{"paper_id":"463fce84-e5bf-44cd-a9e0-8209cb54f5c1","body_text":"Interdecadal Modulation of the Warm Arctic-Cold Eurasia Reversal by the North Atlantic Sea Surface Temperature Tripole | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Interdecadal Modulation of the Warm Arctic-Cold Eurasia Reversal by the North Atlantic Sea Surface Temperature Tripole chen liu, Lei Chen, Stefan Liess This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7737671/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The reversal of the warm Arctic-cold Eurasia (WACE) pattern significantly influences weather and climate extremes across Eurasia. Based on previous studies, the WACE reversal is defined as the third season-reliant empirical orthogonal function of surface air temperature (SAT) variability over the Arctic-Eurasian continent in Northern Hemisphere winter. This study investigates the interdecadal changes in the interannual variability of WACE reversal, revealing that its dominant periodicities have shifted across different decades. Observational analyses identify three characteristic Ural blocking (UB) patterns, each associated with shifts in the Arctic-Eurasian SAT dipole during reversal events. Furthermore, the results indicate that the interdecadal transitions of WACE reversal are largely attributable to the North Atlantic tripole sea surface temperature anomalies (SSTAs). Meanwhile, cold SSTAs over the North Pacific induced by these tripole patterns, may suppress the North Atlantic’s influence by reducing downstream wave activity to the reversal region. Our results demonstrate that North Atlantic SSTAs play an important role in modulating WACE reversal on interannual timescales, with their impact further shaped by coupled Atlantic-Pacific interactions. Climate change atmospheric circulation interdecadal change sea surface temperature Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Since the 20th century, the warm Arctic-cold Eurasia (WACE) pattern has emerged as a prominent mode of atmospheric variability that induces severe cold extremes across Eurasia, particularly during the boreal winter season (Cohen et al. 2012 ; Mori et al. 2014 ; Kug et al. 2015 ; Jin et al. 2020 ). In recent years, growing attention has been given to the subseasonal reversals of WACE (SR-WACE) pattern in early and late winter (Xu et al. 2022 ; Yin et al. 2023 ; Xu et al. 2024 ). The transition on the subseasonal timescale can trigger significant weather and climate anomalies (Zhang et al. 2021 ; Yin et al. 2022 ), increasing the risk of ecological and societal impacts. Therefore, it is necessary to more deeply investigate the physical mechanism of WACE reversal. Early studies on SR-WACE have primarily focused on the evolution of reversal events, highlighting the role of Ural blocking (UB) in driving transitions of the surface air temperature (SAT) dipole (Xu et al. 2022 ; Yin et al. 2023 ). In addition, the impacts of the mid-latitude westerly jet and sea ice further indicate the complexity of the reversal issue (Xu et al. 2022 ; Zhang et al. 2023 ). However, the mechanisms responsible for the transition of the WACE pattern are presently not well understood. Notably, the formation of the WACE pattern itself remains a subject of debate, particularly regarding the dominant roles of internal atmospheric variation and sea ice forcing (Kug et al. 2015 ; Luo et al. 2016 ; Yao et al. 2017 ; Wegmann et al. 2018 ; Sorokina et al. 2016 ; McCusker et al. 2016 ; Sun et al. 2016 ; Blackport and Screen 2020 ). Given these uncertainties, it is plausible that SR-WACE has similar characteristics, potentially influenced by a combination of remote and local factors (Sun et al. 2016 ; Ye and Messori 2020 ; Lin et al. 2022 ). Thus, insights from studies of the WACE pattern may provide valuable guidance for investigating its reversals. Firstly, the characteristics and causes of interdecadal variation of WACE pattern have been widely studied. At the beginning of the 21st century, the winter surface air temperature (SAT) distribution in the Arctic-Eurasian region shifted from a cold Arctic-warm Eurasia configuration to a warm Arctic-cold Eurasia pattern (He et al. 2015). Jin et al. ( 2020 ) further showed that the WACE pattern became the leading EOF mode of winter SAT after 1998, with amplified warm anomalies over the Arctic and enhanced cold anomalies over Eurasia. Additionally, interactions between Rossby waves and the Siberian High have been found to strengthen the WACE pattern on interdecadal timescales (Sung et al. 2018 ). However, whether these interdecadal changes occurred in WACE reversals remains unclear. In combination with previous studies typically focusing on the interdecadal evolution of WACE patterns, it is necessary to investigate the WACE reversal from a long-term perspective. Furthermore, the influence of internal ocean variability has been related to the WACE pattern (Nakanowatari et al. 2014 ; Park et al. 2015 ; Woods and Caballero 2016 ; Jung et al. 2017 ). Positive sea surface temperature anomalies (SSTAs) stimulate Rossby waves over the North Atlantic, altering the propagation of planetary waves and energy, ultimately impacting the SAT over Eurasia (Sato et al. 2014 ; Woods and Caballero 2016 ; Jung et al. 2017 ). The winter cold events in East are associated by SST variations in the North Atlantic (Song and Wu 2017 ). In addition, Sung et al. ( 2018 ) suggested that the interdecadal variability of the WACE pattern is closely linked to warm SSTAs in the North Atlantic. These results raise the hypothesis that the WACE reversal has a potential connection with North Atlantic SSTs. Therefore, one of the aims of the present study is to assess the possible impact of North Atlantic SSTAs on the interannual variability of WACE reversals. In this study, we construct an annual time series of WACE reversals, with particular emphasis on changes in their interannual variability. To further identify the driving factors behind these changes, we specify the contribution of SSTAs over North Atlantic to WACE reversal on interannual timescales. The paper is organized as follows: The data and methods of analysis are described in Section 2 . Section 3 investigates the interdecadal changes in interannual variability of WACE reversal. In Section 4 , we specify the role of SSTAs over North Atlantic on the interdecadal changes during reversal events. Finally, the summary and discussion are presented in Section 5 . 2 Data and analysis methods In this study, we focus on winter during the period 1959–2023, using daily mean reanalysis data from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), on a 2.5°×2.5° grid (Hersbach et al. 2023). The variables include surface air temperature (SAT), geopotential (z), as well as zonal and meridional wind (u, v) and temperature (T) at pressure level ranging from 1000 to 10 hPa. To minimize dataset biases, the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset is also employed for validation (NCEP-1; Kalnay et al. 1996 ). Monthly SST is obtained from the National Oceanic and Atmospheric Administration Extend Reconstructed SST V5 (ERSSTv5) dataset at a 2.0°resolution (Huang et al. 2017 ). The season-reliant empirical orthogonal function (S-EOF) analysis is applied to extract the features of WACE reversal (Wang and An 2005 ; Xu et al. 2024 ). Since each S-EOF mode captures two sequential spatial patterns, the reversal point of WACE is set around 14 January during the winter season (Yin et al. 2023 ). Based on this reversal point, the evolution of WACE is divided into early and late winter. Empirical orthogonal function (EOF) is also applied to derive the time series of SSTAs. Wavelet analysis is used to examine changes in periodicity. The wave activity flux (WAF) is calculated following Takaya and Nakamura ( 2001 ): W = \\(\\:\\frac{\\text{p}\\bullet\\:\\text{c}\\text{o}\\text{s}{\\phi\\:}}{2\\left|\\text{U}\\right|}\\left(\\begin{array}{c}\\frac{\\text{U}}{{\\text{a}}^{2}{\\text{c}\\text{o}\\text{s}}^{2}{\\phi\\:}}\\left[{\\left(\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\lambda\\:}}\\right)}^{2}-{\\psi\\:}{\\prime\\:}\\frac{{\\partial\\:}^{2}{\\psi\\:}{\\prime\\:}}{\\partial\\:{{\\lambda\\:}}^{2}}\\right]+\\frac{\\text{V}}{{\\text{a}}^{2}\\text{c}\\text{o}\\text{s}{\\phi\\:}}\\left[\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\lambda\\:}}\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\phi\\:}}-{\\psi\\:}{\\prime\\:}\\frac{{\\partial\\:}^{2}{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\lambda\\:}\\partial\\:{\\phi\\:}}\\right]\\\\\\:\\frac{\\text{U}}{{\\text{a}}^{2}\\text{c}\\text{o}\\text{s}{\\phi\\:}}\\left[\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\lambda\\:}}\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\phi\\:}}-{\\psi\\:}{\\prime\\:}\\frac{{\\partial\\:}^{2}{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\lambda\\:}\\partial\\:{\\phi\\:}}\\right]+\\frac{\\text{V}}{{\\text{a}}^{2}}\\left[{\\left(\\frac{\\partial\\:{\\psi\\:}{\\prime\\:}}{\\partial\\:{\\phi\\:}}\\right)}^{2}-{\\psi\\:}{\\prime\\:}\\frac{{\\partial\\:}^{2}{\\psi\\:}{\\prime\\:}}{\\partial\\:{{\\phi\\:}}^{2}}\\right]\\end{array}\\right)\\) (1) where U and V represent the climatological mean zonal and meridional wind, respectively. ψ' is perturbation stream function. λ and φ denote longitude and latitude, and p is the specific pressure (pressure divided by 1000hPa). Prior to analysis, the linear trends in the reanalysis data are removed. Statistical significance is assessed using a two-tailed Student’s t-test. 3 The Interdecadal Changes of Reversal of WACE Pattern The S-EOF analysis is applied to identify the reversal of WACE during winters from 1959 to 2023 (Figure S1 ). Figure 1 displays only the third S-EOF mode over the Arctic-Eurasian region (40°-90°N, 20°-130°E). The derived spatial patterns reveal a transition of SAT anomalies from WACE in early winter to cold Arctic-warm Eurasia (CAWE) in late winter, with prominent anomaly centers over the Barents-Kara (B-K) Sea and near Balkhash Lake. To validate the time series of S-EOF, positive and negative cases with normalized values exceeding ± 1 are selected, and their composite SAT anomaly distributions are shown in Figure S2. It is noted that the SAT anomalies experience a reversal in WACE/CAWE. Meanwhile, the centers of anomalous SAT over the B-K sea and Balkhash Lake are consistent with those in the third mode. The correlation between PC3 and observational reversal index is 0.82 (Figure S2e), confirming the time series effectively captures the interannual variability of the WACE reversal. As Fig. 1 c shows, the amplitudes of PC3 are predominantly negative after the 1980s, returning to positive values around 2000. We utilized wavelet analysis to investigate this interdecadal shift in the interannual variability of the WACE reversal (Fig. 2 ). Before 1973, the WACE reversal exhibits a 4–6 years cycle, which shifts to a 7–9 years cycle afterward. Subsequently, the periodicity extends to 6–8 years after 1997. Wavelet power shows that interannual variability (≤ 8 years) dominates the WACE reversal, although a weaker interdecadal signal (9 years) is also present. Given the changes in periodicity, the characteristics of WACE reversals may vary across different periods. Therefore, the period from 1959 to 2023 is divided into three phases: 1959–1973(P1), 1974–1997(P2), 1998–2023(P3). The results are robust to slight shifts in the boundary years (figure not shown). In early winter during 1959–1973 (Fig. 3 a), significantly positive SAT anomalies are observed over southwestern Eurasia and the Barents-Kara (B-K) Sea, while East Asia experiences negative SAT anomalies. After the reversal, strong cold anomalies dominate the B-K Sea, and most of Eurasia exhibits anomalous warming (Fig. 3 b). Corresponding changes are also seen in geopotential height anomalies. The early-winter Ural blocking (UB) exhibits an anomalous southward extension to the Arabian Plateau near 20°N, deviating from its typical climatological latitude. Following the reversal, the negative geopotential height anomalies (GHAs) over the B-K Sea replace the earlier UB, while positive GHAs develop over central and eastern Eurasia. During the second (1974–1997) and third (1998–2023) periods, the dipole structures still appear in the early and late winter (Fig. 3 c-f). However, the distribution and intensity of SAT anomalies is different across these periods. The WACE pattern during 1974–1997 (Fig. 3 c) is stronger than that of 1998–2023 (Fig. 3 e), resulting in a broader area of warm anomalies affecting northern Eurasia. In late winter of 1974–1997, the negative temperature anomalies over northwestern Eurasia are accompanied by negative GHAs around 50°N (Fig. 3 d). Subsequently, a weak meridional dipole of GHAs covers the Eurasia during 1998–2023, similar to that in the first period (Fig. 3 f). Notably, CAWE-related SAT anomalies during 1974–1997 are less significant compared to the third period (Fig. 3 d, f). These results suggest that the spatial structures of WACE/CAWE in three periods have different characteristics with interdecadal changes in the interannual variability of WACE reversal, implying that large-scale atmospheric circulations also have interdecadal changes. To further investigate the underlying interdecadal changes in the atmospheric circulation, wave activity fluxes are shown in Fig. 4 . During the first period, a strong wave train originates from the central North Atlantic and propagates northeastward, reaching the Barents-Kara (B-K) Sea in early winter (Fig. 4 a). An eastward branch of this wave activity extends as far south as 30°N, influencing southern Eurasia and explaining the southward extension of the Ural blocking (UB). In contrast, during late winter, the strong North Atlantic wave train largely disappears (Fig. 4 b). Instead of propagating across the Barents Sea, the wave fluxes shift toward the eastern North Atlantic, exerting only a weak influence on atmospheric circulations over central Eurasia. As shown in Fig. 4 c, North Atlantic wave activity remains important in early winter during 1974–1997. However, the wave train shifts northward, with less energy transferred into B-K Sea through Greenland, and part of the wave flux disperses southward. This weakened wave activity results in a more limited extension of the UB compared to the first period. Following the WACE reversal, the North Atlantic energy source weakens, and most wave fluxes over Europe fail to propagate to the Balkhash Lake region, leading to the disappearance of the WACE spatial structure over central Eurasia. During the period of 1998–2023, the wave train over North Atlantic weakens, and fewer wave fluxes reach Eurasia. Compared to previous periods, it is noted that a relatively stronger wave train over the central North Atlantic delivers energy toward the Greenland Sea in late winter, however, which hardly reached the regions associated with the WACE reversal pattern. These results suggest that variations in North Atlantic wave activity contribute to the distinct large-scale circulation patterns across the three periods, consistent with the findings of Sung et al. ( 2018 ). This also highlights the potential role of the North Atlantic in modulating the interdecadal changes in the interannual variability of WACE reversal. 4 The effect of SSTAs over the North Atlantic on the interdecadal changes This section investigates the relationship between the North Atlantic and the interdecadal changes of WACE reversals across the three periods. The regressed December SSTAs upon PC3 over the North Atlantic upon the annual time series of the WACE reversal is given in Fig. 5 . During the first period, the spatial distribution of SSTAs exhibits a “positive-negative-positive” pattern, with significant warming near eastern Greenland and the Azores Islands (Fig. 5 a). In contrast to the overall positive SSTAs in the first period, the SSTAs during 1974–1997 and 1998–2023 are predominantly negative. Additionally, both periods show a tripole-like structure, which is “negative-positive-negative” in 1974–1997 and “positive-negative-positive” in 1998–2023, consistent with the conventional North Atlantic tripole pattern (Pan 2005 ; Chen et al. 2020 ). The regression of SSTAs over the North Atlantic onto the WACE reversal index is most significant during the second period, while the SSTAs in the third period are largely insignificant. EOF analysis is further employed to examine the interannual variability of North Atlantic SSTAs. Figure 6 presents the first EOF mode of December SSTAs (hereafter referred to as the ATL_SSTA index). As the ATL_SSTA index is predominantly negative during the second period, the regressed SSTAs for this period are multiplied by \\(\\:-\\) 1 to facilitate comparison. Notably, the SST patterns during the first two periods closely resemble those regressions upon the WACE reversal index (Fig. 5 a-b), with pattern correlation coefficients of 0.38 and 0.76, respectively (not shown). Moreover, the correlation between the WACE reversal index and the ATL_SSTA index suggests that the North Atlantic tripole pattern may contribute to the interdecadal changes in WACE reversal. In contrast, the SST pattern shows little resemblance to the regressed SSTAs associated with the normalized PC3 during 1998–2023 (Figs. 5 c and 6 c), with a pattern correlation of only 0.06. The reason for this insignificant correlation in the third period will be discussed in the next section. Here, we suggest that the North Atlantic SSTAs tripole pattern might cause the interdecadal shifts of WACE reversal in three periods. To confirm this hypothesis, Fig. 7 presents the circulation anomalies regressed onto North Atlantic SSTAs for the three periods. It is noted that the SSTA-related regressions during 1959–1973 and 1974–1997 are relatively consistent with the spatial features of the WACE reversal. In the first period, the blocking high near the Arabian plateau shows consistency with the southward extension of the circulations in the WACE reversal, coupled with the positive temperature anomalies over northern and southwestern Eurasia (Figs. 3 a and 7 a). Following the reversal, negative GHAs emerge over the B-K Sea, accompanied by the strong positive GHAs over central Eurasia (Fig. 7 b). Similarly, the SSTA-related regression during the second period illustrates the influence of the North Atlantic tripole on the WACE reversal (Fig. 7 c-d). The center of positive GHAs is located farther north compared to the first period, inducing positive temperature anomalies extending into the Barents Sea (Fig. 7 c). In late winter, negative GHAs dominate northwestern Eurasia, and the weak positive GHAs extends eastward into central Eurasia around 40°N. Although the anomalies are not statistically significant, the SAT pattern still exhibits a clear reversal in late winter. However, the regressed circulations are insignificant during 1998–2023 (Fig. 7 e-f), implying a negligible influence of North Atlantic SSTAs on the spatial distribution of WACE reversal in this period. This also corresponds to the weak correlation coefficient observed in Fig. 6 c. Moreover, the wave activities associated with the ATL_SSTA index illustrate the patterns of energy propagation during the three periods (Fig. 8 ). In the first period, wave activity originating from the North Atlantic propagates northeastward toward Norway and then disperses to affect western Eurasia (Fig. 8 a). Although the direction of wave fluxes over the North Atlantic remains largely unchanged after early winter, a greater portion of the energy is directed toward central Eurasia (Fig. 8 b). During the period of 1974–1997, the wave fluxes over the North Atlantic exhibit a split pattern, with energy propagating both northward and southward (Fig. 8 c-d). Subsequently, a wave train over Greenland transports energy toward the Arctic-Eurasian region. It is noteworthy that the wave activity patterns in both the first and second periods resemble the regressed wave flux structures associated with the WACE reversal, as shown in Fig. 4 . However, during the period of 1998–2023, strong wave fluxes are observed near 50°N over the North Atlantic in early winter. They predominantly propagate southward and southeastward and have limited influence on Greenland (Fig. 8 e). In late winter, although the wave train moves eastward to Eurasia, the wave activity remains weak and statistically insignificant. These comparisons of GHAs and wave activities across the three periods indicate that the observed atmospheric circulations over the North Atlantic are closely linked to North Atlantic SSTAs. The regressed GHAs downstream of the North Atlantic upon the ATL_SSTAs index (Figs. 7 a-d and 8 a-d), which resembles the structure of WACE reversal. It suggests that SSTAs may induce interdecadal changes in WACE reversal by influencing the propagation of wave activity fluxes over the North Atlantic. The cross-wavelet analysis is employed to confirm the close relationship between North Atlantic SSTAs and the WACE reversal. The results indicate that the North Atlantic tripole is significantly associated with the WACE reversal index during the periods of 1959–1973 and 1974–1997 (Fig. 9 ). The vector directions during these periods align with the correlation coefficients shown in Fig. 6 , supporting the connection. Moreover, the significant periodicities identified correspond to the 4- and 8-year periodicity of WACE reversal (Fig. 2 ). Notably, the correlation of periodicity between the December ATL_SSTAs and the WACE reversal index is decreased after 1997. Although the patterns of SSTAs on the first and third periods are similar (Fig. 6 a and c), the significance of coefficient and the wave activities over the North Atlantic in the period 1959–1973 are stronger than that of 1998–2023. Thus, the following section explores the underlying reasons for this shift. First, the connection between North Pacific SSTAs (hereafter PAC_SSTAs) and ATL_SSTAs becomes notably stronger during 1998–2023 (Fig. 6 ). From December to February, the tripole SSTAs pattern and the cold anomalies over the central tropical Pacific are persistent and significant in the third period (Figure S3g-i), resembling a Central La Niña-like pattern. Compared to the first period, positive GHAs over the central North Atlantic are weakened and shift southward, thereby redirecting more wave fluxes toward the south (Figs. 7 e and 8 e). In late winter, positive GHAs near Greenland transition into negative GHAs centered further west (Figs. 7 f and 8 f), which diminishes the northeastward propagation of wave activity toward Eurasia. Therefore, composite analyses are conducted to assess the impacts of different combinations of SSTAs on the WACE reversal. The PAC_SSTAs index is defined as the area-weighted average of SSTAs over the region 20°S-40°N, 180°-100°W (Figure S4). A strong event is identified when the PAC_SSTA index exceeds ± 0.5 standard deviations. Based on this threshold, we select cases of strong and weak cold PAC_SSTA events that occur in conjunction with the North Atlantic SSTA tripole pattern. Under strong PAC_SSTAs, wave activity fluxes from the North Pacific propagate downstream, forming a dipole of GHAs over the North Atlantic (Fig. 10 a-b). Subsequently, wave fluxes along with positive GHAs move into central Eurasia. In contrast, during weak PAC_SSTA events, North Atlantic circulations mainly supply energy toward the B-K Sea (Fig. 10 c-d). As shown in Fig. 10 e, the spatial distribution of GHAs across the Arctic-Eurasian region resembles the CAWE pattern, accompanied by warm anomalies over central Eurasia and cold anomalies over the Arctic. Relative to weak PAC_SSTAs events, positive GHAs in strong cold PAC_SSTAs conditions shift southward, consistent with the WACE reversal structure during the third period (Fig. 3 e). Meanwhile, strong wave activity fluxes over the North Atlantic primarily propagate southeastward toward Africa, following with the northeast-southwest tilt of wave trains (Simmons et al. 1983 ; Wang et al. 2023 ). The weak energy transmission near B-K sea therefore might contribute to the suppression of the WACE pattern during early winter. Similarly, in late winter, negative GHAs develop over western Eurasia, while positive GHAs dominate the B-K sea, producing a dipole of SAT anomalies opposites to the typical CAWE pattern (Fig. 7 b and d). Moreover, the structure of GHAs over North Atlantic is similar to the North Atlantic Oscillation (NAO) pattern. Given that positive NAO phases are known to facilitate the development of the WACE pattern (Luo et al. 2017 ; Zhong et al. 2018 ; Luo et al. 2022 ), the suppressive effect of PAC_SSTAs may therefore arise from their modulation of the North Atlantic atmospheric circulation. In strong PAC_SSTAs events, the wave train from the North Pacific propagates along the enhanced westerly jet downstream, which could impact the circulation over the North Atlantic (Figure S5). Meanwhile, the high-latitude wave train near the Barents Sea is similar to the British-Baikal corridor (BBC pattern) (Xu et al. 2019 ; Li et al. 2020 b), facilitating more energy move southeastward. The circulation pattern is different from the wave pattern path directed to the polar region (Li et al. 2024 ). Therefore, the North Atlantic circulation that facilitated energy transfer has collapsed (Figure S5a and c), resulting in reduced wave fluxes impacting the WACE reversal region. Overall, during 1998–2023, the influence of the North Atlantic tripole SSTAs on WACE reversal is suppressed by North Pacific SSTAs. Pronounced cold anomalies over the northeastern Pacific affect the interdecadal variability of WACE reversal by disrupting the atmospheric circulation patterns over the North Atlantic that typically facilitate energy transfer. Consequently, fewer wave activities reach the Barents-Kara Sea, limiting the development of WACE/CAWE patterns. With weakened enhancement from North Atlantic SSTAs, the coupled Atlantic-Pacific effect produces a weaker WACE reversal structure in the third period compared with the first. Therefore, interdecadal variations of WACE reversal across the three periods are largely attributable to the North Atlantic tripole SSTAs, accompanied by episodic coupled influences from the North Pacific SSTAs. 5 Conclusions and discussions This study investigates the interdecadal changes in interannual variability of WACE pattern reversals and explores their possible mechanisms using reanalysis data. Meanwhile, we also verified these conclusions utilizing the NCEP-NCAR reanalysis data (not shown). The time series of WACE reversals reveal distinct interdecadal variations in atmospheric circulation, characterized by three periods: 1959–1973 (P0), 1974–1997 (P1) and 1998–2023 (P2). During P0, the UB extends southward to 30°N, contributing to warm anomalies over the Arabian Plateau. In P1, the WACE structure strengthens, with significant cold anomalies over central Eurasia. In contrast, P2 shows a notably weaker WACE reversal pattern, particularly in early winter. The spatial distribution of SAT anomalies is related to the intensity and position of blocking highs over the Arctic-Eurasian sector, confirming the necessary role of atmospheric circulation in shaping the dipole structure of the WACE pattern (Luo et al. 2016 ; Xu et al. 2024 ). Wave activity analysis further suggests that interdecadal shift is strongly linked to energy propagation from the North Atlantic. The WACE-related SSTAs in three periods exhibit a tripole structure, consistent with the leading EOF mode of North Atlantic SSTs (ATL_SSTA index). Moreover, regression of circulations and wave fluxes upon the ATL_SSTA index matches the spatial structure of the WACE reversal. In detail, the “positive-negative-positive” tripole favors reversals from WACE to CAWE in P0 and P2, while the promoting SSTA patterns are shown as “negative-positive-negative” in P1. These findings suggest that the North Atlantic tripole SST pattern contributes to interdecadal variability by modulating atmospheric flow. However, during P2, the influence of the North Atlantic SSTAs diminishes. Concurrently, SSTAs over the North Pacific become significantly correlated with the ATL_SSTA index from December, indicating a coupled Atlantic-Pacific influence. Composite analysis shows that negative Pacific SST anomalies (PAC_SSTAs) suppress the promoting impact from the North Atlantic, producing an opposite SAT dipole structure over the Arctic-Eurasian region. Strong positive geopotential height anomalies (GHAs) over the North Atlantic shifts southward in early winter and evolves into negative GHAs located more west in late winter. This change in atmospheric circulation redirects wave activity southeastward, reducing energy transport to the B-K Sea. A schematic diagram illustrating the mechanisms of the joint influence of the North Atlantic and Pacific is shown in Fig. 11 (c). Cold anomalies over the northeast Pacific generate wave trains that propagate along the intensified westerly jet and induce the northeast-southwest tilt of GHAs, resembling the BBC pattern. As more wave fluxes are directed toward southwestern Eurasia, energy input into the polar region is hindered, weakening the development of atmospheric circulations over the B-K Sea. Thus, cold PAC_SSTAs events impact the interdecadal changes of WACE reversal during P2 by modulating upstream wave activity fluxes over the reversal region. Notably, El Niño-Southern Oscillation (ENSO) pattern can also impact atmospheric circulations over the North Atlantic (Toniazzo and Scaife 2006 ; Mezzina et al. 2020). Under La Niña-like PAC_SSTA conditions, the correlation between ATL_SSTA index and ENSO index become significant in the third period (Figure S3g-i), suggesting that ENSO events may regulate the interaction between the North Atlantic and Pacific. Nonetheless, this is beyond the scope of this paper and will be a focus of our future research. In general, this study focuses on the variability of WACE reversals predominately on an interannual timescale, with interdecadal changes detected. Transitions from WACE to CAWE dominate during P0 and P2, while P1 is characterized primarily by CAWE-to-WACE transitions. This differs from the interdecadal variability of the WACE pattern itself (Sung et al. 2018 ), highlighting the need to study these two processes separately. Moreover, the contribution of the North Atlantic tripole SSTAs and their interaction with North Pacific SSTAs are critical to the interdecadal changes in WACE reversals. This conclusion confirms that the development of WACE reversal is not only forced by Arctic Sea ice, pointing to a more complex mechanism involving ocean-atmosphere coupling. This study could also provide a better prediction for the variability of WACE reversal. There is no clear indication of an internally generated signal for Atlantic multidecadal variability, in contrast to the North Pacific (Fernandez et al. 2025 ). Nevertheless, detailed monitoring of North Atlantic tripole SSTAs in early winter could aid in forecasting extreme cold events associated with WACE reversals. Despite these predictive implications, whether North Atlantic SSTAs contribute to WACE reversals on subseasonal timescales remains uncertain. The North Atlantic tripole SST could exert a positive feedback, inducing the reversed NAO pattern in day-to-day analysis (Tao et al. 2023 ). Given that the NAO plays a critical role in shaping the circulation over Eurasia and influencing winter cold events in East Asia (Song and Wu 2017 ), it is plausible that North Atlantic SSTs may have influences on the process of WACE reversals. Furthermore, the coupled impacts of North Atlantic and Pacific SSTAs represent a complex factor governing North Atlantic atmospheric circulation. To clarify these issues and better understand the relative influences of the North Atlantic and Pacific on subseasonal WACE reversals, numerical modeling studies would be valuable. For instance, quantifying the contributions of North Atlantic and Pacific SSTAs, assessing their impacts on the day-to-day evolution of WACE reversals and distinguishing ENSO-related impact from those of North Pacific SSTAs. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This work is jointly supported by the National Natural ScienceFoundation of China (42376250) and the China National Key Research and Development Program (2023YFE0103900). Author Contributions Chen Liu, Lei Chen, and Stefan Liess jointly conceived and designed the study. Chen Liu prepared the first draft of the manuscript. Lei Chen, and Stefan Liess critically reviewed and revised the draft for important intellectual content. All authors read and approved the final version of the manuscript. Acknowledgements This work is jointly supported by the National Natural ScienceFoundation of China (42376250) and the China National Key Research and Development Program (2023YFE0103900). Data Availability The NOAA-RESSTv5 dataset is available at: ( https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html ). The NCEP-NCAR reanalysis data can be freely accessed on the website ( https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html ). The ECMWF Reanalysis v5 (ERA5) data can be freely accessed on the website ( https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5 ). References Blackport R, Screen JA (2020) Weakened evidence for mid-latitude impacts of Arctic warming. Nat. Clim. Change , 10(12):1065-1066. https://doi.org/10.1038/s41558-020-00954-y Chen S, Wu R, Chen W (2020) Strengthened connection between springtime North Atlantic Oscillation and North Atlantic tripole SST pattern since the late 1980s. J.Climate , 33(5):2007-2022. https://doi.org/10.1175/JCLI-D-19-0628.1 Cohen JL, Furtado JC, Barlow MA, Alexeev VA and Cherry JE (2012) Arctic warming, increasing snow cover and widespread boreal winter cooling. Environ. Res. Lett. , 7(1):014007. https://doi.org/10.1088/1748‐9326/7/1/014007 He S, Xu X, Furevik T, Gao Y (2020) Eurasian cooling linked to the vertical distribution of Arctic warming. Geophys. Res. Lett. , 47(10):e2020GL087212. https://doi.org/10.1029/2020GL087212 Fernandez A, Steinman BA, Mann ME, Christiansen SA (2025) Multidecadal temperature variability in the Community Earth System Model Last Millennium Ensemble. Geophysical Research Letters, 52:e2024GL113393. https://doi.org/10.1029/2024GL113393 Hersbach H, Bell B, Berrisford P, Biavati G, Horányi A, Muñoz Sabater J et al (2018) ERA5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds), 10(10.24381). https://doi.org/10.24381/cds.adbb2d47 Huang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. J. Climate , 30(20):8179-8205. https://doi.org/10.1175/JCLI-D-16-0836.1 Jin C, Wang B, Yang YM and Liu J (2020) “Warm Arctic‐cold Siberia” as an internal mode instigated by North Atlantic warming. Geophys. Res. Lett. , 47(9):e2019GL086248. https://doi.org/10.1029/2019GL086248 Jung O, Sung MK, Sato K, Lim YK, Kim SJ, Baek EH et al (2017) How does the SST variability over the western North Atlantic Ocean control Arctic warming over the Barents–Kara Seas?. Environ. Res. Lett. , 12(3):034021. https://doi.org/10.1088/1748-9326/aa5f3b Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven S, Gandin L (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor. Soc. , 77:437–472. https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2 Kug JS, Jeong JH, Jang YS, Kim BM, Folland CK, Min SK, Son SW (2015) Two distinct influences of Arctic warming on cold winters over North America and East Asia. Nat. Geosci. , 8(10):759-762. https://doi.org/10.1038/ngeo2517 Li S, Hu H, Ren X, Perrie W, Yang XQ, Yu P, Mao K (2024) A transmitted subseasonal mode of the winter surface air temperature in the mid-and high-latitudes of the Eurasia and contributions from the North Atlantic and Arctic regions. J GEOPHYS RES-ATMOS , 129(12):e2023JD038627. https://doi.org/10.1029/2023JD038627 Li X, Lu R, Greatbatch RJ, Li G, Hong X (2020) Maintenance mechanism for the teleconnection pattern over the high latitudes of the Eurasian continent in summer. J. Climate , 33(3), 1017-1030. https://doi.org/10.1175/JCLI-D-19-0362.1 Lin H, Yu B, Hall NM (2022) Origin of the warm Arctic–cold North American pattern on the intraseasonal time scale. J. Atmos. Sci. , 79(10):2571-2583. https://doi.org/10.1175/JAS-D-22-0013.1 Luo D, Xiao Y, Yao Y, Dai A, Simmonds I, Franzke CL (2016) Impact of Ural blocking on winter warm Arctic–cold Eurasian anomalies. Part I: Blocking-induced amplification. J. Climate , 29(11):3925-3947. https://doi.org/10.1175/JCLI-D-15-0611.1 Luo B, Luo D, Wu L, Zhong L, Simmonds I (2017) Atmospheric circulation patterns which promote winter Arctic sea ice decline. Environ. Res. Lett. , 12(5):054017. https://doi.org/10.1088/1748-9326/aa69d0 Luo B, Luo D, Dai A, Simmonds I, Wu L (2022) Decadal variability of winter warm Arctic‐cold Eurasia dipole patterns modulated by Pacific decadal oscillation and Atlantic multidecadal oscillation. Earth's Future , 10 (1):e2021EF002351. https://doi.org/10.1029/2021EF002351 McCusker KE, Fyfe JC, Sigmond M (2016) Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss. Nat. Geosci. , 9(11):838-842. https://doi.org/10.1038/ngeo2820 Mezzina B, García-Serrano J, Bladé I et al (2022) Dynamics of the ENSO teleconnection and NAO variability in the North Atlantic-European late winter. J. Climate , 33(3): 907-923. https://doi.org/10.1175/JCLI-D-19-0192.1 Mori M, Watanabe M, Shiogama H, Inoue J, Kimoto M (2014) Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. Nat. Geosci. , 7(12):869-873. https://doi.org/10.1038/ngeo2277 Nakanowatari T, Sato K, Inoue J (2014) Predictability of the Barents Sea ice in early winter: Remote effects of oceanic and atmospheric thermal conditions from the North Atlantic. J. Climate , 27(23):8884-8901. https://doi.org/10.1175/JCLI-D-14-00125.1 Pan LL (2005) Observed positive feedback between the NAO and the North Atlantic SSTA tripole. Geophys. Res. Lett. , 32(6). https://doi.org/10.1029/2005GL022427 Park H S, Lee S, Son SW, Feldstein SB, Kosaka Y (2015) The impact of poleward moisture and sensible heat flux on Arctic winter sea ice variability. J. Climate , 28(13):5030-5040. https://doi.org/10.1175/JCLI-D-15-0074.1 Sato K, Inoue J, Watanabe M (2014) Influence of the Gulf Stream on the Barents Sea ice retreat and Eurasian coldness during early winter. Environ. Res. Lett. , 9(8):084009. https://doi.org/10.1088/1748-9326/9/8/084009 Sardeshmukh PD, Hoskins BJ (1988) The generation of global rotational flow by steady idealized tropical divergence. J. Atmos. Sci. , 45(7):1228-1251. https://doi.org/10.1175/1520-0469(1988)045%3C1228:TGOGRF%3E2.0.CO;2 Simmons AJ, Wallace J, Branstator GW (1983) Barotropic wave propagation and instability, and atmospheric teleconnection patterns. J. Atmos. Sci. , 40(6):1363-1392. https://doi.org/10.1175/1520-0469(1983)040%3C1363:BWPAIA%3E2.0.CO;2 Sorokina SA, Li C, Wettstein JJ, Kvamstø NG (2016) Observed atmospheric coupling between Barents Sea ice and the warm-Arctic cold-Siberian anomaly pattern. J. Climate , 29(2):495-511. https://doi.org/10.1175/JCLI-D-15-0046.1 Song L, Wu R (2017) Processes for occurrence of strong cold events over eastern China. J. Climate , 30(22):9247-9266. https://doi.org/10.1175/JCLI-D-16-0857.1 Sun L, Perlwitz J, Hoerling M (2016) What caused the recent “Warm Arctic, Cold Continents” trend pattern in winter temperatures?. Geophys. Res. Lett. , 43(10):5345-5352. https://doi.org/10.1002/2016GL069024 Sung MK, Kim SH, Kim BM, Choi YS (2018) Interdecadal variability of the warm Arctic and cold Eurasia pattern and its North Atlantic origin. J. Climate , 31(15):5793-5810. https://doi.org/10.1175/JCLI-D-17-0562.1 Takaya K, Nakamura H (2001) A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. J. Atmos. Sci. , 58(6):608-627. https://doi.org/10.1175/1520-0469(2001)058%3C0608:AFOAPI%3E2.0.CO;2 Tao L, Fang J, Yang XQ, Sun X, Cai D, Wang Y (2023) Role of North Atlantic tripole SST in mid‐winter reversal of NAO. Geophys. Res. Lett. , 50(15):e2023GL103502. https://doi.org/10.1029/2023GL103502 Toniazzo T, Scaife AA (2006) The influence of ENSO on winter North Atlantic climate. Geophys. Res. Lett. , 33(24). https://doi.org/10.1029/2006GL027881 Wang B, An SI (2005) A method for detecting season‐dependent modes of climate variability: S‐EOF analysis. Geophys. Res. Lett. , 32(15). https://doi.org/10.1029/2005GL022709 Wang Y, Hu K, Huang G, Tao W (2023) The role of nonlinear energy advection in forming asymmetric structure of ENSO teleconnections over the North Pacific and North America. Geophys. Res. Lett. , 50(17):e2023GL105277. https://doi.org/10.1029/2023GL105277 Wegmann M, Orsolini Y, Zolina O (2018) Warm Arctic− cold Siberia: comparing the recent and the early 20th-century Arctic warmings. Environ. Res. Lett. , 13(2):025009. https://doi.org/10.1088/1748-9326/aaa0b7 Woods C, Caballero R (2016) The role of moist intrusions in winter Arctic warming and sea ice decline. J. Climate , 29(12):4473-4485. https://doi.org/10.1175/JCLI-D-15-0773.1 Xu X, He S, Zhou B, Wang H (2022) Atmospheric contributions to the reversal of surface temperature anomalies between early and late winter over Eurasia. Earth's Future , 10(8):e2022EF002790. https://doi.org/10.1029/2022EF002790 Xu X, He S, Zhou B, Wang H, Outten S (2022) The role of mid‐latitude westerly jet in the impacts of November Ural blocking on early‐winter warmer Arctic‐colder Eurasia pattern. Geophys. Res. Lett. , 49(16):e2022GL099096. https://doi.org/10.1029/2022GL099096 Xu P, Wang L, Chen W (2019) The British–Baikal Corridor: A teleconnection pattern along the summertime polar front jet over Eurasia. J. Climate , 32(3), 877-896. https://doi.org/10.1175/JCLI-D-18-0343.1 Xu T, Yin Z, Zhang Y, Zhou B (2024) Identification of shortcomings in simulating the subseasonal reversal of the warm Arctic–cold Eurasia pattern. Geophys. Res. Lett. , 51(3):e2023GL105430. https://doi.org/10.1029/2023GL105430 Yao Y, Luo D, Dai A, Simmonds I (2017) Increased quasi stationarity and persistence of winter Ural blocking and Eurasian extreme cold events in response to Arctic warming. Part I: Insights from observational analyses. J. Climate , 30(10):3549-3568. https://doi.org/10.1175/JCLI-D-16-0261.1 Ye K, Messori G (2020) Two leading modes of wintertime atmospheric circulation drive the recent warm Arctic–cold Eurasia temperature pattern. J. Climate , 33(13):5565-5587. https://doi.org/10.1175/JCLI-D-19-0403.1 Yin Z, Wan Y, Zhang Y, Wang H (2022) Why super sandstorm 2021 in North China?. Natl. Sci. Rev. , 9(3):nwab165. https://doi.org/10.1093/nsr/nwab165 Yin Z, Zhang Y, Zhou B, Wang H (2023) Subseasonal variability and the “Arctic warming-Eurasia cooling” trend. Sci. Bull. , 68(5):528-535. https://doi.org/10.1016/j.scib.2023.02.009 Zhang Y, Yin Z, Wang H, He S (2021) 2020/21 record-breaking cold waves in east of China enhanced by the ‘Warm Arctic-Cold Siberia’pattern. Environ. Res. Lett. , 16(9):094040. https://doi.org/10.1088/1748‐9326/ac1f46 Zhang Y, Yin Z, Wang H (2023) Subseasonal transition of Barents–Kara sea-ice anomalies in winter related to the reversed warm Arctic–cold Eurasia pattern. Atmospheric and Oceanic Science Letters , 16(5):100392. https://doi.org/10.1016/j.aosl.2023.100392 Zhong L, Hua L, Luo D (2018) Local and external moisture sources for the Arctic warming over the Barents–Kara Seas. J. Climate , 31 (5):1963-1982. https://doi.org/10.1175/JCLI-D-17-0203.1 Supplementary Files SupplementaryMaterials0924.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7737671\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":526779850,\"identity\":\"fd35bfba-540e-4026-aac5-8e393fcbd613\",\"order_by\":0,\"name\":\"chen liu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIie3QMWrDMBSAYRmD3EGJ11danCs4GEqhgRZ6kaclnhIKWTQFm8Lz4jP0Dl06y2joogMEuiT0AvHmqVReS6umWwf9IE36kJ4YC4X+YdPYbaggS5O66o5qkfGk0V7CR7K318V5a2oDdllMhUU/cSs6kJL5rnzUEzLyCe5yP0nE/IgWkO1kpYGbkoAhG9SL52GiADfLOmoPlX4Q5Youah219s1Hrpi7ZRMn4y1ws6JLjXFEvxBJIIk5IvK45ID5aaQ9Gwne4wmEb8A9rADRVQb0ck7ukzvfLGlqnvtBbbPb1+a97z8Ws1nTdPtB/Uy+T//xfCgUCoW+9AlEz1iuW1ChHQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"China University of Geosciences\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"chen\",\"middleName\":\"\",\"lastName\":\"liu\",\"suffix\":\"\"},{\"id\":526779851,\"identity\":\"043d0011-c6ef-4610-b577-867889656132\",\"order_by\":1,\"name\":\"Lei Chen\",\"email\":\"\",\"orcid\":\"https://orcid.org/0000-0002-1104-9200\",\"institution\":\"China University of Geosciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lei\",\"middleName\":\"\",\"lastName\":\"Chen\",\"suffix\":\"\"},{\"id\":526779852,\"identity\":\"751b6cae-0cf4-4321-879c-3c0de7c62e2f\",\"order_by\":2,\"name\":\"Stefan Liess\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"University of Minnesota\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Stefan\",\"middleName\":\"\",\"lastName\":\"Liess\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2025-09-29 04:07:33\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-7737671/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-7737671/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":94134774,\"identity\":\"958d685a-b8ff-4b29-bf32-99b29d80765f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"xml\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":6974,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"cldyCLDYD2501058.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/4e88c48962983cfd2346c5d4.xml\"},{\"id\":94133933,\"identity\":\"0ed76515-f008-4640-b57c-6b225113e6d7\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"xml\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":983,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CLDYD250105822034.go.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/8a81d9b9142313803a8f65a0.xml\"},{\"id\":94135479,\"identity\":\"a5c7f1c9-3f03-4aee-bfee-87fa98db6bd2\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:53:39\",\"extension\":\"xml\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":808,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CLDYD2501058Import.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/1e72a9360a808aa63c946705.xml\"},{\"id\":94134775,\"identity\":\"1df75a17-1d76-4984-b32e-fc74d25585ef\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"xml\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":130457,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CLDYD25010580enriched.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/5ec15f394e9fc80d4b897281.xml\"},{\"id\":94134778,\"identity\":\"42448a78-5f14-425e-bc80-4a269496680c\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"png\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":233281,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/be587b593438e56f5e0a35a1.png\"},{\"id\":94133944,\"identity\":\"7ca69ce0-ed54-485d-bd5f-067093231f83\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":625257,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/890945c5b9a638dadd821c46.png\"},{\"id\":94133946,\"identity\":\"c59e9a2a-c50d-41c9-831c-69d7293558c7\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":408938,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/0fb81ea2b362d632a98a5280.png\"},{\"id\":94133949,\"identity\":\"4dfbc4fa-7aea-4dde-bd1a-d5779474ce48\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":9,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":203752,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/23592055fd0f57a213d1f8a3.png\"},{\"id\":94134781,\"identity\":\"11e208a9-e8fd-4171-a8a1-ec53ab7ee31a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"png\",\"order_by\":10,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":667085,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/aa949199fac1283edbd99ea4.png\"},{\"id\":94133952,\"identity\":\"b5fbbbfd-69b8-411b-8bc9-e5e52c763a8c\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":11,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":588535,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/ed9b2aea7b20468b3e76f717.png\"},{\"id\":94133950,\"identity\":\"1bb4d6e2-60a3-49af-9689-6454c24620ab\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":12,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":219225,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/3eccf507892d82d56423c875.png\"},{\"id\":94135482,\"identity\":\"a7dfb7c2-a4b5-479a-acec-d22363c45ad0\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:53:45\",\"extension\":\"png\",\"order_by\":13,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":246060,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/dbba68230e2207bbc65986f2.png\"},{\"id\":94133964,\"identity\":\"97ace152-8ca4-4c9b-b8ec-383bc6b2ac0a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":14,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":706931,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/00dada4662cc6e5bb97d815e.png\"},{\"id\":94134785,\"identity\":\"253fcd5d-cbfe-48f2-9f8d-464e803fde6c\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"png\",\"order_by\":15,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":598665,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/17b5473031cba8a96dc95fc7.png\"},{\"id\":94134783,\"identity\":\"a557c0c7-a4e0-4163-a006-219bf80cc416\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"png\",\"order_by\":16,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":1062917,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"floatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/0bed58e50d2c8c3f2cbd42f1.png\"},{\"id\":94133958,\"identity\":\"6832fb41-607f-4ab2-b51a-519541a26974\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":17,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":70877,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/95c820cde1255a5e89b6878e.png\"},{\"id\":94133965,\"identity\":\"db7b4cef-7738-4d19-a5f0-b1b4116f965f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":18,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":138942,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/05036d42cfa84297b627a5fd.png\"},{\"id\":94133962,\"identity\":\"02cea0ba-8379-41ed-a9ec-a4f47b72f606\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":19,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":86227,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/b779665ef9bc5fd97618daf2.png\"},{\"id\":94133951,\"identity\":\"ec718d80-339f-4cab-bccc-103bbca6d8ce\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":20,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":55793,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/1a316ebe9119b2f9b7a77064.png\"},{\"id\":94133960,\"identity\":\"ec276a1b-5235-48cd-95c2-e74310cbd5c3\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":21,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":152292,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/6bcb7809ce207de1dca5d14e.png\"},{\"id\":94133961,\"identity\":\"d81e4f75-bcf0-406c-a9c4-e443510dd149\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":22,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":134725,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/4c1e8171d1a9415e80b8529d.png\"},{\"id\":94133955,\"identity\":\"23689f99-8038-4091-88bd-1c7f4e992c7a\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":23,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":55586,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/b0842fc088235ab94d86058a.png\"},{\"id\":94134786,\"identity\":\"1a8b5690-bd35-417f-90e1-2edacc0f0220\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"png\",\"order_by\":24,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":57871,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/16ca981aa57c52e4f6c5c2f1.png\"},{\"id\":94135483,\"identity\":\"4699ed4d-6e76-4274-9057-b68c4ffebada\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:53:47\",\"extension\":\"png\",\"order_by\":25,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":159520,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/51e589c446cc7031c03d0a7b.png\"},{\"id\":94134787,\"identity\":\"4c4a099b-c610-4289-a7c4-6058b0f720c2\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"png\",\"order_by\":26,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":136402,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/3a9d6adb4f38b8adc7647953.png\"},{\"id\":94133968,\"identity\":\"3f650a92-deaf-4bbb-8692-b9b9f4950dcb\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":27,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":173408,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Onlinefloatimage9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/17bd9dfbe6dabd37a5c0cfd8.png\"},{\"id\":94133967,\"identity\":\"9255a85e-ee1c-4b69-8707-360286b51d0b\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"xml\",\"order_by\":28,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":129157,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"CLDYD25010580structuring.xml\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/74bc102493bbde4ec8d9b5bf.xml\"},{\"id\":94133970,\"identity\":\"a956221f-ca30-4a8e-bb18-eb9ab695045d\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"html\",\"order_by\":29,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":137567,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/6584b1a5ab274293d2273f80.html\"},{\"id\":94134773,\"identity\":\"fb3f9901-1d60-43f4-a497-ec200ae2b228\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":121074,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e(a)-(b) The sequential patterns of the third S-EOF mode of SAT anomalies (shading) over Arctic-Eurasian region during 1959-2023. (c) The corresponding standardized PC3 of the S-EOF modes.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/c4427f45d98493d97b627dff.png\"},{\"id\":94133931,\"identity\":\"affd75bf-935b-4901-b5a7-ec7df5581d0f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":150900,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eWavelet analysis and wavelet power of PC3 during 1959-2023. Bold contour and red dash line indicate the value above 90% confidence level. The region outside the solid red line denotes the cone of influence.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/ef12895dc8bed0b59e759d57.png\"},{\"id\":94133936,\"identity\":\"229ab6c3-2057-4feb-a277-04c269508afa\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":478511,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRegressions of surface air temperature (shading; unit: K) and 300-hPa geopotential height (contour; unit: m) on normalized PC3 in panels early (left column) and late (right column) winter during (a-b) 1959-1973, (c-d) 1974-1997 and (e-f) 1998-2023. Stippling indicates the SAT anomalies above 95% confidence level.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/1c1139dcb8240aa33c47e192.png\"},{\"id\":94133935,\"identity\":\"24f307c2-b348-4beb-bb4a-62b8ef421435\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":393530,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRegressions of 300-hPa geopotential height (shading; unit: m, contour; unit: m, zero line is emitted) and wave activity flux (vector; unit: m\\u003csup\\u003e2\\u003c/sup\\u003es\\u003csup\\u003e-2\\u003c/sup\\u003e ) on normalized PC3 in panels early (left column) and late (right column) winter during (a-b) 1959-1973, (c-d) 1974-1997 and (e-f) 1998-2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/a7d0fafd976127b90f26845a.png\"},{\"id\":94133941,\"identity\":\"2ef7b62a-6fba-4161-be77-47edcc948c6f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":167871,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe regression map of SSTAs (shading; unit: K) over North Atlantic in December during the period (a) 1959–1973, (b) the period 1974–1997 and (c) the period 1998-2023 upon normalized PC3. The black shading areas indicate the values above 95% confidence levels based on the Student’s t-test.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/d208cc00f8625aca83cf659d.png\"},{\"id\":94133938,\"identity\":\"2a2d615e-7f77-4c3e-a98b-a47c173b2f60\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:39\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":194631,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe regressions of the December SSTAs (shading; unit: K) upon ATL_SSTAs index during the period (a) 1959–1973, (b) the period 1974–1997 and (c) the period 1998-2023. (d) The normalized PC1 of the EOF mode of December SSTAs over North Atlantic over 1959-2023. The number at the top left is the correlation coefficient between WACE reversal index and ATL_SSTA index (number in parentheses is correlation coefficient without detrending). The black shading areas and values marked with an asterisk (*) are statistically significant at the 90% confidence level based on the Student’s t-test. The regressed SSTAs for the second period are multiplied by -1 due to predominantly negative ATL_SSTA index.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/c8886b0cb80b01db73c76460.png\"},{\"id\":94134776,\"identity\":\"bf4e34b3-2508-4bda-a5f8-dfa1b34f23d3\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":528243,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRegressions of surface air temperature (shading; unit: K) and 300-hPa geopotential height (contour; unit: m) upon the ATL_SSTAs index in panels early (left column) and late (right column) winter during (a-b) 1959-1973, (c-d) 1974-1997 and (e-f) 1998-2023. Stippling indicates the SAT anomalies above 95% confidence level.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/a4ec699470b7fcf0e26c90d8.png\"},{\"id\":94135480,\"identity\":\"1ed715b8-6d7e-41d8-b5cf-bd8c9b35d016\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:53:39\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":467144,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRegressions of 300-hPa geopotential height (shading; unit: m, contour; unit: m, zero line is emitted) and wave activity fluxes (vector; unit: m\\u003csup\\u003e2\\u003c/sup\\u003e s\\u003csup\\u003e-2\\u003c/sup\\u003e upon the ATL_SSTAs index in panels early (left column) and late (right column) winter during (a-b) 1959-1973, (c-d) 1974-1997 and (e-f) 1998-2023.\\u0026nbsp;\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/ba4c6046c8f34a739db96f63.png\"},{\"id\":94134780,\"identity\":\"516b6ecb-6e3f-471a-9468-785cf2074a99\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:39\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":586992,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe\\u003cstrong\\u003e \\u003c/strong\\u003ecross-wavelet analysis between ATL_SSTAs index and WACE reversal index. The enclosed areas denote the values above the 95% confidence level based on the Student’s t-test, and the arrows pointing left (right) indicate that the two time series are anti-phase (in-phase).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/a92804e3f2270453702192cf.png\"},{\"id\":94133963,\"identity\":\"aa11ee4a-338a-4ff9-8010-3802ee211fc6\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":584104,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe surface air temperature (shading; unit: K), 300-hPa geopotential height (contour; unit: m) and wave activity fluxes (vector; unit: m\\u003csup\\u003e2\\u003c/sup\\u003es\\u003csup\\u003e-2\\u003c/sup\\u003e) of strong (a-b), weak (c-d) positive PAC_SSTA events and their differences (e-f) in panels (left) early and (right) late winter .\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/f251ad740a4cfb053405601a.png\"},{\"id\":94133947,\"identity\":\"d9655ec0-6f31-463d-96f0-cac85809268e\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:37:40\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":226901,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eConceptual diagram illustrating the influence of the North Atlantic SST tripole on energy transfer associated with WACE reversal for (a) 1959-1973 and (b) 1974-1997, as well as the combined effect of the North Atlantic SST tripole and PAC_SSTAs during 1998–2023.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/04ec33c1a4c29bfe3e94c84b.png\"},{\"id\":98624164,\"identity\":\"128e7c72-370a-46f2-8a77-1774e3793a24\",\"added_by\":\"auto\",\"created_at\":\"2025-12-19 17:08:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3465508,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/00440f4e-5989-4d12-aeb7-42cff423a1a2.pdf\"},{\"id\":94134782,\"identity\":\"b46d26d0-bca0-48a6-8c05-45d645effd9f\",\"added_by\":\"auto\",\"created_at\":\"2025-10-22 18:45:40\",\"extension\":\"pdf\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":2319588,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryMaterials0924.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7737671/v1/6ccb8ff72ed463483b61f258.pdf\"}],\"financialInterests\":\"\",\"formattedTitle\":\"Interdecadal Modulation of the Warm Arctic-Cold Eurasia Reversal by the North Atlantic Sea Surface Temperature Tripole\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eSince the 20th century, the warm Arctic-cold Eurasia (WACE) pattern has emerged as a prominent mode of atmospheric variability that induces severe cold extremes across Eurasia, particularly during the boreal winter season (Cohen et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Mori et al. \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kug et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Jin et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). In recent years, growing attention has been given to the subseasonal reversals of WACE (SR-WACE) pattern in early and late winter (Xu et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Yin et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Xu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The transition on the subseasonal timescale can trigger significant weather and climate anomalies (Zhang et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e; Yin et al. \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), increasing the risk of ecological and societal impacts. Therefore, it is necessary to more deeply investigate the physical mechanism of WACE reversal.\\u003c/p\\u003e\\u003cp\\u003eEarly studies on SR-WACE have primarily focused on the evolution of reversal events, highlighting the role of Ural blocking (UB) in driving transitions of the surface air temperature (SAT) dipole (Xu et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Yin et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In addition, the impacts of the mid-latitude westerly jet and sea ice further indicate the complexity of the reversal issue (Xu et al. \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). However, the mechanisms responsible for the transition of the WACE pattern are presently not well understood. Notably, the formation of the WACE pattern itself remains a subject of debate, particularly regarding the dominant roles of internal atmospheric variation and sea ice forcing (Kug et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Luo et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Yao et al. \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Wegmann et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Sorokina et al. \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; McCusker et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Sun et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Blackport and Screen \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Given these uncertainties, it is plausible that SR-WACE has similar characteristics, potentially influenced by a combination of remote and local factors (Sun et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Ye and Messori \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Lin et al. \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Thus, insights from studies of the WACE pattern may provide valuable guidance for investigating its reversals.\\u003c/p\\u003e\\u003cp\\u003eFirstly, the characteristics and causes of interdecadal variation of WACE pattern have been widely studied. At the beginning of the 21st century, the winter surface air temperature (SAT) distribution in the Arctic-Eurasian region shifted from a cold Arctic-warm Eurasia configuration to a warm Arctic-cold Eurasia pattern (He et al. 2015). Jin et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) further showed that the WACE pattern became the leading EOF mode of winter SAT after 1998, with amplified warm anomalies over the Arctic and enhanced cold anomalies over Eurasia. Additionally, interactions between Rossby waves and the Siberian High have been found to strengthen the WACE pattern on interdecadal timescales (Sung et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, whether these interdecadal changes occurred in WACE reversals remains unclear. In combination with previous studies typically focusing on the interdecadal evolution of WACE patterns, it is necessary to investigate the WACE reversal from a long-term perspective.\\u003c/p\\u003e\\u003cp\\u003eFurthermore, the influence of internal ocean variability has been related to the WACE pattern (Nakanowatari et al. \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Park et al. \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2015\\u003c/span\\u003e; Woods and Caballero \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Jung et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Positive sea surface temperature anomalies (SSTAs) stimulate Rossby waves over the North Atlantic, altering the propagation of planetary waves and energy, ultimately impacting the SAT over Eurasia (Sato et al. \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Woods and Caballero \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Jung et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). The winter cold events in East are associated by SST variations in the North Atlantic (Song and Wu \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). In addition, Sung et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) suggested that the interdecadal variability of the WACE pattern is closely linked to warm SSTAs in the North Atlantic. These results raise the hypothesis that the WACE reversal has a potential connection with North Atlantic SSTs. Therefore, one of the aims of the present study is to assess the possible impact of North Atlantic SSTAs on the interannual variability of WACE reversals.\\u003c/p\\u003e\\u003cp\\u003eIn this study, we construct an annual time series of WACE reversals, with particular emphasis on changes in their interannual variability. To further identify the driving factors behind these changes, we specify the contribution of SSTAs over North Atlantic to WACE reversal on interannual timescales. The paper is organized as follows: The data and methods of analysis are described in Section \\u003cspan refid=\\\"Sec2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. Section \\u003cspan refid=\\\"Sec3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e investigates the interdecadal changes in interannual variability of WACE reversal. In Section \\u003cspan refid=\\\"Sec4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e, we specify the role of SSTAs over North Atlantic on the interdecadal changes during reversal events. Finally, the summary and discussion are presented in Section \\u003cspan refid=\\\"Sec5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.\\u003c/p\\u003e\"},{\"header\":\"2 Data and analysis methods\",\"content\":\"\\u003cp\\u003eIn this study, we focus on winter during the period 1959\\u0026ndash;2023, using daily mean reanalysis data from the ERA5 dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF), on a 2.5\\u0026deg;\\u0026times;2.5\\u0026deg; grid (Hersbach et al. 2023). The variables include surface air temperature (SAT), geopotential (z), as well as zonal and meridional wind (u, v) and temperature (T) at pressure level ranging from 1000 to 10 hPa. To minimize dataset biases, the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset is also employed for validation (NCEP-1; Kalnay et al. \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e1996\\u003c/span\\u003e). Monthly SST is obtained from the National Oceanic and Atmospheric Administration Extend Reconstructed SST V5 (ERSSTv5) dataset at a 2.0\\u0026deg;resolution (Huang et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe season-reliant empirical orthogonal function (S-EOF) analysis is applied to extract the features of WACE reversal (Wang and An \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Xu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Since each S-EOF mode captures two sequential spatial patterns, the reversal point of WACE is set around 14 January during the winter season (Yin et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Based on this reversal point, the evolution of WACE is divided into early and late winter.\\u003c/p\\u003e\\u003cp\\u003eEmpirical orthogonal function (EOF) is also applied to derive the time series of SSTAs. Wavelet analysis is used to examine changes in periodicity.\\u003c/p\\u003e\\u003cp\\u003eThe wave activity flux (WAF) is calculated following Takaya and Nakamura (\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e):\\u003c/p\\u003e\\u003cp\\u003eW = \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\frac{\\\\text{p}\\\\bullet\\\\:\\\\text{c}\\\\text{o}\\\\text{s}{\\\\phi\\\\:}}{2\\\\left|\\\\text{U}\\\\right|}\\\\left(\\\\begin{array}{c}\\\\frac{\\\\text{U}}{{\\\\text{a}}^{2}{\\\\text{c}\\\\text{o}\\\\text{s}}^{2}{\\\\phi\\\\:}}\\\\left[{\\\\left(\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\lambda\\\\:}}\\\\right)}^{2}-{\\\\psi\\\\:}{\\\\prime\\\\:}\\\\frac{{\\\\partial\\\\:}^{2}{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{{\\\\lambda\\\\:}}^{2}}\\\\right]+\\\\frac{\\\\text{V}}{{\\\\text{a}}^{2}\\\\text{c}\\\\text{o}\\\\text{s}{\\\\phi\\\\:}}\\\\left[\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\lambda\\\\:}}\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\phi\\\\:}}-{\\\\psi\\\\:}{\\\\prime\\\\:}\\\\frac{{\\\\partial\\\\:}^{2}{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\lambda\\\\:}\\\\partial\\\\:{\\\\phi\\\\:}}\\\\right]\\\\\\\\\\\\:\\\\frac{\\\\text{U}}{{\\\\text{a}}^{2}\\\\text{c}\\\\text{o}\\\\text{s}{\\\\phi\\\\:}}\\\\left[\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\lambda\\\\:}}\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\phi\\\\:}}-{\\\\psi\\\\:}{\\\\prime\\\\:}\\\\frac{{\\\\partial\\\\:}^{2}{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\lambda\\\\:}\\\\partial\\\\:{\\\\phi\\\\:}}\\\\right]+\\\\frac{\\\\text{V}}{{\\\\text{a}}^{2}}\\\\left[{\\\\left(\\\\frac{\\\\partial\\\\:{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{\\\\phi\\\\:}}\\\\right)}^{2}-{\\\\psi\\\\:}{\\\\prime\\\\:}\\\\frac{{\\\\partial\\\\:}^{2}{\\\\psi\\\\:}{\\\\prime\\\\:}}{\\\\partial\\\\:{{\\\\phi\\\\:}}^{2}}\\\\right]\\\\end{array}\\\\right)\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (1)\\u003c/p\\u003e\\u003cp\\u003ewhere U and V represent the climatological mean zonal and meridional wind, respectively. ψ' is perturbation stream function. λ and φ denote longitude and latitude, and p is the specific pressure (pressure divided by 1000hPa).\\u003c/p\\u003e\\u003cp\\u003ePrior to analysis, the linear trends in the reanalysis data are removed. Statistical significance is assessed using a two-tailed Student\\u0026rsquo;s t-test.\\u003c/p\\u003e\"},{\"header\":\"3 The Interdecadal Changes of Reversal of WACE Pattern\",\"content\":\"\\u003cp\\u003eThe S-EOF analysis is applied to identify the reversal of WACE during winters from 1959 to 2023 (Figure \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e displays only the third S-EOF mode over the Arctic-Eurasian region (40\\u0026deg;-90\\u0026deg;N, 20\\u0026deg;-130\\u0026deg;E). The derived spatial patterns reveal a transition of SAT anomalies from WACE in early winter to cold Arctic-warm Eurasia (CAWE) in late winter, with prominent anomaly centers over the Barents-Kara (B-K) Sea and near Balkhash Lake. To validate the time series of S-EOF, positive and negative cases with normalized values exceeding\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1 are selected, and their composite SAT anomaly distributions are shown in Figure S2. It is noted that the SAT anomalies experience a reversal in WACE/CAWE. Meanwhile, the centers of anomalous SAT over the B-K sea and Balkhash Lake are consistent with those in the third mode. The correlation between PC3 and observational reversal index is 0.82 (Figure S2e), confirming the time series effectively captures the interannual variability of the WACE reversal.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eAs Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003ec shows, the amplitudes of PC3 are predominantly negative after the 1980s, returning to positive values around 2000. We utilized wavelet analysis to investigate this interdecadal shift in the interannual variability of the WACE reversal (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Before 1973, the WACE reversal exhibits a 4\\u0026ndash;6 years cycle, which shifts to a 7\\u0026ndash;9 years cycle afterward. Subsequently, the periodicity extends to 6\\u0026ndash;8 years after 1997. Wavelet power shows that interannual variability (\\u0026le;\\u0026thinsp;8 years) dominates the WACE reversal, although a weaker interdecadal signal (9 years) is also present.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eGiven the changes in periodicity, the characteristics of WACE reversals may vary across different periods. Therefore, the period from 1959 to 2023 is divided into three phases: 1959\\u0026ndash;1973(P1), 1974\\u0026ndash;1997(P2), 1998\\u0026ndash;2023(P3). The results are robust to slight shifts in the boundary years (figure not shown).\\u003c/p\\u003e\\u003cp\\u003eIn early winter during 1959\\u0026ndash;1973 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea), significantly positive SAT anomalies are observed over southwestern Eurasia and the Barents-Kara (B-K) Sea, while East Asia experiences negative SAT anomalies. After the reversal, strong cold anomalies dominate the B-K Sea, and most of Eurasia exhibits anomalous warming (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eb). Corresponding changes are also seen in geopotential height anomalies. The early-winter Ural blocking (UB) exhibits an anomalous southward extension to the Arabian Plateau near 20\\u0026deg;N, deviating from its typical climatological latitude. Following the reversal, the negative geopotential height anomalies (GHAs) over the B-K Sea replace the earlier UB, while positive GHAs develop over central and eastern Eurasia.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eDuring the second (1974\\u0026ndash;1997) and third (1998\\u0026ndash;2023) periods, the dipole structures still appear in the early and late winter (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec-f). However, the distribution and intensity of SAT anomalies is different across these periods. The WACE pattern during 1974\\u0026ndash;1997 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ec) is stronger than that of 1998\\u0026ndash;2023 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ee), resulting in a broader area of warm anomalies affecting northern Eurasia. In late winter of 1974\\u0026ndash;1997, the negative temperature anomalies over northwestern Eurasia are accompanied by negative GHAs around 50\\u0026deg;N (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ed). Subsequently, a weak meridional dipole of GHAs covers the Eurasia during 1998\\u0026ndash;2023, similar to that in the first period (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ef). Notably, CAWE-related SAT anomalies during 1974\\u0026ndash;1997 are less significant compared to the third period (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ed, f). These results suggest that the spatial structures of WACE/CAWE in three periods have different characteristics with interdecadal changes in the interannual variability of WACE reversal, implying that large-scale atmospheric circulations also have interdecadal changes.\\u003c/p\\u003e\\u003cp\\u003eTo further investigate the underlying interdecadal changes in the atmospheric circulation, wave activity fluxes are shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. During the first period, a strong wave train originates from the central North Atlantic and propagates northeastward, reaching the Barents-Kara (B-K) Sea in early winter (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ea). An eastward branch of this wave activity extends as far south as 30\\u0026deg;N, influencing southern Eurasia and explaining the southward extension of the Ural blocking (UB). In contrast, during late winter, the strong North Atlantic wave train largely disappears (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003eb). Instead of propagating across the Barents Sea, the wave fluxes shift toward the eastern North Atlantic, exerting only a weak influence on atmospheric circulations over central Eurasia.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eAs shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003ec, North Atlantic wave activity remains important in early winter during 1974\\u0026ndash;1997. However, the wave train shifts northward, with less energy transferred into B-K Sea through Greenland, and part of the wave flux disperses southward. This weakened wave activity results in a more limited extension of the UB compared to the first period. Following the WACE reversal, the North Atlantic energy source weakens, and most wave fluxes over Europe fail to propagate to the Balkhash Lake region, leading to the disappearance of the WACE spatial structure over central Eurasia. During the period of 1998\\u0026ndash;2023, the wave train over North Atlantic weakens, and fewer wave fluxes reach Eurasia. Compared to previous periods, it is noted that a relatively stronger wave train over the central North Atlantic delivers energy toward the Greenland Sea in late winter, however, which hardly reached the regions associated with the WACE reversal pattern.\\u003c/p\\u003e\\u003cp\\u003eThese results suggest that variations in North Atlantic wave activity contribute to the distinct large-scale circulation patterns across the three periods, consistent with the findings of Sung et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). This also highlights the potential role of the North Atlantic in modulating the interdecadal changes in the interannual variability of WACE reversal.\\u003c/p\\u003e\"},{\"header\":\"4 The effect of SSTAs over the North Atlantic on the interdecadal changes\",\"content\":\"\\u003cp\\u003eThis section investigates the relationship between the North Atlantic and the interdecadal changes of WACE reversals across the three periods. The regressed December SSTAs upon PC3 over the North Atlantic upon the annual time series of the WACE reversal is given in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eDuring the first period, the spatial distribution of SSTAs exhibits a \\u0026ldquo;positive-negative-positive\\u0026rdquo; pattern, with significant warming near eastern Greenland and the Azores Islands (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea). In contrast to the overall positive SSTAs in the first period, the SSTAs during 1974\\u0026ndash;1997 and 1998\\u0026ndash;2023 are predominantly negative. Additionally, both periods show a tripole-like structure, which is \\u0026ldquo;negative-positive-negative\\u0026rdquo; in 1974\\u0026ndash;1997 and \\u0026ldquo;positive-negative-positive\\u0026rdquo; in 1998\\u0026ndash;2023, consistent with the conventional North Atlantic tripole pattern (Pan \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Chen et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The regression of SSTAs over the North Atlantic onto the WACE reversal index is most significant during the second period, while the SSTAs in the third period are largely insignificant.\\u003c/p\\u003e\\u003cp\\u003eEOF analysis is further employed to examine the interannual variability of North Atlantic SSTAs. Figure\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e presents the first EOF mode of December SSTAs (hereafter referred to as the ATL_SSTA index). As the ATL_SSTA index is predominantly negative during the second period, the regressed SSTAs for this period are multiplied by \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:-\\\\)\\u003c/span\\u003e\\u003c/span\\u003e1 to facilitate comparison.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eNotably, the SST patterns during the first two periods closely resemble those regressions upon the WACE reversal index (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea-b), with pattern correlation coefficients of 0.38 and 0.76, respectively (not shown). Moreover, the correlation between the WACE reversal index and the ATL_SSTA index suggests that the North Atlantic tripole pattern may contribute to the interdecadal changes in WACE reversal. In contrast, the SST pattern shows little resemblance to the regressed SSTAs associated with the normalized PC3 during 1998\\u0026ndash;2023 (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ec and \\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ec), with a pattern correlation of only 0.06. The reason for this insignificant correlation in the third period will be discussed in the next section. Here, we suggest that the North Atlantic SSTAs tripole pattern might cause the interdecadal shifts of WACE reversal in three periods.\\u003c/p\\u003e\\u003cp\\u003eTo confirm this hypothesis, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e presents the circulation anomalies regressed onto North Atlantic SSTAs for the three periods. It is noted that the SSTA-related regressions during 1959\\u0026ndash;1973 and 1974\\u0026ndash;1997 are relatively consistent with the spatial features of the WACE reversal.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn the first period, the blocking high near the Arabian plateau shows consistency with the southward extension of the circulations in the WACE reversal, coupled with the positive temperature anomalies over northern and southwestern Eurasia (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ea and \\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ea). Following the reversal, negative GHAs emerge over the B-K Sea, accompanied by the strong positive GHAs over central Eurasia (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eb). Similarly, the SSTA-related regression during the second period illustrates the influence of the North Atlantic tripole on the WACE reversal (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ec-d). The center of positive GHAs is located farther north compared to the first period, inducing positive temperature anomalies extending into the Barents Sea (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ec). In late winter, negative GHAs dominate northwestern Eurasia, and the weak positive GHAs extends eastward into central Eurasia around 40\\u0026deg;N. Although the anomalies are not statistically significant, the SAT pattern still exhibits a clear reversal in late winter. However, the regressed circulations are insignificant during 1998\\u0026ndash;2023 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ee-f), implying a negligible influence of North Atlantic SSTAs on the spatial distribution of WACE reversal in this period. This also corresponds to the weak correlation coefficient observed in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ec.\\u003c/p\\u003e\\u003cp\\u003eMoreover, the wave activities associated with the ATL_SSTA index illustrate the patterns of energy propagation during the three periods (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e). In the first period, wave activity originating from the North Atlantic propagates northeastward toward Norway and then disperses to affect western Eurasia (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ea). Although the direction of wave fluxes over the North Atlantic remains largely unchanged after early winter, a greater portion of the energy is directed toward central Eurasia (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003eb). During the period of 1974\\u0026ndash;1997, the wave fluxes over the North Atlantic exhibit a split pattern, with energy propagating both northward and southward (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ec-d). Subsequently, a wave train over Greenland transports energy toward the Arctic-Eurasian region. It is noteworthy that the wave activity patterns in both the first and second periods resemble the regressed wave flux structures associated with the WACE reversal, as shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e. However, during the period of 1998\\u0026ndash;2023, strong wave fluxes are observed near 50\\u0026deg;N over the North Atlantic in early winter. They predominantly propagate southward and southeastward and have limited influence on Greenland (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ee). In late winter, although the wave train moves eastward to Eurasia, the wave activity remains weak and statistically insignificant.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eThese comparisons of GHAs and wave activities across the three periods indicate that the observed atmospheric circulations over the North Atlantic are closely linked to North Atlantic SSTAs. The regressed GHAs downstream of the North Atlantic upon the ATL_SSTAs index (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ea-d and \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ea-d), which resembles the structure of WACE reversal. It suggests that SSTAs may induce interdecadal changes in WACE reversal by influencing the propagation of wave activity fluxes over the North Atlantic.\\u003c/p\\u003e\\u003cp\\u003eThe cross-wavelet analysis is employed to confirm the close relationship between North Atlantic SSTAs and the WACE reversal. The results indicate that the North Atlantic tripole is significantly associated with the WACE reversal index during the periods of 1959\\u0026ndash;1973 and 1974\\u0026ndash;1997 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e). The vector directions during these periods align with the correlation coefficients shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e, supporting the connection. Moreover, the significant periodicities identified correspond to the 4- and 8-year periodicity of WACE reversal (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Notably, the correlation of periodicity between the December ATL_SSTAs and the WACE reversal index is decreased after 1997. Although the patterns of SSTAs on the first and third periods are similar (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003ea and c), the significance of coefficient and the wave activities over the North Atlantic in the period 1959\\u0026ndash;1973 are stronger than that of 1998\\u0026ndash;2023. Thus, the following section explores the underlying reasons for this shift.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFirst, the connection between North Pacific SSTAs (hereafter PAC_SSTAs) and ATL_SSTAs becomes notably stronger during 1998\\u0026ndash;2023 (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). From December to February, the tripole SSTAs pattern and the cold anomalies over the central tropical Pacific are persistent and significant in the third period (Figure S3g-i), resembling a Central La Ni\\u0026ntilde;a-like pattern. Compared to the first period, positive GHAs over the central North Atlantic are weakened and shift southward, thereby redirecting more wave fluxes toward the south (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ee and \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ee). In late winter, positive GHAs near Greenland transition into negative GHAs centered further west (Figs.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003ef and \\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003ef), which diminishes the northeastward propagation of wave activity toward Eurasia.\\u003c/p\\u003e\\u003cp\\u003eTherefore, composite analyses are conducted to assess the impacts of different combinations of SSTAs on the WACE reversal. The PAC_SSTAs index is defined as the area-weighted average of SSTAs over the region 20\\u0026deg;S-40\\u0026deg;N, 180\\u0026deg;-100\\u0026deg;W (Figure S4). A strong event is identified when the PAC_SSTA index exceeds\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5 standard deviations. Based on this threshold, we select cases of strong and weak cold PAC_SSTA events that occur in conjunction with the North Atlantic SSTA tripole pattern.\\u003c/p\\u003e\\u003cp\\u003eUnder strong PAC_SSTAs, wave activity fluxes from the North Pacific propagate downstream, forming a dipole of GHAs over the North Atlantic (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003ea-b). Subsequently, wave fluxes along with positive GHAs move into central Eurasia. In contrast, during weak PAC_SSTA events, North Atlantic circulations mainly supply energy toward the B-K Sea (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003ec-d). As shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003ee, the spatial distribution of GHAs across the Arctic-Eurasian region resembles the CAWE pattern, accompanied by warm anomalies over central Eurasia and cold anomalies over the Arctic. Relative to weak PAC_SSTAs events, positive GHAs in strong cold PAC_SSTAs conditions shift southward, consistent with the WACE reversal structure during the third period (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003ee). Meanwhile, strong wave activity fluxes over the North Atlantic primarily propagate southeastward toward Africa, following with the northeast-southwest tilt of wave trains (Simmons et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e1983\\u003c/span\\u003e; Wang et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). The weak energy transmission near B-K sea therefore might contribute to the suppression of the WACE pattern during early winter. Similarly, in late winter, negative GHAs develop over western Eurasia, while positive GHAs dominate the B-K sea, producing a dipole of SAT anomalies opposites to the typical CAWE pattern (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003eb and d).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eMoreover, the structure of GHAs over North Atlantic is similar to the North Atlantic Oscillation (NAO) pattern. Given that positive NAO phases are known to facilitate the development of the WACE pattern (Luo et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Zhong et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Luo et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e), the suppressive effect of PAC_SSTAs may therefore arise from their modulation of the North Atlantic atmospheric circulation. In strong PAC_SSTAs events, the wave train from the North Pacific propagates along the enhanced westerly jet downstream, which could impact the circulation over the North Atlantic (Figure S5). Meanwhile, the high-latitude wave train near the Barents Sea is similar to the British-Baikal corridor (BBC pattern) (Xu et al. \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Li et al. \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003eb), facilitating more energy move southeastward. The circulation pattern is different from the wave pattern path directed to the polar region (Li et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Therefore, the North Atlantic circulation that facilitated energy transfer has collapsed (Figure S5a and c), resulting in reduced wave fluxes impacting the WACE reversal region.\\u003c/p\\u003e\\u003cp\\u003eOverall, during 1998\\u0026ndash;2023, the influence of the North Atlantic tripole SSTAs on WACE reversal is suppressed by North Pacific SSTAs. Pronounced cold anomalies over the northeastern Pacific affect the interdecadal variability of WACE reversal by disrupting the atmospheric circulation patterns over the North Atlantic that typically facilitate energy transfer. Consequently, fewer wave activities reach the Barents-Kara Sea, limiting the development of WACE/CAWE patterns. With weakened enhancement from North Atlantic SSTAs, the coupled Atlantic-Pacific effect produces a weaker WACE reversal structure in the third period compared with the first. Therefore, interdecadal variations of WACE reversal across the three periods are largely attributable to the North Atlantic tripole SSTAs, accompanied by episodic coupled influences from the North Pacific SSTAs.\\u003c/p\\u003e\"},{\"header\":\"5 Conclusions and discussions\",\"content\":\"\\u003cp\\u003eThis study investigates the interdecadal changes in interannual variability of WACE pattern reversals and explores their possible mechanisms using reanalysis data. Meanwhile, we also verified these conclusions utilizing the NCEP-NCAR reanalysis data (not shown). The time series of WACE reversals reveal distinct interdecadal variations in atmospheric circulation, characterized by three periods: 1959\\u0026ndash;1973 (P0), 1974\\u0026ndash;1997 (P1) and 1998\\u0026ndash;2023 (P2).\\u003c/p\\u003e\\u003cp\\u003eDuring P0, the UB extends southward to 30\\u0026deg;N, contributing to warm anomalies over the Arabian Plateau. In P1, the WACE structure strengthens, with significant cold anomalies over central Eurasia. In contrast, P2 shows a notably weaker WACE reversal pattern, particularly in early winter. The spatial distribution of SAT anomalies is related to the intensity and position of blocking highs over the Arctic-Eurasian sector, confirming the necessary role of atmospheric circulation in shaping the dipole structure of the WACE pattern (Luo et al. \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Xu et al. \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWave activity analysis further suggests that interdecadal shift is strongly linked to energy propagation from the North Atlantic. The WACE-related SSTAs in three periods exhibit a tripole structure, consistent with the leading EOF mode of North Atlantic SSTs (ATL_SSTA index). Moreover, regression of circulations and wave fluxes upon the ATL_SSTA index matches the spatial structure of the WACE reversal. In detail, the \\u0026ldquo;positive-negative-positive\\u0026rdquo; tripole favors reversals from WACE to CAWE in P0 and P2, while the promoting SSTA patterns are shown as \\u0026ldquo;negative-positive-negative\\u0026rdquo; in P1. These findings suggest that the North Atlantic tripole SST pattern contributes to interdecadal variability by modulating atmospheric flow.\\u003c/p\\u003e\\u003cp\\u003eHowever, during P2, the influence of the North Atlantic SSTAs diminishes. Concurrently, SSTAs over the North Pacific become significantly correlated with the ATL_SSTA index from December, indicating a coupled Atlantic-Pacific influence. Composite analysis shows that negative Pacific SST anomalies (PAC_SSTAs) suppress the promoting impact from the North Atlantic, producing an opposite SAT dipole structure over the Arctic-Eurasian region. Strong positive geopotential height anomalies (GHAs) over the North Atlantic shifts southward in early winter and evolves into negative GHAs located more west in late winter. This change in atmospheric circulation redirects wave activity southeastward, reducing energy transport to the B-K Sea. A schematic diagram illustrating the mechanisms of the joint influence of the North Atlantic and Pacific is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e(c). Cold anomalies over the northeast Pacific generate wave trains that propagate along the intensified westerly jet and induce the northeast-southwest tilt of GHAs, resembling the BBC pattern. As more wave fluxes are directed toward southwestern Eurasia, energy input into the polar region is hindered, weakening the development of atmospheric circulations over the B-K Sea. Thus, cold PAC_SSTAs events impact the interdecadal changes of WACE reversal during P2 by modulating upstream wave activity fluxes over the reversal region. Notably, El Ni\\u0026ntilde;o-Southern Oscillation (ENSO) pattern can also impact atmospheric circulations over the North Atlantic (Toniazzo and Scaife \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e; Mezzina et al. 2020). Under La Ni\\u0026ntilde;a-like PAC_SSTA conditions, the correlation between ATL_SSTA index and ENSO index become significant in the third period (Figure S3g-i), suggesting that ENSO events may regulate the interaction between the North Atlantic and Pacific. Nonetheless, this is beyond the scope of this paper and will be a focus of our future research.\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eIn general, this study focuses on the variability of WACE reversals predominately on an interannual timescale, with interdecadal changes detected. Transitions from WACE to CAWE dominate during P0 and P2, while P1 is characterized primarily by CAWE-to-WACE transitions. This differs from the interdecadal variability of the WACE pattern itself (Sung et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), highlighting the need to study these two processes separately. Moreover, the contribution of the North Atlantic tripole SSTAs and their interaction with North Pacific SSTAs are critical to the interdecadal changes in WACE reversals. This conclusion confirms that the development of WACE reversal is not only forced by Arctic Sea ice, pointing to a more complex mechanism involving ocean-atmosphere coupling.\\u003c/p\\u003e\\u003cp\\u003eThis study could also provide a better prediction for the variability of WACE reversal. There is no clear indication of an internally generated signal for Atlantic multidecadal variability, in contrast to the North Pacific (Fernandez et al. \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Nevertheless, detailed monitoring of North Atlantic tripole SSTAs in early winter could aid in forecasting extreme cold events associated with WACE reversals.\\u003c/p\\u003e\\u003cp\\u003eDespite these predictive implications, whether North Atlantic SSTAs contribute to WACE reversals on subseasonal timescales remains uncertain. The North Atlantic tripole SST could exert a positive feedback, inducing the reversed NAO pattern in day-to-day analysis (Tao et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Given that the NAO plays a critical role in shaping the circulation over Eurasia and influencing winter cold events in East Asia (Song and Wu \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), it is plausible that North Atlantic SSTs may have influences on the process of WACE reversals. Furthermore, the coupled impacts of North Atlantic and Pacific SSTAs represent a complex factor governing North Atlantic atmospheric circulation. To clarify these issues and better understand the relative influences of the North Atlantic and Pacific on subseasonal WACE reversals, numerical modeling studies would be valuable. For instance, quantifying the contributions of North Atlantic and Pacific SSTAs, assessing their impacts on the day-to-day evolution of WACE reversals and distinguishing ENSO-related impact from those of North Pacific SSTAs.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003ch2\\u003eCompeting Interests\\u003c/h2\\u003e\\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e\\u003c/p\\u003e\\u003ch2\\u003eFunding\\u003c/h2\\u003e\\u003cp\\u003eThis work is jointly supported by the National Natural ScienceFoundation of China (42376250) and the China National Key Research and Development Program (2023YFE0103900).\\u003c/p\\u003e\\u003ch2\\u003eAuthor Contributions\\u003c/h2\\u003e\\u003cp\\u003eChen Liu, Lei Chen, and Stefan Liess jointly conceived and designed the study. Chen Liu prepared the first draft of the manuscript. Lei Chen, and Stefan Liess critically reviewed and revised the draft for important intellectual content. All authors read and approved the final version of the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e\\u003cp\\u003eThis work is jointly supported by the National Natural ScienceFoundation of China (42376250) and the China National Key Research and Development Program (2023YFE0103900).\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eThe NOAA-RESSTv5 dataset is available at: (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html\\u003c/span\\u003e\\u003cspan address=\\\"https://psl.noaa.gov/data/gridded/data.noaa.ersst.v5.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eThe NCEP-NCAR reanalysis data can be freely accessed on the website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html\\u003c/span\\u003e\\u003cspan address=\\\"https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e). The ECMWF Reanalysis v5 (ERA5) data can be freely accessed on the website (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5\\u003c/span\\u003e\\u003cspan address=\\\"https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e).\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBlackport R, Screen JA (2020) Weakened evidence for mid-latitude impacts of Arctic warming. \\u003cem\\u003eNat. Clim. Change\\u003c/em\\u003e, 10(12):1065-1066. https://doi.org/10.1038/s41558-020-00954-y\\u003c/li\\u003e\\n\\u003cli\\u003eChen S, Wu R, Chen W (2020) Strengthened connection between springtime North Atlantic Oscillation and North Atlantic tripole SST pattern since the late 1980s. \\u003cem\\u003eJ.Climate\\u003c/em\\u003e, 33(5):2007-2022. https://doi.org/10.1175/JCLI-D-19-0628.1\\u003c/li\\u003e\\n\\u003cli\\u003eCohen JL, Furtado JC, Barlow MA, Alexeev VA and Cherry JE (2012) Arctic warming, increasing snow cover and widespread boreal winter cooling. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 7(1):014007. https://doi.org/10.1088/1748‐9326/7/1/014007\\u003c/li\\u003e\\n\\u003cli\\u003eHe S, Xu X, Furevik T, Gao Y (2020) Eurasian cooling linked to the vertical distribution of Arctic warming. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 47(10):e2020GL087212. https://doi.org/10.1029/2020GL087212\\u003c/li\\u003e\\n\\u003cli\\u003eFernandez A, Steinman BA, Mann ME, Christiansen SA (2025) Multidecadal temperature variability in the Community Earth System Model Last Millennium Ensemble. Geophysical Research Letters, 52:e2024GL113393. https://doi.org/10.1029/2024GL113393\\u003c/li\\u003e\\n\\u003cli\\u003eHersbach H, Bell B, Berrisford P, Biavati G, Hor\\u0026aacute;nyi A, Mu\\u0026ntilde;oz Sabater J et al (2018) ERA5 hourly data on single levels from 1979 to present. Copernicus climate change service (c3s) climate data store (cds), 10(10.24381). https://doi.org/10.24381/cds.adbb2d47\\u003c/li\\u003e\\n\\u003cli\\u003eHuang B, Thorne PW, Banzon VF, Boyer T, Chepurin G, Lawrimore JH et al (2017) Extended reconstructed sea surface temperature, version 5 (ERSSTv5): upgrades, validations, and intercomparisons. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 30(20):8179-8205. https://doi.org/10.1175/JCLI-D-16-0836.1\\u003c/li\\u003e\\n\\u003cli\\u003eJin C, Wang B, Yang YM and Liu J (2020) \\u0026ldquo;Warm Arctic‐cold Siberia\\u0026rdquo; as an internal mode instigated by North Atlantic warming. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 47(9):e2019GL086248. https://doi.org/10.1029/2019GL086248\\u003c/li\\u003e\\n\\u003cli\\u003eJung O, Sung MK, Sato K, Lim YK, Kim SJ, Baek EH et al (2017) How does the SST variability over the western North Atlantic Ocean control Arctic warming over the Barents\\u0026ndash;Kara Seas?. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 12(3):034021. https://doi.org/10.1088/1748-9326/aa5f3b\\u003c/li\\u003e\\n\\u003cli\\u003eKalnay E, Kanamitsu M, Kistler R, Collins W, Deaven S, Gandin L (1996) The NCEP/NCAR 40-year reanalysis project. \\u003cem\\u003eBull. Amer. Meteor. Soc.\\u003c/em\\u003e, 77:437\\u0026ndash;472. https://doi.org/10.1175/1520-0477(1996)077\\u0026lt;0437:TNYRP\\u0026gt;2.0.CO;2\\u003c/li\\u003e\\n\\u003cli\\u003eKug JS, Jeong JH, Jang YS, Kim BM, Folland CK, Min SK, Son SW (2015) Two distinct influences of Arctic warming on cold winters over North America and East Asia. \\u003cem\\u003eNat. Geosci.\\u003c/em\\u003e, 8(10):759-762. https://doi.org/10.1038/ngeo2517\\u003c/li\\u003e\\n\\u003cli\\u003eLi S, Hu H, Ren X, Perrie W, Yang XQ, Yu P, Mao K (2024) A transmitted subseasonal mode of the winter surface air temperature in the mid-and high-latitudes of the Eurasia and contributions from the North Atlantic and Arctic regions. \\u003cem\\u003eJ GEOPHYS RES-ATMOS\\u003c/em\\u003e, 129(12):e2023JD038627. https://doi.org/10.1029/2023JD038627\\u003c/li\\u003e\\n\\u003cli\\u003eLi X, Lu R, Greatbatch RJ, Li G, Hong X (2020) Maintenance mechanism for the teleconnection pattern over the high latitudes of the Eurasian continent in summer. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 33(3), 1017-1030. https://doi.org/10.1175/JCLI-D-19-0362.1\\u003c/li\\u003e\\n\\u003cli\\u003eLin H, Yu B, Hall NM (2022) Origin of the warm Arctic\\u0026ndash;cold North American pattern on the intraseasonal time scale. \\u003cem\\u003eJ. Atmos. Sci.\\u003c/em\\u003e, 79(10):2571-2583. https://doi.org/10.1175/JAS-D-22-0013.1\\u003c/li\\u003e\\n\\u003cli\\u003eLuo D, Xiao Y, Yao Y, Dai A, Simmonds I, Franzke CL (2016) Impact of Ural blocking on winter warm Arctic\\u0026ndash;cold Eurasian anomalies. Part I: Blocking-induced amplification. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 29(11):3925-3947. https://doi.org/10.1175/JCLI-D-15-0611.1\\u003c/li\\u003e\\n\\u003cli\\u003eLuo B, Luo D, Wu L, Zhong L, Simmonds I (2017) Atmospheric circulation patterns which promote winter Arctic sea ice decline. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 12(5):054017. https://doi.org/10.1088/1748-9326/aa69d0\\u003c/li\\u003e\\n\\u003cli\\u003eLuo B, Luo D, Dai A, Simmonds I, Wu L (2022) Decadal variability of winter warm Arctic‐cold Eurasia dipole patterns modulated by Pacific decadal oscillation and Atlantic multidecadal oscillation. \\u003cem\\u003eEarth\\u0026apos;s Future\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e(1):e2021EF002351. https://doi.org/10.1029/2021EF002351\\u003c/li\\u003e\\n\\u003cli\\u003eMcCusker KE, Fyfe JC, Sigmond M (2016) Twenty-five winters of unexpected Eurasian cooling unlikely due to Arctic sea-ice loss. \\u003cem\\u003eNat. Geosci.\\u003c/em\\u003e, 9(11):838-842. https://doi.org/10.1038/ngeo2820\\u003c/li\\u003e\\n\\u003cli\\u003eMezzina B, Garc\\u0026iacute;a-Serrano J, Blad\\u0026eacute; I et al (2022) Dynamics of the ENSO teleconnection and NAO variability in the North Atlantic-European late winter. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 33(3): 907-923. https://doi.org/10.1175/JCLI-D-19-0192.1\\u003c/li\\u003e\\n\\u003cli\\u003eMori M, Watanabe M, Shiogama H, Inoue J, Kimoto M (2014) Robust Arctic sea-ice influence on the frequent Eurasian cold winters in past decades. \\u003cem\\u003eNat. Geosci.\\u003c/em\\u003e, 7(12):869-873. https://doi.org/10.1038/ngeo2277\\u003c/li\\u003e\\n\\u003cli\\u003eNakanowatari T, Sato K, Inoue J (2014) Predictability of the Barents Sea ice in early winter: Remote effects of oceanic and atmospheric thermal conditions from the North Atlantic. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 27(23):8884-8901. https://doi.org/10.1175/JCLI-D-14-00125.1\\u003c/li\\u003e\\n\\u003cli\\u003ePan LL (2005) Observed positive feedback between the NAO and the North Atlantic SSTA tripole. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 32(6). https://doi.org/10.1029/2005GL022427\\u003c/li\\u003e\\n\\u003cli\\u003ePark H S, Lee S, Son SW, Feldstein SB, Kosaka Y (2015) The impact of poleward moisture and sensible heat flux on Arctic winter sea ice variability. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 28(13):5030-5040. https://doi.org/10.1175/JCLI-D-15-0074.1\\u003c/li\\u003e\\n\\u003cli\\u003eSato K, Inoue J, Watanabe M (2014) Influence of the Gulf Stream on the Barents Sea ice retreat and Eurasian coldness during early winter. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 9(8):084009. https://doi.org/10.1088/1748-9326/9/8/084009\\u003c/li\\u003e\\n\\u003cli\\u003eSardeshmukh PD, Hoskins BJ (1988) The generation of global rotational flow by steady idealized tropical divergence. \\u003cem\\u003eJ. Atmos. Sci.\\u003c/em\\u003e, 45(7):1228-1251. https://doi.org/10.1175/1520-0469(1988)045%3C1228:TGOGRF%3E2.0.CO;2\\u003c/li\\u003e\\n\\u003cli\\u003eSimmons AJ, Wallace J, Branstator GW (1983) Barotropic wave propagation and instability, and atmospheric teleconnection patterns. \\u003cem\\u003eJ. Atmos. Sci.\\u003c/em\\u003e, 40(6):1363-1392. https://doi.org/10.1175/1520-0469(1983)040%3C1363:BWPAIA%3E2.0.CO;2\\u003c/li\\u003e\\n\\u003cli\\u003eSorokina SA, Li C, Wettstein JJ, Kvamst\\u0026oslash; NG (2016) Observed atmospheric coupling between Barents Sea ice and the warm-Arctic cold-Siberian anomaly pattern. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 29(2):495-511. https://doi.org/10.1175/JCLI-D-15-0046.1\\u003c/li\\u003e\\n\\u003cli\\u003eSong L, Wu R (2017) Processes for occurrence of strong cold events over eastern China. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 30(22):9247-9266. https://doi.org/10.1175/JCLI-D-16-0857.1\\u003c/li\\u003e\\n\\u003cli\\u003eSun L, Perlwitz J, Hoerling M (2016) What caused the recent \\u0026ldquo;Warm Arctic, Cold Continents\\u0026rdquo; trend pattern in winter temperatures?. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 43(10):5345-5352. https://doi.org/10.1002/2016GL069024\\u003c/li\\u003e\\n\\u003cli\\u003eSung MK, Kim SH, Kim BM, Choi YS (2018) Interdecadal variability of the warm Arctic and cold Eurasia pattern and its North Atlantic origin. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 31(15):5793-5810. https://doi.org/10.1175/JCLI-D-17-0562.1\\u003c/li\\u003e\\n\\u003cli\\u003eTakaya K, Nakamura H (2001) A formulation of a phase-independent wave-activity flux for stationary and migratory quasigeostrophic eddies on a zonally varying basic flow. \\u003cem\\u003eJ. Atmos. Sci.\\u003c/em\\u003e, 58(6):608-627. https://doi.org/10.1175/1520-0469(2001)058%3C0608:AFOAPI%3E2.0.CO;2\\u003c/li\\u003e\\n\\u003cli\\u003eTao L, Fang J, Yang XQ, Sun X, Cai D, Wang Y (2023) Role of North Atlantic tripole SST in mid‐winter reversal of NAO. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 50(15):e2023GL103502. https://doi.org/10.1029/2023GL103502\\u003c/li\\u003e\\n\\u003cli\\u003eToniazzo T, Scaife AA (2006) The influence of ENSO on winter North Atlantic climate. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 33(24). https://doi.org/10.1029/2006GL027881\\u003c/li\\u003e\\n\\u003cli\\u003eWang B, An SI (2005) A method for detecting season‐dependent modes of climate variability: S‐EOF analysis. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 32(15). https://doi.org/10.1029/2005GL022709\\u003c/li\\u003e\\n\\u003cli\\u003eWang Y, Hu K, Huang G, Tao W (2023) The role of nonlinear energy advection in forming asymmetric structure of ENSO teleconnections over the North Pacific and North America. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 50(17):e2023GL105277. https://doi.org/10.1029/2023GL105277\\u003c/li\\u003e\\n\\u003cli\\u003eWegmann M, Orsolini Y, Zolina O (2018) Warm Arctic\\u0026minus; cold Siberia: comparing the recent and the early 20th-century Arctic warmings. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 13(2):025009. https://doi.org/10.1088/1748-9326/aaa0b7\\u003c/li\\u003e\\n\\u003cli\\u003eWoods C, Caballero R (2016) The role of moist intrusions in winter Arctic warming and sea ice decline. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 29(12):4473-4485. https://doi.org/10.1175/JCLI-D-15-0773.1\\u003c/li\\u003e\\n\\u003cli\\u003eXu X, He S, Zhou B, Wang H (2022) Atmospheric contributions to the reversal of surface temperature anomalies between early and late winter over Eurasia. \\u003cem\\u003eEarth\\u0026apos;s Future\\u003c/em\\u003e, 10(8):e2022EF002790. https://doi.org/10.1029/2022EF002790\\u003c/li\\u003e\\n\\u003cli\\u003eXu X, He S, Zhou B, Wang H, Outten S (2022) The role of mid‐latitude westerly jet in the impacts of November Ural blocking on early‐winter warmer Arctic‐colder Eurasia pattern. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 49(16):e2022GL099096. https://doi.org/10.1029/2022GL099096\\u003c/li\\u003e\\n\\u003cli\\u003eXu P, Wang L, Chen W (2019) The British\\u0026ndash;Baikal Corridor: A teleconnection pattern along the summertime polar front jet over Eurasia. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 32(3), 877-896. https://doi.org/10.1175/JCLI-D-18-0343.1 \\u003c/li\\u003e\\n\\u003cli\\u003eXu T, Yin Z, Zhang Y, Zhou B (2024) Identification of shortcomings in simulating the subseasonal reversal of the warm Arctic\\u0026ndash;cold Eurasia pattern. \\u003cem\\u003eGeophys. Res. Lett.\\u003c/em\\u003e, 51(3):e2023GL105430. https://doi.org/10.1029/2023GL105430\\u003c/li\\u003e\\n\\u003cli\\u003eYao Y, Luo D, Dai A, Simmonds I (2017) Increased quasi stationarity and persistence of winter Ural blocking and Eurasian extreme cold events in response to Arctic warming. Part I: Insights from observational analyses. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 30(10):3549-3568. https://doi.org/10.1175/JCLI-D-16-0261.1\\u003c/li\\u003e\\n\\u003cli\\u003eYe K, Messori G (2020) Two leading modes of wintertime atmospheric circulation drive the recent warm Arctic\\u0026ndash;cold Eurasia temperature pattern. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, 33(13):5565-5587. https://doi.org/10.1175/JCLI-D-19-0403.1\\u003c/li\\u003e\\n\\u003cli\\u003eYin Z, Wan Y, Zhang Y, Wang H (2022) Why super sandstorm 2021 in North China?. \\u003cem\\u003eNatl. Sci. Rev.\\u003c/em\\u003e, 9(3):nwab165. https://doi.org/10.1093/nsr/nwab165\\u003c/li\\u003e\\n\\u003cli\\u003eYin Z, Zhang Y, Zhou B, Wang H (2023) Subseasonal variability and the \\u0026ldquo;Arctic warming-Eurasia cooling\\u0026rdquo; trend. \\u003cem\\u003eSci. Bull.\\u003c/em\\u003e, 68(5):528-535. https://doi.org/10.1016/j.scib.2023.02.009\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Y, Yin Z, Wang H, He S (2021) 2020/21 record-breaking cold waves in east of China enhanced by the \\u0026lsquo;Warm Arctic-Cold Siberia\\u0026rsquo;pattern. \\u003cem\\u003eEnviron. Res. Lett.\\u003c/em\\u003e, 16(9):094040. https://doi.org/10.1088/1748‐9326/ac1f46\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Y, Yin Z, Wang H (2023) Subseasonal transition of Barents\\u0026ndash;Kara sea-ice anomalies in winter related to the reversed warm Arctic\\u0026ndash;cold Eurasia pattern. \\u003cem\\u003eAtmospheric and Oceanic Science Letters\\u003c/em\\u003e, 16(5):100392. https://doi.org/10.1016/j.aosl.2023.100392\\u003c/li\\u003e\\n\\u003cli\\u003eZhong L, Hua L, Luo D (2018) Local and external moisture sources for the Arctic warming over the Barents\\u0026ndash;Kara Seas. \\u003cem\\u003eJ. Climate\\u003c/em\\u003e, \\u003cem\\u003e31\\u003c/em\\u003e(5):1963-1982. https://doi.org/10.1175/JCLI-D-17-0203.1\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Climate change, atmospheric circulation, interdecadal change, sea surface temperature\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7737671/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7737671/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe reversal of the warm Arctic-cold Eurasia (WACE) pattern significantly influences weather and climate extremes across Eurasia. Based on previous studies, the WACE reversal is defined as the third season-reliant empirical orthogonal function of surface air temperature (SAT) variability over the Arctic-Eurasian continent in Northern Hemisphere winter. This study investigates the interdecadal changes in the interannual variability of WACE reversal, revealing that its dominant periodicities have shifted across different decades. Observational analyses identify three characteristic Ural blocking (UB) patterns, each associated with shifts in the Arctic-Eurasian SAT dipole during reversal events. Furthermore, the results indicate that the interdecadal transitions of WACE reversal are largely attributable to the North Atlantic tripole sea surface temperature anomalies (SSTAs). Meanwhile, cold SSTAs over the North Pacific induced by these tripole patterns, may suppress the North Atlantic\\u0026rsquo;s influence by reducing downstream wave activity to the reversal region. Our results demonstrate that North Atlantic SSTAs play an important role in modulating WACE reversal on interannual timescales, with their impact further shaped by coupled Atlantic-Pacific interactions.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Interdecadal Modulation of the Warm Arctic-Cold Eurasia Reversal by the North Atlantic Sea Surface Temperature Tripole\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-22 18:37:34\",\"doi\":\"10.21203/rs.3.rs-7737671/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"978acab8-fbc3-45c0-9276-5b1bc4dc1a94\",\"owner\":[],\"postedDate\":\"October 22nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-12-18T01:42:00+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-10-22 18:37:34\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7737671\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7737671\",\"identity\":\"rs-7737671\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}