1. Introduction
In the summer of 2018, several record-breaking heat wave (HW) events were experienced over northwestern Europe (Drouard et al. 2019), northeast Asia (Hsu et al. 2020), and the middle of the Yangtze River basin (Lu et al. 2022), resulting in increased mortality and socioeconomic losses in relevant countries. The summer Eurasian HW events in 2018 were related to the propagations of atmospheric intraseasonal oscillation (ISO) along different latitudes and longitudes. Indeed, the HW events in the past five years have been found to be closely related to the ISO (Xu et al. 2019; Lin et al. 2022).
The ISO, especially the Madden–Julian oscillation (Madden and Julian 1971, 1972), is the dominant intraseasonal phenomenon in the tropics (Zhang 2005). However, atmospheric intraseasonal variations exist not only in the tropics, but also in the mid–high latitudes (Anderson and Rosen 1983; Weickmann et al. 1985; Ghil and Mo 1991; Yang and Li 2003; Li et al. 2018; X. Qi et al. 2019; Wei et al. 2019; Zhang et al. 2022; Xiu et al. 2022). In general, the origin of the intraseasonal variability in mid–high latitudes may arise from the tropical forcing (Lau and Phillips 1986; Kawamura et al. 1996; Mori and Watanabe 2008; He et al. 2011; Abdillah et al. 2018; Song and Wu 2019a,b, 2020), topographic effects (Jin and Ghil 1990; Lott et al. 2004a,b), local air–sea interaction (Wang et al. 2012), atmospheric internal variability (Terao 1998; Wang et al. 2013; Xu et al. 2020; Xiu et al. 2022), and tropical–extratropical interaction (Ding and Wang 2007; Park et al. 2010; Yang et al. 2010; Stan et al. 2017; Zhu and Yang 2021). For instance, Jin and Ghil (1990) suggested that the intraseasonal oscillations may arise due to the wave–mean flow interaction, which is partially attributed to the topographic drag effect. Recently, Xu et al. (2020) highlighted that the growth and decay of the intraseasonal teleconnection pattern over Eurasia along the polar front jet is mainly driven by the atmospheric internal processes, the nonlinear wave–wave interaction in particular. The intraseasonal teleconnections along the upper-level jet waveguide have also been investigated by other studies (e.g., Watanabe 2004; Jiao et al. 2019).
The intraseasonal variability in the mid–high latitudes over Eurasia can further affect the subseasonal climate variability in the Northern Hemisphere independently or synergistically with the tropical ISO. The midlatitude subseasonal variability can affect the Indian monsoon droughts when the tropical signal is absent (Borah et al. 2020). The variability of summer rainfall over the eastern China is significantly affected by the midlatitude ISO (Yang et al. 2013; Qi et al. 2013, Y. Qi et al. 2019; Li and Mao 2019) and by the coordinated tropical–extratropical ISO (Yang and Li 2003; Yang et al. 2010). Unlike the rainfall variability, the intraseasonal variability of Eurasian temperature has been paid much less attention. Previous studies mainly focused on the intraseasonal temperature variability in wintertime (Yang and Li 2016; Yao et al. 2016; Song and Wu 2019a,b, 2020) instead of boreal summer. The summer (June–August) intraseasonal temperature variability over Eurasia and its associated physical mechanisms might differ from those of winter. Using the reanalysis data, Zhao et al. (2017) and Liang et al. (2018) investigated the intraseasonal variability of summer air temperature in eastern China. They pointed out that the ISO in mid–high latitudes is important for China’s summer temperature variability. On the other hand, Eurasian HW events, which are high-impact weather phenomena, are affected by the tropical and midlatitude ISO (Chen and Zhai 2017; Hsu et al. 2017; X. Qi et al. 2019; Kornhuber et al. 2020; Hsu et al. 2020; Xu et al. 2021). However, the summer intraseasonal temperature variability and its relationship with the HW events over entire Eurasia have not been investigated.
The present study investigates the intraseasonal variability of summer (June–August) surface air temperature (SAT) over all of Eurasia and the related HW occurrence. In fact, Wei et al. (2017) found a summer rainfall seesaw over northern China and central Asia on the interannual time scale. Chen et al. (2016) pointed out an interannual SAT tripole with two centers over eastern Eurasia and central Eurasia during boreal spring. Some studies found intraseasonal surface air temperature variations over the Eurasian continent in winter (Yang and Li 2016; Yao et al. 2016; Xiu et al. 2022). However, it is unclear if the variability of summer SAT anomalies across the entire Eurasian continent exists on the intraseasonal time scale. The intraseasonal SAT variability during the summer season remains insufficiently studied. More importantly, there is a lack of knowledge regarding the association of the intraseasonal SAT variability with HW events. In this study we will investigate the intraseasonal variability of summer SAT over the Eurasian continent and its forming mechanism, as well as the associated HW events.
2. Data and methods
a. Data
The three-dimensional daily wind velocity, air temperature, and geopotential height are obtained from the National Centers for Environmental Prediction–National Center for Atmospheric Research reanalysis 1 products (NCEP–NCAR1) with a horizontal resolution of 2.5° × 2.5° (Kalnay et al. 1996). The daily SAT at 2 m and surface pressure data are also from NCEP–NCAR1, and the daily SAT at 2-m data are Gaussian gridded data with 192 × 94 horizontal points. We also use the SAT data from the Japanese 55-year Reanalysis (JRA-55; Kobayashi et al. 2015) with a horizontal resolution of 1.25° × 1.25°.
b. Methods
To evaluate the intraseasonal variability, first we calculate the long-term daily climatology and the first four harmonics of climatological annual cycle for the period of 1979–2018. Then, the daily anomalies are calculated by subtracting the smoothed daily climatology from the original data, and then a 10–90-day Lanczos bandpass filter (Duchon 1979) is applied to obtain the intraseasonal variability. Subsequently, we utilize only the daily data from 1 June to 31 August during boreal summer of 1979–2018 for further analysis. This method was widely used in previous studies to extract the intraseasonal variability over extratropical regions (Yang and Li 2016; Zhu and Yang 2021; Xiu et al. 2022), although there may be differences in the specific time range selection.
Heat waves (HW) are defined as the local daily SAT exceeding the 95th percentile for at least 3 consecutive days. This definition of HW is similar to that defined in previous studies (Hsu et al. 2017; Lau and Nath 2014). Here, we utilize the raw summer SAT anomalies without any filtering to calculate the HW occurrence, while the intraseasonal variability of SAT is calculated by using the data with a 10–90-day bandpass filter being applied. In calculating the autocorrelation of the time series, we apply the method of equivalent sample size introduced by Zwiers and von Storch (1995) to obtain the effective degree of freedom.
3. Results
a. Relationships of intraseasonal SAT variability between central and eastern Eurasia
During boreal summer, the intraseasonal SAT variance explains more than 40% of total variance over Eurasia (Fig. 1a), suggesting the importance of intraseasonal variability of SAT. We further evaluate the variability of SAT over Eurasia on the intraseasonal time scale of 10–90 days. The highest intraseasonal variability of SAT occurs in northern Eurasia, with values exceeding 3.5°C over central Siberia. Two other hot spots with high intraseasonal variability can be found in the midlatitude between 45° and 55°N (Fig. 1b). One is located over the western region of northeast China, and the other over the northwest of Lake Balkhash. The results using JRA-55 data (Fig. 1c) show a similar pattern of intraseasonal SAT standard deviation with two midlatitude hot spots in the same regions as the NCEP–NCAR1 results (Fig. 1b), although the magnitudes are relatively weaker. Indeed, the main results of our study remain unchanged when using the JRA-55 or ERA5 data (figure not shown). Thus, we only show the NCEP–NCAR1 results hereafter. Given the strong intraseasonal variability of SAT anomalies in the mid–high latitudes of the eastern and central Eurasia with latitudes ranging widely from 40° to 70°N (Fig. 1), we define two indices, ICE and IEE, to represent the intraseasonal SAT variability in the central Eurasia (CE) and eastern Eurasia (EE) regions, respectively. The ICE is the 1979–2018 summer (June–August) daily area-averaged intraseasonal SAT anomalies over central Eurasia in the domain of 40°–70°N, 60°–90°E, while the IEE is the same over eastern Eurasia in 40°–70°N, 110°–140°E.
(a) Percentage (units: %) of summer (JJA) total surface air temperature variance explained by 10–90-day intraseasonal variance in NCEP–NCAR1. Summer 10–90-day intraseasonal standard deviations of surface air temperature (shading; units: °C) in (b) NCEP–NCAR1 and (c) JRA-55. The red crosses in (b) and (c) represent the centers of maximum standard deviation in midlatitude regions in NCEP–NCAR1. Gray areas show elevations exceeding 1800 m.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Figure 2 shows the correlation coefficients of the intraseasonal SAT anomalies in Eurasia with summer daily ICE and IEE, respectively. A clear seesaw pattern can be observed over Eurasia (Figs. 2a,b). The local SAT anomalies over central Eurasia are highly correlated with ICE, with a maximum correlation coefficient exceeding 0.7 (Fig. 2a). Notably, SAT anomalies in the eastern Eurasia are negatively correlated with ICE, with a maximum correlation coefficient of −0.27, which exceeds the 95% significance level. The correlation map with IEE exhibits a similar pattern (Fig. 2b). The SAT anomalies associated with IEE increase in eastern Eurasia with a maximum correlation coefficient of 0.83 and decrease in central Eurasia, with a correlation coefficient of −0.31. Figures 2a and 2b suggest that the intraseasonal SAT variability in central and eastern Eurasia are mutually correlated. The power spectra of both ICE and IEE indicate a significant period on the intraseasonal time scale, the 10–50-day period in particular (Fig. 2c). Moreover, the power spectra of both ICE and IEE exceed the 95% significance level of red noise, as determined by the F distribution test for analysis of variance, suggesting that ICE and IEE as defined in this study are not determined by stochastic (noisy) processes. Although the power spectra exhibit alternative peaks for ICE and IEE, the cross-coherence spectrum shows significant coherence between them at around 20–30- and 40–50-day periods (figure not shown). ICE and IEE are negatively correlated (the correlation coefficient r = −0.28, exceeding the 95% significance level) at 0-day time lag (Fig. 2d), consistent with Figs. 2a and 2b. Moreover, ICE is positively correlated with IEE at an 8-day time lead, although the correlation coefficient is marginally significant at 90% confidence level based on the equivalent sample size (Zwiers and von Storch 1995). In the next subsection, we will demonstrate that this lead–lag correlation between ICE and IEE is attributed to the large-scale atmospheric intraseasonal variability.
Correlation coefficients (shadings) of the 10–90-day intraseasonal SAT anomalies in Eurasia with summer (June–August) daily indices (a) ICE and (b) IEE from 1979 to 2018. (c) Power spectra (solid thick lines) and the corresponding 95% red noise significance level (dashed lines) of ICE (red line) and IEE (black line), based on the F distribution test. (d) Lead–lag correlations between ICE and IEE. The black boxes indicate the domain of CE and EE. The stippling in (a) and (b) and the red dashed lines in (d) represent the correlation coefficients exceeding the 95% confidence level based on the equivalent sample size introduced by Zwiers and von Storch (1995).
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
b. The SAT seesaw between the central and eastern Eurasia
Figure 3a exhibits the correlation coefficients between the summer daily intraseasonal SAT anomalies over Eurasia and ITSI. The pattern resembles those in Figs. 2a and 2b, suggesting that the ITSI can capture the dominant dipolar feature of SAT in central and eastern Eurasia. Note that intraseasonal SAT in eastern Eurasia associated with ICE is at its lowest in the south of Lake Baikal (Fig. 2a). Although this area lies outside the defined eastern Eurasia region, the ITSI still captures the SAT variability in this region. The power spectrum of ITSI reveals a significant intraseasonal period of approximately 10–50 days (Fig. 3b). This period is also evident in the autocorrelations of ITSI (Fig. 3c), with significant autocorrelation coefficients observed in the leading or lagged time at around day ±17 and day ±8.
(a) Correlation coefficients (shading) between the intraseasonal SAT anomalies in Eurasia and ITSI. (b) Power spectrum (black solid line), theoretical Markov red noise spectrum (red dashed line) obtained by using the lag-1 autocorrelation model, and the 95% significance level (green dashed line) for ITSI based on the F distribution test. (c) The autocorrelations of ITSI. Stippling and black boxes in (a) show the correlation coefficient exceeding the 95% confidence level and the domains of CE (40°–70°N, 60°–90°E) and EE (40°–70°N, 110°–140°E), respectively. The red dashed lines in (c) represent the 95% confidence level based on the equivalent sample size.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Considering that the ITSI well captures the intraseasonal SAT variability in central and eastern Eurasia, we now examine the temporal evolutions of anomalous fields of large-scale circulation and SAT associated with ITSI using lead–lag regressions (Xiu et al. 2022). In calculating the lead–lag regressions day 0 represents the reference day, which depicts the contemporaneous anomalous fields regressed against the normalized daily ITSI during summer of 1979–2018, while day +n (−n) indicates the regression of anomalous fields leading (lagging) the normalized ITSI by n days onto the normalized ITSI. Figure 4 displays the intraseasonal SAT and wind anomalies at 850-hPa regressed onto ITSI from day −8 to day +6 with a 2-day interval. Due to the leading period of ITSI is mainly at around 20 days (Fig. 3c), a regression map from day −8 to day +6 allows us to investigate the almost complete cycle of the atmospheric circulations associated with the SAT seesaw. Positive SAT anomalies are found in the northwest of the Tibetan Plateau at day −8 (Fig. 4a). In the upstream European regions, both SAT warming and cooling can be found in northern and eastern Europe, respectively. This SAT pattern at day −8 implies that the SAT dipole may originate from high-latitude regions upstream. Note that the SAT is cooling in the eastern Eurasia at day −8, consistent with the negative correlation coefficients in Fig. 3c. The low-level wind anomalies from the North Atlantic to eastern Europe feature a wavy pattern at day −8. At day −6, the SAT and low-level wind anomalies propagate eastward slowly (Fig. 4b). At day −4, the SAT warming crosses the Altai Mountains (the mountains in the north of Tibetan Plateau) and reaches Lake Baikal (Fig. 4c), with intensified SAT and wind anomalies in Siberia. At day −2, the local SAT is warming in eastern Eurasia, while cooling in central Eurasia (Fig. 4d). The SAT seesaw pattern between eastern and central Eurasia is intensified. Low-level wind anomalies also exhibit an anomalous anticyclone–cyclone–anticyclone pattern in Eurasia. The maximum SAT warming at day 0 occurs in eastern Eurasia (Fig. 4e), followed by a rapid decay from day +2 (Fig. 4f) to day +6 (Fig. 4h). Low-level winds propagate eastward continuously from day 0 (Fig. 4e) to day +2 (Fig. 4f). At day +4 (Fig. 4g) and day +6 (Fig. 4h), the SAT anomalies remain significant in Eurasia, although the wavy structure of low-level wind anomalies disappears.
The 10–90-day bandpass filtered intraseasonal SAT (shading; units: °C) and wind anomalies at 850 hPa (vectors; units: m s−1) regressed onto the normalized daily ITSI during summer from 1979 to 2018 at day (a) −8, (b) −6, (c) −4, (d) −2, (e) 0, (f) +2, (g) +4, and (h) +6 by using the lead–lag regressions. Stippling shows the regressed SAT anomalies exceeding the 95% confidence level based upon the equivalent sample size (Zwiers and von Storch 1995). The wind anomalies lower than the 95% confidence level or 0.2 m s−1 are omitted. Gray areas show elevations exceeding 1800 m.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
SAT and low-level wind anomalies associated with ITSI suggest that the surface temperature seesaw mainly arises from the upstream North Atlantic and European regions and then propagates eastward to central and eastern Eurasia. In addition, upper-level circulations exhibit an intensified eastward propagating wave train over Eurasia from day −8 to day +6 (Fig. 5). It is noted that at day −6 (Fig. 5b), two wave trains are coexisted over the Eurasian mid–high latitudes. One is located over regions from the North Atlantic to northeastern Europe, while the other is mainly located in the northwest of India to northern China. The latter may be related to the intraseasonal latent heating anomalies over northern India (Ding and Wang 2007; Wei et al. 2019). Ding and Wang (2007) pointed out that the strong convection over the northern Indian summer monsoon region excites a Rossby wave response to reinforce the downstream circulations of the wave train. Wei et al. (2019) also highlighted the role of convection over India in modulating the wave train through the intraseasonal movement of the South Asian high. These two wave trains propagate eastward from day −2 to day +4, resulting in the SAT seesaw over the mid–high latitudes (Fig. 3a) and the cyclonic anomaly in China (Figs. 5d–g). At day +8, the anomalous upper-tropospheric circulations associated with ITSI weaken, and the SAT seesaw disappears.
As in Fig. 4, but for the 10–90-day intraseasonal geopotential height (shading; units: gpm) and wind anomalies (vectors; units: m s−1) at 200 hPa. Wind anomalies below the 95% confidence level or <0.4 m s−1 are omitted.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
c. Mechanisms associated with the SAT seesaw
The preceding subsection suggests that the SAT seesaw is related to the eastward propagating wave train at the upper level over Eurasia (Fig. 5). Since the tropospheric wave train exhibits a quasi-barotropic structure (Figs. 4 and 5) and the tropospheric temperature is highly correlated with SAT (figure not shown), we use the column-integrated temperature budget from the surface pressure to 100 hPa [Eq. (1)] to examine the underlying mechanisms. Figure 6 shows the column-integrated temperature tendency term
Time evolution of column-integrated temperature tendency term
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Considering that the SAT seesaw pattern is defined by the local SAT anomalies averaged over the two boxes in central and eastern Eurasia (Fig. 3a), Fig. 7 further shows the time evolution of each term in Eqs. (1)–(3) averaged over these two areas. In the central Eurasia, the temperature tendency (Fig. 6) becomes negative at day −7 and reaches its minimum at day −3 (Fig. 7a). The vertical adiabatic advection term
Column-integrated temperature budget from day −19 to day 0 regressed onto ITSI averaged in (a) CE (40°–70°N, 60°–90°E) and (b) EE (40°–70°N, 110°–140°E). Red solid line represents the temperature tendency term
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Figure 7 suggests that dynamic processes play a more significant role than diabatic heating processes in regulating the SAT seesaw. Generally, the propagation and maintenance of dynamic-driven wave trains at upper troposphere are closely tied to the energy propagation of the Rossby wave train. Figure 8 shows the wave activity flux and geopotential height anomalies at 200 hPa from day −8 to reference day 0 regressed onto daily ITSI during summer of 1979–2018. The spatial distribution of wave activity flux confirms that the large-scale circulation anomalies associated with the SAT seesaw propagate eastward from the North Atlantic to the eastern Eurasia on the intraseasonal time scale, especially along the polar front jet in the north of subtropical westerly jet (Kosaka et al. 2011; Xu et al. 2020) (also see Figs. 5 and 6). Previous studies have highlighted the importance of baroclinic instability and barotropic energy conversion in the Eurasian ISO. The intraseasonal wave train over Eurasia gains energy from both the climatological mean westerly and the synoptic transient eddies through the barotropic or eddy available potential energy (Xu et al. 2020; Dai et al. 2021; Zhu et al. 2023). Also, the intraseasonal energy feeds back to transient eddies and results in the energy dispersion of Eurasian ISO. The results presented here further reinforce the significance of atmospheric internal dynamical processes, particularly in terms of temperature budget and wave activity flux, which align with the findings on energy conversion.
The 10–90-day intraseasonal geopotential height (shading; units: gpm) and wave activity flux (vectors; units: m2 s−2) anomalies at 200 hPa regressed against ITSI (top to bottom) from day −8 to day 0 with an interval of 2 days.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
The eastward propagation of tropospheric temperature tendency and Rossby wave train occur mainly along the polar front jet at upper troposphere (Figs. 6 and 8). The u2 term in Fig. 7 is associated with the climatological mean westerly, and the υ1 term is related to intraseasonal meridional wind anomalies. To extract the dominant modes of the Eurasian polar front jet, we conduct an empirical orthogonal function (EOF) analysis on the intraseasonal meridional wind anomalies at 200 hPa over the domain of 50°–80°N, 30°–130°E according to Xu et al. (2020) and Li et al. (2020). The geopotential height and wind anomalies regressed onto the first and second EOF modes of upper-level meridional winds are shown in Figs. 9a and 9b, respectively. The first EOF mode (EOF1) accounts for 22% of total variance, with a wavy pattern in Eurasian high latitudes. Positive centers are located over Europe and Siberia, while negative centers are located over northeastern Europe and eastern Eurasia (Fig. 9a). The second EOF mode (EOF2), which accounts for 19.5% of total intraseasonal variance, resembles EOF1 with a slightly eastward shift (Fig. 9b). The lead–lag correlations between the principal components (PCs) of EOF1 and EOF2 suggest a significant correlation at lagged time (Fig. 9c). The highest correlation between EOF PC1 and PC2 is 0.27 when PC1 leads PC2 by 4 days, which exceeds the 90% significance level using the equivalent sample size (Zwiers and von Storch 1995). The negative correlation reaches −0.23 when PC1 lags PC2 by approximately 4 days (Fig. 9c). The lead–lag correlations of EOF PC1 and PC2 feature a sinusoidal structure from −10 to 10 days.
The 10–90-day intraseasonal geopotential height (shading; units: gpm) and wind (vectors; units: m s−1) anomalies at 200 hPa regressed onto the (a) first and (b) second EOF modes of meridional wind anomalies at 200 hPa over the domain of 50°–80°N, 30°–130°E. (c) Lead–lag correlations between the first and second principal components. In (a) and (b) the wind anomalies below the 95% confidence level or <0.4 m s−1 are omitted. The red dashed lines in (c) represent the 95% confidence level based on the equivalent sample size.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Combining the spatial patterns of EOF1 and EOF2 suggests that the leading modes of Eurasian intraseasonal meridional winds at upper levels are a propagating wave train (Xu et al. 2020; Zhu et al. 2023). To clarify the propagating features of this wave train, we perform a composite life cycle of SAT and wind anomalies at 200 hPa based on the EOF PC1 and PC2 (Fig. 10), which is widely used in the tropical MJO community (Wheeler and Hendon 2004; Lee et al. 2013). Here, we use this method to extract the phase structures of extratropical intraseasonal wave train. Due to the orthogonality of PC1 and PC2, PC1 can be considered as the x axis and PC2 as the y axis of the Cartesian coordinate system. The phase space can be divided into eight phases by an angle interval of 45°. For instance, 0° ≤ angle ≤ 45° represents phase 1, 45° ≤ angle ≤ 90° represents phase 2, and so on. In Fig. 10, phase 8 and phase 2 represent the EOF1 and EOF2, respectively. Also, the value of
Composite life cycle of SAT (shading; units: °C) and winds (vectors; units: m s−1) at 200 hPa based on the meridional wind EOF PC1 and PC2. Wind anomalies < 1 m s−1 are omitted. The green contours show the summer (JJA) mean climatological zonal winds at 200 hPa with an interval of 8 m s−1. The number shown in the bottom-right corner of each phase is the total days used to create the composite.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
d. Impacts of intraseasonal SAT seesaw on regional HW in Eurasia
In this final subsection, we investigate the influence of the SAT seesaw between the central and eastern Eurasia on the occurrence of summer Eurasian heat waves. The spatial distribution of the 95th-percentile threshold value of SAT over Eurasia during boreal summer is displayed in Fig. 11. The distribution of SAT with this threshold is similar to that of summer mean SAT. Note that the two hot spots in the distribution of 10–90-day SAT standard deviation (Fig. 1a) are also the regions with high values of the 95th-percentile threshold. The maximum SAT of the 95th-percentile threshold reaches 34°C in the central Eurasia and exceeds 26°C in the eastern Eurasia.
Distribution of the 95th percentile of summer SAT (shading and contours; units: °C). Black boxes represent the domains of CE and EE.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
To examine the characteristics of HW influenced by the SAT seesaw, we conduct the composite analysis of HW in three different scenarios, namely the normalized SAT seesaw index ITSI > 1.5, ITSI < −1.5, and |ITSI| < 0.1. When ITSI > 1.5 (ITSI < −1.5), the intraseasonal SAT variability in Eurasia is active with strong warming (cooling) in the eastern Eurasia but cooling (warming) in the central Eurasia. When |ITSI| < 0.1, the intraseasonal SAT variability is inactive in Eurasia. Figures 12a–c show the composite of total HW days in these three different scenarios. The HW is observed in the entire eastern Eurasia with the largest number exceeding 25 days over northeast China when ITSI > 1.5 (Fig. 12a). No HW can be found in central Eurasia under this scenario. On the contrary, the HW is found in the northeast of the Caspian and Aral Sea with the total number of HW days amounting to 25 days when ITSI < −1.5 (Fig. 12b). In this situation, no HW is observed in eastern Eurasia. The total HW days over Eurasia is rarely observed in inactive scenario with the largest number less than 13 days in Europe (Fig. 12c). Table 1 presents the area-averaged total number of HW days in central and eastern Eurasia across three different scenarios. When ITSI < −1.5 (ITSI > 1.5), the frequency of HW events is higher in central (eastern) Eurasia, whereas such events are suppressed when |ITSI| < 0.1. Moreover, we show that the maximum intensity of HW is associated with the SAT seesaw. The HW is more intense in active phases (|ITSI| > 1.5) of SAT seesaw than in inactive phases (|ITSI| < 0.1; Figs. 12d–f). Our results suggest that the occurrence of HW in Eurasia is highly modulated by the intraseasonal SAT seesaw pattern.
Composite of (a)–(c) total HW days (shading; units: days) and (d)–(f) maximum HW intensity (shading; units: °C) when (a),(d) ITSI > 1.5, (b),(e) ITSI < −1.5, and (c),(f) |ITSI| < 0.1.
Citation: Journal of Climate 36, 22; 10.1175/JCLI-D-22-0761.1
Area-averaged total HW days over the CE and EE domains in three scenarios.
4. Summary and discussion
SAT over Eurasia exhibits a significant intraseasonal component. By using two reanalysis datasets, we show that the intraseasonal SAT variability is noticeable in central and eastern Eurasia. The SAT anomaly in eastern Eurasia is negatively correlated with that in central Eurasia. Thus, a SAT seesaw index is proposed to examine the coupling of intraseasonal surface temperature variability between central and eastern Eurasia. A positive seesaw index indicates that the local intraseasonal SAT anomaly increases (decreases) in eastern (central) Eurasia and vice versa. The power spectrum analysis indicates that the SAT seesaw is significant for a 10–50-day period.
In boreal summer, the upper tropospheric Silk Road pattern is demonstrated to be the atmospheric leading internal mode in Eurasia on the interannual time scale (Lu et al. 2002; Enomoto 2004). The circumglobal teleconnection pattern (Ding and Wang 2005) trapped in the subtropical westerly jet in the Eurasian sector is a component of the Silk Road pattern (Zhou et al. 2019). Our present study illustrates that the SAT seesaw is strongly associated with the large-scale intraseasonal wave train in the troposphere in the Eurasian mid–high latitudes. The analyses of column-integrated temperature budget and wave activity flux associated with the Rossby wave train suggest that the intraseasonal SAT seesaw is mainly driven by the eastward propagating wave train at the mid–high latitudes of Eurasia, which is different from the Silk Road pattern along the subtropical Asian jet on the interannual time scale. A further decomposition of the temperature budget suggests that the increase (decrease) of local SAT in eastern (central) Eurasia is dominated by the dynamical processes of both the horizontal and vertical adiabatic advection terms, while the thermodynamic heating process plays a role of negative feedback. Moreover, the decomposition of horizontal advection suggests that the summer climatological mean westerly and intraseasonal meridional wind anomalies are two leading contributors for the SAT seesaw. The former zonal advection term is associated with the role of the polar front jet as mentioned in Fig. 6 (Xu et al. 2020; Zhu et al. 2023). We also conduct an EOF analysis of intraseasonal meridional wind anomalies at 200 hPa in Eurasia to confirm the role of intraseasonal meridional wind anomalies. Our results indicate that the SAT seesaw is highly correlated with the Eurasian atmospheric leading patterns and features an eastward propagating wave train along the polar front jet. Our findings highlight the important role played by the wave train in the north of the subtropical westerly jet stream in modulating local climate anomalies on the intraseasonal time scale, in addition to traditional views of the Silk Road pattern and subtropical westerly jet.
Last, we investigate the influence of the SAT seesaw on local extreme hot events. The results show that the local HW in Eurasia is mainly modulated by the intraseasonal SAT seesaw. Few HW events can be found in the inactive phase of the SAT seesaw. Both the maximum intensity and total days of HW increase in the active phase of the SAT seesaw. These suggest potential implications of the SAT seesaw on subseasonal prediction of local extreme hot events. Over the past decades, eastern and central Eurasia have suffered severe climate-related challenges such as extreme HW events and heat-related mortality (Revich and Shaposhnikov 2010; Lau and Kim 2012; Tao and Zhang 2019; Xu et al. 2019; Hsu et al. 2020; Ren et al. 2020). Previous study also suggested that the subseasonal forecast skill of HW in northeastern Asia could be about 3 weeks (Zhu et al. 2021). Our result highlights the predominant role of the intraseasonal SAT seesaw in driving the local HW events, which may be helpful in improving regional subseasonal forecasts of extreme weather events.
Acknowledgments.
We thank the three anonymous reviewers and editor for their constructive comments that helped improve our paper. X. W. and R. Z. are supported by the National Natural Science Foundation of China (42288101, 41790472). X. W. is also supported by the National Natural Science Foundation of China (42205016). D. J. is supported by the National Natural Science Foundation of China (41905061). Y. Z. is supported by the Fundamental Research Funds for the Central Universities (202213050) and the project funded by China Postdoctoral Science Foundation (2021M703034).
Data availability statement.
The NCEP/NCAR Reanalysis data are openly available from the NOAA/OAR/ESRLPSL, Boulder, Colorado, USA, including daily surface air temperature from their web site at https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.surfaceflux.html and three-dimensional wind velocity, air temperature, and geopotential height daily data from https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html. The JRA-55 data are available at https://rda.ucar.edu/.
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