1. Introduction
The Northern Hemisphere (NH) climate has significantly changed over recent decades. Some of the most notable changes occur in the Arctic, where the trend in surface air temperature is larger than for the rest of the globe (Screen et al. 2018), a phenomenon known as the Arctic amplification. This temperature trend has been accompanied by a rapid sea ice reduction in the Arctic Ocean (Screen and Simmonds 2010; Polyakov et al. 2012; Simmonds 2015; Lee et al. 2017). Along with the changes in high-latitude climate, the NH midlatitudes have seen significant changes in the circulation including, but not limited to, weakening of the jet (Coumou et al. 2015, 2018), increase in the frequency and intensity of extreme heat and rainfall events and decrease in spring and summer snow cover, this last reducing at nearly twice the rate of the September Arctic sea ice loss (Cohen et al. 2014; Lehmann et al. 2015; Fischer and Knutti 2015). At the same time, Cohen et al. (2014) showed that the frequency of the unusually cold months and the annual number of days below freezing show an upward trend since the 1990s, a timing that coincides with the acceleration in the high-latitude warming in the polar regions of the NH. They pointed out that, while the increasing number of high temperature and precipitation extremes is consistent with the global warming theory, the increase in cold extremes cannot be directly explained in those terms.
Profound changes in Arctic climate and the increased number of extreme events in the midlatitudes have spurred research activities to determine whether these trends are linked, and what the relevant physical processes might be responsible for any such link. Over the past decade there have been a considerable number of climate model experiments, theoretical investigations, and observational studies aimed at addressing these questions. The variety of conclusions reached in these range from clear signals, right through to signals that do not exceed the noise level of natural variability (e.g., Francis and Vavrus 2012; Screen et al. 2014; Coumou et al. 2018; Meleshko et al. 2018; Cohen et al. 2020; Dai and Song 2020; Ringgaard et al. 2020). Recent progress on identifying reasons for this disagreement has come from the application of the potential vorticity (PV) framework to the problem. Studies have shown that the magnitude of the meridional gradient of PV can act as a barrier or “gate” to the propagation of quasi-stationary Rossby waves (e.g., Luo et al. 2018; D. Luo et al. 2019; Xie et al. 2020). A number of dynamical phenomena known to encourage Arctic–midlatitude connections are intimately related to the changes in this gradient, which is reduced following amplified warming in the Arctic. For example, changes in the storm track through a shift in the North Atlantic Oscillation (NAO)/Arctic Oscillation (Rudeva and Simmonds 2015; Luo et al. 2016; Vihma et al. 2020), weakening of the polar jet stream and planetary waves (e.g., Cohen et al. 2014; Pedersen et al. 2016; B. Luo et al. 2019; Semmler et al. 2020). This offers an avenue for understanding why a given circulation pattern may or may not facilitate a remote response in a given region.
Research attention has also been paid to the potential for climate change in both the midlatitudes and the Arctic being forced by teleconnection patterns originating from the tropical west Pacific. This region is host to high and increasing sea surface temperature (SST) (Trenberth et al. 2014; Palmer 2014). This temperature increase leads to an enhancement of atmospheric water loading followed by an intensification of latent heat release and creates a Rossby wave source. Ding et al. (2014) have shown that such waves can account for the recent warming in northeastern Canada and Greenland through contributing to the negative trend in the NAO. Goss et al. (2016) demonstrated that enhanced warm pool convection in the tropical Pacific is followed by an increase in the Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas [see also the studies of Yim et al. (2017) and Krishnamurti and Kumar (2017)]. In an analogous fashion significant SST anomalies in the midlatitudes of the west Atlantic Ocean, associated with a poleward shift of the Gulf Stream, induce a wave train causing Barents Sea sea ice decrease and cooling over Eurasia (e.g., Sato et al. 2014; Simmonds and Govekar 2014; Jung et al. 2017; Matsumura and Kosaka 2019; Wolf et al. 2020).
A number of studies suggested that midlatitude weather extremes can be associated with near-stationary and highly amplified Rossby waves (Blackburn et al. 2008; Woollings et al. 2008; Francis and Vavrus 2012; Petoukhov et al. 2013, 2016; Saeed et al. 2014; Coumou et al. 2014; Huntingford et al. 2014; Yuan et al. 2015, 2018; Stadtherr et al. 2016; Kornhuber et al. 2016, 2019, 2020; Simmonds 2018; Wolf et al. 2018). Screen and Simmonds (2014) showed that half of monthly temperature and precipitation extremes in the midlatitudes are associated with amplification of at least one of the synoptic waves. They also speculated that in any specific location amplified waves may favor one type of extreme event, and that one particular wavenumber or wave phase may be more important than others (the latter explains the importance of quasi-stationary waves for extreme events and also allows for a persistence of amplified wave conditions in sensitive locations). In line with this suggestion, Kornhuber et al. (2019) showed that extreme summer temperatures in western Europe and the European part of Russia are often associated with amplified wavenumber 7. A mechanism, proposed by Petoukhov et al. (2013, 2016), called “quasi-resonant amplification” of Rossby waves, was used to attempt to explain this and some earlier extreme events (Coumou et al. 2015; Kornhuber et al. 2017). However, this proposed mechanism has a number of theoretical and observational obstacles (see, e.g., Teng and Branstator 2019), and enhanced forcings may provide a more convincing mechanism for the amplification of waveguide teleconnections.
The growing body of evidence that climate extremes are often connected to wave activity has led to the conclusion that predicting waveguide teleconnections and associated structures may help to improve prediction of extreme events (Hoskins 2013; Teng et al. 2013; Teng and Branstator 2019). Teng and Branstator (2019) proposed that, while most studies focus on the steady forcing of planetary waves, short-lived forcing (e.g., lasting for 2 days) could also excite a wave response. Possible short-term forcing hotspots could be associated with anomalous sea surface temperatures or temperature and/or moisture anomalies over land due to heat waves or droughts (Douville 2002; Wang et al. 2010, 2019). Anomalous short-lived eddy fluxes, originating from those hotspots, are likely induced by waves and can, in their turn, reinforce those wave patterns.
As a preliminary to our investigation we build on the analyses of Xiao et al. (2018) and Ge et al. (2020) by determining the correlations between the seasonal sea ice extent (SIE) over the entire Arctic basin and the temperature in our eight midlatitude “target” regions (Table 1; see Fig. 1 for geographical regions). It has been established (e.g., Pedersen et al. 2016) that specific regions of the Arctic sea ice loss play differing roles in determining the large-scale circulation response. Our analysis, based on Pearson’s correlation and tested by using the two-sided Student’s t test, shows that the Barents–Kara SIE is correlated with the temperatures in China, North America, and some of the Eurasian regions (both SIE and air temperature time series had linear trends removed prior to the correlation analysis). The SIE in the Pacific sector of the Arctic is correlated with the air temperature in western China and some Eurasian regions. However, the sign of correlation in detrended time series differs across regions, implying a complex dynamical response of the atmospheric circulation to the anomalies in the Arctic.
Correlation coefficient between the SIE in the Arctic and regional Arctic seas with the JFM air temperature at 2 m in the eight midlatitude regions shown in Fig. 1. Only values that are significant at p < 0.10 are shown; values marked with an asterisk (*) are significant at p < 0.05 and values shown in plain (boldface) text represent correlations for raw (detrended) SIE and T2m. No correlations are shown for the Laptev and eastern Siberian Seas due to the full ice coverage in JFM.
The geographical regions used in this study. Green filling indicates regions that are the main focus of the paper, hatching marks additional (eastern) regions. The Arctic seas marked in yellow are the Greenland (GrS), Kara (KarS), Laptev (LapS), and Beaufort (BeaS) Seas and Baffin Bay (BafB).
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Interestingly, while the ice decline is the strongest in September, previous studies have found that the atmospheric response is the strongest in winter (Vihma 2014). To explain this difference in timing, a lagged response to the summer/autumn sea ice anomalies has been suggested; however, Petoukhov and Semenov (2010), Semmler et al. (2012), and Tang et al. (2013) argue that important factors are changes in the meridional winter surface temperature gradient and the associated baroclinicity. In agreement with this, Luo et al. (2018) and D. Luo et al. (2019) found that the PV gradient plays a key role in forming cold extremes in the midlatitudes: while a strong PV gradient may be considered as a barrier, a reduction in this gradient allows for a southward intrusion of cold Arctic air. Thus, even a small winter anomaly in polar regions may lead to a midlatitude temperature extreme, whereas summer Arctic anomalies, being stronger, are less likely to cause a large-scale atmospheric response due to an overall reduced meridional PV gradient.
Semmler et al. (2020) simulated an atmospheric response to a sudden “thinning” of the sea ice all over the Arctic. They argue that the most important process is the fast tropospheric response developing within a few days of an anomaly before temperatures in the lower troposphere had time to adjust. Their results suggest that anomalies in the Arctic play a more important role on the synoptic time scale, while slow atmospheric processes, associated with seasonal anomalies, only intensify the synoptic response. This may be responsible for very modest correlations between the SIE and the midlatitude extremes.
In this study we test the hypothesis that winter temperature extremes in the midlatitudes can, under certain conditions, be associated with anomalous wave propagation and teleconnection patterns from remote Arctic and tropical regions, where temperature variability and change suggests periods of enhanced forcing. An important feature of our analysis is identification of propagation windows on the daily time scale. Our approach is described and justified in the next section. We further connect temperature extremes on seasonal and daily time scales with the anomalous waveguides in the NH. This approach allows us to capture both long- and short-lived wave forcing, as well as to identify synoptic situations that make the atmosphere more conducive for wave propagation from/to particular locations. We conduct our analysis by exploring wave propagation characteristics when subregions of midlatitude North America, Europe, and Asia are subject to temperature extremes. Finally, we explore how warm Arctic regions create cold outbreaks in both Siberia and North America.
2. Data and methods
a. Data
We used daily-average geopotential height and zonal wind speed at 300 hPa (Z300 and U300, respectively) and 2-m air temperature (T2m) from the ERA-Interim (Dee et al. 2011). The analysis focuses on the T2m extremes during the months of January, February, and March (JFM) for the period 1980–2017, but the data were also extracted for December and April to allow for smoothing of daily temperatures and lead–lag relationships. The horizontal resolution is 1° × 1°.
This study also uses the sea ice extent from the Gridded Monthly Sea Ice Extent and Concentration, 1850 onward, version 2 dataset (Walsh et al. 2019) for the period 1980–2017. As of 1979, this dataset relies on NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration merged values. The monthly sea ice extent is given for the NH and individual seas within the Arctic basin.
b. Methods
A description of the theory of Rossby stationary wave propagation for the zonally symmetric case can be found in Hoskins and Karoly (1981). Later studies (e.g., Karoly 1983; Held 1983; Hoskins and Ambrizzi 1993) extended this theory to the zonally varying background flow.
Hoskins and Ambrizzi (1993) showed that, even though the theory of wave propagation was derived for the zonally symmetric case, it can still be useful in a zonally varying flow given the dominance of U over the meridional wind component and of the latitudinal over longitudinal gradients of the basic flow. They found that even though the strict mathematical validity for the application of the theory was in question, it provided a very useful theoretical basis for discussion of teleconnection behavior in the atmosphere. The above equations have been used for the zonally varying case with considerable success in a number of studies, including those of Freitas and Ambrizzi (2012), Freitas and Rao (2011, 2014), McIntosh and Hendon (2018), and Wills et al. (2019).
The dispersion relation [Eq. (1)] for the barotropic Rossby wave perturbations to the westerly flow was derived from the equation for horizontal streamfunction [Eq. (2) in Karoly 1983], which was linearized assuming that Rossby wave perturbations have smaller length scale than those of the mean field. In previous studies the background fields were taken from long-term temporal averages (e.g., seasonal means), meaning that these fields evolve more slowly than the time scale of the disturbances. In this study we explore the potential of using shorter time scales for averaging of the background field for the analysis of Rossby waves, even if, as in Hoskins and Ambrizzi (1993), the strict mathematical validity might be violated.
As an example of the global distribution of Ks, we show its structure in Fig. 2b derived from U300 for JFM 2006 (Fig. 2a), which is one of the anomalously cold seasons in central Europe. Most of the midlatitudes provide an environment conducive to the propagation of long stationary waves [i.e., there is a real solution to Eq. (2)], and this is true to a lesser extent in the high latitudes. Large parts of the tropics are dominated by easterlies, meaning that quasi-stationary Rossby waves cannot propagate in that region; however, over small domains over the tropical Pacific and Atlantic Oceans the averaged wind is westerly, making the interaction between the Northern and Southern Hemispheres possible. A summary of the meridional structure of Ks is presented in Fig. 2c; the black curve shows the longitudinal means of the values (when these are defined), while the orange line shows Ks%, which is the percentage of the latitude circle for which Ks is defined (i.e., is conducive to wave propagation).
(a) Zonal component of wind U, (b) stationary total wavenumber Ks, and (c) meridional profiles of Ks% and zonally averaged Ks at the 300-hPa level in JFM 2006. In (b) the area subject to easterly wind is blanked. In (c) Ks profiles are shown in black and for the bottom axis, while that for Ks% are displayed in orange for the top axis (%). Note that Ks in (b) and solid lines in (c) stand for values derived from seasonally averaged U300; dashed lines in (c) indicate seasonal averages from daily Ks and Ks%. (d)–(f) As in (a)–(c), but for the wind in January 2010. (g)–(i) Results for the wind taken on 21 Jan 2010. In (i) only solid lines are shown as they already represent daily values (dashed lines would be identical).
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
To allow an appreciation of the influence of averaging period used for the calculation of the background flow, we present in the second row of Figs. 2d–f plots analogous to those discussed above but determined from the mean flow for the subperiod of January 2006. The plots are very similar to those for the seasonal case but a little noisier, as would be expected. To emphasize the importance of examining the dynamics at the synoptic time scale the bottom row in Fig. 2 shows these characteristics for an anomalously cold day within the period considered above (21 January 2006). Now Ks exhibits a considerably more perturbed pattern, showing narrow regions of defined Ks that allow Rossby wave propagation. In addition, there are some propagation windows between polar and middle latitudes with Ks values reaching 6–9, meaning that synoptic-scale waves at these times could propagate out of the polar regions and impact on conditions at lower latitudes. Our example makes clear that wave propagation (and remote impacts) is possible for certain subperiods, while the mean wind field over a longer period might indicate that this could not be achieved. We regard these temporary “windows” as an important component of understanding remote connections.
We base our analysis on the mean Ks% values, rather than the Ks metric, as it better reflects the presence or absence of regions favorable for wave propagation at a given latitude (cf. orange and black lines in the right column of Fig. 2). This is most important in the tropics and at around 60°–70°N, the areas where waves often cannot propagate, either due to easterly winds or low β* values (Fig. 2, center column). We stress that the stationary wavenumbers were calculated daily, and then averaged; had seasonally or monthly averaged fields been used, these profiles would have looked rather different. (Compare the solid and dashed lines in Figs. 2c,f, where solid lines indicate Ks and Ks% values from seasonal and monthly mean U300 and dashed lines show seasonally and monthly averaged daily Ks and Ks% values. Hereafter, seasonal Ks and Ks% refer to seasonal average of daily values.)
It should be noted that we consider the areas where Ks is defined as wave propagation zones, although a possibility that waves are absorbed within those areas cannot be ruled out. Hoskins and Ambrizzi (1993) described waveguides as regions with relatively high total wavenumbers, K (in the general case or Ks for stationary waves), bounded by lower values to the north and south. In this case, once a wave with the zonal wavenumber k reaches low values of K such that when K gets close to k, the wave is reflected back to higher values of K. Indeed, elongated filaments of Ks on the daily time scale (Fig. 2h) often look like waveguides, particularly in the extratropics, but the Ks% metric cannot guarantee that the necessary criterion on the Ks distribution is met. However, Wirth (2020) argues that the presence of turning points is not a good predictor of so-called waveguidability. Another approach to defining a waveguide is given by Wirth et al. (2018). They argue that atmospheric jet streams can be seen as elongated bands of strong PV gradients on jet-crossing isentropes. Rossby waves, being fundamentally related to material displacements across the background PV gradient, can only propagate along a PV front. This implies that the area along the jet can be seen as a waveguide and, as filaments of defined Ks in Fig. 2h mainly follow jets, we treat them as waveguides.
3. Results
a. Atmospheric circulation at the time of midlatitude air temperature anomalies
To explore the atmospheric circulation during midlatitude temperature extremes and a potential role of propagating stationary Rossby waves in those anomalies, we present here the difference between warm and cold Z300 and U300 and then the time-averaged profiles of daily Ks%. For synoptic extremes, atmospheric variables are averaged for 11-day period centered on the date of extreme.
Differences between cold and warm winters in central Europe (Figs. 3a,b) are associated with zonally elongated Z300 that resembles the NAO. Cold (warm) winter seasons in central Europe are associated the negative (positive) NAO and a strong subtropical (polar) jet, in agreement with the analysis of Röthlisberger et al. (2016) and Li et al. (2020a). The positive Z300 anomaly in the high latitudes of the North Atlantic during cold European seasons is in agreement with positive (negative) correlations with the SIE in Baffin Bay and the Beaufort Sea (the Barents Sea), shown in Table 1, due to the wind-induced sea ice drift.
Anomalies in (a),(d) Z300 (m) and (b),(e) U300 (m s−1) for cold relative to warm extremes (cold minus warm) in central Europe at (a),(b) seasonal and (d),(e) synoptic time scales for JFM 1980–2017. Maps are centered on the target region that is marked with a blue rectangle. Cross-hatching indicates differences significant at the 95% level. Numbers along the top axis mark the location of each of nine longitudinal bands associated with the target region. (c) Seasonal and (f) synoptic profiles of Ks% averaged over (left) the circle of latitude and (other panels) 40° longitudinal bands. Band number and corresponding longitudes are marked on top of each panel. The black line indicates the climatological mean; red and blue lines indicate profiles during warm and cold extremes, respectively. Latitudes where the difference between cold and warm Ks% is statistically significant at the 95% level are marked with circles. Synoptic composites in (d)–(f) are built for the 11-day average centered on the date of the extreme.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
The anomalies in the wave propagation during extremes are investigated using Ks% profiles (Fig. 2c). The left panel in Fig. 2c presents the zonal mean Ks%, while the other panels show Ks% profiles for selected longitudinal bands. In the midlatitudes the fraction of the latitude circle where Ks is defined in the zonal mean is between 60% and 90%. In the tropics this percentage drops to 20%, meaning that only parts of the tropics are open to Rossby wave propagation in JFM, mainly over the oceans. This is in qualitative agreement with Freitas and Rao (2014, see their Fig. 7), who also suggested a potential for Rossby wave propagation over the tropical Pacific and Atlantic. At the time of warm and cold European seasons the zonal means of Ks% are very similar to the climatological profile. Using a different approach, Woollings et al. (2018) compared zonal profiles of wavenumbers of nonstationary waves calculated using zonally averaged wind speed for the strongest and weakest jet months and did not detect any significant difference in those profiles. Although their method is quite different from ours, as we focus not on the actual values of wavenumbers but rather on the presence of waveguides, the findings of Woollings et al. reinforce our result that during anomalously warm and cold seasons in both winter and summer the waveguides, averaged over Earth’s circumference, are essentially unmodified from the climatological mean.
From our earlier discussion, however, it is clear that there will be ranges of longitude that present Ks% profiles that will differ from the climatological mean during extremes, despite not showing any such variance when the full latitude circle is considered. To explore this, we calculated Ks% profiles in nine 40° longitudinal bands. These longitudinal sectors differ between regions, being relative to the region under consideration, with band 1 being over the selected region, band 2 located “downstream,” and band 9 just upstream of the longitude of the selected region (sectors are marked along the top axis of all maps in Figs. 3–9). The Ks% profiles were calculated in all nine 40° sectors, but here we show only the sectors, where the difference between warm and cold profiles is significant. In agreement with the location of the jet anomalies shown in Fig. 2b, Ks% profiles in the sectors surrounding the region (particularly in bands 9 and 1) suggest an increase (decrease) in Ks% immediately to the north (south) of the region in the case of a cold season in central Europe (Fig. 3c).
Similar to the seasonal extremes, Z300 anomalies on the synoptic time scale resemble the NAO with the southern center shifted farther east, while anomalies in the wind speed and Ks% profiles look like the amplified seasonal pattern (Figs. 3d,e).
Cold (warm) winters in western Siberia (Figs. 4a–c) are associated with a positive Z300 anomaly to the north of the Atlantic–European sector and negative anomalies in the middle latitudes, leading to an equatorward shift of the jet. The Ks% anomalies are the strongest to the north of the region, with increased (reduced) Ks% during cold (warm) anomalies. Furthermore, Ks%, together with Z300 and the wind fields, suggest anomalous wave patterns over the tropical oceans, particularly over the Pacific (sector 4). This result agrees with findings by Goss et al. (2016), who connected warming in the tropical Pacific with anomalies in the Barents and Kara Seas, suggesting that there is a second wave train connecting tropical oceans via the upper troposphere in northern Eurasia. Synoptic extremes over western Siberia (Figs. 4d–f) are associated with patterns similar to the seasonal anomalies with a little stronger Z300 anomaly over the Barents Sea. Higher Z300 over the Arctic basin and particularly over the Kara and Barents Seas was previously linked to circulation anomalies and cold outbreaks in northwestern Siberia and northeastern Europe (Petoukhov and Semenov 2010; Outten and Esau 2012; McCusker et al. 2016; Mori et al. 2019; Semmler et al. 2020). In contrast to the seasonal patterns, the signal coming out from the tropics is much weaker on the synoptic time scale.
As in Fig. 3, but for western Siberia.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Cold seasons in western North America (Fig. 5) are associated with weakening of the Aleutian low and lower Z300 over the tropical central Pacific, leading to a weaker North Pacific jet stream, but stronger jet over the North Atlantic. Z300 anomalies over the Pacific and North America show a well-defined PNA pattern. While previous studies identified the tropical Atlantic (Cassou et al. 2005) and tropical Pacific and Caribbean (Ding et al. 2014; Wulff et al. 2017; O’Reilly et al. 2018) as important wave forcing regions, our results confirm that there is an extratropical wave train in the upper troposphere that connects tropical oceans via North America (Cai et al. 2019). In contrast to western Siberia, Ks% anomalies in the topics are seen on both time scales, while high-latitude anomalies, being strong for the synoptic extremes, are very modest in seasonal patterns.
As in Fig. 3, but for western North America.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Figure 6 shows that in western China, cold winters are associated with lower Z300 at low midlatitudes and higher Z300 in northern Siberia (although the latter is not statistically significant). In agreement with this, the jet to the south (north) of the target region is amplified (weakened). As for other regions, synoptic anomalies resemble those of the amplified seasonal patterns. However, for western China anomalies in Ks% are very weak at the seasonal time scale and are not significant in either tropical or polar regions even for the synoptic extremes.
As in Fig. 3, but for western China.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Despite regional differences in temperature anomalies in selected regions, we have found that 1) the cold temperature anomaly is associated with weaker (stronger) jet to the north (south) of the target region. This change in the jet steam can be related to a decrease (increase) in the meridional temperatures gradient in the high (low) midlatitudes and 2) the area of weaker (stronger) jet is associated with larger (smaller) Ks% in sectors that correspond to jet anomalies; also, 3) anomalies in Z300 and jets streams, being specific for each region, show similar patterns on both seasonal and synoptic time scales. For warm temperature extremes the sign of anomalies mentioned above reverses. The mechanism of the development of those circulation patterns is a subject of the following section. Importantly, given the similarity between seasonal and synoptic circulation patterns in Figs. 3–6, we may suggest that seasonal Z300 anomalies in remote centers create a foundation for frequent but not so strong synoptic anomalies similar to those that lead to short-term extremes. In this case, seasonal midlatitude anomalies are created by those more frequent synoptic circulation patterns.
b. Evolution of synoptic air temperature anomalies
In the previous section we presented a diagnostic of the atmospheric circulation at the time of seasonal and synoptic cold (warm) extremes in four key regions. We now focus on the evolution of synoptic extremes to identify conditions that favor the development of midlatitude extremes. We begin this analysis by showing temperature composites 10–20 days prior to the extreme event in the four regions. Figure 7 reveals areas of negative temperature anomalies poleward of each region, these being significant for the most part. It was found that at a longer lead time (not shown) there was no agreement between regions, suggesting various mechanisms that create these initial anomalies.
Difference in T2m (°C) between cold and warm (cold minus warm) synoptic extremes at 10–20-day lead time for (a) central Europe, (b) western Siberia, (c) western North America, and (d) western China. Maps are centered on the target region marked with a blue rectangle. Cross-hatching indicates differences significant at the 95% level.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Temperature anomalies 10–20 days before the heat extreme are associated with very weak Z300 anomalies that are often shifted with respect to their location during the extreme (cf. Fig. 8, left column, and Figs. 3d, 4d, 5d, and 6d). Importantly, at this stage local Z300 anomalies are small (or not significant), but anomalies in the key remote regions are already present: an NAO-like pattern in the North Atlantic for central Europe, a high anomaly in the Arctic for western Siberia, a wave train in the Pacific for western America, and an anomaly along the Barents and Kara Seas coastline. While these anomalies are present, they are very weak and unlikely to be responsible for triggering midlatitude extremes. Five days later Z300 anomalies (both local and remote) are stronger and shift to where they are located at the time of extremes (cf. Fig. 8, middle column, and Figs. 3d, 4d, 5d, and 6d). During the following days these Z300 anomalies stay in place and continue to strengthen (Fig. 8, right column). At the time of initial temperature anomalies (10–20 days before the extreme), Ks% is very close to the climatological values in both warm and cold extremes (not shown). Anomalies in Ks% develop along with amplification of Z300 lows and high.
(a)–(d) As in Fig. 7, but for Z300. (e)–(h),(i)–(l) As in (a)–(d), but at 5–15- and 0–10-day lead time, respectively.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
Despite different hemispheric circulation patterns associated with each of midlatitude regional extremes, amplification of the temperature anomaly can be associated with similar local conditions: cold temperature anomalies lead to a reduction in Z300 over the target region and associated weakening (strengthening) of the jet to the north (south) of the region. The Ks% profiles indicate that areas of a weaker jet are associated with an increase in Ks%. We speculate here that anomalies in Ks% may be associated with a change in the wave activity. Therefore, in the case of a cold midlatitude anomaly, in line with the southward shift of the jet and increase in Ks% to the north of the region, waveguide regions enlarge on the poleward side of the jet. This may, in turn, lead to a cyclonic Rossby wave breaking and an amplification of the cold anomaly as well as the jet speed (Takaya and Nakamura 2005; Messori and Caballero 2015). Similarly, in the case of a warm local anomaly, the jet shifts to the north and Ks% increases to the south of the region, indicative of increased a waveguide on the equatorward side of the jet. This may lead to an anticyclonic Rossby wave breaking and an amplification of the warm anomaly in the target region (anticyclonic wave breaking was shown to be responsible for heat waves in Australia; see Engel et al. 2013).
We note that at the lead time of 10–20 days, the most significant anomaly found in all regions is a surface temperature anomaly to the north of each region, while associated Z300 anomalies are very weak. We propose that the development of extreme events starts regionally. Around 10 days before the temperature in the region reaches its extreme value, the local Z300 anomaly connects with pre-existing remote centers of action that are specific for every region. Once connected, Z300 anomalies start to grow, helping to amplify the local temperature extreme. It is worth repeating that that seasonal midlatitude extremes must be created by a series of reoccurring synoptic anomalies as follows from a good agreement in the location of Z300 and U300 patterns between those time scales (Figs. 3–6).
c. Influence of the Arctic on the midlatitude temperature anomalies
In previous sections we showed that cold extremes in NH midlatitudes are often associated with a high pressure system in high latitudes, but they may not be the sole factor inducing the midlatitude anomalies. [For example, Li et al. (2020b) show that sea ice in the Barents–Kara Seas plays an important role in “anchoring” the teleconnection from the North Atlantic in inducing temperature extremes over Asia.] In this section we investigate whether large temperature anomalies in two of the Artic regions, introduced in section 2c, can create anomalous temperatures in the midlatitude in the absence of a pre-existing anomaly.
In agreement with Semmler et al. (2020), who argued that fast tropospheric response to the thinning of the Arctic sea ice is more important than slow processes, our analysis shows that on the seasonal time scale Z300 anomalies in the midlatitudes during extreme Arctic seasons are not significant and Ks% to the south of the polar anomalies do not differ from the climatological mean (see Figs. S2 and S3 in the online supplemental material). In contrast, the synoptic heat extremes produce strong and significant anomalies of opposite sign to the southeast of the Arctic regions.
The evolution of polar extremes and their associated anomalies in the midlatitudes are presented in Fig. 9 (note that, in contrast to Figs. 3–8, Fig. 9 shows warm anomalies with respect to cold anomalies in the target polar regions that correspond to cooling in the midlatitudes). Similar to the approach taken above (section 3b), we begin with composites 10–20 days before the event. At this lead time, the temperature anomaly over the eastern Siberian and Chukchi Seas is not yet developed, while in the Barents–Kara Seas it is already quite strong. Nevertheless, for both regions circulation anomalies in the midlatitudes are hardly detectable (despite a weak NAO pattern, which is often associated with an anomaly in the Barents–Kara Seas; see Yang et al. 2016). In just five days (5–15 days before the Arctic extreme), the surface temperature anomaly over both Arctic regions becomes strong, causing a local increase in Z300 and affecting the jet to the south of this anomaly. In the following days, a negative Z300 and surface temperature anomaly develop in the midlatitudes downstream of the polar high (in sectors 2 and 3). While cold temperature anomalies over Eurasia have been associated with a blocking over the Barents–Kara Seas in a number of papers (discussed in section 2c), we find a similar pattern in America as a response to the anomalously warm eastern Siberian–Chukchi Seas (note that extreme temperatures over the eastern Siberian–Chukchi Seas are associated with raised temperatures all over the Arctic seas except to the north of the North Atlantic; Fig. 9l).
Differences in (a)–(c) T2m (K), (d)–(f) Z300 (m), and (g)–(i) U300 (m s−1) between warm and cold (warm minus cold) synoptic extremes at (left) 10–20-, (center) 5–15-, and (right) 0–10-day lead times of extremes over the Barents–Kara Seas. Maps are centered on the target region that is marked with a dark blue rectangle. Cross-hatching indicates differences significant at the 95% level. (j)–(r) As in (a)–(i), but for the East Siberian and Chukchi Seas.
Citation: Journal of Climate 34, 10; 10.1175/JCLI-D-20-0371.1
We find that the location of midlatitude extremes is closely related to the position of the jet. High temperatures in the Arctic cause equatorward displacement of the jet due to a decrease in the meridional gradient. This was discussed in other studies (e.g., Petoukhov and Semenov 2010; Screen et al. 2015); however, they argue that the change in the temperature meridional gradient is limited to the high midlatitudes. In our cases, as we focus on extreme temperatures in the Arctic seas, the change in meridional gradient may be stronger leading to a shift of the jet into low midlatitudes and subtropics (Figs. 9i,r). Due to a large distance between the polar temperature anomaly and the jet location, increased waveguides, observed to the south of the Arctic anomalies (Figs. S2 and S3c), do not reach the jet. However, farther downstream, where the jet shifts back into midlatitudes, increased waveguides on the poleward side of the jet (sectors 2 and 3; Figs. S2 and S3c) may lead to wave breaking and formation of a cold anomaly in the midlatitudes downstream of the Arctic anomaly (Figs. 9c,l). The exact location of the midlatitude anomaly depends on the size and strength of the polar anomaly: weaker Arctic anomalies may cause midlatitude anomalies shifted northwest with respect to those found in this study (as in, e.g., McCusker et al. 2016).
Cooling in North America following the warm eastern Siberian and Chukchi Seas is in apparent disagreement with Screen et al. (2015). They find that North American cold extremes are expected to become less frequent as a result of the Arctic sea ice loss. However, their analysis is limited to a region that is to the southeast of the cold anomaly shown in Fig. 9l. We argue that in a warmer world, where jets are shifted poleward, the target region in Screen et al. (2015) will be on the equatorward side of the jet in case of an Arctic warming similar to the one shown in Fig. 9l, while the area on the other side of the jet may still be experiencing cold outbreaks.
We emphasize that cooling in the midlatitudes develop following synoptic Arctic anomalies. The observed seasonal anomalies in the Arctic region have not been able to influence the seasonal temperature in the midlatitudes (Fig. S4). As shown by Gong et al. (2017) and Lee et al. (2017), it is the synoptic-scale intrusions of moisture into the Arctic that play a key role in local temperature extremes. Thus, the synoptic circulation creates local anomalies in the Arctic that are strong enough to affect the midlatitudes, but only for about two weeks around the date of the extreme. This may explain why studies that focus on the seasonal and subseasonal anomalies find that effect of the Arctic amplification on the midlatitudes is “insignificant” (e.g., Manney and Hegglin 2018; Blackport et al. 2019; Blackport and Screen 2020).
As a final note here, some studies (Petoukhov and Semenov 2010; Mori et al. 2019) have suggested that the sign of the midlatitude anomalies may change in case of a larger sea ice loss. Our analysis is based on reanalysis data; hence, we are limited with the observed range of temperatures in the Arctic, which might increase should the sea ice loss continue. In that case, other mechanisms may be involved in the circulation changes, leading to different results.
4. Discussion
We have examined Rossby waveguides at the time of warm and cold events in the NH midlatitudes to identify teleconnection patterns that may be responsible for those extremes. An important aspect of our analysis is that waveguides are identified on the daily time scale that allow propagation of synoptic waves (Ks > 4) in high latitudes, which is in contrast to the seasonal or monthly analysis that only allow planetary waves (Ks ≤ 4) to the north of 40°–50°N (Fig. 2). Even when seasonal extremes are analyzed, we first identify areas open for wave propagation in daily data and then produce seasonally averaged Ks% statistics.
Wirth et al. (2018) note that amplified Rossby waves are often precursors to extreme weather. Importantly, they suggest distinguishing between different types of Rossby waves: one being the low-frequency variety of waves traditionally referred to as “Rossby wave trains” and the other a more transient (high-frequency) variety propagating along the midlatitude jets. While separation between the “waves” and background flow gets ambiguous at high temporal resolution, we believe that our results show two types of waves associated with extreme events:
Wave trains, seen as a series of Z300 anomalies across the NH, suggest an amplification of low-frequency Rossby waves on the synoptic scale (recall weak pre-existing anomalies in Z300). On the seasonal time scale these low-frequency (or stationary) waves are seen as seasonal anomalies in Z300.
Anomalies in daily Ks% reflect the synoptic-scale (high frequency) type of Rossby waves that develop along sharp PV gradients following an amplification of planetary wave trains (low-frequency Rossby waves). These synoptic-scale waves can amplify existing local temperature anomalies or create a new one.
Even though our focus has been on the stationary wavenumber Ks, identified waveguides will also be conducive to nonstationary wave propagation. This is because the phase speed of waves with nonzero frequency offsets the flow speed in Eq. (2) [see the equation for nonstationary waves in Karoly (1983)], increasing Ks values but not having a significant effect on the location of the axis of a waveguide. Thus, our results do not imply stationarity of the synoptic-scale waves. However, even though the phase speed of stationary Rossby waves is zero, their group velocity does not have to be zero since they are dispersive waves. This explains the downstream development of stationary Rossby waves (Orlanski and Katzfey 1991; Chang and Orlanski 1994; Wirth et al. 2018). Furthermore, we chose not to focus on any particular wavenumber, so that all possible waves that can facilitate communication between our regions of interest are captured by our approach. We note here that the zonal wavelength at different latitudes varies with the cosine of latitude, meaning that, for example, wavenumber 6 at 40°N would have the same length as wave 4 at 60°N and wave 3 closer to 70°N.
Our analysis has been conducted for horizontal wave propagation at the 300-hPa level. Studies at other levels may offer additional teleconnection patterns that may not be apparent in our results. Moreover, vertically propagating Rossby waves, which can be associated with Madden–Julian oscillation or sudden stratospheric warming events, are known to cause temperature extremes in different regions of the NH (Woollings et al. 2010; Kodera et al. 2016; Karpechko et al. 2017; Screen et al. 2018; King et al. 2019; Barrett 2019).
5. Conclusions
Our study examines the role of Rossby waves in the upper troposphere in creating temperature extremes in selected regions of the NH. We show that on the daily time scale midlatitudes are often connected to both polar and tropical latitudes via elongated filaments of areas that allow wave propagation. These areas usually stretch along atmospheric jets but can also be found in regions of local wind anomalies created by synoptic systems (Fig. 2h). As opposed to the seasonal time scale, Ks calculated from daily wind fields reach higher values in all latitudes, thereby allowing for propagation of synoptic waves even in high latitudes. We note here that despite using the stationary wavenumber, Ks, nonstationary synoptic waves can show similar patterns.
Anomalously cold/warm winter seasons in Europe were associated with amplified stationary wave trains in the North Atlantic. Northwestern North America is influenced by anomalies in both tropical Pacific and tropical Atlantic. Western Siberia is, on the one hand, connected to the tropical oceans similar to American regions and on the other hand influenced by anomalous wave propagation from the Arctic.
On the synoptic time scale, pre-existing temperature anomalies in the midlatitudes lead to amplification of the NH wave trains, similar to those identified on the seasonal time scale, accompanied by a development of synoptic-scale Rossby waves along the sharp PV gradients. These synoptic-scale waves, developing either on the poleward or equatorward sides of the jet, help amplify cold or warm temperature anomalies, respectively, leading to extreme events.
Warming of the Arctic seas can create, subject to the criteria identified here, local low-frequency circulation anomalies in the midlatitudes to the south and southeast of the Arctic warming. These low-frequency anomalies, in their turn, promote development of the synoptic-scale Rossby waves and create temperature anomalies in the midlatitudes downstream of the polar warming.
Our analysis demonstrates how polar, middle, and tropical latitudes can interact with each other, producing anomalous weather patterns in the northern midlatitudes. We will apply our approach to investigate the consequences of Antarctic sea ice changes and Southern Hemisphere (SH) Hadley circulation trends on weather patterns and Rossby waves. Rudeva et al. (2019) have demonstrated key links between trends in SH synoptic behavior and the Hadley circulation, and Freitas and Ambrizzi (2012) and Freitas et al. (2017) have already highlighted some of the connections between all of these factors. Waveguides identified in the upper troposphere at daily time scale seem to be the actual pathways for long distance horizontal Rossby wave propagation across different latitudes. Our approach helps to identify regions that are potentially more sensitive to the warming Arctic and tropical oceans. Further analysis is planned on possible changes of teleconnection patterns in the future climates, and the consequences of these in both the Northern and Southern Hemispheres.
Acknowledgments
We thank three anonymous reviewers for their very constructive comments on the previous version of this paper that helped significantly improve the manuscript. We thank James Screen for his input at the early stage of this work. We also thank Guomin Wang and Roseanna McKay for their comments on the first draft. This research was supported by an Australian Research Council grant (DP160101997) and the Department of Environment, Land, Water and Planning through the Victorian Water and Climate Initiative.
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