Relationship between Pacific–South America teleconnections in austral fall and ENSO evolution

Relationship between Pacific–South America teleconnections in austral fall and ENSO evolution | 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 Article Relationship between Pacific–South America teleconnections in austral fall and ENSO evolution Jae-Heung Park, Geon-Il Kim, Jong-Seong Kug, Mi-Kyung Sung, Young-Min Yang, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7757839/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 South Pacific atmospheric teleconnections and their relationship with the tropical Pacific have remained less explored than their North Pacific counterparts. Using observational reanalysis datasets since the mid-20th century, we first identified three atmospheric teleconnections by applying Empirical Orthogonal Function (EOF) analysis to the 200hPa geopotential height from the South Pacific to South America during the austral fall, when the variability of El Niño–Southern Oscillation (ENSO) is weak. We then investigated the relationship between these EOFs and ENSO. We found that EOF1 is a direct Rossby wave response to the tropical convection associated with previous ENSO events. In contrast, both EOF2 and EOF3—similar to Pacific–South America teleconnections—are significantly correlated with subsequent ENSO events. In detail, EOF2 exhibits a great-circle Rossby wave energy propagation pattern, initiated by enhanced convection over the equatorial western Pacific. That is, this convection is largely responsible for the development of both ENSO and EOF2. Meanwhile, EOF3 is characterized by wave energy propagation toward the tropical Pacific from the high-latitudes, potentially contributing to ENSO development. These results suggest that further investigation of tropical–South Pacific interactions could expand our understanding of Pacific climate variability. Earth and environmental sciences/Climate sciences Earth and environmental sciences/Ocean sciences Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Due to the dominance of ocean over land in the Southern Hemisphere (SH), atmospheric circulation tends to be zonally symmetric (wave number 0). As a result, the leading variability mode is characterized by shifts in zonal wind strength linked to pressure differences between mid- and high-latitudes. It is known as the Southern Annular Mode 1 , 2 (SAM) or the Antarctic Oscillation 3 (AAO). In contrast, other dominant modes in the SH midlatitudes are zonally asymmetric, typically represented by wave numbers 1 to 4. Among these, the atmospheric structure with repeated anticyclonic-cyclonic circulations at wave number 3 is known as the Pacific–South America (PSA) teleconnections 4 – 6 . Both SAM and PSA are observed around the year, with various time scales ranging from intraseasonal 7 , 8 to interannual and multi-decadal time scales 9 . They explain much of the SH mid-latitude atmospheric variability on planetary scales. Previous studies have identified SAM and PSA by applying Empirical Orthogonal Function (EOF) analysis to sea level pressure (SLP) and geopotential height (GPH) over the SH 10,11 . From intraseasonal to interannual time scales, the EOF1 mode corresponds to the SAM, and the EOF2 and EOF3 modes to PSA1 and PSA2 (interchangeable, together PSAs), respectively. PSAs exhibit large amplitudes, particularly from the South Pacific to South America. Partly due to the inherent properties of EOF analysis, these modes show a quadrature relation, resulting in a less clearly defined distinction between them 12 . Nevertheless, because they can also be obtained through Rotating-EOF 9 and they are consistently observed in diverse observational reanalysis datasets, climate model simulations, and AGCM experiments 6 , 10 , 13 , PSAs, as defined via EOF analysis, are generally understood as physical modes. Previous studies reported that PSAs were formed by the internal atmospheric variability in the SH mid-latitudes 10 , 14 , similar to the North Atlantic Oscillation in the Northern Hemisphere (NH). In addition, PSAs can be influenced by external forcing through Rossby wave sources from convection over the tropical Pacific 4 , 5 , 15 , like the Pacific–North America (PNA) teleconnection in the NH 16 . In this context, Mo (2000) 9 investigated the relationship between El Niño–Southern Oscillation (ENSO) and PSAs using reanalysis data from 1949 to 1998. Through spectral density analysis of monthly indices, the study found that PSA1 is associated with the low-frequency component of ENSO variability (~ 40–48 months), whereas PSA2 corresponds to its high-frequency component (~ 26 months). Tropical Pacific climate variability exerts a significant influence on climate variability over the South Pacific. Meanwhile, it has also been suggested that climate variability in the South Pacific influences the tropical Pacific climate. For instance, the South Pacific Meridional Mode (SPMM), which arises from interactions between low-level wind and SST, has been proposed to affect the tropical Pacific through the Wind–Evaporation–SST (WES) feedback, in a manner analogous to the North Pacific Meridional Mode (NPMM) 17 , 18 . In addition, it has been suggested that the South Pacific Quadrupole (SPQ), featured by a quadrupole SSTA variability formed by the lower-level atmosphere variability, can affect the tropical Pacific through a process similar to the Seasonal Footprinting Mechanism (SFM) by the North Pacific Oscillation (NPO) in the NH 19,20 . The above studies depicted how mid-latitude climate variability can influence the tropical Pacific via the subtropical ocean, underscoring the importance of lower-level atmospheric variability coupled with upper-ocean processes. Meanwhile, a recent study has proposed that the upper-level atmospheric stationary waves propagating from mid-latitudes to the tropics in the NH can affect ENSO 21 , 22 . Specifically, NPO-relevant upper-level atmospheric variability has been shown to propagate wave energy into the tropical Pacific, where the baroclinic structure (upper-level wind anomalies induce lower-level wind anomalies of opposite direction) contributes to the development of ENSO events. While the influence of upper-level atmospheric wave propagation from the North Pacific on the tropical Pacific has been reported, relatively little attention has been given to the corresponding influence from the South Pacific. This highlights the need to investigate how upper-level atmospheric variability in the SH is involved in ENSO development. To address this, we particularly focused on the austral fall season (i.e., March–April–May; MAM), a period when ENSO is weak and most unpredictable (i.e., ENSO predictability barrier). We apply EOF analysis to identify primary upper-level atmospheric stationary waves over the South Pacific to South Atlantic, where PSA activity is strongest 14 , and investigate their relationship with previous and subsequent ENSO. Our results show that EOF1 is strongly influenced by previous ENSO events (i.e., ENSO decaying phase), while EOF2 and EOF3—similar to PSA1 and PSA2—have a significant relationship with subsequent ENSO events (i.e., ENSO developing phase). Particularly, EOF3, featured by a pronounced variability in high latitudes, appears to have the potential to contribute to ENSO development, different from EOF2, which is likely induced by tropical convection as ENSO develops. Result Primary atmospheric stationary waves over the South Pacific and their relationship with ENSO To examine upper-level atmospheric variability over the South Pacific during the austral fall season, EOF analysis was applied to 200hPa GPH anomalies over the South Pacific–South Atlantic for March–May from 1950 to 2023 (74 years in total). Figure 1 a–c shows the three leading EOF patterns along with their normalized principal component (PC) index. EOF1, EOF2, and EOF3 account for 37.8%, 13.2%, and 11.2% of the total variance, respectively. These modes are robustly defined even when the analysis domain is slightly modified or when alternative datasets are used (e.g., NCEP-R1; Supplementary Fig. 1). EOF1 exhibits the most prominent positive GPH anomalies located over the subtropical South Pacific with negative anomalies extending toward the mid-latitudes and positive anomalies reappearing over higher latitudes (Fig. 1 a). EOF1 exhibits a spatial structure that is nearly symmetric to the PNA teleconnection across the equator. Based on this similarity, we examined the lead–lag correlation between the EOF1-PC index and the Niño 3.4 index from the preceding boreal winter (December–January–February; DJF). The correlation was found to be notably high, reaching 0.89. In contrast, the correlation with the Niño3.4 index for the following boreal winter was much lower, at 0.004. These results suggest that EOF1 represents an upper-level atmospheric response to the preceding ENSO event. Thus, it is considered as a response to tropical convection during the ENSO decaying phase. Additionally, based on the GPH centers over the Pacific region (yellow boxes in Fig. 1 a), here we defined EOF1-regional index as (Z1 + Z3)/2 – (Z2 + Z4)/2 (normalized), wherein Z1 (120°–160°W, 5°–20°S), Z2 (130°–160°W, 35°–45°S), Z3 (85°–125°W, 55°–65°S), and Z4 (80°–100°W, 35°–45°S). This definition is advantageous for applying to other datasets (e.g., climate model data) to complement the fact that EOF PC index can slightly differ depending on the selection of region. This EOF1-regional index shows a high correlation with the EOF1-PC index (0.80), implying that the results in this study are not dependent on the index selection between EOF-PC index and its regional index. In contrast to EOF1, EOF2 and EOF3 feature strong GPH anomalies centered in the mid-latitudes over the South Pacific (Fig. 1 b-c). It would be beneficial to mention that EOF2 and EOF3 are similar to the PSA1 and PSA2 in the following aspects: they have a GPH center in the east (for PSA1) and far southeast (for PSA2) of New Zealand, with a wave number 3 structure in the mid-latitudes. Thus, in this study, EOF2 and EOF3 are referred to as a PSA1-like mode (simply PSA1) and a PSA2-like mode (simply PSA2), respectively. However, we also note that it would be challenging to directly compare EOF2 and EOF3 to the PSA1 and PSA2 of previous studies, because they were defined based on different spatiotemporal scales. We then assessed the relationship between EOF2/EOF3 in austral fall and previous and subsequent ENSO. The lead–lag correlation coefficients between the EOF2/EOF3 PC index and Niño3.4 index during the preceding boreal winter were only 0.18/0.02. These low values indicate that EOF2 and EOF3 are largely independent of previous ENSO. This result further supports the idea that only EOF1 is directly related to the previous ENSO, as evidenced by its strong correlation of 0.89. By contrast, the correlation coefficients between EOF2/EOF3 PC index and Niño3.4 index in the subsequent boreal winter were 0.36/0.37, respectively (similar values are obtained for other datasets; Supplementary Fig. 2), both statistically significant at the 95% confidence level. Next, we defined the EOF2- and EOF3-regional indices based on their GPH core regions (yellow boxes in Fig. 1 b-c), which are conveniently named as PSA1 and PSA2 indices in this study, respectively. For the PSA1 index (yellow time series in Fig. 1 b), we used the following core regions: Z1 (180°–155°W, 35°–45°S), Z2 (120°–145°E, 55°–65°S), and Z3 (65°–85°W, 45°–55°S). The PSA1 index was defined as: Z2 – (Z1 + Z3)/2 (normalized). Similarly, the PSA2 index was constructed based on two GPH centers (yellow time series in Fig. 1 c): Z1 (150°–180°W, 50°–65°S) and Z2 (100°–130°W, 35°–45°S), using the formula: Z1 – Z2 (normalized). These regionally defined indices showed high correlations with the corresponding EOF-PC index, with correlation coefficients of 0.89 and 0.92, respectively. In this context, the correlation coefficients between PSA1/PSA2 index and Niño3.4 index in the subsequent boreal winter were 0.36/0.36, similar to those obtained when using the EOF2/EOF3 PC index (0.36/0.37). Hereafter, the following analysis was performed based on the PSA1 and PSA2 indices. We also note that there is no significant difference in the results when EOF PC index is used instead. Based on these results, the remainder of this study focuses more on EOF2 and EOF3, which possibly represent primary stationary atmospheric modes over the South Pacific in austral fall (i.e., PSAs) that are potentially involved in the development of ENSO. Propagation of MAM-PSA1 and its relationship with subsequent ENSO evolution In this section, we examine how the PSAs in austral fall season are associated with the tropical Pacific circulation (Fig. 2 for PSA1 (i.e., EOF2) and Fig. 3 for PSA2 (i.e., EOF3); Supplementary Fig. 3 for EOF1). To first investigate the propagating characteristics of PSA1, we performed lag-regression analyses of GPH and wind anomalies at 200hPa onto the PSA1 index from MAM (i.e., no lag) through the following DJF season (i.e., 9-month lag) (Fig. 2 ; Supplementary Fig. 4 for preceding season; Supplementary Fig. 5 for NCEP-R1). During March–May (Fig. 2 a), a cyclonic circulation with negative GPH is evident to the east of New Zealand, while an anticyclonic circulation with positive GPH anomalies appears to its southeast. Simultaneously, there are negative and positive GPH anomalies over the southern tip of South America and Southeastern South America (SESA). At this time, the wave activity flux (WAF) reveals a propagation pathway extending from the cyclonic anomaly at the east of New Zealand to the downstream anticyclone to its southeast, and subsequently toward South America (Fig. 4 a; Supplementary Fig. 6 for FMA). It is noted that subtropical and polar jet streams flow along the northern and southern areas of New Zealand in this season. These results suggest that Rossby wave propagation from the western South Pacific to South America plays a role in establishing the PSA1 teleconnection, in association with the jet stream. In April–June (Fig. 2 b), these upper-level circulation features are well maintained, which weaken during the May–July (Fig. 2 c) and June–August (Fig. 2 d). Notably, in March–May (Fig. 2 a), there is also an anomalous anticyclonic circulation in the subtropical southwestern Pacific (near 150°W, 15°S). Comprehensively, the PSA1 teleconnection forms a great circle over the South Pacific, seemingly emerging from the tropical southwestern Pacific. It is noted that precipitation enhances along the Intertropical Convergence Zone (ITCZ) over the equatorial western Pacific in this season (Fig. 2 e). In this context, it is plausible to interpret PSA1, despite the strong WAF observed in the midlatitudes, as primarily originating from tropical convection. Associated with this enhanced precipitation along the Pacific ITCZ (Fig. 2 e), there are low-level westerly wind anomalies over the equatorial western Pacific. As time goes by, the westerly wind and positive precipitation anomalies become more pronounced, promoting SST warming in the equatorial Pacific (Fig. 2 f-g). Through the Bjerknes feedback, these warm SST anomalies are sustained and amplified, contributing to the development of El Niño (Fig. 2 h; Supplementary Fig. 2). In summary, it is reasonable to infer that enhanced tropical convection, accompanied by low-level westerly wind anomalies, plays a key role in driving both PSA1 and ENSO. Propagation of MAM-PSA2 and its role in ENSO development To examine the potential influence of PSA2 on ENSO development, we applied the same regression analysis for PSA1 as in the previous section. Figure 3 presents the temporal evolution of GPH and wind anomalies at 200hPa associated with PSA2 (Supplementary Fig. 7 for other seasons; Supplementary Fig. 8 for NCEP-R1). During March–May (Fig. 3 a), the most prominent feature is an anticyclonic circulation (i.e., positive GPH anomalies) over the southeastern region of New Zealand. Cyclonic anomalies are located to the northeast of this anticyclone. Another anticyclonic anomaly is found over the southern tip of South America, while a cyclonic anomaly is present over SESA. The WAF analysis during March–May indicates that wave energy propagates from the mid-latitude South Pacific toward the subtropical Pacific and South America (Fig. 4 b; Supplementary Fig. 6 for FMA). According to this WAF, anticyclonic circulation is observed over the subtropical South Pacific, contributing to the northeastward expansion of easterly wind anomalies into the tropical Pacific in the following April–June (Fig. 3 b). This suggests that PSA2 propagates from mid-latitudes to the equator, later influencing circulations in the tropical Pacific. From April–June to June–August (Fig. 3 b-d), an anticyclonic anomaly intensifies over the subtropical South Pacific, accompanying easterly wind anomalies in the upper troposphere over the equatorial Pacific. Although the PSA2 pattern weakens during May–July and June–August, the subtropical anticyclone persists, and the easterly anomalies aloft over the equatorial Pacific remain. Given the inherent baroclinic structure of the tropical atmosphere, these upper-level easterly anomalies are likely to induce westerly anomalies near the surface (Fig. 3 f), which are expected to facilitate the initiation of El Niño. Meanwhile, lower-level atmospheric responses associated with PSA2 during March–May also exhibit a coherent pattern (Fig. 3 e). A cyclonic anomaly is located to the south of Australia, and an anticyclonic anomaly appears to the southeast of New Zealand, with an additional cyclonic circulation extending equatorward into the midlatitudes. Similar to PSA1, these structures suggest a vertically barotropic configuration in the mid-to-high latitudes. The PSA2 gradually weakens afterward (Fig. 3 f), but westerly anomalies emerge near the equator, potentially contributing to SST warming. These surface anomalies, vertically coupled with upper-level structures, are sustained through the growing season of ENSO and promote El Niño development via Bjerknes feedback (Fig. 3 h). In summary, PSA2 appears to influence El Niño development by facilitating the equatorward propagation of upper-level atmospheric wave energy from the midlatitudes of the South Pacific. This leads to the establishment of easterly anomalies in the upper troposphere over the equatorial Pacific. Through the baroclinic structure of the tropical atmosphere, these anomalies induce surface westerly wind anomalies, which trigger and maintain warm SST anomalies and support the onset of ENSO events. Stationary Wave Model experiments The core regions of PSA1 and PSA2—located to the east and far southeast of New Zealand, respectively—lie within the Southern Hemisphere jet stream belt, where wave energy appears to propagate along the jet. Based on this, we conducted Stationary Wave Model experiments (see Methods), using these core regions of PSA1 and PSA2 as forcing regions, to investigate the direction of wave energy propagation. First, we conducted the PSA1-related SWM experiment. Corresponding to the western core of the PSA1 (Fig. 5 a), we prescribed an upper-level vorticity forcing over the region east of New Zealand under the March–May climatological mean atmospheric state (1950–2023). Figure 5 b presents the corresponding steady-state response. Wave energy propagation associated with the cyclonic circulation east of New Zealand appears to follow two distinct pathways. First, in relation to PSA1, the primary wave energy propagates southeastward toward western Antarctic, and subsequently eastward toward South America. Second, the cyclonic flow east of New Zealand also directs wave energy northeastward, inducing an anticyclonic circulation over the subtropical southwestern Pacific. Regarding this anticyclonic circulation over the subtropical southwestern Pacific, the WAF analysis also showed northward energy propagation around 130°W and 30°S, although that is confined to a relatively narrow region (Fig. 4 a; Supplementary Fig. 6). Notably, the location of this anticyclonic circulation coincides with the upper-level anticyclonic response, which is mainly triggered by enhanced tropical convection over the western tropical Pacific (Fig. 2 a). Therefore, if PSA1 develops independently of tropical forcing (cf. Supplementary Figs. 4–6), it is plausible that PSA1 may itself exert an influence on the tropics. This possibility, however, warrants further investigation through targeted experiments. We also conducted a similar analysis for PSA2, to understand wave energy propagation characteristics. Note that during the developing period of PSA2 (Fig. 3 a; Fig. 3 e; Supplementary Fig. 7–8), there is no noticeable signal over the equatorial Pacific, different from the case of PSA1. In this case, upper-level atmospheric waves appear to originate in the southeastern region of New Zealand (~ 160°W, 60°S) and then propagate sequentially toward the tropical Pacific. Based on this result, we further aimed to clarify whether PSA2 plays a role in shaping tropical Pacific variability through upper-level atmospheric wave propagation. To this end, we performed SWM experiments for PSA2 as well. It is known that a strong anticyclonic circulation is observed southeast of New Zealand during March–May (Fig. 5 c). We used this feature as the vorticity forcing in the SWM experiment. The resulting wave response shows northward and northeastward propagation of atmospheric waves (Fig. 5 d), consistent with observed patterns and wave activity fluxes (Fig. 4 b; Supplementary Fig. 6). These results suggest that upper-level atmospheric wave activity associated with PSA2, originating from mid-latitudes, propagates equatorward and ultimately induces zonal wind anomalies over the tropical Pacific. This wave-induced circulation could facilitate ENSO development. Summary and Discussion In this study, we investigated how the primary atmospheric stationary waves in the SH during austral autumn are associated with tropical Pacific climate variability, based on observational reanalysis data from 1950 to 2023. By applying EOF analysis to 200hPa GPH anomalies in March–May, we identified three leading atmospheric stationary waves. Among them, EOF1 was found to be a direct atmospheric response associated with preceding ENSO events, while EOF2 and EOF3, considered as a PSA1-like and PSA2-like mode, appear to be significantly correlated with following ENSO events. Specifically, EOF2 exhibits a a distinct propagation path from the southwestern subtropical Pacific to the South Pacific–South America sector, which seems to be largely influenced by convection over the equatorial Pacific. In contrast, EOF3 is characterized by northward and northeastward wave propagation from the mid-latitudes of the South Pacific and is expected to contribute to ENSO. The lead-lag relationship between SH atmospheric stationary waves and ENSO was examined, particularly focusing on the MAM season when ENSO predictability is the lowest. Therefore, PSA teleconnections during MAM season (particularly PSA2) could be another predictor to overcome ENSO predictability barrier. However, it is noted that the correlation coefficients between PSAs and subsequent Niño3.4 index are relatively modest over the past seven decades (slightly greater than 0.35), despite being statistically significant at the 95% confidence level. Therefore, specific climate model experiments should be conducted to exactly assess the role of PSAs in ENSO evolution in the near future. Previous studies have shown that surface coupled modes such as the SPQ and SPMM can influence ENSO during the austral fall season. Additionally, it is possible that SPQ and SPMM may be related to PSA2. In this regard, we also examined potential statistical connections between PSA2 and SPQ/SPMM, finding no significant correlations between them. Nevertheless, further investigation into the relationship between upper-level PSA teleconnections and lower-tropospheric coupled modes remains warranted. Lastly, our analysis is mostly based on observational reanalysis datasets. When we examined the PSA as well as PSA–ENSO relationship in climate models participating in CMIP6, only a few models can capture the characteristics (Supplementary Table 1), indicating large discrepancies across models and notable differences from observations. Furthermore, climate models tend to exhibit different EOF modes, compared to those from observational reanalysis (not shown). We speculate that this may also be related to the fact that CMIP6 models have some bias in simulating the western Antarctic mean state (cf., Amundsen Sea Low) 23 . Therefore, future work should explore how well current climate models represent PSAs and their teleconnections with ENSO. Methods Data availability. Reanalysis Dataset: We utilized the ECMWF Reanalysis v5 (ERA5) 24 . ERA5 is the fifth generation of ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades, produced using 4D-Var data assimilation in CY41R2 of ECMWF’s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure levels in the vertical, with the top level at 0.01 hPa. Additionally, we also use NCEP/NCAR Reanalysis 1 (NCEP-R1) 25 , which provides monthly atmospheric model output from 1948 to near present (2.5° x 2.5° global grids (144x73), with 17 pressure levels). The analysis period is from 1950 to 2023. All observational reanalysis datasets can be downloaded from open URL. ERA5: https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. NCEP-R1: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html Stationary Wave Model (SWM) SWM is a nonlinear baroclinic atmospheric model with a dry dynamical core. It has 14 vertical levels on sigma coordinates. The horizontal resolution of SWM is truncated at rhomboidal 30. This model was devised to understand how the atmospheric stationary waves propagate given atmospheric perturbations. To conduct SWM experiments, background atmospheric states of a month or a season is first fixed. Then, under the fixed background state, steady atmospheric heating or vorticity are prescribed until stationary atmospheric waves are formed. It takes about 30 to 60 days. The response to the given forcing shown in Fig. 5 is averaged for the first 50 days, since the steady forcing is exerted. For further details of the model equations or information, please refer to Ting and Yu (1998) 26 and Wang and Ting (1999) 27 . Declarations Competing interests. The authors declare no competing interests. Author Contribution J.-H. Park and G.-I. Kim started the research with the initial idea and produced the initial results. J.-S. Kug developed the initial ideas and results by giving constructive opinions. J.-H. Park and G.-I. Kim wrote the first draft of the paper. All co-authors actively participated in discussion and revised the manuscript. Acknowledgments. J.-H. Park was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2023R1A2C1004083 and RS-2023-00219830). Data availability. Reanalysis Dataset: We utilized the ECMWF Reanalysis v5 (ERA5) 24 . ERA5 is the fifth generation of ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades, produced using 4D-Var data assimilation in CY41R2 of ECMWF’s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure levels in the vertical, with the top level at 0.01 hPa. Additionally, we also use NCEP/NCAR Reanalysis 1 (NCEP-R1) 25 , which provides monthly atmospheric model output from 1948 to near present (2.5° x 2.5° global grids (144x73), with 17 pressure levels). The analysis period is from 1950 to 2023. All observational reanalysis datasets can be downloaded from open URL. ERA5: https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html . NCEP-R1: https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html Code availability. Codes used in the manuscript are available upon reasonable requests from G.-l. Kim References Fogt, R. L. & Marshall, G. J. The Southern Annular Mode: Variability, trends, and climate impacts across the Southern Hemisphere. WIREs Climate Change 11, e652 (2020). Marshall, G. J. Trends in the Southern Annular Mode from Observations and Reanalyses. (2003). Gong, D. & Wang, S. Definition of Antarctic Oscillation index. Geophysical Research Letters 26, 459–462 (1999). Goyal, R., Jucker, M., Sen Gupta, A., Hendon, H. 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Improvements in Circumpolar Southern Hemisphere Extratropical Atmospheric Circulation in CMIP6 Compared to CMIP5. Earth and Space Science 7, e2019EA001065 (2020). Hersbach, H. et al. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 1999–2049 (2020). Kalnay, E. et al. The NCEP/NCAR 40-Year Reanalysis Project. (1996). Ting, M. & Yu, L. Steady Response to Tropical Heating in Wavy Linear and Nonlinear Baroclinic Models. (1998). Wang, H. & Ting, M. Seasonal Cycle of the Climatological Stationary Waves in the NCEP–NCAR Reanalysis. (1999). Additional Declarations No competing interests reported. Supplementary Files PSAENSOSupplementary20251001.docx 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. 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08:23:37","extension":"xml","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":76162,"visible":true,"origin":"","legend":"","description":"","filename":"e08037734a9a4a379f06dd2ec86263511structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/f8cdbe321ccab10c8c10feb4.xml"},{"id":94073993,"identity":"a98c969e-8a60-47bf-a370-f812b1b35899","added_by":"auto","created_at":"2025-10-22 08:23:42","extension":"html","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":85888,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/0de8b9a6e23c5a30b93dc2db.html"},{"id":94073975,"identity":"d1425faf-bb86-4193-90ef-118a2311d51f","added_by":"auto","created_at":"2025-10-22 08:23:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":367445,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrimary upper-level atmospheric stationary waves in austral fall season over South Pacific to South America from ERA5 |\u003c/strong\u003e(a) The first EOF mode of 200hPa-GPH anomalies in MAM (1950-2023), wherein core regions over the South Pacific are marked by yellow rectangles. Bottom shows its PC time series (dotted line). Yellow line indicates its regional index based on the core regions (see manuscript). (b) and (c) are similar to (a), but for the second and third EOF modes, respectively. The regional indices of the second and third EOF modes are referred respectively to as the PSA1 and PSA2 index in this study. These three EOF modes explain 37.8%, 13.2%, and 11.2% of the total variability, respectively.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/0fb70e7c2037b3bb01eeaa0f.png"},{"id":94073984,"identity":"58120632-c6dd-4a76-ba30-d90d43eeae25","added_by":"auto","created_at":"2025-10-22 08:23:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":861402,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of the PSA1-like atmospheric stationary waves and its relevant surface pattern |\u003c/strong\u003e (a) GPH (shading) and wind anomalies (vector) at 200hPa in MAM season regressed onto the PSA1 index, wherein the core regions of PSA1-like mode are marked by yellow rectangles. The areas satisfying 95% confidence level by Student’s t-test are shaded for GPH. Wind vectors significant at the 95% and 90–95% confidence levels are shown in black and gray, respectively. (b), (c) and (d) are similar to (a), but in AMJ, MJJ, JJA season, respectively. (e) SST (shading), wind (vector, 850hPa), and precipitation (green and purple dot) anomalies in MAM season regressed onto the PSA1 index. (f), (g), and (h) are similar to (e), but in JJA, SON, and DJF season, respectively. The areas satisfying 95% confidence level by Student’s t-test are shaded (dotted) for SST (precipitation). Wind vectors significant at the 95% and 90–95% confidence levels are shown in black and gray, respectively.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/fc3c7fff791ab45367c41596.png"},{"id":94073990,"identity":"c0819827-9781-47b2-b469-75c2468a3fb5","added_by":"auto","created_at":"2025-10-22 08:23:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":822047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEvolution of the PSA2-like atmospheric stationary waves and its relevant surface pattern |\u003c/strong\u003e (a) GPH (shading) and wind anomalies (vector) at 200hPa in MAM season regressed onto the PSA2 index, wherein the core regions of PSA2-like mode are marked by yellow rectangles. The areas satisfying 95% confidence level by Student’s t-test are shaded for GPH. Wind vectors significant at the 95% and 90–95% confidence levels are shown in black and gray, respectively. (b), (c) and (d) are similar to (a), but in AMJ, MJJ, JJA season, respectively. (e) SST (shading), wind (vector, 850hPa), and precipitation (dot) anomalies in MAM season regressed onto the PSA2 index. (f), (g), and (h) are similar to (e), but in JJA, SON, and DJF season, respectively. The areas satisfying 95% confidence level by Student’s t-test are shaded for SST and marked by vectors (dots) for wind (precipitation).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/a1e6b5736d63b516fd30bc6b.png"},{"id":94073912,"identity":"bc549f3b-b532-4109-9528-e533794e3c27","added_by":"auto","created_at":"2025-10-22 08:23:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":287668,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGPH and Wave activity flux relevant to PSA1-like and PSA2-like teleconnections |\u003c/strong\u003eGPH (shading) and wave activity flux (vector) associated with the PSA1-like mode for (a) MAM. (b) The same fields as (a), respectively, but are associated with the PSA2-like mode.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/fabbc3813510e3bd48525f18.png"},{"id":94073915,"identity":"7d8df308-41a7-4d89-b9e4-3deec30ddef2","added_by":"auto","created_at":"2025-10-22 08:23:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":583600,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVorticity forcings and its steady atmospheric responses of Stationary wave model |\u003c/strong\u003e(a) Horizontal (left panel) and vertical (right panel, σ-coordinate) structure of steady vorticity forcing associated with PSA1-like mode. (b) Steady response of atmospheric circulation (i.e., stream function) at 0.258σ level to the vorticity forcing described in (a) under the MAM climatological mean state of atmosphere variables of 1950-2023. (c) and (d) are similar to (a) and (b), but associated with PSA2-like mode.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/01f254c1789214902fb66d70.png"},{"id":96251395,"identity":"d195fc28-a01a-4321-812c-61d48267a7bf","added_by":"auto","created_at":"2025-11-19 07:39:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3653331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/cc0e2e00-9c21-4a41-87b6-b61a9ac282f8.pdf"},{"id":94073987,"identity":"6115ac47-eeb4-4104-9bbf-728bc1fc162c","added_by":"auto","created_at":"2025-10-22 08:23:36","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":3769372,"visible":true,"origin":"","legend":"","description":"","filename":"PSAENSOSupplementary20251001.docx","url":"https://assets-eu.researchsquare.com/files/rs-7757839/v1/97e752d1584e2c7b21d35d86.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Pacific–South America teleconnections in austral fall and ENSO evolution","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDue to the dominance of ocean over land in the Southern Hemisphere (SH), atmospheric circulation tends to be zonally symmetric (wave number 0). As a result, the leading variability mode is characterized by shifts in zonal wind strength linked to pressure differences between mid- and high-latitudes. It is known as the Southern Annular Mode\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e (SAM) or the Antarctic Oscillation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e (AAO). In contrast, other dominant modes in the SH midlatitudes are zonally asymmetric, typically represented by wave numbers 1 to 4. Among these, the atmospheric structure with repeated anticyclonic-cyclonic circulations at wave number 3 is known as the Pacific\u0026ndash;South America (PSA) teleconnections\u003csup\u003e\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Both SAM and PSA are observed around the year, with various time scales ranging from intraseasonal\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e to interannual and multi-decadal time scales\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. They explain much of the SH mid-latitude atmospheric variability on planetary scales.\u003c/p\u003e\u003cp\u003ePrevious studies have identified SAM and PSA by applying Empirical Orthogonal Function (EOF) analysis to sea level pressure (SLP) and geopotential height (GPH) over the SH\u003csup\u003e10,11\u003c/sup\u003e. From intraseasonal to interannual time scales, the EOF1 mode corresponds to the SAM, and the EOF2 and EOF3 modes to PSA1 and PSA2 (interchangeable, together PSAs), respectively. PSAs exhibit large amplitudes, particularly from the South Pacific to South America. Partly due to the inherent properties of EOF analysis, these modes show a quadrature relation, resulting in a less clearly defined distinction between them\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nevertheless, because they can also be obtained through Rotating-EOF\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e and they are consistently observed in diverse observational reanalysis datasets, climate model simulations, and AGCM experiments\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, PSAs, as defined via EOF analysis, are generally understood as physical modes.\u003c/p\u003e\u003cp\u003ePrevious studies reported that PSAs were formed by the internal atmospheric variability in the SH mid-latitudes\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, similar to the North Atlantic Oscillation in the Northern Hemisphere (NH). In addition, PSAs can be influenced by external forcing through Rossby wave sources from convection over the tropical Pacific\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, like the Pacific\u0026ndash;North America (PNA) teleconnection in the NH\u003csup\u003e16\u003c/sup\u003e. In this context, Mo (2000)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e investigated the relationship between El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) and PSAs using reanalysis data from 1949 to 1998. Through spectral density analysis of monthly indices, the study found that PSA1 is associated with the low-frequency component of ENSO variability (~\u0026thinsp;40\u0026ndash;48 months), whereas PSA2 corresponds to its high-frequency component (~\u0026thinsp;26 months).\u003c/p\u003e\u003cp\u003eTropical Pacific climate variability exerts a significant influence on climate variability over the South Pacific. Meanwhile, it has also been suggested that climate variability in the South Pacific influences the tropical Pacific climate. For instance, the South Pacific Meridional Mode (SPMM), which arises from interactions between low-level wind and SST, has been proposed to affect the tropical Pacific through the Wind\u0026ndash;Evaporation\u0026ndash;SST (WES) feedback, in a manner analogous to the North Pacific Meridional Mode (NPMM)\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, it has been suggested that the South Pacific Quadrupole (SPQ), featured by a quadrupole SSTA variability formed by the lower-level atmosphere variability, can affect the tropical Pacific through a process similar to the Seasonal Footprinting Mechanism (SFM) by the North Pacific Oscillation (NPO) in the NH\u003csup\u003e19,20\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe above studies depicted how mid-latitude climate variability can influence the tropical Pacific via the subtropical ocean, underscoring the importance of lower-level atmospheric variability coupled with upper-ocean processes. Meanwhile, a recent study has proposed that the upper-level atmospheric stationary waves propagating from mid-latitudes to the tropics in the NH can affect ENSO\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Specifically, NPO-relevant upper-level atmospheric variability has been shown to propagate wave energy into the tropical Pacific, where the baroclinic structure (upper-level wind anomalies induce lower-level wind anomalies of opposite direction) contributes to the development of ENSO events.\u003c/p\u003e\u003cp\u003eWhile the influence of upper-level atmospheric wave propagation from the North Pacific on the tropical Pacific has been reported, relatively little attention has been given to the corresponding influence from the South Pacific. This highlights the need to investigate how upper-level atmospheric variability in the SH is involved in ENSO development. To address this, we particularly focused on the austral fall season (i.e., March\u0026ndash;April\u0026ndash;May; MAM), a period when ENSO is weak and most unpredictable (i.e., ENSO predictability barrier). We apply EOF analysis to identify primary upper-level atmospheric stationary waves over the South Pacific to South Atlantic, where PSA activity is strongest\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, and investigate their relationship with previous and subsequent ENSO. Our results show that EOF1 is strongly influenced by previous ENSO events (i.e., ENSO decaying phase), while EOF2 and EOF3\u0026mdash;similar to PSA1 and PSA2\u0026mdash;have a significant relationship with subsequent ENSO events (i.e., ENSO developing phase). Particularly, EOF3, featured by a pronounced variability in high latitudes, appears to have the potential to contribute to ENSO development, different from EOF2, which is likely induced by tropical convection as ENSO develops.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePrimary atmospheric stationary waves over the South Pacific and their relationship with ENSO\u003c/h2\u003e\u003cp\u003eTo examine upper-level atmospheric variability over the South Pacific during the austral fall season, EOF analysis was applied to 200hPa GPH anomalies over the South Pacific\u0026ndash;South Atlantic for March\u0026ndash;May from 1950 to 2023 (74 years in total). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea\u0026ndash;c shows the three leading EOF patterns along with their normalized principal component (PC) index. EOF1, EOF2, and EOF3 account for 37.8%, 13.2%, and 11.2% of the total variance, respectively. These modes are robustly defined even when the analysis domain is slightly modified or when alternative datasets are used (e.g., NCEP-R1; Supplementary Fig.\u0026nbsp;1).\u003c/p\u003e\u003cp\u003eEOF1 exhibits the most prominent positive GPH anomalies located over the subtropical South Pacific with negative anomalies extending toward the mid-latitudes and positive anomalies reappearing over higher latitudes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). EOF1 exhibits a spatial structure that is nearly symmetric to the PNA teleconnection across the equator. Based on this similarity, we examined the lead\u0026ndash;lag correlation between the EOF1-PC index and the Ni\u0026ntilde;o 3.4 index from the preceding boreal winter (December\u0026ndash;January\u0026ndash;February; DJF). The correlation was found to be notably high, reaching 0.89. In contrast, the correlation with the Ni\u0026ntilde;o3.4 index for the following boreal winter was much lower, at 0.004. These results suggest that EOF1 represents an upper-level atmospheric response to the preceding ENSO event. Thus, it is considered as a response to tropical convection during the ENSO decaying phase.\u003c/p\u003e\u003cp\u003eAdditionally, based on the GPH centers over the Pacific region (yellow boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), here we defined EOF1-regional index as (Z1\u0026thinsp;+\u0026thinsp;Z3)/2 \u0026ndash; (Z2\u0026thinsp;+\u0026thinsp;Z4)/2 (normalized), wherein Z1 (120\u0026deg;\u0026ndash;160\u0026deg;W, 5\u0026deg;\u0026ndash;20\u0026deg;S), Z2 (130\u0026deg;\u0026ndash;160\u0026deg;W, 35\u0026deg;\u0026ndash;45\u0026deg;S), Z3 (85\u0026deg;\u0026ndash;125\u0026deg;W, 55\u0026deg;\u0026ndash;65\u0026deg;S), and Z4 (80\u0026deg;\u0026ndash;100\u0026deg;W, 35\u0026deg;\u0026ndash;45\u0026deg;S). This definition is advantageous for applying to other datasets (e.g., climate model data) to complement the fact that EOF PC index can slightly differ depending on the selection of region. This EOF1-regional index shows a high correlation with the EOF1-PC index (0.80), implying that the results in this study are not dependent on the index selection between EOF-PC index and its regional index.\u003c/p\u003e\u003cp\u003eIn contrast to EOF1, EOF2 and EOF3 feature strong GPH anomalies centered in the mid-latitudes over the South Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c). It would be beneficial to mention that EOF2 and EOF3 are similar to the PSA1 and PSA2 in the following aspects: they have a GPH center in the east (for PSA1) and far southeast (for PSA2) of New Zealand, with a wave number 3 structure in the mid-latitudes. Thus, in this study, EOF2 and EOF3 are referred to as a PSA1-like mode (simply PSA1) and a PSA2-like mode (simply PSA2), respectively. However, we also note that it would be challenging to directly compare EOF2 and EOF3 to the PSA1 and PSA2 of previous studies, because they were defined based on different spatiotemporal scales.\u003c/p\u003e\u003cp\u003eWe then assessed the relationship between EOF2/EOF3 in austral fall and previous and subsequent ENSO. The lead\u0026ndash;lag correlation coefficients between the EOF2/EOF3 PC index and Ni\u0026ntilde;o3.4 index during the preceding boreal winter were only 0.18/0.02. These low values indicate that EOF2 and EOF3 are largely independent of previous ENSO. This result further supports the idea that only EOF1 is directly related to the previous ENSO, as evidenced by its strong correlation of 0.89. By contrast, the correlation coefficients between EOF2/EOF3 PC index and Ni\u0026ntilde;o3.4 index in the subsequent boreal winter were 0.36/0.37, respectively (similar values are obtained for other datasets; Supplementary Fig.\u0026nbsp;2), both statistically significant at the 95% confidence level.\u003c/p\u003e\u003cp\u003eNext, we defined the EOF2- and EOF3-regional indices based on their GPH core regions (yellow boxes in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb-c), which are conveniently named as PSA1 and PSA2 indices in this study, respectively. For the PSA1 index (yellow time series in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), we used the following core regions: Z1 (180\u0026deg;\u0026ndash;155\u0026deg;W, 35\u0026deg;\u0026ndash;45\u0026deg;S), Z2 (120\u0026deg;\u0026ndash;145\u0026deg;E, 55\u0026deg;\u0026ndash;65\u0026deg;S), and Z3 (65\u0026deg;\u0026ndash;85\u0026deg;W, 45\u0026deg;\u0026ndash;55\u0026deg;S). The PSA1 index was defined as: Z2 \u0026ndash; (Z1\u0026thinsp;+\u0026thinsp;Z3)/2 (normalized). Similarly, the PSA2 index was constructed based on two GPH centers (yellow time series in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec): Z1 (150\u0026deg;\u0026ndash;180\u0026deg;W, 50\u0026deg;\u0026ndash;65\u0026deg;S) and Z2 (100\u0026deg;\u0026ndash;130\u0026deg;W, 35\u0026deg;\u0026ndash;45\u0026deg;S), using the formula: Z1 \u0026ndash; Z2 (normalized). These regionally defined indices showed high correlations with the corresponding EOF-PC index, with correlation coefficients of 0.89 and 0.92, respectively. In this context, the correlation coefficients between PSA1/PSA2 index and Ni\u0026ntilde;o3.4 index in the subsequent boreal winter were 0.36/0.36, similar to those obtained when using the EOF2/EOF3 PC index (0.36/0.37). Hereafter, the following analysis was performed based on the PSA1 and PSA2 indices. We also note that there is no significant difference in the results when EOF PC index is used instead.\u003c/p\u003e\u003cp\u003eBased on these results, the remainder of this study focuses more on EOF2 and EOF3, which possibly represent primary stationary atmospheric modes over the South Pacific in austral fall (i.e., PSAs) that are potentially involved in the development of ENSO.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePropagation of MAM-PSA1 and its relationship with subsequent ENSO evolution\u003c/h3\u003e\n\u003cp\u003eIn this section, we examine how the PSAs in austral fall season are associated with the tropical Pacific circulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e for PSA1 (i.e., EOF2) and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e for PSA2 (i.e., EOF3); Supplementary Fig.\u0026nbsp;3 for EOF1). To first investigate the propagating characteristics of PSA1, we performed lag-regression analyses of GPH and wind anomalies at 200hPa onto the PSA1 index from MAM (i.e., no lag) through the following DJF season (i.e., 9-month lag) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; Supplementary Fig.\u0026nbsp;4 for preceding season; Supplementary Fig.\u0026nbsp;5 for NCEP-R1).\u003c/p\u003e\u003cp\u003eDuring March\u0026ndash;May (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), a cyclonic circulation with negative GPH is evident to the east of New Zealand, while an anticyclonic circulation with positive GPH anomalies appears to its southeast. Simultaneously, there are negative and positive GPH anomalies over the southern tip of South America and Southeastern South America (SESA). At this time, the wave activity flux (WAF) reveals a propagation pathway extending from the cyclonic anomaly at the east of New Zealand to the downstream anticyclone to its southeast, and subsequently toward South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; Supplementary Fig.\u0026nbsp;6 for FMA). It is noted that subtropical and polar jet streams flow along the northern and southern areas of New Zealand in this season. These results suggest that Rossby wave propagation from the western South Pacific to South America plays a role in establishing the PSA1 teleconnection, in association with the jet stream. In April\u0026ndash;June (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), these upper-level circulation features are well maintained, which weaken during the May\u0026ndash;July (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec) and June\u0026ndash;August (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003eNotably, in March\u0026ndash;May (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), there is also an anomalous anticyclonic circulation in the subtropical southwestern Pacific (near 150\u0026deg;W, 15\u0026deg;S). Comprehensively, the PSA1 teleconnection forms a great circle over the South Pacific, seemingly emerging from the tropical southwestern Pacific. It is noted that precipitation enhances along the Intertropical Convergence Zone (ITCZ) over the equatorial western Pacific in this season (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee). In this context, it is plausible to interpret PSA1, despite the strong WAF observed in the midlatitudes, as primarily originating from tropical convection.\u003c/p\u003e\u003cp\u003eAssociated with this enhanced precipitation along the Pacific ITCZ (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee), there are low-level westerly wind anomalies over the equatorial western Pacific. As time goes by, the westerly wind and positive precipitation anomalies become more pronounced, promoting SST warming in the equatorial Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef-g). Through the Bjerknes feedback, these warm SST anomalies are sustained and amplified, contributing to the development of El Ni\u0026ntilde;o (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh; Supplementary Fig.\u0026nbsp;2). In summary, it is reasonable to infer that enhanced tropical convection, accompanied by low-level westerly wind anomalies, plays a key role in driving both PSA1 and ENSO.\u003c/p\u003e\n\u003ch3\u003ePropagation of MAM-PSA2 and its role in ENSO development\u003c/h3\u003e\n\u003cp\u003eTo examine the potential influence of PSA2 on ENSO development, we applied the same regression analysis for PSA1 as in the previous section. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the temporal evolution of GPH and wind anomalies at 200hPa associated with PSA2 (Supplementary Fig.\u0026nbsp;7 for other seasons; Supplementary Fig.\u0026nbsp;8 for NCEP-R1). During March\u0026ndash;May (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the most prominent feature is an anticyclonic circulation (i.e., positive GPH anomalies) over the southeastern region of New Zealand. Cyclonic anomalies are located to the northeast of this anticyclone. Another anticyclonic anomaly is found over the southern tip of South America, while a cyclonic anomaly is present over SESA.\u003c/p\u003e\u003cp\u003eThe WAF analysis during March\u0026ndash;May indicates that wave energy propagates from the mid-latitude South Pacific toward the subtropical Pacific and South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Supplementary Fig.\u0026nbsp;6 for FMA). According to this WAF, anticyclonic circulation is observed over the subtropical South Pacific, contributing to the northeastward expansion of easterly wind anomalies into the tropical Pacific in the following April\u0026ndash;June (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). This suggests that PSA2 propagates from mid-latitudes to the equator, later influencing circulations in the tropical Pacific.\u003c/p\u003e\u003cp\u003eFrom April\u0026ndash;June to June\u0026ndash;August (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-d), an anticyclonic anomaly intensifies over the subtropical South Pacific, accompanying easterly wind anomalies in the upper troposphere over the equatorial Pacific. Although the PSA2 pattern weakens during May\u0026ndash;July and June\u0026ndash;August, the subtropical anticyclone persists, and the easterly anomalies aloft over the equatorial Pacific remain. Given the inherent baroclinic structure of the tropical atmosphere, these upper-level easterly anomalies are likely to induce westerly anomalies near the surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), which are expected to facilitate the initiation of El Ni\u0026ntilde;o.\u003c/p\u003e\u003cp\u003eMeanwhile, lower-level atmospheric responses associated with PSA2 during March\u0026ndash;May also exhibit a coherent pattern (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee). A cyclonic anomaly is located to the south of Australia, and an anticyclonic anomaly appears to the southeast of New Zealand, with an additional cyclonic circulation extending equatorward into the midlatitudes. Similar to PSA1, these structures suggest a vertically barotropic configuration in the mid-to-high latitudes. The PSA2 gradually weakens afterward (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ef), but westerly anomalies emerge near the equator, potentially contributing to SST warming. These surface anomalies, vertically coupled with upper-level structures, are sustained through the growing season of ENSO and promote El Ni\u0026ntilde;o development via Bjerknes feedback (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eh).\u003c/p\u003e\u003cp\u003eIn summary, PSA2 appears to influence El Ni\u0026ntilde;o development by facilitating the equatorward propagation of upper-level atmospheric wave energy from the midlatitudes of the South Pacific. This leads to the establishment of easterly anomalies in the upper troposphere over the equatorial Pacific. Through the baroclinic structure of the tropical atmosphere, these anomalies induce surface westerly wind anomalies, which trigger and maintain warm SST anomalies and support the onset of ENSO events.\u003c/p\u003e\n\u003ch3\u003eStationary Wave Model experiments\u003c/h3\u003e\n\u003cp\u003eThe core regions of PSA1 and PSA2\u0026mdash;located to the east and far southeast of New Zealand, respectively\u0026mdash;lie within the Southern Hemisphere jet stream belt, where wave energy appears to propagate along the jet. Based on this, we conducted Stationary Wave Model experiments (see Methods), using these core regions of PSA1 and PSA2 as forcing regions, to investigate the direction of wave energy propagation.\u003c/p\u003e\u003cp\u003eFirst, we conducted the PSA1-related SWM experiment. Corresponding to the western core of the PSA1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea), we prescribed an upper-level vorticity forcing over the region east of New Zealand under the March\u0026ndash;May climatological mean atmospheric state (1950\u0026ndash;2023). Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb presents the corresponding steady-state response. Wave energy propagation associated with the cyclonic circulation east of New Zealand appears to follow two distinct pathways. First, in relation to PSA1, the primary wave energy propagates southeastward toward western Antarctic, and subsequently eastward toward South America. Second, the cyclonic flow east of New Zealand also directs wave energy northeastward, inducing an anticyclonic circulation over the subtropical southwestern Pacific.\u003c/p\u003e\u003cp\u003eRegarding this anticyclonic circulation over the subtropical southwestern Pacific, the WAF analysis also showed northward energy propagation around 130\u0026deg;W and 30\u0026deg;S, although that is confined to a relatively narrow region (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea; Supplementary Fig.\u0026nbsp;6). Notably, the location of this anticyclonic circulation coincides with the upper-level anticyclonic response, which is mainly triggered by enhanced tropical convection over the western tropical Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). Therefore, if PSA1 develops independently of tropical forcing (cf. Supplementary Figs.\u0026nbsp;4\u0026ndash;6), it is plausible that PSA1 may itself exert an influence on the tropics. This possibility, however, warrants further investigation through targeted experiments.\u003c/p\u003e\u003cp\u003eWe also conducted a similar analysis for PSA2, to understand wave energy propagation characteristics. Note that during the developing period of PSA2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee; Supplementary Fig.\u0026nbsp;7\u0026ndash;8), there is no noticeable signal over the equatorial Pacific, different from the case of PSA1. In this case, upper-level atmospheric waves appear to originate in the southeastern region of New Zealand (~\u0026thinsp;160\u0026deg;W, 60\u0026deg;S) and then propagate sequentially toward the tropical Pacific. Based on this result, we further aimed to clarify whether PSA2 plays a role in shaping tropical Pacific variability through upper-level atmospheric wave propagation.\u003c/p\u003e\u003cp\u003eTo this end, we performed SWM experiments for PSA2 as well. It is known that a strong anticyclonic circulation is observed southeast of New Zealand during March\u0026ndash;May (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). We used this feature as the vorticity forcing in the SWM experiment. The resulting wave response shows northward and northeastward propagation of atmospheric waves (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed), consistent with observed patterns and wave activity fluxes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb; Supplementary Fig.\u0026nbsp;6). These results suggest that upper-level atmospheric wave activity associated with PSA2, originating from mid-latitudes, propagates equatorward and ultimately induces zonal wind anomalies over the tropical Pacific. This wave-induced circulation could facilitate ENSO development.\u003c/p\u003e"},{"header":"Summary and Discussion","content":"\u003cp\u003eIn this study, we investigated how the primary atmospheric stationary waves in the SH during austral autumn are associated with tropical Pacific climate variability, based on observational reanalysis data from 1950 to 2023. By applying EOF analysis to 200hPa GPH anomalies in March\u0026ndash;May, we identified three leading atmospheric stationary waves. Among them, EOF1 was found to be a direct atmospheric response associated with preceding ENSO events, while EOF2 and EOF3, considered as a PSA1-like and PSA2-like mode, appear to be significantly correlated with following ENSO events. Specifically, EOF2 exhibits a a distinct propagation path from the southwestern subtropical Pacific to the South Pacific\u0026ndash;South America sector, which seems to be largely influenced by convection over the equatorial Pacific. In contrast, EOF3 is characterized by northward and northeastward wave propagation from the mid-latitudes of the South Pacific and is expected to contribute to ENSO.\u003c/p\u003e\u003cp\u003eThe lead-lag relationship between SH atmospheric stationary waves and ENSO was examined, particularly focusing on the MAM season when ENSO predictability is the lowest. Therefore, PSA teleconnections during MAM season (particularly PSA2) could be another predictor to overcome ENSO predictability barrier. However, it is noted that the correlation coefficients between PSAs and subsequent Ni\u0026ntilde;o3.4 index are relatively modest over the past seven decades (slightly greater than 0.35), despite being statistically significant at the 95% confidence level. Therefore, specific climate model experiments should be conducted to exactly assess the role of PSAs in ENSO evolution in the near future.\u003c/p\u003e\u003cp\u003ePrevious studies have shown that surface coupled modes such as the SPQ and SPMM can influence ENSO during the austral fall season. Additionally, it is possible that SPQ and SPMM may be related to PSA2. In this regard, we also examined potential statistical connections between PSA2 and SPQ/SPMM, finding no significant correlations between them. Nevertheless, further investigation into the relationship between upper-level PSA teleconnections and lower-tropospheric coupled modes remains warranted.\u003c/p\u003e\u003cp\u003eLastly, our analysis is mostly based on observational reanalysis datasets. When we examined the PSA as well as PSA\u0026ndash;ENSO relationship in climate models participating in CMIP6, only a few models can capture the characteristics (Supplementary Table\u0026nbsp;1), indicating large discrepancies across models and notable differences from observations. Furthermore, climate models tend to exhibit different EOF modes, compared to those from observational reanalysis (not shown). We speculate that this may also be related to the fact that CMIP6 models have some bias in simulating the western Antarctic mean state (cf., Amundsen Sea Low)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Therefore, future work should explore how well current climate models represent PSAs and their teleconnections with ENSO.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData availability.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReanalysis Dataset: We utilized the ECMWF Reanalysis v5 (ERA5)\u003csup\u003e24\u003c/sup\u003e. ERA5 is the fifth generation of ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades, produced using 4D-Var data assimilation in CY41R2 of ECMWF\u0026rsquo;s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure levels in the vertical, with the top level at 0.01 hPa. Additionally, we also use NCEP/NCAR Reanalysis 1 (NCEP-R1)\u003csup\u003e25\u003c/sup\u003e, which provides monthly atmospheric model output from 1948 to near present (2.5\u0026deg; x 2.5\u0026deg; global grids (144x73), with 17 pressure levels). The analysis period is from 1950 to 2023.\u0026nbsp;All observational reanalysis datasets can be downloaded from open URL. ERA5:\u0026nbsp;https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html. NCEP-R1: \u0026nbsp;https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.html\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStationary Wave Model (SWM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSWM is a nonlinear baroclinic atmospheric model with a dry dynamical core. It has 14 vertical levels on sigma coordinates. The horizontal resolution of SWM is truncated at rhomboidal 30. This model was devised to understand how the atmospheric stationary waves propagate given atmospheric perturbations. To conduct SWM experiments, background atmospheric states of a month or a season is first fixed. Then, under the fixed background state, steady atmospheric heating or vorticity are prescribed until stationary atmospheric waves are formed. It takes about 30 to 60 days. The response to the given forcing shown in Fig. 5 is averaged for the first 50 days, since the steady forcing is exerted. For further details of the model equations or information, please refer to Ting and Yu (1998)\u003csup\u003e26\u003c/sup\u003e and Wang and Ting (1999)\u003csup\u003e27\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests.\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.-H. Park and G.-I. Kim started the research with the initial idea and produced the initial results. J.-S. Kug developed the initial ideas and results by giving constructive opinions. J.-H. Park and G.-I. Kim wrote the first draft of the paper. All co-authors actively participated in discussion and revised the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments.\u003c/h2\u003e\u003cp\u003eJ.-H. Park was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (NRF-2023R1A2C1004083 and RS-2023-00219830).\u003c/p\u003e\u003ch2\u003eData availability.\u003c/h2\u003e\u003cp\u003eReanalysis Dataset: We utilized the ECMWF Reanalysis v5 (ERA5)\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. ERA5 is the fifth generation of ECMWF reanalysis for the global climate and weather for the past 4 to 7 decades, produced using 4D-Var data assimilation in CY41R2 of ECMWF\u0026rsquo;s Integrated Forecast System (IFS), with 137 hybrid sigma/pressure levels in the vertical, with the top level at 0.01 hPa. Additionally, we also use NCEP/NCAR Reanalysis 1 (NCEP-R1)\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, which provides monthly atmospheric model output from 1948 to near present (2.5\u0026deg; x 2.5\u0026deg; global grids (144x73), with 17 pressure levels). The analysis period is from 1950 to 2023. All observational reanalysis datasets can be downloaded from open URL. ERA5: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metoffice.gov.uk/hadobs/hadisst/data/download.html\u003c/span\u003e\u003cspan address=\"https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. NCEP-R1: \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\u003c/p\u003e\u003ch2\u003eCode availability.\u003c/h2\u003e\u003cp\u003eCodes used in the manuscript are available upon reasonable requests from G.-l. Kim\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFogt, R. L. \u0026amp; Marshall, G. J. The Southern Annular Mode: Variability, trends, and climate impacts across the Southern Hemisphere. \u003cem\u003eWIREs Climate Change\u003c/em\u003e 11, e652 (2020).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMarshall, G. J. Trends in the Southern Annular Mode from Observations and Reanalyses. 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Steady Response to Tropical Heating in Wavy Linear and Nonlinear Baroclinic Models. (1998).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWang, H. \u0026amp; Ting, M. Seasonal Cycle of the Climatological Stationary Waves in the NCEP\u0026ndash;NCAR Reanalysis. (1999).\u003c/span\u003e\u003c/li\u003e\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":"[email protected]","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":"","lastPublishedDoi":"10.21203/rs.3.rs-7757839/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7757839/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSouth Pacific atmospheric teleconnections and their relationship with the tropical Pacific have remained less explored than their North Pacific counterparts. Using observational reanalysis datasets since the mid-20th century, we first identified three atmospheric teleconnections by applying Empirical Orthogonal Function (EOF) analysis to the 200hPa geopotential height from the South Pacific to South America during the austral fall, when the variability of El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) is weak. We then investigated the relationship between these EOFs and ENSO. We found that EOF1 is a direct Rossby wave response to the tropical convection associated with previous ENSO events. In contrast, both EOF2 and EOF3\u0026mdash;similar to Pacific\u0026ndash;South America teleconnections\u0026mdash;are significantly correlated with subsequent ENSO events. In detail, EOF2 exhibits a great-circle Rossby wave energy propagation pattern, initiated by enhanced convection over the equatorial western Pacific. That is, this convection is largely responsible for the development of both ENSO and EOF2. Meanwhile, EOF3 is characterized by wave energy propagation toward the tropical Pacific from the high-latitudes, potentially contributing to ENSO development. These results suggest that further investigation of tropical\u0026ndash;South Pacific interactions could expand our understanding of Pacific climate variability.\u003c/p\u003e","manuscriptTitle":"Relationship between Pacific–South America teleconnections in austral fall and ENSO evolution","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-22 08:16:23","doi":"10.21203/rs.3.rs-7757839/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"46e25942-cab8-4eb4-b447-a856424c7775","owner":[],"postedDate":"October 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56123250,"name":"Earth and environmental sciences/Climate sciences"},{"id":56123251,"name":"Earth and environmental sciences/Ocean sciences"}],"tags":[],"updatedAt":"2025-11-19T02:08:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-22 08:16:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7757839","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7757839","identity":"rs-7757839","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}