Understanding the Basin Asymmetry in Surface Response to Sudden Stratospheric Warmings from an Ocean–Atmosphere Coupled Perspective

Ying Dai aDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

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Peter Hitchcock aDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

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Abstract

The canonical tropospheric response to a weakening of the stratospheric vortex—an equatorward shift of the eddy-driven jet—is mostly limited to the North Atlantic following sudden stratospheric warmings (SSWs). A coherent change in the Pacific eddy-driven jet is notably absent. Why is this so? Using daily reanalysis data, we show that air–sea interactions over the North Pacific are responsible for the basin-asymmetric response to SSWs. Prior to the onset of some SSWs, their tropospheric precursors produce a dipolar SST pattern in the North Pacific, which then persists as the stratospheric polar vortex breaks down following the onset of the SSW. By reinforcing the lower-tropospheric baroclinicity, the dipolar SST pattern helps sustain the generation of baroclinic eddies, strengthening the near-surface Pacific eddy-driven jet and maintaining its near-climatological-mean state. This prevents the jet from being perturbed by the downward influence of the stratospheric anomalies. As a result, these SSWs exhibit a highly basin-asymmetric surface response with only the Atlantic eddy-driven jet shifted equatorward. For SSWs occurring without the atmospheric precursors in the North Pacific troposphere, the dipolar SST pattern is absent due to the lack of the atmospheric forcing. In the absence of the dipolar SST pattern and the resultant eddy–mean flow feedbacks, these SSWs exhibit a basin-symmetric surface response with both the Atlantic and the Pacific eddy-driven jets shifted equatorward. Our results provide an ocean–atmosphere coupled perspective on stratosphere–troposphere interaction following SSW events and have potential for improving subseasonal to seasonal forecasts for surface weather and climate.

Significance Statement

Stratospheric sudden warming events (SSWs) happen roughly two out of every three winters and are known to contribute to predictability and weather extremes at the surface (e.g., cold-air outbreaks, strong winds, and heavy rain). These SSW-driven high-impact extremes are mostly seen within or surrounding the North Atlantic, with a relatively small signal over the North Pacific. We therefore wanted to understand why the surface response to SSWs is muted over the North Pacific. We find that the surface response over the Pacific is weakened because of air–sea interactions that occur over the North Pacific. Our results suggest that air–sea interactions are important for forming a complete understanding of the downward influence of SSWs and for improving subseasonal predictions following SSWs.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter Hitchcock, aph28@cornell.edu

Abstract

The canonical tropospheric response to a weakening of the stratospheric vortex—an equatorward shift of the eddy-driven jet—is mostly limited to the North Atlantic following sudden stratospheric warmings (SSWs). A coherent change in the Pacific eddy-driven jet is notably absent. Why is this so? Using daily reanalysis data, we show that air–sea interactions over the North Pacific are responsible for the basin-asymmetric response to SSWs. Prior to the onset of some SSWs, their tropospheric precursors produce a dipolar SST pattern in the North Pacific, which then persists as the stratospheric polar vortex breaks down following the onset of the SSW. By reinforcing the lower-tropospheric baroclinicity, the dipolar SST pattern helps sustain the generation of baroclinic eddies, strengthening the near-surface Pacific eddy-driven jet and maintaining its near-climatological-mean state. This prevents the jet from being perturbed by the downward influence of the stratospheric anomalies. As a result, these SSWs exhibit a highly basin-asymmetric surface response with only the Atlantic eddy-driven jet shifted equatorward. For SSWs occurring without the atmospheric precursors in the North Pacific troposphere, the dipolar SST pattern is absent due to the lack of the atmospheric forcing. In the absence of the dipolar SST pattern and the resultant eddy–mean flow feedbacks, these SSWs exhibit a basin-symmetric surface response with both the Atlantic and the Pacific eddy-driven jets shifted equatorward. Our results provide an ocean–atmosphere coupled perspective on stratosphere–troposphere interaction following SSW events and have potential for improving subseasonal to seasonal forecasts for surface weather and climate.

Significance Statement

Stratospheric sudden warming events (SSWs) happen roughly two out of every three winters and are known to contribute to predictability and weather extremes at the surface (e.g., cold-air outbreaks, strong winds, and heavy rain). These SSW-driven high-impact extremes are mostly seen within or surrounding the North Atlantic, with a relatively small signal over the North Pacific. We therefore wanted to understand why the surface response to SSWs is muted over the North Pacific. We find that the surface response over the Pacific is weakened because of air–sea interactions that occur over the North Pacific. Our results suggest that air–sea interactions are important for forming a complete understanding of the downward influence of SSWs and for improving subseasonal predictions following SSWs.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Peter Hitchcock, aph28@cornell.edu

1. Introduction

While significant progress has been made in understanding the essential driving mechanisms of major sudden stratospheric warmings (SSWs) (Charney and Drazin 1961; Matsuno 1971; Schoeberl 1978; Andrews et al. 1987), the physical processes responsible for the downward influence of SSWs are still not well understood (Kidston et al. 2015). Observational studies suggest that the strongest downward coupling takes place over the North Atlantic, whereas the signal over the North Pacific is relatively small (e.g., Baldwin and Dunkerton 2001; Charlton and Polvani 2007; Kolstad et al. 2010; Sigmond et al. 2013; Hitchcock and Simpson 2014). This asymmetry between the responses over the two ocean basins in the Northern Hemisphere has been known for several decades, but the reasons for it have not been explained in any detail. Identifying the reasons for this basin asymmetry may shed light on the mechanisms responsible for the downward influence of stratospheric anomalies. Furthermore, while models do produce a surface response that is strongly localized over the North Atlantic, the Pacific sector response tends to be stronger than those seen in observations (Sigmond et al. 2013; Lehtonen and Karpechko 2016; Ayarzagüena et al. 2020). Even if the shortness of the observational record and the large internal variability suggest the need for caution when comparing observations and models, this disagreement between observed and simulated responses lowers our confidence in forecasts for surface weather provided by subseasonal and seasonal forecast systems initialized at or after the occurrence of an SSW event.

There are several potential reasons why the surface response to stratospheric warmings may differ between the Atlantic and Pacific. These may include, but are not limited to, the following four hypotheses. 1) Zonal asymmetries in the stratospheric anomalies associated with the initial disruption of the vortex at the time of the SSW may be responsible for the asymmetric surface response. However, this seems unlikely to be the case, since asymmetries in the stratosphere itself rarely persist for more than a week or two while the asymmetric surface response persists for several months (Maycock and Hitchcock 2015). 2) There are dynamical differences between the tropospheric jets over the two ocean basins that could account for the basin asymmetric surface response to SSWs. In view of the fact that the Atlantic jet is primarily eddy driven while the Pacific jet is both thermally driven and eddy driven (Lee and Kim 2003; Li and Wettstein 2012), one may reasonably expect the two jets to respond in different manners to the downward influence of SSWs. 3) Closely related to the previous hypothesis, the eddy feedbacks in the Pacific storm track may be weaker than those in the Atlantic. Some indication that this may be the case arises from the suppression of the midwinter storm track in the Pacific (Nakamura 1992; Penny et al. 2010; Schemm and Schneider 2018; Yuval et al. 2018). 4) In addition to these atmospheric internal processes, differences in “external” forcing arising, for instance, from interactions with sea surface temperature anomalies may play a role in shaping the surface response to SSWs. Interactions between the ocean and atmosphere have been known to influence the strength of stratosphere–troposphere coupling and alter the annularity of the annular mode (Ogawa et al. 2015; Omrani et al. 2019), illustrating the importance of including ocean–atmosphere coupling mechanisms in understanding the downward influence of SSWs.

In this study, we will explore the effect of ocean–atmosphere coupling on the observed basin asymmetric surface response to SSWs. The major motivation for considering ocean–atmosphere coupling mechanisms is the fact that, in the aftermath of the SSW, in addition to the well-known negative NAO pattern and the equatorward displaced Atlantic surface westerlies (Figs. 1a,b), there is a significant dipolar sea surface temperature (SST) pattern in the North Pacific Ocean with above-normal SSTs at subtropical latitudes and below-normal SSTs at subpolar latitudes (Figs. 1c,d). This dipolar SST pattern over the North Pacific, to the best of our knowledge, has not been shown or discussed in previous studies of SSWs. We will demonstrate that the air–sea interactions associated with this dipolar SST pattern and the resultant eddy–mean flow feedbacks play a prominent role in establishing the basin-asymmetric surface response to SSWs.

Fig. 1.
Fig. 1.

Composites of anomalous (a) SLP, (b) U10m, and (c) SST averaged over days [3 45] following all 38 SSW events. (d) As in (c), but using the OISST.v2 dataset and the 23 SSWs during 1981/82–2018/19 winters. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

Following this section, a description of the data and methods used in this study is given in section 2. The main results are reported in section 3, and the summary and discussion are presented in section 4.

2. Data and methodology

a. Data

We use daily data from the Japanese 55-year Reanalysis (JRA-55) dataset (Ebita et al. 2011; Kobayashi et al. 2015), which has a 1.25° × 1.25° latitude–longitude resolution and 37 pressure levels from 1000 hPa up to 1 hPa. The reason for choosing the JRA-55 is threefold: 1) JRA-55 begins prior to the satellite era, and including the presatellite period of the reanalyses can help reduce the sampling uncertainty in SSW-related studies, which is a larger source of error than that inferred from inter-reanalysis differences (Hitchcock 2019); 2) JRA-55 assimilates upper-air observations using an advanced data assimilation scheme during the presatellite era (Fujiwara et al. 2017), thereby providing valuable constraints on the upper-atmospheric state; and 3) JRA-55 is based on a high-top model with a well-resolved stratosphere, which has a more realistic representation of the stratosphere than low-top models (Charlton-Perez et al. 2013; Ayarzagüena et al. 2019). The data analyzed cover the years 1958–2019. The atmospheric fields examined include sea level pressure (SLP), 10-m zonal wind (U10m), 10-m meridional wind (V10m), 2-m temperature (T2m), total precipitation, surface heat flux [SHF: sum of surface sensible and latent heat flux; the sensible and latent heat flux anomalies are considered as a sum because they are strongly correlated over the extratropical oceans and both drive changes in SST, as shown by earlier studies (Cayan 1992a,b,c)], 850-hPa meridional temperature gradient and 10-day high-pass-filtered eddy heat flux [defined as υt′, where the primes denote high-frequency components of the 850-hPa meridional wind and air temperature computed with a 101-point Lanczos filter (Duchon 1979) with a cutoff frequency of 10 days], and 100-hPa temperature and zonal wind, as well as 10-hPa temperature, zonal wind, and geopotential height.

Monthly SST data from the National Oceanic and Atmospheric Administration (NOAA) Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5) dataset (Huang et al. 2017) over the years 1958–2019 are applied. The monthly data have been interpolated to daily data, assuming the monthly values are centered on the 16th of the month. Additionally, to account for the potential deficiencies in the temporal interpolation and to verify the robustness of our results, daily SST data from the NOAA Optimum Interpolation SST version 2 (OISSTv2) dataset (Reynolds et al. 2007) over the years 1981–2019 are tested.

Daily anomalies at each grid point are obtained by subtracting the seasonal cycle on that calendar day. The seasonal cycle is defined as the first three Fourier harmonics of the daily climatology.

b. Definition of SSWs

In this study, SSWs are defined by the reversal of the zonal mean westerlies at 60°N and 10 hPa following Charlton and Polvani (2007). In the 61 extended winters [November–March (NDJFM)] considered here, 38 events are identified (Table 1). Of these, 23 SSWs occur during the 1981/82–2018/19 winters.

Table 1.

List of 38 central dates of SSWs in JRA-55 during winters (NDJFM) from 1958/59 to 2018/19. SSWs without and with the North Pacific dipolar SST pattern (NP SST) are identified using 0.5 and 1.0 as the threshold value, respectively (see section 3c for details).

Table 1.

c. Statistical significance assessment

Statistical significance of the composite analysis based on SSWs is assessed with a two-tailed Monte Carlo test. One thousand sets of composites are computed around N randomly chosen “central dates” from the pool of days during 1958/59–2018/19 NDJFM, where N is equal to the number of SSWs involved in the composite analysis.

Statistical significance of the regression analysis used in this study is evaluated using a two-tailed Student’s t test, where the effective degrees of freedom are calculated following Eq. (1) of Kosaka et al. (2012).

3. Results

a. North Pacific SST and SLP anomalies

While the atmospheric features shown in Figs. 1a and 1b have been robustly demonstrated as surface responses to SSWs (e.g., Hitchcock and Simpson 2014; Kidston et al. 2015; Butler et al. 2017; Ayarzagüena et al. 2018), we do not expect the oceanic features in the Pacific shown in Figs. 1c and 1d to be a direct response to the stratospheric anomalies associated with SSWs. The reasons are as follows: 1) The downward influence of SSWs on SLP and U10m is confined largely to the North Atlantic (Figs. 1a,b), and therefore is unlikely to cause SST responses over the North Pacific; also, 2) as can be seen from the lagged composites of SST anomalies shown in Fig. 2, the broad pattern of the dipolar SST over North Pacific begins to emerge 45 days prior to the central date of the SSW and grows steadily over lag days −30 and −15 to a peak at lag day 0 (the SSW central date). The stratospheric anomalies in the aftermath of the SSW cannot be considered as a driver of the dipolar SST pattern that appears prior to the SSW central date. The temporal evolution shown in Fig. 2 suggests that the dipolar SST pattern is more likely a response to the tropospheric precursors to the SSWs, rather than to their subsequent downward influence.

Fig. 2.
Fig. 2.

Lag composites of anomalous SST based on (left) ERSST.v5 and 38 SSWs during 1958/59–2018/19 winters and (right) OISST.v2 and 23 SSWs during 1981/82–2018/19 winters. The lag days are labeled in the upper-right corner of each panel. The anomalies on each lag day are a pentad-mean centered on that day. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

As has been shown in previous studies of SSWs, composite mean SLP prior to the central date of SSWs feature a subtropical–subpolar dipole in the North Pacific [e.g., Figs. 1a and 1c in Cohen and Jones (2011); upper-right panel of Fig. 2 in Lehtonen and Karpechko (2016)]. Does this SLP precursor to SSWs emerge early enough to produce the SST anomalies? To address this question, we show in Fig. 3 lagged composites of SLP anomalies around the SSW central dates (black contour lines). These are shown superimposed on the 43-day mean SLP precursor pattern (averaged from day −45 to day −3; colored contour lines). The lagged composite results show that this North Pacific SLP precursor begins to emerge about 30 days prior to the SSW (Fig. 3b). The temporal evolution shown in Fig. 3 suggests that the SLP precursor to SSWs persists well beyond synoptic time scales. Previous studies have shown that SSWs arise from more persistent forcings (Newman et al. 2001; Polvani and Waugh 2004; Sjoberg and Birner 2012). The unusual persistence of this anomaly can thus be explained by the fact that SSW central dates will tend to follow particularly persistent or recurrent SLP anomalies that can be expected to occur every so often just by chance. The persistent SLP anomalies that precede SSWs in the composite average can thus be expected to significantly perturb the North Pacific SSTs.

Fig. 3.
Fig. 3.

Lag composites of anomalous SLP based on (left) 38 SSWs during 1958/59–2018/19 winters and (right) 23 SSWs during 1981/82–2018/19 winters. The lag days are labeled in the upper-right corner of each panel. The anomalies on each lag day are an 11-day-mean centered on that day. We choose an 11-day average to extract quasi-stationary fluctuations in the SLP fields. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively. The anomalous SLP averaged over days [−45 –3] is superimposed as colored contours. Red and blue contours denote positive and negative anomalies, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

Composites for the 23 SSWs that occurred during shorter record spanning the winters of 1981/82–2018/19 for which the daily OISSTv2 dataset is available show patterns of SST (Figs. 2f–j) and SLP precursors (Figs. 3f–j) that are consistent with the longer record, attesting to the robustness of the spatial pattern and temporal evolution of North Pacific SST and SLP anomalies.

b. Dynamics of the NP SST anomalies

To investigate the dynamical connection between the SSTs and the atmospheric anomalies, we first define a daily index measuring the intensity of the dipolar SST pattern over North Pacific. This is done by projecting the SST anomalies each day in extended winter (NDJFM) onto the dipolar SST pattern at the SSW central date (Fig. 2d) for the North Pacific sector (15°–60°N, 120°E–105°W). The projected time series is normalized and used as the SST index. The North Pacific sector is chosen because there are no significant SST anomalies in the tropical Pacific (Fig. S1). The dynamical processes underlying this dipolar SST pattern can then be investigated by analyzing the lag regressions of various quantities against the SST index.

Lag regressions of SLP, 10-m winds, SHF, and SST against this daily index are shown in Fig. 4, with negative lags indicating that the atmospheric quantity leads the SST index, and positive lags indicating that the SST index leads the atmospheric quantity.

Fig. 4.
Fig. 4.

Lag regressions of anomalous (a) SLP, (b) SHF and 10-m winds (arrows), (c) SST, and (d) SHF and 10-m winds (arrows) against the daily SST index. The lag days are labeled in the upper-right corner of each panel. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Student’s t test. Surface wind vectors displayed are statistically significant at least for one component at the p < 0.10 level based on a two-tailed Student’s t test. Scaling for arrows is given at the lower-right corners of (b) and (d).

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

The regressed patterns at the growth [−45 –3], peak [−2 2], and decay [3 45] stages of the dipolar SST pattern are shown in Figs. 4a–d. The SLP anomaly during the growth stage (Fig. 4a) closely resembles the SLP precursor seen prior to SSWs (Fig. 3, colored contour lines). The growth-stage SHF pattern (Fig. 4b), arising from the influence of surface winds anomalies (red arrows in Fig. 4b), has a structure that closely resembles the peak-stage SST pattern (Fig. 4c) with heat loss from the ocean to the atmosphere suppressed over the above-normal SSTs at subtropical latitudes and enhanced over the below-normal SSTs at subpolar latitudes. This is consistent with the SST anomalies being driven by the atmospheric anomalies (Cayan 1992a,b,c). The decay-stage SHF pattern (Fig. 4d) also has a structure that resembles the peak-stage SST pattern (Fig. 4c) but with the opposite sign to the SHF anomalies seen in the growth stage (Fig. 4b). Due to the lack of strong atmospheric circulation anomalies in the decay stage (red arrows in Fig. 4d), the decay-stage SHF (Fig. 4d) is much weaker than that seen in the growth stage (Fig. 4b). This suggests that the dipolar SST anomalies decay gradually by releasing more heat from the ocean to the atmosphere over the above-normal SSTs at subtropical latitudes and by releasing less heat over the below-normal SSTs at subpolar latitudes. Lead–lag regressions against the SST index from the OISSTv2 dataset give similar results (Fig. S2). These lagged regression results indicate that the dipolar North Pacific SST pattern is produced by surface heat fluxes modified by atmospheric circulation anomalies, and then in turn produces anomalous surface heat fluxes as it decays.

The close resemblance of the SLP anomalies that precede the SST pattern in general and those that precede SSWs supports the hypothesis that the composite North Pacific SLP precursor seen prior to SSWs also leads to the dipolar SST anomaly pattern. We further confirm this hypothesis by considering how surface heat fluxes prior to and following SSWs vary from event to event. For this purpose, we define an SSW-onset SST index by averaging the daily SST index over days [−2 2] around the central date of each SSW event. Since there are 38 SSWs in the record we are considering, the SSW-onset SST index has a length of 38, with each element corresponding to a single SSW event. Composites of various fields of interest are computed for each SSW event and then regressed against the SSW-onset SST index (Fig. 5). Day 0 corresponds here to the SSW central date; days [−45 –3], [−2 2], and [3 45] correspond to the precursor, onset, and aftermath phases of SSWs, respectively. As can be seen, SSW events that have stronger NP SST anomalies at their onset are preceded by stronger SLP precursors and surface heat flux anomalies, consistent with atmospheric forcing of the SST anomalies. These SSWs are also followed by stronger surface heat fluxes consistent with the decay of the SST anomalies. The regressed patterns against the SSW-onset SST index (Fig. 5) closely resemble those regressed against the daily SST index (Fig. 4). In particular, during the SSW events, the SHF patterns change their sign from driving the onset-phase SST pattern (Fig. 5c) during the precursor phase (Fig. 5b) to dissipating it during the aftermath phase (Fig. 5d). This indicates that the dipolar SST pattern at the onset of SSWs develops under the driving effect of the atmospheric forcing and then acts as an additional forcing on the lower troposphere through the anomalous surface heat fluxes. We argue below that this feedback from the dipolar SST pattern generated by the SLP precursors plays an important role in shaping the surface response to SSWs.

Fig. 5.
Fig. 5.

As in Fig. 4, but for lag regressions against the SSW-onset SST index.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

c. Surface response to SSWs conditioned on North Pacific SST anomalies

To identify the role of the dipolar SST pattern in shaping the surface response to SSWs, we will consider two subsets of SSWs separately. An SSW event is defined to occur with the dipolar SST pattern if the value of the SSW-onset SST index is greater than 1.0. It is defined to occur without the dipolar SST pattern if the absolute value of the same index is less than 0.5. The average value of the index over all SSWs is slightly greater than 0.5 (a histogram is shown in Fig. S3). These thresholds are chosen to ensure that 1) the sample size for each subset is sufficiently large for composite analysis and 2) North Pacific SST features in the two subsets are in sharp contrast. Our conclusions are not sensitive to the choice of thresholds as is demonstrated in the online supplemental material (Table S1, Figs. S4–S6). Among the 38 SSW events, 14 SSWs occur without the dipolar SST pattern and 11 SSWs occur with the dipolar SST pattern (Table 1).

By construction, the SST anomalies in the Pacific sector differ substantially between the two subsets, with weak and insignificant anomalies for SSWs without the dipolar SST pattern (Fig. 6a) compared to large and significant anomalies for SSWs with the dipolar SST pattern (Fig. 6f). Additionally, only the subset of SSWs with the dipolar SST pattern is preceded by the SLP precursor over North Pacific (Fig. 7b), which is absent in the cases without the dipolar SST pattern (Fig. 7a). The differences in the SST anomalies and the SLP precursor over North Pacific between the two subsets of SSWs are statistically significant (see Figs. S7 and S8). The dependence of the dipolar SST pattern on the SLP precursor again supports the driving effect of the SLP precursor on the formation of the dipolar SST pattern. It is important to note that while the SLP precursor is apparent in the composite average preceding SSWs (Fig. 3, colored contour lines), it is not essential to trigger SSWs. SSWs can arise due to tropospheric blocking precursors (Barriopedro and Calvo 2014) or stratospheric internal dynamics (Scott and Polvani 2004, 2006; Hitchcock and Haynes 2016; de la Cámara et al. 2019).

Fig. 6.
Fig. 6.

Composites of anomalous (a),(f) SST, (b),(g) SLP, (c),(h) U10m, (d),(i) T2m, and (e),(j) total precipitation averaged over days [3 45] following the central date of SSWs (left) without and (right) with the North Pacific dipolar SST pattern. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

Fig. 7.
Fig. 7.

Composites of anomalous SLP averaged over days [−45 –3] prior to the central date of SSWs (a) without and (b) with the North Pacific dipolar SST pattern. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

In the absence of the dipolar SST pattern, the SLP and U10m responses are similar in the Pacific and Atlantic basins (Figs. 6b,c), suggesting a more basin-symmetric downward influence of the stratospheric anomalies. By contrast, in the presence of the dipolar SST pattern, the SLP and U10m responses are highly basin-asymmetric (Figs. 6g,h). The negative NAO-like SLP pattern and the equatorward shifted surface westerlies in the Atlantic sector remain identifiable here (Figs. 6g,h), although they are not identical to the average responses to all 38 SSWs shown in Fig. 1. The most distinctive feature in the SLP and U10m response following SSWs with the SST dipole are found in the Pacific sector, where there is a north–south tripolar structure for both SLP and U10m fields (Figs. 6g,h). This contrasts sharply with the anomalies following SSWs without the dipolar SST pattern, which exhibit anomalies more consistent with the canonical equatorward shift response associated with SSWs. There are differences between the positive SLP anomalies in the Arctic too: the Arctic center for SSWs with the dipolar SST pattern (Fig. 6g) is tighter and somewhat more centered over the North Pole with respect to the other (Fig. 6b).

The dipolar SST pattern, along with its effect on SLP and U10m responses to SSWs, has broader consequences as well. For instance, the cold anomalies in the United States seen in the average responses to all 38 SSWs (see Fig. S9a) are much stronger following SSWs without the dipolar SST pattern (Fig. 6d), as was the case following the January 2019 SSW (Table 1). The strong and extensive warm anomalies over subtropical Africa and Asia shown in Fig. S9a are much more pronounced following SSWs with the dipolar SST pattern (Fig. 6i). Precipitation responses also differ in the two subsets. SSWs without the dipolar SST pattern are followed by above-average marine precipitation with banded structures over both the Pacific and the Atlantic basins (Fig. 6e); by contrast, precipitation anomalies following SSWs with the dipolar SST pattern exhibit less large-scale structure (Fig. 6j).

The distinctive features in surface response to the two subsets of SSWs cannot be attributed to SST anomalies in the Niño-3.4 region (5°S–5°N, 120°–170°W), which are modest in the composite mean for both subsets of SSWs (Figs. 8a,e), indicating a rather limited role of remote influence arising from the tropical Pacific Ocean related to ENSO—at least from a linear viewpoint. Neither can the differences between the two subsets be explained by differences in stratospheric conditions. The strength, depth, and morphology of the stratospheric anomalies are not substantially different between the two subsets (Figs. 8b–d and 8f–h). While SSWs occurring without the dipolar SST pattern appear slightly stronger and more persistent in the lower stratosphere (100 hPa) than the ones occurring with the dipolar SST pattern (Figs. 8c,g), these differences are not statistically significant, and we do not expect the differences in duration to account for the substantial differences in the surface responses. Ayarzagüena et al. (2020) has compared surface responses to all SSWs with those to the more persistent polar night-jet oscillation (PJO) events (cf. their Figs. 7 and S1), and their results indicate that SSWs with more persistent stratospheric conditions are not related to more basin-symmetric surface responses.

Fig. 8.
Fig. 8.

Composites for SSWs (left) without and (right) with the North Pacific dipolar SST pattern. (a),(e) SST anomalies averaged over days [3 45]. The magenta rectangular box denotes the Niño-3.4 region (5°S–5°N, 120°–170°W). Contours start from ±0.1 K with an interval of 0.2 K. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively. (b),(f) Temporal evolutions of zonal-averaged zonal wind along 60°N (blue dotted curves) and area-weighted polar cap (50°–90°N) mean temperature (red dotted curves) at 10 hPa. Blue and red solid curves denote temporal evolutions of zonal wind and temperature for all 38 SSWs, respectively. The scales of zonal wind (m s−1) and temperature (K) are labeled on the left and right axes, respectively. Black solid vertical line indicates the SSW central date. Filled dot marks lag days for which the composite value is statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test. Green and magenta triangles indicate lag days for which the differences in wind and temperature between the two subsets of SSWs are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively. (c),(g) As in (b),(f), but for 100 hPa. (d),(h) Geopotential height anomalies at 10 hPa averaged over days [−5 10]. Contours start from ±200 m with an interval of 400 m. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively. Colored contours represent differences between SSWs without and with the dipolar SST pattern (subtract the latter from the former). Red and blue contours denote positive and negative values, respectively. For colored contours, contours start from ±40 m with an interval of 80 m. Stippling indicates differences that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

Identical calculations using 0.75 as the threshold value to identify the two subsets of SSWs produce broadly similar results (Table S1, Figs. S4–S6), lending robustness to our conclusion. Still, a dynamical explanation is required to support the claim that the dipolar SST pattern plays an important role in shaping the surface response to SSWs.

d. Influence of air–sea interactions on North Pacific storm track response

In this section, we illustrate the dynamical processes through which the dipolar SST pattern acts to shape the downward influence of SSWs.

For this purpose, we examine patterns of the lower-tropospheric baroclinicity, synoptic eddy activity, U10m, and SLP fields over the North Pacific during the aftermath of SSWs. Composites of these fields are computed over days [3 45] after the central date of each SSW event and then regressed against the SSW-aftermath SST index, which is defined by averaging the daily SST index over days [3 45] after the central date of each SSW event. These lower-tropospheric fields are of interest in view of the potential for eddy–mean flow feedbacks. The inter-SSW regression allows us to examine how the eddy–mean flow feedbacks in the aftermath of SSWs vary from event to event.

The meridional temperature gradient along the well-defined baroclinic zone in the North Pacific is enhanced following those SSW events that also have stronger NP SST anomalies in their aftermath (Fig. 9a). In association, strengthened synoptic eddies develop in the region of enhanced near-surface baroclinicity (Fig. 9b). Consequently, the near-surface westerlies become stronger in the core region of the climatological jet (Fig. 9c). These results are consistent with Brayshaw et al. (2011) and Baker et al. (2017) showing that any ocean variability that strengthens the low-level meridional temperature gradient across the storm tracks is likely to strengthen the jet.

Fig. 9.
Fig. 9.

Regressions of anomalous lower-tropospheric fields averaged over days [3 45] following each SSW event against the SSW-aftermath SST index (black contours and color shading). (a) 850-hPa meridional temperature gradient, (b) 850-hPa eddy heat flux, (c) U10m, and (d) SLP. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Student’s t test, respectively. Colored contours represent the NDJFM climatology of (a) 850-hPa meridional temperature gradient (K degree−1; contour interval 0.4 K degree−1 starting from ±0.2 K degree−1), (b) 850-hPa eddy heat flux (K m s−1; contour interval 4 K m s−1 starting from ±2 K m s−1), (c) U10m (m s−1, contour interval 2 m s−1 starting from ±1 m s−1), and (d) SLP (hPa; contour interval 5.0 hPa starting from 1000 hPa). Red and blue contours denote positive and negative values, respectively. The climatological mean fields have been smoothed with a seven-point Gaussian-weighted moving-average filter to reduce the noise over land.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

The regressed SLP pattern (Fig. 9d) features a north–south dipolar structure consistent with the wind response. Above-average pressures are found near Hawaii, enhancing the subtropical high, while below-average pressures to the north deepen the Aleutian low.

In summary, the North Pacific dipolar SST pattern, through regional air–sea interactions and the resultant eddy–mean flow feedbacks, favors positive surface wind anomalies in the Pacific sector that are precisely positioned to strengthen the climatological surface westerlies. Woollings et al. (2018) has documented a general and robust dependence of the variability of the midlatitude jet position on its average speed: when a jet gets stronger, its variability in latitudinal position decreases. We therefore suggest that the dipolar SST pattern strengthens the low-level Pacific jet, reducing its susceptibility to the downward influence of SSWs that tends to shift the midlatitude jets equatorward. This behavior can be explained following the barotropic mechanism proposed in Woollings et al. (2018). Generally, when a jet gets stronger, the relative vorticity gradients on the jet flank increase (as can be seen in Fig. 10a). As a result, the poleward turning latitude moves closer to the jet, which reduces the region favorable for cyclonic wave breaking. Consequently, very little cyclonic wave breaking occurs on the poleward side of the jet. The majority waves are turned to propagate equatorward and add to potential for anticyclonic wave breaking on the equatorward side of the jet. Therefore, when the Pacific jet is strengthened in the presence of the dipolar SST pattern, the wave breaking occurs more frequently on the equatorward side of the jet. Because wave breaking acts to decelerate the zonal wind locally, frequent wave breaking on the equatorward side of the jet will push it poleward, acting against the downward influence from the stratosphere that tends to push the jet equatorward. As a result, when the dipolar SST pattern is present, the Pacific jet is less susceptible to the downward influence from the stratosphere.

Fig. 10.
Fig. 10.

Latitude–height sections of zonal wind anomalies in the (left) Pacific (150°E–120°W) and (right) Atlantic (1.25°–60°W) sectors of the Northern Hemisphere. (a),(b) Regressions of the sectoral-mean zonal wind anomalies averaged over days [3 45] following each SSW event against the SSW-aftermath SST index (black contours and color shading). Colored contours represent the NDJFM climatology of the sectoral-mean zonal wind. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Student’s t test, respectively. (c),(d) Composites of the sectoral-mean zonal wind anomalies averaged over days [3 45] following all 38 SSW events. (e),(f) As in (c) and (d), but for the subset of SSWs without the North Pacific dipolar SST pattern. Orange and blue shading indicates positive and negative anomalies that are statistically significant at the p < 0.10 level as determined with a two-tailed Monte Carlo test, respectively. Number of SSWs in each composite is indicated at the lower left of each latitude–height section.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

Both observational and modeling studies (Sutton et al. 2001; Xiao et al. 2016) suggest that the extratropical meridional dipolar SST anomalies can induce barotropic tropospheric zonal wind response. In that case, the tropospheric zonal wind anomalies associated with the North Pacific SST dipole should be able to affect the Pacific jet response to SSWs throughout the entire troposphere. This expectation is supported by the fact that the North Pacific wind anomalies associated with the North Pacific SST dipole are equivalent-barotropic (Fig. 10a). Additionally, the deep and positive wind anomaly at around 45°N is located to strengthen the Pacific midlatitude westerlies throughout the troposphere (Fig. 10a). By contrast, over the North Atlantic, there are no significant wind anomalies in association with the North Pacific SST dipole (Fig. 10b), indicating that the North Pacific SST dipole does not modulate the Atlantic jet response to SSWs significantly. To test this implication, we show the latitude–height sections of the composite zonal wind response to SSWs (Figs. 10c–f). Consistent with what we expect, over the North Pacific, there are no characteristic changes following all 38 SSWs throughout the entire troposphere (Fig. 10c), confirming the equivalent-barotropic influence of the North Pacific SST dipole on the zonal wind response to the stratospheric forcing. Following the subset of SSWs without the North Pacific SST dipole, an equatorward shifted North Pacific jet is identifiable throughout the entire troposphere (Fig. 10e). This is confirmation that the stratospheric forcing, in the absence of the North Pacific SST dipole, is able to influence the North Pacific jet at all levels. Unlike the North Pacific jet, the North Atlantic jet response to the stratospheric forcing is not influenced by the North Pacific SST dipole, and an equatorward shifted North Atlantic jet is identifiable regardless of whether the North Pacific SST dipole is present or not (Figs. 10d,f).

4. Conclusions

A summary schematic of our key findings is presented in Fig. 11. The top row of panels illustrates the evolution of the surface anomalies during SSWs that are “coupled” with dipolar SST anomalies in the North Pacific Ocean (hereafter referred to as coupled SSWs). Prior to the onset of the coupled SSW, atmospheric precursors form in the North Pacific troposphere. These include a subtropical–subpolar SLP dipole (Fig. 11a, purple contours). This SLP precursor plays a dual role: it is associated with enhanced upward wave activity into the stratosphere (Fig. 11a, blue upward-pointing curved arrow), and the associated wind-driven surface heat flux anomalies produce the dipolar SST pattern in the North Pacific (Fig. 11a, blue downward-pointing curved arrow). The dipolar SST pattern then persists as the stratospheric polar vortex breaks down (Figs. 11b,c). By reinforcing the lower-tropospheric baroclinicity, the North Pacific SST anomalies help to sustain the generation of baroclinic eddies, strengthening the low-level, eddy-driven jet and maintaining its mean state (Figs. 11b,c, magenta upward-pointing curved arrow). This prevents the Pacific eddy-driven jet from being perturbed by the downward influence of the stratospheric anomalies in the aftermath of the SSW (Figs. 11b,c, red downward-pointing curved arrow). As a result, these SSWs exhibit a highly basin-asymmetric surface response with only the Atlantic eddy-driven jet shifted equatorward (Fig. 11c, red arrows).

Fig. 11.
Fig. 11.

Schematic presentation of the stratosphere–troposphere–ocean coupling processes that lead to the basin asymmetric surface response to SSWs. Rows illustrates the evolution of the surface anomalies during SSWs that are (a)–(c) “coupled” and (d)–(f) “uncoupled” to the North Pacific Ocean, respectively. Various quantities used to illustrate the physical picture include stratospheric circumpolar wind (U10hPa, gray slightly curved arrow), tropospheric precursor pattern of SSWs (SLP′; purple contours), tropospheric eddy-driven jets (U10m; green shaded ellipse), anomalous U10m (U10m′; green open contours), and anomalous SST (SST′; black contours with dashed lines shaded blue and solid lines shaded orange). Equatorward shift of the tropospheric jet is indicated with a red arrow. Solid and dashed contours denote positive and negative anomalies, respectively. Orange and blue shading indicates positive and negative anomalies, respectively.

Citation: Journal of Climate 34, 21; 10.1175/JCLI-D-21-0314.1

In contrast, the bottom row of panels in Fig. 11 illustrates the evolution of surface anomalies during SSWs that are “uncoupled” from the North Pacific Ocean (hereafter referred to as uncoupled SSWs). In these cases, the uncoupled SSWs occur without the atmospheric precursors in the North Pacific troposphere. For these uncoupled SSWs, the dipolar SST pattern is absent due to the lack of the atmospheric forcing (Fig. 11d). In the absence of the dipolar SST pattern and the resultant eddy–mean flow feedbacks, the uncoupled SSWs exhibit a basin-symmetric surface response with both the Atlantic and the Pacific eddy-driven jets shifted equatorward (Fig. 11f, red arrows).

SSW events can occur with or without the dipolar SST pattern. However, while both types of events occur, the North Pacific SST pattern is present on average, leading to the more basin-asymmetric response seen following the coupled events (Figs. 11a–c) dominating the composite average following all SSWs. These results provide an ocean–atmosphere coupled perspective on stratosphere–troposphere interaction during the SSW events, pointing toward the importance of including air–sea interactions in forming a complete understanding of the downward influence of SSWs. On this basis, our results highlight the crucial role of eddy–zonal flow feedbacks in establishing the surface response to SSWs, which agrees well with the results of Hitchcock and Simpson (2014) and Afargan-Gerstman and Domeisen (2020) in view of the emphasis put on synoptic-scale eddies.

While the explanation put forth here for the basin-asymmetric response does not invoke the effects of different dynamical properties of the jet and storm track over the Atlantic and Pacific basins (Nakamura 1992; Nakamura and Sampe 2002; Lee and Kim 2003; Eichelberger and Hartmann 2007; Penny et al. 2010; Li and Wettstein 2012; Schemm and Schneider 2018; Yuval et al. 2018), it does not explicitly exclude them. A modeling study of the downward influence of SSWs (Hitchcock and Simpson 2014) may provide insight into the existence of their roles in establishing the highly basin-asymmetric surface response to SSWs. Specifically, simulations performed and analyzed by Hitchcock and Simpson (2014) have two key features: 1) all integrations are carried out with climatological repeated annual cycle SSTs and 2) SSWs are artificially imposed in the stratosphere by nudging the stratospheric circulation toward the reference SSW events, starting just prior to the central date—this nudging technique precludes the existence of tropospheric precursors to SSWs. For both of these reasons, the air–sea interaction we highlight in this study will not be present in their AMIP-type SSW ensembles. On the one hand, the prescribed climatological SSTs are fixed and hence not free to respond to atmospheric changes; on the other hand, there are no SSW precursors to force the underlying SSTs. Consistent with what we expect, given the missing air–sea interaction, their AMIP-type SSW ensembles do exhibit more basin-symmetric surface responses to SSWs with an equatorward shifted North Pacific jet, as can be seen in Figs. 11d and 11h in Hitchcock and Simpson (2014). Even so, it is worth noting that in their AMIP-type SSW ensembles, the North Pacific jet response is weaker than the North Atlantic jet response. This is because while the air–sea interaction is missing in their AMIP-type SSW ensembles, the effects of different dynamical properties of the jet and storm track over the Atlantic and Pacific basins are still present. These two factors may contribute to the magnitude differences between the simulated jet responses over the two ocean basins. Full consideration of their potential importance is left for future study.

One may also notice that the dipolar SST pattern over North Pacific during 1981–2018 has larger magnitude than that during 1958–2018 (Figs. 1c,d; Fig. 2), and so does the SLP precursor in the Pacific troposphere (Fig. 3). This is consistent with the fact that most of the SSWs with the dipolar SST pattern occur during the post-1981 period (Table 1; Table S1). This suggests further that there might be decadal variability in the stratosphere–troposphere–ocean coupling during SSWs; for instance, one may speculate that the Pacific decadal oscillation (PDO) could be modulating the dipolar SST pattern revealed in this study, in view of the fact that the shift in the phase of the PDO in the late 1970s (Miller et al. 1994; Hare and Mantua 2000) roughly coincides with the shift to increased occurrences of SSWs with the dipolar SST pattern. Kren et al. (2016) and Ayarzagüena et al. (2021) have provided modeling evidence for higher likelihood of SSWs with PDO+ than with PDO−, demonstrating low-frequency connection between the state of the PDO and the occurrence of SSWs. Despite these indications of a connection between PDO and SSW, whether and how PDO may have influenced the dipolar SST pattern is complicated by the fact that the PDO-related SST pattern (see Fig. 1 in Kren et al. 2016) does not match geographically well with the SST pattern studied here. In particular, the PDO-related SST extrema in the western and central North Pacific lies along 40°N, just between the positive–negative polarities in the dipolar SST pattern revealed in this study. However, a detailed investigation of the underlying physical processes acting at decadal time scales is beyond the scope of this study. Furthermore, the short observational record is not sufficient to examine variations at decadal time scales. For that purpose, long controlled numerical experiments should be analyzed to derive robust and reliable results.

Additionally, this study has focused on reanalysis data. While we have demonstrated composite anomalies consistent with the proposed dynamical mechanisms, the evidence is ultimately correlative and would be further strengthened by appropriate controlled numerical experiments. Moreover, questions remain about whether the physical mechanisms involved in the stratosphere–troposphere–ocean coupling are properly represented in state-of-the-art climate models, and whether dynamical subseasonal to seasonal predictions can benefit from the combined effects of stratospheric and ocean memory. An important next step, therefore, will be to conduct controlled numerical experiments to better understand the effects of North Pacific SSTs on the surface response to SSWs in climate models.

Acknowledgments

This work was supported by Cornell University. We thank Thomas Reichler and an anonymous reviewer for insightful comments.

Data availability statement

The JRA-55 data used in this study were obtained from https://rda.ucar.edu/datasets/ds628.0/, the NOAA ERSST.v5 data from https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.ersst.v5.html, and the NOAA OISST.v2 data from https://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html.

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  • Yuval, J., H. Afargan, and Y. Kaspi, 2018: The relation between the seasonal changes in jet characteristics and the Pacific midwinter minimum in eddy activity. Geophys. Res. Lett., 45, 9995–10 002, https://doi.org/10.1029/2018GL078678.

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Supplementary Materials

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  • Polvani, L. M., and D. W. Waugh, 2004: Upward wave activity flux as a precursor to extreme stratospheric events and subsequent anomalous surface weather regimes. J. Climate, 17, 35483554, https://doi.org/10.1175/1520-0442(2004)017<3548:UWAFAA>2.0.CO;2.

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  • Reynolds, R. W., T. M. Smith, C. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature. J. Climate, 20, 54735496, https://doi.org/10.1175/2007JCLI1824.1.

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    • Search Google Scholar
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  • Schemm, S., and T. Schneider, 2018: Eddy lifetime, number, and diffusivity and the suppression of eddy kinetic energy in midwinter. J. Climate, 31, 56495665, https://doi.org/10.1175/JCLI-D-17-0644.1.

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  • Scott, R. K., and L. M. Polvani, 2006: Internal variability of the winter stratosphere. Part I: Time-independent forcing. J. Atmos. Sci., 63, 27582776, https://doi.org/10.1175/JAS3797.1.

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    • Search Google Scholar
    • Export Citation
  • Sigmond, M., J. F. Scinocca, V. V. Kharin, and T. G. Shepherd, 2013: Enhanced seasonal forecast skill following stratospheric sudden warmings. Nat. Geosci., 6, 98102, https://doi.org/10.1038/ngeo1698.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sjoberg, J. P., and T. Birner, 2012: Transient tropospheric forcing of sudden stratospheric warmings. J. Atmos. Sci., 69, 34203432, https://doi.org/10.1175/JAS-D-11-0195.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sutton, R. T., W. A. Norton, and S. P. Jewson, 2001: The North Atlantic Oscillation—What role for the ocean? Atmos. Sci. Lett., 1, 89–100, https://doi.org/10.1006/asle.2000.0021.

    • Search Google Scholar
    • Export Citation
  • Woollings, T., and Coauthors, 2018: Daily to decadal modulation of jet variability. J. Climate, 31, 12971314, https://doi.org/10.1175/JCLI-D-17-0286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xiao, B., Y. Zhang, X.-Q. Yang, and Y. Nie, 2016: On the role of extratropical air–sea interaction in the persistence of the Southern Annular Mode. Geophys. Res. Lett., 43, 88068814, https://doi.org/10.1002/2016GL070255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuval, J., H. Afargan, and Y. Kaspi, 2018: The relation between the seasonal changes in jet characteristics and the Pacific midwinter minimum in eddy activity. Geophys. Res. Lett., 45, 9995–10 002, https://doi.org/10.1029/2018GL078678.