
Variance of the anomalous SAT field over the Eurasia continent in boreal summer is illustrated in Fig. 1a. It is found that midtohighlatitude land regions (51°–75°N, 60°–141°E; denoted by the box in Fig. 1a) show pronounced SAT variability. As suggested by the power spectra of the arealaveraged SAT time series over the box area (Fig. 1b), the SAT anomalies over this domain exhibit a significant intraseasonal period of about 10–50 days based on the red noise spectrum at the 95% confidence level. Shown in Fig. 1c is the variance of 10–50day SAT anomalies filtered by the Lanczos BP filter (Duchon, 1979). It is noted that relatively larger intraseasonal SAT variability is found over MHE as depicted in Fig. 1a as well. Accordingly, in the analysis below, we will focus on the 10–50day intraseasonal SAT variability over MHE during boreal summer.
Figure 1. Distributions of the variance (shading; K^{2}) of (a) daily SAT (namely T2m) anomaly and (c) 10–50day filtered SAT anomaly during boreal summer (June–September; JJAS) of 1979–2016. (b) The power spectra of the arealaveraged SAT anomaly (K^{2}) over the box area in (a), including the Markov red noise spectrum (dashed curve) and the 95% confidence level (dashanddot curve).
An EOF analysis is utilized to derive the prominent modes of MHE SAT on intraseasonal timescale, and it is conducted for the anomalous SAT fields in the region of 36°–81°N, 21°–141°E during boreal summer (June–September) from 1979 to 2016. Illustrated in Figs. 2a, b are the first two spatial patterns of the EOF analysis (i.e., EOF1 and EOF2), which are not separable and are independent from higher EOF patterns in accordance with the criteria of North et al. (1982). The percentage of variance explained by the first two modes is 15.4% and 13.3%, respectively. EOF1 captures the west–east dipole mode of the intraseasonal SAT anomalies over the MHE domain and EOF2 exhibits a triple mode of the SAT perturbation centers over this domain, representing the propagating features of the ISV of SAT over MHE. The correlation between the two PCs reaches the maximum with PC1 leading (lagging) PC2 by about four days (Fig. 2c). It is noteworthy that the same EOF analysis has been applied to the intraseasonal SAT obtained by using the abovementioned nonBPfiltering method. The result demonstrates that the spatial distribution of the two leading EOF patterns and the lead–lag relationship between the corresponding PCs show common characteristics with that derived from traditional BPfiltering method (figures omitted).
Figure 2. The (a) first and (b) second EOF modes of the intraseasonal SAT field during summer (JJAS) of 1979–2016. Hatching indicates the statistical significance at the 95% confidence level for shaded anomalies. (c) The lead–lag correlation between the first two PCs. The red dashed lines represent the 95% confidence level. (d) Schematic phase–space diagram formed by PC1 and PC2. The location of the positive SAT anomaly for the corresponding phase space is labeled in the diagram.
As illustrated in Fig. 2d, based on PC1 and PC2, eight phases of the intraseasonal SAT anomalies over MHE are then defined by using the same method developed in Wheeler and Hendon (2004). The ISO at a certain time can be denoted as a twodimensional spatial vector T.
$$\hspace{6pt} {\boldsymbol{T}}\left( t \right)=\left[ {{\rm{PC1}}\left( t \right),{\rm{PC2}}\left( t \right)} \right], $$ (1) $$\hspace{6pt} {{A}}\left( t \right) = {\rm{PC}}1^{2}{{\left(t \right)}}+ {\rm{PC}}2^2{{\left( t \right)}}, $$ (2) $$\hspace{18pt} {{a}}\left(t \right){\rm{ = ta}}{{\rm{n}}^{{\rm{  1}}}}\left[ {\dfrac{{{\rm{PC2}}\left( t \right)}}{{{\rm{PC1}}\left( t \right)}}} \right], $$ (3) where A(t) represents the amplitude of an ISO event, and a(t) is the phase angle between PC1 and PC2 in the range from −
$ –\pi $ to$ \pi $ . It is assumed that a(t) equals${{a}}\left( t \right) +{\rm{ 2}}{\pi}$ when a(t) is negative in this study. Taking the 16day life cycle as an example, the duration of each phase is about 2 days. As depicted in Fig. 2c, when PC1 leads PC2 by four days, their positive correlation coefficient reaches a peak. As shown in Fig. 2d, when PC1 and PC2 are both positive and PC1 is greater than PC2, which means that the value of a(t) is between 0 and π/4, the ISO case is in Phase 1. The eight phase angle range is from 0 to 2π and the angle interval is π/4.According to the SAT phases defined by PC1 and PC2, composites of 10–50day filtered SAT and 850hPa horizontal wind anomalies for the eight phases are illustrated in Fig. 3, clearly depicting the eastwardpropagating characteristic of the ISV of SAT over MHE. The positive SAT perturbation center is located over western Russia at about 60°N, 70°E, which is approximately to the north of the Ural Mountains in Phase 2. The subsequent phases witness the continuous eastward propagation of the anomalous positive SAT center and it is situated to the north of Lake Baikal at about 60°N, 110°E in Phase 5. From Phase 6 to 1, southward shifting of the positive SAT center from about 60° to 40°N is detected, which indicates that the anomalous SAT filed over East China can be affected by the ISO signal, albeit with the relatively small amplitude of positive SAT signals in these phases. The same analysis using the anomalous SAT field derived from the nonBPfiltering method for the composites exhibits similar propagating and evolutionary features, as shown in Fig. 3.
Figure 3. Composites of the intraseasonal SAT (shading; K) and 850hPa horizontal wind (vector; m s^{−1}) anomalies for (a–h) eight SAT phases during boreal summer for the period of 1979–2016. Refer to Section 3 for the definition of eight SAT phases. Shaded areas indicate the SAT anomalies exceeding the 95% confidence level (Student’s ttest).
Considering the significant ISO signals of SAT anomalies over MHE, we further investigate the possible impacts of the leading intraseasonal SAT mode on the occurrence frequency of extreme events over China, which can exert considerable impacts on the society and economy. Composite maps of SAT anomalies for different phases over China are illustrated in Fig. 4. Taking the positive anomalous SAT signals for example, significant positive SAT anomalies are located over Northwest China in Phases 5 and 6, which strengthen and move southeastward, and reside over central China and Northeast China in Phases 7 and 8. In Phases 1 and 2, the positive SAT anomalies are mainly observed over South China with a relatively small amplitude. In Phases 3 and 4, the impact of the positive anomalous SAT signals is mainly detected over Northwest China. Daily mean SAT from the CN05.1 dataset is utilized to identify extreme weather events in summer (June–September) following Hsu et al. (2017). The extreme hot (cool) day is defined as the date when daily mean SAT goes over (falls below) the climatological 95thpercentile (5thpercentile) threshold. Being largely in accord with spatial patterns of the intraseasonal SAT mode, the relatively frequent occurrence of extreme hot (cool) days is observed over Northwest China in Phases 5–6 (1–2), and over South China in Phases 7–8 (5–6) (Figs. 5a, b). For North China, one of the most populated regions in China, it tends to have the relatively more frequent extreme hot (cool) days in Phases 7–8 (3–4).
Figure 4. Composites of intraseasonal SAT anomalies for (a–h) eight SAT phases during boreal summer for the period of 1979–2016. The black dots denote the areas exceeding the 95% confidence level (Student’s ttest).
Figure 5. Distributions of the occurrence frequency of (a) extreme hot days and (b) extreme cool days for eight SAT phases in boreal summer of 1979–2016. See Section 3 for definitions of extreme hot/cool days.
In a nutshell, the ISO signals of SAT over MHE can modulate the occurrence of extreme temperature events over China during boreal summer, which may be favorable to the prediction of these extreme events over China. Since we mainly focus on the observational ISO signals in the analysis above, next we will further investigate how well the stateoftheart operational models predict the ISO signals of SAT over MHE.
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Cui, J., S. Y. Yang, and T. Li, 2021: Intraseasonal variability of summertime surface air temperature over mid–highlatitude Eurasia and its prediction skill in S2S models. J. Meteor. Res., 35(5), 815–830, doi: 10.1007/s133510211131x 
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