric circulation features during El Niño years is the anomalous low-pressure system located in the vicinities of the southeastern United States (Wallace y Gutzler 1981; Klein et al. 1999). In the case of the springs following La Niña events, the GPH anomaly patterns are slightly significant at the 95% confidence level (Fig. 12). This result implies a small impact of La Niña phase on the GPH variability during the boreal spring, mainly over North America and the North Atlantic basin. Therefore, unlike El Niño, La Niña does not seem decisive to modulate the DM.
Additionally, we found a great resemblance between the GPH anomaly patterns for the positive DM and negative NAO, as well as between the composites of negative DM and positive NAO (Figs. 12, S3, and S4). These pairs of cases reveal significant changes in the NASH region and emphasize that a winter NAO can trigger or enhance the tropospheric anomalies in the North Atlantic side that favor the development of the DM positive or negative phase.
Besides the above comparison of GPH anomalies, we performed a composite analysis of separated phases for the jet stream. Figures S5 and S6 present the jet anomaly patterns analogous to Fig. 7 for the DM, but for opposite phases of NAO and ENSO, respectively. Here, we also found a considerable consistency between the composites of positive DM and the spring following an El Niño event, showing a southward migration of the subtropical jet, increasing the jet core frequency at latitudes around 30°N and across the GM and subtropical North Atlantic. A pattern very similar to this but less significant is also observed for negative NAO. Thus, in addition to the modulation of the trade winds, the anomalous southward shift of the subtropical jet, with the associated alteration of the storm tracks, would be another mechanism by which the relationship between the positive DM and El Niño, and to a lesser extent with the negative NAO, is established.
However, as shown in Figure S6, La Niña does not reflect a significant change in the subtropical jet stream, but the pattern presents a configuration very close to climatology. Likewise, the positive NAO shows almost no significant changes in the jet stream, although some gridpoints with significant positive anomalies in the jet frequency are observed over latitudes between 40° and 50°N (Fig. S5). This response is not as robust as the pattern obtained for the negative DM (Fig. 7). Strictly speaking, the changes in the jet associated with the positive NAO and La Niña seem to be negligible compared with neutral conditions. Notwithstanding, the arguments discussed in this section point to that a positive NAO could contribute to the development of the negative phase of DM, through mechanisms that might involve other systems as the NASH and CLLJ. La Niña, on the contrary, does not influence or, in any case, it has a lesser impact on the development of the negative DM.