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Article

Seasonal Temperature Extremes in the North Eurasian Regions Depending on ENSO Phase Transitions

by
Igor I. Mokhov
1,2,* and
Alexander V. Timazhev
1
1
A.M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, 119017 Moscow, Russia
2
Faculty of Physics, Lomonosov Moscow State University, 119991 Moscow, Russia
*
Author to whom correspondence should be addressed.
Atmosphere 2022, 13(2), 249; https://doi.org/10.3390/atmos13020249
Submission received: 9 December 2021 / Revised: 28 January 2022 / Accepted: 29 January 2022 / Published: 31 January 2022
(This article belongs to the Special Issue ENSO Atmospheric Teleconnections to the Mid-to-High Latitudes)
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Abstract

:
Seasonal anomalies of surface air temperature were analyzed for the North Eurasian regions in mid-latitudes using long-term data from the end of the 19th century with an assessment of El Niño Southern Oscillation (ENSO) effects. In particular, temperature anomalies in the spring–summer months for the European (ER) and Asian (AR) Russian regions for different phase transitions of the ENSO phenomena were estimated using the Niño3, Niño3.4 and Niño4 indices. The largest frequency of the extremely high-temperature and drought conditions in spring–summer months in ER was detected for years starting in the El Niño phase with the transition to the La Niña phase at the end of the year. Such conditions were realized in ER in summer 2010 (“Russian heatwave”). The corresponding largest frequency of high temperature in AR was obtained for conditions with the continuation of the El Niño phase during the whole year. Such conditions in AR were noted, in particular, in the summer of 2015, with an extremely high temperature and extremely low precipitation in the Lake Baikal basin.

1. Introduction

Extratropical continental regions are characterized by high interannual climate variability, particularly in the North Eurasian regions. The most significant contribution to interannual variability in global surface air temperature is associated with El Niño/Southern Oscillation (ENSO) effects [1,2]. The impact of the El Niño and La Niña phenomena has been found in many regions, including the regions of Northern Eurasia [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29].
The first results concerning the ENSO influence on the extratropical latitudes were obtained by J. Bjerknes in the 1960s [3,4]. He noted that the large positive anomalies of sea surface temperature at the equator in the central and eastern Pacific are accompanied by abnormally intense westerlies in the mid-latitude atmosphere. This is related to the intensification of convection and Hadley circulation in the equatorial atmosphere with the increase in the angular momentum flux towards westerlies in the mid-latitude atmosphere.
The mechanisms and peculiarities of the influence of tropics and ENSO, in particular, on extratropical latitudes are analyzed in many studies. Some mechanisms of the influence of El Niño events on extratropical latitudes are considered in review [16]. It was noted that the El Niño effects on the extratropical latitudes are associated with the dynamics of planetary waves, with the displacement of cyclone trajectories, with interaction between ENSO and monsoons.
The possible influence of ENSO warm and cold extremes on mid-latitude circulation in the North Atlantic/European sector was considered in [5] in terms of phenomenological, statistical and physical analysis of observations. ENSO-related differences in blocking frequency were found in [6] to be associated with changes in both the mean and variance of the atmospheric circulation over the North Pacific. The results of an analysis of the relationship between ENSO and climate anomalies in Russian regions from observations were presented in [9]. The interannual variability of atmospheric blockings was also examined in [11] with respect to ENSO-related variability. It was found that Northern (Southern) Hemisphere blocking events were stronger and more frequent during La Niña (El Niño) years (see also [15]). The mutual influence of the ENSO, North Atlantic Oscillation and Arctic Oscillation from the time series of their various indices based on nonlinear methods of coupling diagnosis was analyzed in [15]. The potential predictability of seasonal variability of the meteorological fields in different regions of the Northern Hemisphere mid-latitudes was estimated in [17] for different ENSO events. The goals of the work [21] were to determine whether blocking can be used as an indicator of drought potential and determine whether ENSO or the transition of the ENSO phase may be influential in determining drought-producing flow regimes, such as that in summer 2010 in the European part of Russia (see also [19]). Physical processes of the ENSO summer teleconnections and remote impacts on the extratropical regions in the Northern Hemisphere during a multiyear La Niña life cycle are analyzed in [27].
The connection with the ENSO is manifested in the peculiarities of the anomalies of the atmospheric centers of action (see, for example, [7,13,16,24,28]). In [14], for example, a mechanism for teleconnections from the tropics is proposed with an allowance for the propagation of Rossby waves to extratropical latitudes. The influence of El Niño phenomena on blocking activity in the atmosphere was analyzed in many studies [25]. Connections with the ENSO features were noted in [18,20,22,26] for prolonged atmospheric blocking conditions and, consequently, strong heatwaves, droughts and forest fires in the summer of 2010 in the European part of Russia. Significant El Niño effects on variations in the Volga River runoff and Caspian Sea level were revealed in [8,10].
Different types of El Niño phenomena have been identified, and a number of indices have been proposed for their detection. The canonical El Niño phenomenon is characterized by quite strong positive surface temperature anomalies at the equatorial latitudes of the eastern part of the Pacific Ocean. The opposite phase with corresponding rather strong negative temperature anomalies is called La Niña. Along with this, another type of manifestation of El Niño processes is distinguished with rather strong surface temperature anomalies in the central part of the Pacific Ocean at equatorial latitudes—the so-called El Niño Modoki [30]. According to [23,31], the propagation of the influence of tropical anomalies into the extratropical atmosphere differs significantly for different types of El Niño events. A significant number of El Niño events during the last few decades have been detected in the central rather than eastern Pacific [2] (see also [32]).
In this paper, the relationship of seasonal temperature anomalies in the North Eurasian regions with the ENSO phase transitions using data from observations from the end of the 19th century is estimated. Regional effects of different types of El Niño events are evaluated using different ENSO indices.

2. Materials and Methods

Spring–summer (May–June–July, MJJ) surface air temperature (SAT) δT anomalies in European (ER) and Asian (AR) mid-latitude regions in North Eurasia based on observations for the 1891–2015 period [33,34].
Relationships of regional SAT anomalies in ER and AR with ENSO were analyzed. Regional effects were assessed with the use of various El Niño indices as characteristics of ENSO phenomena of different types. As well as canonical El Niño events with significant positive anomalies of sea surface temperature (SST) in the eastern equatorial Pacific (EP El Niño), El Niño events with the most significant anomalies of SST in the central equatorial Pacific (CP El Niño) have often been observed in recent decades [2,30].
For estimation of the El Niño/La Niña effects, we used their indices characterized by the sea surface temperature (SST) in the Niño3 (150°–90° W, 4° N–4° S), Niño3.4 (170°–120° W, 4° N–4° S) and Niño4 (160° E–150° W, 4° N–4° S) regions in the equatorial latitudes of the Pacific Ocean (ftp://www.coaps.fsu.edu/, accessed on 31 January 2022). The El Niño (E) and La Niña (L) phases were distinguished using the 5-month moving averaging of the SST anomaly in the Niño3 region (JMA index). El Niño (warm) and La Niña (cold) phases were defined by the index values of at least 0.5 °C and at most −0.5 °C, respectively, over six consecutive months (including October–December). All the other cases were characterized as neutral phases (N).
Nine possible phases of transition for ENSO were analyzed, including N→E as a transition from the neutral phase in the beginning of the year (winter in the Northern Hemisphere) to the El Niño phase at the beginning of the next year, N→L as a transition from neutral to La Niña phase and N→N as a prolongation of the neutral phase. The number of years (n) starting from the neutral phase is approximately half of the total number (125) of analyzed years. The number of years starting from the El Niño and La Niña phases amounted to approximately a quarter of the total number of analyzed years.
The frequency for each ENSO phase transition was calculated as the number of cases with a positive (negative) SAT anomaly (including extreme events) related to all cases of this transition.
To assess the frequency of possible regional climate anomalies, the ERA-Interim reanalysis data [35] for the period of 1979–2015 and the results of ensemble model forecasts of El Niño evolution during a year (http://iri.columbia.edu, accessed 31 January 2022) were used along with the observational data.

3. Results

Figure 1a presents estimates of the frequencies of various ENSO phase transitions characterized by the indices Niño3, Niño3.4 and Niño4 for a 125-year period (1891–2015). According to Figure 1, the statistics of phase transitions for ENSO differ significantly when using different indices. If the indices Niño3 and Niño4 are used to classify transitions, then the number of years starting from the neutral phase N in 1891–2015 is 68, and in the case of using the Niño3.4 index, their number is 53. The number of years starting in phases E and L is less than a quarter of all years (28 or 29), depending on the index used—Niño3 or Niño4. In the case of using the Niño3.4 index, the number of years beginning with phases E and L (36) is noticeably larger—approximately a third of all years.
The most common transitions are N→N. For the ENSO processes diagnosed using the Niño3 index, which characterizes the canonical El Niño with anomalously high SST at the equatorial latitudes of the eastern part of the Pacific Ocean, the E→E transitions are rarest. Moreover, they appear twice as often in the case of using the Niño4 index, which characterizes the El Niño conditions in the equatorial latitudes of the central part of the Pacific Ocean. When using the Niño4 index, L→E transitions are rarest; when using the Niño3 index, they are diagnosed almost twice as often.
Figure 2 shows the estimates of the frequency of positive and negative SAT anomalies δT, including estimates of frequencies for extreme positive (δT > 1 K) and extreme negative (δT ≤ −1 K) SAT anomalies, in MJJ for ER and AR for various ENSO phase transitions over 125 years using the indices Niño3, Niño3.4 and Niño4. In general, the frequency of positive temperature anomalies and extreme positive anomalies (δT > 1 K) in ER in MJJ is higher in the years beginning in the El Niño (E) phase. The frequency of positive SAT anomalies in ER in the spring–summer months is the lowest for the years beginning in the La Niña phase. This was noted using different ENSO indices. The highest frequency of positive SAT anomalies was estimated for the transition from El Niño to the La Niña phase E→L. Positive SAT anomalies for ER in MJJ for the E→L transitions were noted in 8 cases out of 9 using the Niño3 index, in 7 out of 8 cases using the Niño4 index and in 10 cases out of 12 using the Niño3.4 index.
For years starting in the El Niño phase, the minimum frequency of negative SAT anomalies in MJJ in ER was obtained with the lowest frequency for the E→L transition. The frequency of positive SAT anomalies in ER for the E→L transition, determined by the Niño3 index, was estimated to be eight-times higher than the frequency of negative (and zero) SAT anomalies. It is significant that, for the E→L transition (using all used indices), extreme negative SAT anomalies (δT ≤ −1 K) have never been observed in the ER for 125 years.
The frequency of positive SAT anomalies in MJJ in ER for the E→L transition, detected using the Niño3 index, is two-times higher than in AR. Moreover, for this transition, detected using different indices, the frequency of extreme positive anomalies in ER with δT > 1 K was estimated to be 4–5-times higher than in AR.
In general, for the years beginning with the El Niño phase, the frequency of realizing extreme SAT anomalies of different signs in both ER and AR is increased regardless of the used El Niño index.
The highest frequency of extreme positive SAT anomalies in AR in MJJ was revealed for the E→E transition. This was noted in 6 out of 8 cases using the Niño4 index and in 2 cases out of 4 using the Niño3 index.
With all the used ENSO indices, extreme negative SAT anomalies were never observed in MJJ in ER for the E→L transition or in AR for the N→L and L→L transitions. It should be noted the effect of the manifestation of a relatively high frequency of regional SAT anomalies when using the combined Niño3.4 index with significantly higher (2–3 times) estimates of the frequency when using the Niño3 and Niño4 indices, as, for example, for extreme negative SAT anomalies in the ER for transition N→L. When using the Niño3.4 index, a fairly high frequency of both positive and negative extreme SAT anomalies can be estimated, particularly in AR for the E→E transition, while, when using the Niño3 and Niño4 indices, the frequency of extreme positive SAT anomalies in AR estimates is much higher (in particular, six-times larger when using Niño4).
Among the significant differences in the estimates obtained using different El Niño indices, we should note the differences in the frequency of negative temperature anomalies for ER during the E→E phase transition (see Figure 2). Using the Niño4 index for this phase transition, along with the highest frequency of extremely high temperature in the AR, the maximum frequency of extreme negative temperature anomalies in the ER was also noted. When using the Niño3 index, this has not been noted even once in 125 years. Conversely, when using the Niño4 index for the L→E phase transition, extreme negative temperature anomalies in the ER were never noted, while when using the Niño3 index, the frequency of such anomalies is quite high.
For relatively rare events, it is necessary to evaluate the statistical significance of the results obtained in a comparative analysis for different time intervals. Along with the analysis of the data for the 125-year period, the corresponding analysis was carried out for the last few decades. Figure 1b,c present estimates of the frequencies of various ENSO phase transitions, characterized by the indices Niño3, Niño3.4 and Niño4, for the periods 1950–2015 (b) and 1980–2015 (c). According to Figure 1, the statistics of phase transitions for ENSO differ significantly not only when using different indices, but also for different periods.
A more detailed analysis was carried out for the transitions E→L and E→E, for which, for a 125-year period, the highest estimates of the frequency of extreme values of surface air temperature in ER and AR were revealed. Table 1 presents estimates of the frequency of positive and extreme positive (>1 K) SAT anomalies δT in MJJ for ER during the E→L transition (a) and for AR during the E→E transition (b) using the indices Niño3, Niño3.4 and Niño4 for different periods: 1891–2015, 1950–2015 and 1980–2015. The Table 1 shows the number of corresponding (> 0 or > 1 K) anomalies (in the numerator) and the total number of phase transitions (in the denominator) E→L for ER (a) and E→E for AR (b).
The analysis carried out for different time intervals revealed differences in the significance of estimates of the frequencies of regional temperature anomalies associated with different ENSO phase transitions. At the same time, the results of the analysis for recent decades generally confirm the conclusions drawn using data from the late 19th century on the peculiarities of the relationship between extreme anomalies of surface air temperature in the spring–summer months in the European and Asian regions of Northern Eurasia with phase transitions of El Niño phenomena, particularly with transitions E→L (for ER) and E→E (for AR). It is significant that the estimates for shorter intervals are more statistically significant than for longer intervals (before the 1980s and even more so until the 1950s) with less reliable data for earlier periods. According to the most reliable data since 1980, in all cases of such transitions in the spring-summer months, positive temperature anomalies were noted in the ER (AR) when using different ENSO indices: Niño3–4 (2) cases, Niño3.4–6 (2) cases and Niño4–5 (6) cases. Also, in all cases, during the E→L (4 cases) and E→E (2 cases) transitions for the canonical El Niño, characterized by the Niño3 index, extreme positive temperature anomalies were noted in the ER and AR, respectively. For El Niño, characterized by the Niño4 index, in all (6) cases with E→E transitions, extreme positive temperature anomalies in the AR are noted, and in the ER, extreme positive temperature anomalies are noted for all E→L transitions, except for one—with a positive anomaly, but not with extremely high temperature (4 cases). The identified regional features for the E→L and E→E transitions using different indicators of El Niño events are noted for different analyzed periods and stand out significantly against the background of remarkable variations in the frequencies of other interphase transitions with significant differences for different types of El Niño events.
The significance of the noted features is characterized by the ratio of the number of positive and negative anomalies of surface air temperature during the E→L transition for the ER and during the E→E transition for the AR, not only using data from the 1980s, but also for earlier periods. For example, for the period 1891–2015 with 9 E→L phase transitions using the Niño3 index and 8 E→L phase transitions using the Niño4 index, in only one case the temperature anomaly for ER was negative. For the period 1950–2015 for all 7 E→L phase transitions using the Niño4 index, the temperature anomaly for ER was negative.
Figure 3 shows an example of the spatial distribution of SAT anomalies in MJJ and June–July–August (JJA) in Northern Eurasia for the E→L transition in 2010 using ERA-Interim reanalysis data. The transition from the El Niño phase in early 2010 to the La Niña phase by the end of this year was characterized by the strongest positive SAT anomaly in the summer in the ER over a 125-year period, with unprecedented consequences of drought and fires associated with extremely prolonged atmospheric blocking conditions [18]. An abnormally high SAT is noted in ER for the E→L transition, both for individual months and for seasonal intervals (for MJJ and JJA). Such anomalies are typical for the conditions of blocking the zonal tropospheric flow in middle latitudes [26].
Spatial features of the SAT anomalies in Figure 3 reveal a wave structure with alternating longitudinal sectors with positive and negative SAT anomalies from west to east—from Eastern Europe to the regions of Western and Central Siberia, and then to the East Asian regions. The features of the noted structure can be associated with the stationing of the corresponding planetary wave (Rossby wave) with wavenumber 4 at mid-latitudes [18]. The longitudinal extent of Eurasia corresponds to approximately two Rossby waves (see also [36]). A possible mechanism of the influence of the tropics on extratropical latitudes and the propagation of Rossby waves is proposed, for example, in [14].
Figure 4 shows the corresponding spatial distribution of average SAT anomalies in MJJ and JJA in Northern Eurasia for the E→E transition in 2015 using ERA-Interim reanalysis data.
SAT anomalies in 2010 and 2015 on Figure 3 and Figure 4 are presented just as illustrations of characteristic spatial structure of anomalies for two selected phase transitions (E→L for ER and E→E for AR). Similar figures were obtained for other case studies for E→L transitions in 1988, 1998 and 2007 (in addition to 2010). The corresponding composites for all E→L transitions after 1980 are very similar to the case of the E→L transition in 2010.

4. Discussion

The results of the performed analysis indicate the highest frequency of an extremely high surface air temperature in the spring–summer months in ER for the E→L transition. This was noted for all indices used for ENSO detection. The peculiarity of the E→L transition was manifested, in particular, in the summer of 2010, when extremely positive SAT anomalies and severe drought were accompanied by fires and unprecedented socio-economic and environmental consequences [18,37].
In AR, the greatest frequency of abnormally high SAT values in the spring–summer months was obtained for the E→E transition with the use of different ENSO indices. Such a transition was noted, in particular, in 2015. According to meteorological observations (https://www.meteorf.ru, accessed on 31 January 2022), in the summer of 2015, along with the extremely high surface air temperature in Asian regions in Russia, including the Lake Baikal region, there was a record shortage of precipitation in Cisbaikalia and Transbaikalia, which affected the level of Lake Baikal.
The proposed approach can be used to obtain predictive estimates of the risk of anomalous regional weather and climatic conditions using predictive model simulations for the evolution of ENSO processes. In particular, the estimates of anomalously high SAT values in MJJ in AR for the E→E transition, made in [22], were confirmed in 2015.
The results of the analysis for recent decades generally confirm the conclusions drawn using data from the late 19th century on the peculiarities of the relationship between extreme anomalies of surface air temperature in the spring–summer months in the European and Asian regions of Northern Eurasia with phase transitions of El Niño phenomena, particularly with transitions E→L and E→E. At the same time, estimates of the realizability of positive temperature anomalies, including extremely high values, in the spring–summer months in ER for the E→L transition and in AR for the E→E transition in recent decades (with more reliable data) were found to be higher (from 80 up to 100%). The identified regional features for the E→L and E→E transitions when using different indicators of El Niño events were noted for different periods against the background of significant changes in the frequencies of interphase transitions and significant differences for different types of El Niño events.
Various factors influence the formation of regional temperature anomalies in the North Eurasian regions, as evidenced by many publications. In this paper, emphasis is placed on a more detailed phase analysis of the potential impact of El Niño phenomena, which are associated with the strongest interannual variations in global surface air temperature with a corresponding effect on different processes in various regions of the world, including Northern Eurasia regions. In particular, a significant relationship with the ENSO was noted for atmospheric centers of action, including the Arctic, Hawaiian and Greenland Highs [28], with the influence on the formation of temperature anomalies. According to [15], for example, the influence of ENSO on the atmospheric circulation in the middle latitudes is possible through the influence on the North Atlantic Oscillation. The corresponding conditions for the propagation and stationing of planetary waves and the formation of atmospheric blockings also change, as a result of which a prolonged influx of warm dry air can lead to extremely high regional temperatures, as, for example, in the summer of 2010 in the eastern part of Europe [18,38,39,40,41,42,43].
The noted anomalies of individual years differ against the background of a general tendency towards a decrease in precipitation for ER and AR in the spring–summer months with an increase in SAT based on observations since the late 19th century [44]. Longer data series, along with ensemble modeling, should lead to more statistically significant estimates, especially for relatively rare transitions such as E→E. It is important to note that there is a risk of an increase in the frequency of stronger regional climate anomalies due to a possible increase in the amplitude and frequency of El Niño/La Niña events under the continuation of global warming [2,45].
The purpose of this work is a statistical analysis of the features of regional temperature anomalies for various ENSO phase transitions. To analyze the comparative role of various regional and global mechanisms, including interrelated ones, it is necessary to use numerical model experiments.

Author Contributions

Methodology, supervision, writing—original draft preparation, I.I.M.; software, visualization, A.V.T., formal analysis, validation, both co-authors. All authors have read and agreed to the published version of the manuscript.

Funding

The study was supported by Ministry of Science and Higher Education of the Russian Federation (Agreement No. 075-15-2021-577 with A.M. Obukhov Institute of Atmospheric Physics RAS). The analysis of different types of El Niño was carried out within the framework of the Russian Science Foundation project (19-17-00240).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All used datasets, such as ERA-Interim reanalysis and other information, are mentioned in the text.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

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Atmosphere 13 00249 g001a 550Atmosphere 13 00249 g001b 550
Figure 1. Estimates of frequency for different ENSO phase transitions characterized by various ENSO indices (Niño3, Niño3.4, Niño4) for the periods (a) 1891–2015; (b) 1950–2015; (c) 1980–2015.
Figure 1. Estimates of frequency for different ENSO phase transitions characterized by various ENSO indices (Niño3, Niño3.4, Niño4) for the periods (a) 1891–2015; (b) 1950–2015; (c) 1980–2015.
Atmosphere 13 00249 g001aAtmosphere 13 00249 g001b
Atmosphere 13 00249 g002 550
Figure 2. Frequency estimates of different positive (δT > 0 K—pink color; δT > 1 K—red color) and negative (δT ≤ 0 K—blue color; δT ≤ −1 K—dark blue color) SAT anomalies δT for ER and AR in MJJ for different ENSO phase transitions from observations during 1891–2015 with the use of Niño3, Niño3.4 and Niño4 indices.
Figure 2. Frequency estimates of different positive (δT > 0 K—pink color; δT > 1 K—red color) and negative (δT ≤ 0 K—blue color; δT ≤ −1 K—dark blue color) SAT anomalies δT for ER and AR in MJJ for different ENSO phase transitions from observations during 1891–2015 with the use of Niño3, Niño3.4 and Niño4 indices.
Atmosphere 13 00249 g002
Atmosphere 13 00249 g003 550
Figure 3. Spatial distributions of mean SAT anomalies in MJJ and JJA in 2010 with the E→L transitions from ERA-Interim reanalysis data.
Figure 3. Spatial distributions of mean SAT anomalies in MJJ and JJA in 2010 with the E→L transitions from ERA-Interim reanalysis data.
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Atmosphere 13 00249 g004 550
Figure 4. Spatial distributions of mean SAT anomalies in MJJ and JJA in 2015 with the E→E transitions from ERA-Interim reanalysis data.
Figure 4. Spatial distributions of mean SAT anomalies in MJJ and JJA in 2015 with the E→E transitions from ERA-Interim reanalysis data.
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Table
Table 1. Estimates of the frequency of positive and extreme positive (>1 K) SAT anomalies δT in MJJ (in the numerator) and the total number of phase transitions (in the denominator) using the indices Niño3, Niño3.4 and Niño4 for different periods, 1891–2015, 1950–2015 and 1980–2015, (a) for ER during the E→L transition; (b) for AR during the E→E transition.
Table 1. Estimates of the frequency of positive and extreme positive (>1 K) SAT anomalies δT in MJJ (in the numerator) and the total number of phase transitions (in the denominator) using the indices Niño3, Niño3.4 and Niño4 for different periods, 1891–2015, 1950–2015 and 1980–2015, (a) for ER during the E→L transition; (b) for AR during the E→E transition.
(a)
ER
E→L		Niño4Niño3.4Niño3
>0>1 K>0>1 K>0>1 K
1891–20157/84/810/125/128/95/9
1950–20157/74/78/95/96/74/7
1980–20155/54/56/65/64/44/4
(b)
AR
E→E		Niño4Niño3.4Niño3
>0>1 K>0>1 K>0>1 K
1891–20156/86/85/94/92/42/4
1950–20156/76/73/43/42/22/2
1980–20156/66/62/22/22/22/2
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Mokhov, I.I.; Timazhev, A.V. Seasonal Temperature Extremes in the North Eurasian Regions Depending on ENSO Phase Transitions. Atmosphere 2022, 13, 249. https://doi.org/10.3390/atmos13020249

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Mokhov II, Timazhev AV. Seasonal Temperature Extremes in the North Eurasian Regions Depending on ENSO Phase Transitions. Atmosphere. 2022; 13(2):249. https://doi.org/10.3390/atmos13020249

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Mokhov, Igor I., and Alexander V. Timazhev. 2022. "Seasonal Temperature Extremes in the North Eurasian Regions Depending on ENSO Phase Transitions" Atmosphere 13, no. 2: 249. https://doi.org/10.3390/atmos13020249

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