Zonal asymmetry of the Quasi Biennial Oscillation

Zonal asymmetry of the Quasi Biennial Oscillation | 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 Short Report Zonal asymmetry of the Quasi Biennial Oscillation Ryo Hayakawa, Yoshio Kawatani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7877638/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Zonal asymmetry in the amplitude of the Quasi-Biennial Oscillation (QBO) is investigated using JRA-55 reanalysis data and in-situ IGRA radiosonde observations. In addition to the previously reported asymmetry near 10 hPa, a significant longitudinal variation with the largest amplitudes over the western and central Pacific is found around 70 hPa. Unlike the dominant wavenumber-1 pattern near 10 hPa, the structure at 70 hPa exhibits more localized variations. Longitudinal differences in the QBO zonal wind at 70 hPa reach up to 30%, indicating that the descent of the QBO into the lower stratosphere and upper troposphere exhibits pronounced longitudinal variation. The asymmetry results were confirmed in five additional reanalysis datasets. Similar zonal variations of QBO amplitudes are also identified in radiosonde observations. These findings suggest that such significant longitudinal variations in QBO amplitude could play a role in interactions with the tropical tropopause and associated teleconnections. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction The Quasi-Biennial Oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere, characterized by alternating easterly and westerly zonal winds with a mean period of about 28 months (Baldwin et al. 2001; Anstey et al. 2022). While the QBO has traditionally been considered a zonally symmetric phenomenon, several studies have indicated the presence of longitudinal asymmetries. The observed zonally asymmetric structures in the monthly mean zonal wind in the equatorial middle stratosphere (around 10 hPa) can be characterized fairly simply. Notably winter hemisphere mid-latitude planetary waves (primarily zonal wavenumber-1) penetrate into the equatorial region during westerly phase of the QBO (Hamilton et al., 2004; Kawatani et al., 2016) but not in the easterly phase. More recently, Sakazaki and Hamilton (2022) refined this picture by identifying quasi-stationary wavenumber-1 equatorially trapped Rossby and Kelvin wave structures which sometimes appear near 10 hPa in the peak westerly and easterly QBO jets. While these studies have focused on the QBO in the middle stratosphere, it seems the simple wavenumber-1 dominated picture does not apply below ~ 20 hPa (Hamilton et al., 2004), and zonally asymmetric structures in the lower stratosphere have received comparatively less attention. Understanding the zonal variation of QBO circulation in the lower stratosphere is important as this region connects most directly with tropospheric variability. In particular, recent studies have indicated that QBO variations of lower-stratospheric circulation may be linked to the activity of tropical cyclones, the Asian monsoon and the Madden–Julian Oscillation (e.g., Ho et al., 2009; Seo et al., 2013; Yoo and Son, 2016). The present study examines the structure of QBO related prevailing zonal wind variations, focusing on their longitudinal, vertical, and QBO phase dependence, with particular emphasis on the 70-hPa level. The analysis is based on the JRA-55 reanalysis data, five other comparable reanalyses, and in-situ near equatorial radiosonde observations. 2. Data and study method Monthly mean zonal wind ( u ) data from the JRA-55 reanalysis (Kobayashi et al., 2015) were used, with a horizontal resolution of 1.25° and 37 pressure levels from 1000 to 1 hPa. Data from January 1958 to December 2015 were analyzed, which excludes the QBO disruption events (Osprey et al., 2016; Newman et al., 2016; Wang et al., 2023) in early 2016 and during 2019/20 boreal winter to focus on the fundamental QBO structures in their undisturbed state. Some calculations were also repeated using the ERA5 (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA (Rienecker et al., 2011), MERRA-2 (Gelaro et al., 2017), and NCEP-CFSR (Saha et al., 2010) reanalyses, in each case considering data from January 1980 to December 2015 (note that MERRA-2 are available only after 1980). Two different versions of each time series were processed with different filtering applied to isolate the QBO signal. The first filter follows Dunkerton and Delisi (1985; hereinafter “DD”). It removes the climatological annual cycle from the raw time series at each level, and applies a 5-month running mean to smooth the data, resulting in a filtered time series denoted as “ U DD ”. The second filter applies a Fast Fourier Transform (FFT) to the raw data to extract components with periods between 18 and 36 months and resulting in a filtered time series denoted as “ U FFT ”. While the DD method is designed to include the rapid non-sinusoidal variations in the QBO evolution, it may also retain longer-period signals. The FFT method removes such long-period variability but tends to smooth over the values during the rapid QBO transitions, leading to generally smaller estimates of the QBO amplitude than obtained using the DD filtering. From the U DD and U FFT filtered series of JRA-55 zonal mean equatorial 20 hPa winds, the months of both westerly-to-easterly (WE) and easterly-to-westerly (EW) transitions were identified separately for each filtering method (dates listed in the Supplementary material). These dates were used both in the construction of QBO composites (see below) and to define a series of individual QBO cycles at each grid point in longitude, latitude and pressure level, each cycle being the interval from one transition month to the month before the next transition of the same type during 1958–2015. For each of the filtered time series the standard deviation \(\:{\sigma\:}_{u}\) was calculated for each QBO cycle, and the QBO amplitude for that cycle estimated as \(\:{{A}_{u}=\sqrt{2}\sigma\:}_{u}\) . The climatological QBO amplitude was then calculated by averaging the amplitudes over all 24 EW QBO cycles. For composite analysis, WE and EW transitions identified from each filtering method were used as reference months. The filtered wind series were composited separately for EW and WE transitions using a ± 18-month window centered on the transition month. Because of this window length, the first of both EW and WE, and the last of EW transitions in the record could not be employed as reference months. Consequently, composites are based on 23 cycles for EW and 24 cycles for WE. Radiosonde observations from the Integrated Global Radiosonde Archive (IGRA) v2.2 archive (Durre et al. 2016) were also analyzed to provide another observational perspective. Monthly mean zonal wind data at 70 hPa from stations within ± 10° latitude were used. QBO amplitudes were calculated using the DD method, and only stations with sufficient temporal coverage were retained. The detailed processing procedures are described in the Supplementary Material. 3. Results Figure 1 presents time–height cross-sections of the composite zonal wind U DD over the equator at 0°E and 180°E, along with their difference (180ºE minus 0ºE) presented for EW and WE transitions. Two largest differences are concentrated in two layers near 10 hPa and 70 hPa, with larger contrasts near 70 hPa for both cases. The zonal differences around 70 hPa peak when the QBO extends most strongly into the lowermost stratosphere, reaching up to 2.4 m s − 1 relative to zonal mean zonal winds of ~ 8 m s − 1 , corresponding to a variation of about 30%. Similar features are also seen in zonal wind composites based on U FFT (Fig. S1 ). Figures 2 a-d show the geographical distribution of the climatological QBO amplitude at 10 hPa and 70 hPa calculated using U DD , along with deviations from the zonal mean. At each location amplitude deviations were calculated for each QBO cycle, and a Student’s t-test was applied over all 24 cycles to assess statistical significance. Stippling indicates regions significant at the 95% confidence level. At 10 hPa, QBO amplitude over the equator is larger over a sector stretching east from the Atlantic to the western Pacific, while amplitude is smaller over central to eastern Pacific. There is a clear wave number-1 patterns in the deviation from zonal mean (Figs. 2 a, c). In contrast, at 70 hPa, the equatorial QBO amplitude is significantly enhanced from the Maritime Continent to the central Pacific, with additional small peaks over Africa and South America (Fig. 2 b). The largest positive anomalies are somewhat narrowly confined in the western and central Pacific (Fig. 2 d). Figures 2 e and 2 f show longitude-height cross-sections of QBO amplitude anomalies from the zonal mean, calculated from U DD and U FFT . A clear wavenumber-1 structure is present in the middle stratosphere, with maximum near 10 hPa, consistent with previous studies (Hamilton et al. 2004; Sakazaki and Hamilton. 2022). Zonally asymmetric structures are also evident around 70 hPa, particularly between 140°E and 140ºW, indicating a more localized zonal structure compared to that around 10 hPa. In both methods, tstronger anomalies are found in the upper troposphere, peaking particularly around 150 hPa. These features will be discussed further in connection with Fig. 5 below. Figure 3 presents longitude–time cross-sections of the EW and WE transition composite zonal wind U DD and its deviation from the zonal mean at 10 hPa and 70 hPa. At 10 hPa, a zonal wavenumber-1 pattern is evident, with relatively weak westerlies (easterlies) over the eastern Pacific during the westerly (easterly) phase, contributing to a reduced mean QBO amplitude in this region. In contrast, at 70 hPa, stronger westerlies (easterlies) are found over the western and central Pacific during the westerly (easterly) phases, with the strongest anomalies confined between approximately 120°E and 130°W, exhibiting a more localized structure than at 10 hPa. Similar features are also found based on U FFT (Fig. S2). Figure 4 shows time–height cross-sections of the longitudinal standard deviation of the composite U DD , along with the corresponding zonal-mean zonal wind composite, for both transition types. Peaks in longitudinal standard deviation appear around 10 hPa and 70 hPa during both transition phases. Between 50 and 100 hPa, substantial standard deviations are seen near the lowest altitudes to which the QBO westerly or easterly phases descend, indicating that the QBO penetration into the lowermost stratosphere depends significantly on longitude. These characteristics are also confirmed by the composites based on U FFT (Fig. S3). The QBO amplitude deviations from the zonal mean, calculated using both U FFT and U DD (Figs. 2 e and 2 f), show pronounced positive anomalies over the western and central Pacific around 70 hPa. Additional large positive anomalies are also evident in the troposphere above 500 hPa, with a maximum near 150 hPa around 150°W. Two amplitude peaks at 70 hPa and 150 hPa appear as distinct maxima. To further investigate these peaks, frequency–height cross-sections of the power spectra were examined (Fig. S4), based on U DD at 0°E and 150°W, and U FFT at 150°W. In the stratosphere, QBO components are well captured in U DD , but spectral power also appears at longer periods (e.g., 42–84 months), which are likely unrelated to the QBO. In the troposphere, these long-period components are weak at 0°E but more prominent at 150°W. Spectral power in the 18–36 month range is also considerably stronger at 150°W than at 0°E below about 150 hPa, raising the question of whether this signal originates from the QBO or is associated with ENSO. In contrast to U DD , U FFT effectively eliminates signals with periods longer than 42 months and shorter than 18 months, thereby isolating the typical QBO periodic components. To clarify the possible role of ENSO in producing the “QBO” amplitude peaks at 70 hPa and 150 hPa in the Pacific, correlations were calculated separately between the monthly NINO3 index defined by the Japan Meteorological Agency and both U DD and U FFT . Figures 5 a–f are scatter plots of the NINO3 index versus U DD or U FFT at 150°W at 70, 100 and 150 hPa. At 150 and 100 hPa, clear negative correlations of about − 0.8 are evident in U DD . Although the magnitudes are smaller, clear negative correlations are also found in U FFT . At 100 hPa, the relationship between U FFT and NINO3 is unclear when the index is within ± 1. However, stronger ENSO events appear to contribute more clearly to the negative correlation. In contrast, the correlations at 70 hPa are − 0.1 for U DD and close to zero for U FFT , suggesting that the zonal asymmetry at this level is primarily attributable to the QBO itself rather than to ENSO variability. Figures 5 g and 5 h present the longitude–height cross-section of the correlation between NINO3 index and both U DD and U FFT . Statistically significant correlations exceeding 0.2 in absolute value are found throughout the troposphere, even for U FFT , whereas correlations above 70 hPa are generally negligible. These results indicate that the zonal asymmetry of the QBO amplitude at 70 hPa is not influenced by ENSO. The basic computation of 70-hPa QBO amplitude was repeated using five additional reanalysis datasets: ERA5, ERA-Interim, MERRA, MERRA-2, and NCEP-CFSR in addition to JRA-55, for the period 1980–2015 (with MERRA-2 data available only after 1980). Figure 6 shows the same diagnostics as Fig. 2 d. Note that the results using JRA-55 data during 1980–2015 (Fig. 6 c) are very similar to those for the entire 1958–2015 period. All datasets exhibit quite similar patterns of zonal asymmetry at 70 hPa, although the MERRA-2 results notably have a somewhat weaker western Pacific maximum. Finally, the spatial distribution of 70-hPa QBO amplitude is examined using radiosonde observations from IGRA v2.2. Stations with complete data for more than 8 out of 24 QBO cycles during 1958–2015 were selected, and QBO amplitudes were estimated using the DD method (see Methods in Supplementary Material for details). Figure 7 a shows the latitudinal distribution of individual climatological QBO amplitudes at 70 hPa, along with the JRA-55 zonal-mean climatology and the ± 1σ and ± 2σ envelopes, where σ represents the longitudinal standard deviation of climatological QBO amplitude at each latitude. Most stations fall within the ± 2σ envelope of the JRA-55 zonal-mean climatology. Figure 7 b shows the radiosonde stations within ± 10° latitude that met the minimum data coverage threshold. Stations with over 90% coverage are concentrated over Singapore and near 7–9°N in the western–central Pacific, while coverage is much sparser elsewhere (Kawatani and Hamilton, 2013). The QBO amplitude exhibits a strong meridional dependence, peaking at the equator with a typical half-width of about 12° latitude (Baldwin et al., 2001). For each station, an “adjusted” amplitude was also computed to represent to an equivalent equatorial value. This latitudinal correction was based on the zonal-mean meridional profile of the QBO amplitude derived from JRA-55 data, and was used to normalize amplitude estimates across stations at different latitudes. The resulting equator-adjusted amplitudes enable fairer comparisons of longitudinal structure. Although a strict comparison of climatological amplitudes would require averaging over identical observation periods at all stations, as cycle-to-cycle variability and a long-term negative trend in QBO amplitude have been reported (Kawatani and Hamilton, 2013), averaging over at least 8 cycles provides a more consistent basis for comparison across stations. These stations shown in Fig. 7 b therefore offer a reasonable basis for examining the longitudinal structure. Figure 7 c shows the longitudinal distribution of the adjusted climatological amplitudes, as well as those for individual QBO cycles, revealing substantial cycle-to-cycle variability at most stations. The sharp amplitude contrast (approximately 3 m s⁻¹) across a narrow longitudinal band between about 100°E and 120°E is primarily due to the particularly low value at KOTA BHARU (station 6) and high value at JUANDA (station 10). Nevertheless, longitudinal variations in 70-hPa QBO amplitude are also evident in the radiosonde data. The pattern of increasing amplitude from 100°E to central Pacific, followed by a decrease toward the eastern Pacific and a secondary increase near South America, closely resembles that seen in the reanalyses. An enhancement near 60–80° E is less evident in the reanalyses, but the presence of three major longitudinal maxima is a consistent feature. In summary, despite the limited spatial coverage, the selected radiosonde observations provide support for the presence of longitudinal asymmetry in the 70-hPa QBO amplitude. 4. Summary and Discussion This study investigated the longitudinal variations of the QBO amplitude in the lower stratosphere, with a particular focus on the 70-hPa level, using JRA-55 and five additional reanalysis datasets, as well as radiosonde observations from the IGRA v2.2 archive. Two different methods were applied to extract the QBO signal: one following Dunkerton and Delisi (1985), and the other using Fast Fourier Transform (FFT) spectral decomposition. Both methods generally produced similar overall structures. While several previous studies have demonstrated that the QBO exhibits pronounced zonal asymmetry in the middle stratosphere near 10 hPa (e.g., Hamilton et al., 2004; Kawatani et al., 2016; Sakazaki and Hamilton, 2022), the longitudinal structure of the QBO in the lower stratosphere, especially near 70 hPa, has not been well documented. The present results revealed that at 70 hPa, the QBO amplitude displays significant zonal asymmetry, characterized by a maximum over the western and central Pacific. This longitudinal pattern was consistently identified across all reanalysis products, despite differences in model formulation and data assimilation systems. In addition, radiosonde observations from IGRA were analyzed to provide an independent observational perspective. Stations with sufficient temporal coverage to estimate reliable QBO amplitude climatology were selected. After scaling the station amplitudes to the equator to account for the meridional structure of the QBO, the IGRA results showed a clear longitudinal variation that closely resembled the reanalysis-based pattern. In particular, the amplitude enhancement over the central Pacific and a secondary peak near South America were also evident in the radiosonde data. This agreement across reanalyses and in-situ observations increases confidence in the robustness of the longitudinal asymmetry in the 70-hPa QBO amplitude. In addition to the amplitude maximum at 70 hPa over the western and central Pacific, large signals extracted by the QBO filtering methods were also found near 150 hPa around 150°W. Correlation analyses between the NINO3 index and the QBO components of zonal wind ( U DD and U FFT ) revealed strong link at this level, suggesting an influence of ENSO variability. In contrast, correlations at 70 hPa were negligible, indicating that the zonal asymmetry at this level is not much influenced by ENSO effects. The longitudinal asymmetry of QBO-related temperature anomalies near the cold-point tropopause and at 100 hPa, as reported by Tegtmeier et al. (2020), featured maxima over the Eastern Hemisphere. These anomalies likely reflect both QBO dynamical effects and convective influences. In contrast, the zonal wind amplitude analyzed in this study, primarily representing the dynamical QBO descent, shows a western and central Pacific maximum. This contrast highlights the need to analyze zonal wind fields alongside temperature data for a more complete understanding of the QBO’s longitudinal structure. A possible explanation for the longitudinal pattern in QBO amplitude involves reduced background flow near 180°E associated with the Walker circulation, which may allow small-phase-speed gravity waves to propagate into the lower stratosphere more effectively (e.g., Kawatani et al., 2010). Further investigation using high-frequency data would help clarify this mechanism. In summary, the QBO amplitude at 70 hPa exhibits significant and robust zonal asymmetry. This asymmetry is apparent in both reanalysis and radiosonde datasets. This feature may have implications for stratosphere–troposphere coupling and tropical teleconnections. Further research will be necessary to quantify its impact on convection, large-scale circulation, and teleconnections, and to examine the processes that maintain these longitudinal differences. Declarations Supplements There are four supplementary figure (Figs. S1-S4), three supplementary tables (Tables S1-S3) and details of the methods. Author Contribution R.H. processed the IGRA radiosonde data, conducted the analyses, and prepared the figures and tables. Y.K. designed the study, performed additional analyses, and contributed to the interpretation of the results. R.H. and Y.K. wrote the manuscript together. All authors reviewed the manuscript. Acknowledgement We thank Prof. K. Hamilton for valuable discussion and helpful comments on the manuscript. Data Availability The datasets analyzed in this study are publicly available. JRA-55 reanalysis data can be accessed at https://jra.kishou.go.jp/JRA-55/index_en.html. ERA5 reanalysis data are available from ECMWF via https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. ERA-Interim reanalysis data are available via ECMWF at https://www.ecmwf.int/en/about/media-centre/news/2008/era-interim-products-available. MERRA reanalysis data can be accessed at https://gmao.gsfc.nasa.gov/gmao-products/merra/. 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R. Park, and J.-Y. Kim, 2013: Relationship between the stratospheric quasi-biennial oscillation and the spring rainfall in the western North Pacific. Geophys. Res. Lett., 40, 5949–5953, https://doi.org/10.1002/2013GL058266. Wang, Y., J. Rao, X. Lu, Z. Ju, and J. Luo, 2023: A revisit and comparison of the quasi-biennial oscillation disruption events in 2015/16 and 2019/20. Atmos. Res., 294, 106970, doi:10.1016/j.atmosres.2023.106970. Yoo, C., and S.-W. Son, 2016: Modulation of the boreal wintertime Madden–Julian oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett., 43, 1392–1398, doi:10.1002/2016GL067762. Additional Declarations No competing interests reported. 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16:51:47","extension":"html","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":62192,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/0490c96ad8dfe9bb191968c9.html"},{"id":94786362,"identity":"8a46b28c-cb40-4f48-8486-16a031ded8c1","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":969651,"visible":true,"origin":"","legend":"\u003cp\u003eTime–height cross-sections of the composite QBO zonal wind over the equator, derived by applying DD filtering to JRA-55 monthly mean time series (\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e). Upper panels show composites for the QBO phase transition: (a) easterly-to-westerly and (b) westerly-to-easterly at 20 hPa, with month 0 denoting the transition month. The composites cover ±18 months from month 0. Blue and red contours indicate zonal wind at 0°E and 180°E, respectively, with a contour interval of 5 m s⁻¹. Lower panels show the difference (180°E minus 0°E) for the corresponding phases, with black contours indicating zonal mean zonal wind (contour interval: 5 m s⁻¹) and shading showing the difference (color interval: 0.5 m s⁻¹).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/265017f916abee98c7d2f7b1.png"},{"id":94786363,"identity":"fbe2ae03-9f08-4a87-a8ec-8b99822ae7e7","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1588085,"visible":true,"origin":"","legend":"\u003cp\u003eHorizontal distributions of (a, b) QBO amplitude and (c, d) deviation from the zonal mean at 10 hPa (a, c) and 70 hPa (b, d), calculated using the DD method. Panels (e) and (f) show longitude–height cross-sections of the QBO amplitude deviation from the zonal mean over the equator, calculated using the DD method (e) and the FFT method (f). The color interval is 2 m s⁻¹ in (a), 1 m s⁻¹ in (b), and 0.3 m s⁻¹ in (c, d). Stippling indicates regions that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/41064e87695af5eed5c098d2.png"},{"id":94824682,"identity":"65266816-b9b7-4d74-aa3a-bed8537b4fe3","added_by":"auto","created_at":"2025-10-31 06:49:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":835769,"visible":true,"origin":"","legend":"\u003cp\u003eLongitude–time cross-sections of the composite deviations of \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e from the zonal mean at 10 hPa (upper panels) and 70 hPa (lower panels). Month 0 denotes the QBO phase transition at 20 hPa: easterly-to-westerly (left) and westerly-to-easterly (right). Black contours show the composite \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, with a contour interval of 2 m s⁻¹. The color interval is 0.3 m s⁻¹.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/32eb2a914c3844fb82a0df63.png"},{"id":94786368,"identity":"65f7ed9c-565b-4baa-adc6-4038423ceb7b","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":770786,"visible":true,"origin":"","legend":"\u003cp\u003eTime–height cross-sections of the longitudinal standard deviation of composite \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, where larger values indicate stronger zonal asymmetry. Contours indicate the composite zonal-mean \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e with a contour interval of 5 m s⁻¹. The color interval is 0.1 m s⁻¹, starting from 0.3 m s⁻¹. Panel (a) corresponds to the easterly-to-westerly phase at 20 hPa, and panel (b) to the westerly-to-easterly transition.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/d9dea1e4cbf0c463608cb0a9.png"},{"id":94786367,"identity":"dfc548e7-2495-40dc-aee6-55d9d9c37f2d","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":461381,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of the Niño index versus QBO-related zonal wind component \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e (left column) and its FFT-filtered counterpart \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e (right column) at 70 hPa (a, b), 100 hPa (c, d), and 150 hPa (e, f). The correlation coefficient is shown in each panel. Panels (g) and (h) present longitude–height cross-sections of the correlation between the Niño index and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e over the equator.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/08e374a608fe55c185f3d1cc.png"},{"id":94786369,"identity":"9d14d576-9f99-4a8e-b4be-c1b94f3add37","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":436895,"visible":true,"origin":"","legend":"\u003cp\u003eSame as Fig. 2d, but for (a) ERA5, (b) ERA-Interim, (c) JRA-55, (d) MERRA, (e) MERRA-2, and (f) NCEP-CFSR over the period 1980–2015. Only regions statistically significant at the 95% confidence level are shaded. Both contour and color intervals are 0.3 m s⁻¹.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/d9e23c70bf7d56ff8aad0312.png"},{"id":94825177,"identity":"067a6420-f9fe-4de6-a9b3-2426ec8ae04c","added_by":"auto","created_at":"2025-10-31 06:49:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":998602,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Latitudinal structure of the climatological QBO amplitude at 70 hPa from JRA-55 data for 1958–2015. The black solid curve denotes the zonal-mean amplitude, while the blue dashed and red solid curves indicate ±1σ and ±2σ envelopes of the longitudinal standard deviation, respectively. Filled circles show climatological amplitudes derived from radiosonde observations at each station, with station numbers labeled on the right. (b) Location of radiosonde stations within ±10° latitude with at least 30% data coverage for estimating the 70-hPa QBO amplitude during 1958–2015. Marker color denotes the fraction of QBO cycles with complete 70-hPa data (30–50%, 50–70%, 70–90%, 90–100%). (c) Longitudinal distribution of 70-hPa QBO amplitudes, showing the climatological average for each station (top; with station numbers) and amplitudes from individual QBO cycles (bottom; colored dots).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/b0a18a269005b975d49a4042.png"},{"id":94985010,"identity":"17cc4629-186f-4aeb-97a7-0f1e3258f822","added_by":"auto","created_at":"2025-11-03 06:57:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6923932,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/b4e83e9b-8894-4327-a634-0676629fb4ec.pdf"},{"id":94786372,"identity":"4c9d52dc-847d-46d6-af99-a9f9fe85bbe3","added_by":"auto","created_at":"2025-10-30 16:51:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4730444,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryHayakawaKawatani2025SOLA.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7877638/v1/c37ccb6ba676974aafdff735.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Zonal asymmetry of the Quasi Biennial Oscillation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Quasi-Biennial Oscillation (QBO) is the dominant mode of interannual variability in the tropical stratosphere, characterized by alternating easterly and westerly zonal winds with a mean period of about 28 months (Baldwin et al. 2001; Anstey et al. 2022). While the QBO has traditionally been considered a zonally symmetric phenomenon, several studies have indicated the presence of longitudinal asymmetries. The observed zonally asymmetric structures in the monthly mean zonal wind in the equatorial middle stratosphere (around 10 hPa) can be characterized fairly simply. Notably winter hemisphere mid-latitude planetary waves (primarily zonal wavenumber-1) penetrate into the equatorial region during westerly phase of the QBO (Hamilton et al., 2004; Kawatani et al., 2016) but not in the easterly phase. More recently, Sakazaki and Hamilton (2022) refined this picture by identifying quasi-stationary wavenumber-1 equatorially trapped Rossby and Kelvin wave structures which sometimes appear near 10 hPa in the peak westerly and easterly QBO jets.\u003c/p\u003e\u003cp\u003eWhile these studies have focused on the QBO in the middle stratosphere, it seems the simple wavenumber-1 dominated picture does not apply below ~\u0026thinsp;20 hPa (Hamilton et al., 2004), and zonally asymmetric structures in the lower stratosphere have received comparatively less attention. Understanding the zonal variation of QBO circulation in the lower stratosphere is important as this region connects most directly with tropospheric variability. In particular, recent studies have indicated that QBO variations of lower-stratospheric circulation may be linked to the activity of tropical cyclones, the Asian monsoon and the Madden\u0026ndash;Julian Oscillation (e.g., Ho et al., 2009; Seo et al., 2013; Yoo and Son, 2016).\u003c/p\u003e\u003cp\u003eThe present study examines the structure of QBO related prevailing zonal wind variations, focusing on their longitudinal, vertical, and QBO phase dependence, with particular emphasis on the 70-hPa level. The analysis is based on the JRA-55 reanalysis data, five other comparable reanalyses, and in-situ near equatorial radiosonde observations.\u003c/p\u003e"},{"header":"2. Data and study method","content":"\u003cp\u003eMonthly mean zonal wind (\u003cem\u003eu\u003c/em\u003e) data from the JRA-55 reanalysis (Kobayashi et al., 2015) were used, with a horizontal resolution of 1.25\u0026deg; and 37 pressure levels from 1000 to 1 hPa. Data from January 1958 to December 2015 were analyzed, which excludes the QBO disruption events (Osprey et al., 2016; Newman et al., 2016; Wang et al., 2023) in early 2016 and during 2019/20 boreal winter to focus on the fundamental QBO structures in their undisturbed state. Some calculations were also repeated using the ERA5 (Hersbach et al., 2020), ERA-Interim (Dee et al., 2011), MERRA (Rienecker et al., 2011), MERRA-2 (Gelaro et al., 2017), and NCEP-CFSR (Saha et al., 2010) reanalyses, in each case considering data from January 1980 to December 2015 (note that MERRA-2 are available only after 1980).\u003c/p\u003e\u003cp\u003eTwo different versions of each time series were processed with different filtering applied to isolate the QBO signal. The first filter follows Dunkerton and Delisi (1985; hereinafter \u0026ldquo;DD\u0026rdquo;). It removes the climatological annual cycle from the raw time series at each level, and applies a 5-month running mean to smooth the data, resulting in a filtered time series denoted as \u0026ldquo;\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e\u0026rdquo;. The second filter applies a Fast Fourier Transform (FFT) to the raw data to extract components with periods between 18 and 36 months and resulting in a filtered time series denoted as \u0026ldquo;\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e\u0026rdquo;.\u003c/p\u003e\u003cp\u003eWhile the DD method is designed to include the rapid non-sinusoidal variations in the QBO evolution, it may also retain longer-period signals. The FFT method removes such long-period variability but tends to smooth over the values during the rapid QBO transitions, leading to generally smaller estimates of the QBO amplitude than obtained using the DD filtering.\u003c/p\u003e\u003cp\u003eFrom the \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e filtered series of JRA-55 zonal mean equatorial 20 hPa winds, the months of both westerly-to-easterly (WE) and easterly-to-westerly (EW) transitions were identified separately for each filtering method (dates listed in the Supplementary material). These dates were used both in the construction of QBO composites (see below) and to define a series of individual QBO cycles at each grid point in longitude, latitude and pressure level, each cycle being the interval from one transition month to the month before the next transition of the same type during 1958\u0026ndash;2015. For each of the filtered time series the standard deviation \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{u}\\)\u003c/span\u003e\u003c/span\u003e was calculated for each QBO cycle, and the QBO amplitude for that cycle estimated as \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{{A}_{u}=\\sqrt{2}\\sigma\\:}_{u}\\)\u003c/span\u003e\u003c/span\u003e. The climatological QBO amplitude was then calculated by averaging the amplitudes over all 24 EW QBO cycles.\u003c/p\u003e\u003cp\u003eFor composite analysis, WE and EW transitions identified from each filtering method were used as reference months. The filtered wind series were composited separately for EW and WE transitions using a\u0026thinsp;\u0026plusmn;\u0026thinsp;18-month window centered on the transition month. Because of this window length, the first of both EW and WE, and the last of EW transitions in the record could not be employed as reference months. Consequently, composites are based on 23 cycles for EW and 24 cycles for WE.\u003c/p\u003e\u003cp\u003eRadiosonde observations from the Integrated Global Radiosonde Archive (IGRA) v2.2 archive (Durre et al. 2016) were also analyzed to provide another observational perspective. Monthly mean zonal wind data at 70 hPa from stations within \u0026plusmn;\u0026thinsp;10\u0026deg; latitude were used. QBO amplitudes were calculated using the DD method, and only stations with sufficient temporal coverage were retained. The detailed processing procedures are described in the Supplementary Material.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents time\u0026ndash;height cross-sections of the composite zonal wind \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e over the equator at 0\u0026deg;E and 180\u0026deg;E, along with their difference (180\u0026ordm;E minus 0\u0026ordm;E) presented for EW and WE transitions. Two largest differences are concentrated in two layers near 10 hPa and 70 hPa, with larger contrasts near 70 hPa for both cases. The zonal differences around 70 hPa peak when the QBO extends most strongly into the lowermost stratosphere, reaching up to 2.4 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e relative to zonal mean zonal winds of ~\u0026thinsp;8 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, corresponding to a variation of about 30%. Similar features are also seen in zonal wind composites based on \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea-d show the geographical distribution of the climatological QBO amplitude at 10 hPa and 70 hPa calculated using \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, along with deviations from the zonal mean. At each location amplitude deviations were calculated for each QBO cycle, and a Student\u0026rsquo;s t-test was applied over all 24 cycles to assess statistical significance. Stippling indicates regions significant at the 95% confidence level. At 10 hPa, QBO amplitude over the equator is larger over a sector stretching east from the Atlantic to the western Pacific, while amplitude is smaller over central to eastern Pacific. There is a clear wave number-1 patterns in the deviation from zonal mean (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, c). In contrast, at 70 hPa, the equatorial QBO amplitude is significantly enhanced from the Maritime Continent to the central Pacific, with additional small peaks over Africa and South America (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). The largest positive anomalies are somewhat narrowly confined in the western and central Pacific (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef show longitude-height cross-sections of QBO amplitude anomalies from the zonal mean, calculated from \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e. A clear wavenumber-1 structure is present in the middle stratosphere, with maximum near 10 hPa, consistent with previous studies (Hamilton et al. 2004; Sakazaki and Hamilton. 2022). Zonally asymmetric structures are also evident around 70 hPa, particularly between 140\u0026deg;E and 140\u0026ordm;W, indicating a more localized zonal structure compared to that around 10 hPa. In both methods, tstronger anomalies are found in the upper troposphere, peaking particularly around 150 hPa. These features will be discussed further in connection with Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003e below.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents longitude\u0026ndash;time cross-sections of the EW and WE transition composite zonal wind \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and its deviation from the zonal mean at 10 hPa and 70 hPa. At 10 hPa, a zonal wavenumber-1 pattern is evident, with relatively weak westerlies (easterlies) over the eastern Pacific during the westerly (easterly) phase, contributing to a reduced mean QBO amplitude in this region. In contrast, at 70 hPa, stronger westerlies (easterlies) are found over the western and central Pacific during the westerly (easterly) phases, with the strongest anomalies confined between approximately 120\u0026deg;E and 130\u0026deg;W, exhibiting a more localized structure than at 10 hPa. Similar features are also found based on \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e (Fig. S2).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows time\u0026ndash;height cross-sections of the longitudinal standard deviation of the composite \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, along with the corresponding zonal-mean zonal wind composite, for both transition types. Peaks in longitudinal standard deviation appear around 10 hPa and 70 hPa during both transition phases. Between 50 and 100 hPa, substantial standard deviations are seen near the lowest altitudes to which the QBO westerly or easterly phases descend, indicating that the QBO penetration into the lowermost stratosphere depends significantly on longitude. These characteristics are also confirmed by the composites based on \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e (Fig. S3).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe QBO amplitude deviations from the zonal mean, calculated using both \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ee and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ef), show pronounced positive anomalies over the western and central Pacific around 70 hPa. Additional large positive anomalies are also evident in the troposphere above 500 hPa, with a maximum near 150 hPa around 150\u0026deg;W. Two amplitude peaks at 70 hPa and 150 hPa appear as distinct maxima.\u003c/p\u003e\u003cp\u003eTo further investigate these peaks, frequency\u0026ndash;height cross-sections of the power spectra were examined (Fig. S4), based on \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e at 0\u0026deg;E and 150\u0026deg;W, and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e at 150\u0026deg;W. In the stratosphere, QBO components are well captured in \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, but spectral power also appears at longer periods (e.g., 42\u0026ndash;84 months), which are likely unrelated to the QBO. In the troposphere, these long-period components are weak at 0\u0026deg;E but more prominent at 150\u0026deg;W. Spectral power in the 18\u0026ndash;36 month range is also considerably stronger at 150\u0026deg;W than at 0\u0026deg;E below about 150 hPa, raising the question of whether this signal originates from the QBO or is associated with ENSO. In contrast to \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e effectively eliminates signals with periods longer than 42 months and shorter than 18 months, thereby isolating the typical QBO periodic components.\u003c/p\u003e\u003cp\u003eTo clarify the possible role of ENSO in producing the \u0026ldquo;QBO\u0026rdquo; amplitude peaks at 70 hPa and 150 hPa in the Pacific, correlations were calculated separately between the monthly NINO3 index defined by the Japan Meteorological Agency and both \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e. Figures\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;f are scatter plots of the NINO3 index versus \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e or \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e at 150\u0026deg;W at 70, 100 and 150 hPa. At 150 and 100 hPa, clear negative correlations of about \u0026minus;\u0026thinsp;0.8 are evident in \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e. Although the magnitudes are smaller, clear negative correlations are also found in \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e. At 100 hPa, the relationship between \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e and NINO3 is unclear when the index is within \u0026plusmn;\u0026thinsp;1. However, stronger ENSO events appear to contribute more clearly to the negative correlation. In contrast, the correlations at 70 hPa are \u0026minus;\u0026thinsp;0.1 for \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and close to zero for \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e, suggesting that the zonal asymmetry at this level is primarily attributable to the QBO itself rather than to ENSO variability.\u003c/p\u003e\u003cp\u003eFigures \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003eg and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e5\u003c/span\u003eh present the longitude\u0026ndash;height cross-section of the correlation between NINO3 index and both \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e. Statistically significant correlations exceeding 0.2 in absolute value are found throughout the troposphere, even for \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e, whereas correlations above 70 hPa are generally negligible. These results indicate that the zonal asymmetry of the QBO amplitude at 70 hPa is not influenced by ENSO.\u003c/p\u003e\u003cp\u003eThe basic computation of 70-hPa QBO amplitude was repeated using five additional reanalysis datasets: ERA5, ERA-Interim, MERRA, MERRA-2, and NCEP-CFSR in addition to JRA-55, for the period 1980\u0026ndash;2015 (with MERRA-2 data available only after 1980). Figure\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the same diagnostics as Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed. Note that the results using JRA-55 data during 1980\u0026ndash;2015 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec) are very similar to those for the entire 1958\u0026ndash;2015 period. All datasets exhibit quite similar patterns of zonal asymmetry at 70 hPa, although the MERRA-2 results notably have a somewhat weaker western Pacific maximum.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, the spatial distribution of 70-hPa QBO amplitude is examined using radiosonde observations from IGRA v2.2. Stations with complete data for more than 8 out of 24 QBO cycles during 1958\u0026ndash;2015 were selected, and QBO amplitudes were estimated using the DD method (see Methods in Supplementary Material for details). Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea shows the latitudinal distribution of individual climatological QBO amplitudes at 70 hPa, along with the JRA-55 zonal-mean climatology and the \u0026plusmn;\u0026thinsp;1σ and \u0026plusmn;\u0026thinsp;2σ envelopes, where σ represents the longitudinal standard deviation of climatological QBO amplitude at each latitude. Most stations fall within the \u0026plusmn;\u0026thinsp;2σ envelope of the JRA-55 zonal-mean climatology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb shows the radiosonde stations within \u0026plusmn;\u0026thinsp;10\u0026deg; latitude that met the minimum data coverage threshold. Stations with over 90% coverage are concentrated over Singapore and near 7\u0026ndash;9\u0026deg;N in the western\u0026ndash;central Pacific, while coverage is much sparser elsewhere (Kawatani and Hamilton, 2013). The QBO amplitude exhibits a strong meridional dependence, peaking at the equator with a typical half-width of about 12\u0026deg; latitude (Baldwin et al., 2001). For each station, an \u0026ldquo;adjusted\u0026rdquo; amplitude was also computed to represent to an equivalent equatorial value. This latitudinal correction was based on the zonal-mean meridional profile of the QBO amplitude derived from JRA-55 data, and was used to normalize amplitude estimates across stations at different latitudes. The resulting equator-adjusted amplitudes enable fairer comparisons of longitudinal structure.\u003c/p\u003e\u003cp\u003eAlthough a strict comparison of climatological amplitudes would require averaging over identical observation periods at all stations, as cycle-to-cycle variability and a long-term negative trend in QBO amplitude have been reported (Kawatani and Hamilton, 2013), averaging over at least 8 cycles provides a more consistent basis for comparison across stations. These stations shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb therefore offer a reasonable basis for examining the longitudinal structure.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec shows the longitudinal distribution of the adjusted climatological amplitudes, as well as those for individual QBO cycles, revealing substantial cycle-to-cycle variability at most stations. The sharp amplitude contrast (approximately 3 m s⁻\u0026sup1;) across a narrow longitudinal band between about 100\u0026deg;E and 120\u0026deg;E is primarily due to the particularly low value at KOTA BHARU (station 6) and high value at JUANDA (station 10). Nevertheless, longitudinal variations in 70-hPa QBO amplitude are also evident in the radiosonde data. The pattern of increasing amplitude from 100\u0026deg;E to central Pacific, followed by a decrease toward the eastern Pacific and a secondary increase near South America, closely resembles that seen in the reanalyses. An enhancement near 60\u0026ndash;80\u0026deg; E is less evident in the reanalyses, but the presence of three major longitudinal maxima is a consistent feature. In summary, despite the limited spatial coverage, the selected radiosonde observations provide support for the presence of longitudinal asymmetry in the 70-hPa QBO amplitude.\u003c/p\u003e"},{"header":"4. Summary and Discussion","content":"\u003cp\u003eThis study investigated the longitudinal variations of the QBO amplitude in the lower stratosphere, with a particular focus on the 70-hPa level, using JRA-55 and five additional reanalysis datasets, as well as radiosonde observations from the IGRA v2.2 archive. Two different methods were applied to extract the QBO signal: one following Dunkerton and Delisi (1985), and the other using Fast Fourier Transform (FFT) spectral decomposition. Both methods generally produced similar overall structures.\u003c/p\u003e\u003cp\u003eWhile several previous studies have demonstrated that the QBO exhibits pronounced zonal asymmetry in the middle stratosphere near 10 hPa (e.g., Hamilton et al., 2004; Kawatani et al., 2016; Sakazaki and Hamilton, 2022), the longitudinal structure of the QBO in the lower stratosphere, especially near 70 hPa, has not been well documented. The present results revealed that at 70 hPa, the QBO amplitude displays significant zonal asymmetry, characterized by a maximum over the western and central Pacific. This longitudinal pattern was consistently identified across all reanalysis products, despite differences in model formulation and data assimilation systems.\u003c/p\u003e\u003cp\u003eIn addition, radiosonde observations from IGRA were analyzed to provide an independent observational perspective. Stations with sufficient temporal coverage to estimate reliable QBO amplitude climatology were selected. After scaling the station amplitudes to the equator to account for the meridional structure of the QBO, the IGRA results showed a clear longitudinal variation that closely resembled the reanalysis-based pattern. In particular, the amplitude enhancement over the central Pacific and a secondary peak near South America were also evident in the radiosonde data. This agreement across reanalyses and in-situ observations increases confidence in the robustness of the longitudinal asymmetry in the 70-hPa QBO amplitude.\u003c/p\u003e\u003cp\u003eIn addition to the amplitude maximum at 70 hPa over the western and central Pacific, large signals extracted by the QBO filtering methods were also found near 150 hPa around 150\u0026deg;W. Correlation analyses between the NINO3 index and the QBO components of zonal wind (\u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eDD\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003eU\u003c/em\u003e\u003csub\u003e\u003cem\u003eFFT\u003c/em\u003e\u003c/sub\u003e) revealed strong link at this level, suggesting an influence of ENSO variability. In contrast, correlations at 70 hPa were negligible, indicating that the zonal asymmetry at this level is not much influenced by ENSO effects.\u003c/p\u003e\u003cp\u003eThe longitudinal asymmetry of QBO-related temperature anomalies near the cold-point tropopause and at 100 hPa, as reported by Tegtmeier et al. (2020), featured maxima over the Eastern Hemisphere. These anomalies likely reflect both QBO dynamical effects and convective influences. In contrast, the zonal wind amplitude analyzed in this study, primarily representing the dynamical QBO descent, shows a western and central Pacific maximum. This contrast highlights the need to analyze zonal wind fields alongside temperature data for a more complete understanding of the QBO\u0026rsquo;s longitudinal structure.\u003c/p\u003e\u003cp\u003eA possible explanation for the longitudinal pattern in QBO amplitude involves reduced background flow near 180\u0026deg;E associated with the Walker circulation, which may allow small-phase-speed gravity waves to propagate into the lower stratosphere more effectively (e.g., Kawatani et al., 2010). Further investigation using high-frequency data would help clarify this mechanism.\u003c/p\u003e\u003cp\u003eIn summary, the QBO amplitude at 70 hPa exhibits significant and robust zonal asymmetry. This asymmetry is apparent in both reanalysis and radiosonde datasets. This feature may have implications for stratosphere\u0026ndash;troposphere coupling and tropical teleconnections. Further research will be necessary to quantify its impact on convection, large-scale circulation, and teleconnections, and to examine the processes that maintain these longitudinal differences.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eSupplements\u003c/h2\u003e\u003cp\u003eThere are four supplementary figure (Figs. S1-S4), three supplementary tables (Tables S1-S3) and details of the methods.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eR.H. processed the IGRA radiosonde data, conducted the analyses, and prepared the figures and tables. Y.K. designed the study, performed additional analyses, and contributed to the interpretation of the results. R.H. and Y.K. wrote the manuscript together. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank Prof. K. Hamilton for valuable discussion and helpful comments on the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets analyzed in this study are publicly available. JRA-55 reanalysis data can be accessed at https://jra.kishou.go.jp/JRA-55/index_en.html. ERA5 reanalysis data are available from ECMWF via https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5. ERA-Interim reanalysis data are available via ECMWF at https://www.ecmwf.int/en/about/media-centre/news/2008/era-interim-products-available. MERRA reanalysis data can be accessed at https://gmao.gsfc.nasa.gov/gmao-products/merra/. MERRA-2 reanalysis data are available at https://gmao.gsfc.nasa.gov/gmao-products/merra-2/data-access_merra-2/. NCEP-CFSR reanalysis data are available from NCAR RDA at https://rda.ucar.edu/datasets/ds093.0/. IGRA radiosonde data are available from NOAA NCEI at https://www.ncei.noaa.gov/products/weather-balloon/integrated-global-radiosonde-archive.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnstey, J. A., S. M. Osprey, J. Alexander, M. P. Baldwin, N. Butchart, L. Gray, Y. Kawatani, P. A. Newman, and J. H. Richter, 2022: The quasi-biennial oscillation: Impacts, processes, and projections. Nat. Rev. Earth Environ., https://doi.org/10.1038/s43017-022-00323-7.\u003c/li\u003e\n \u003cli\u003eBaldwin, M. P., L. J. Gray, T. J. Dunkerton, K. Hamilton, P. H. Haynes, W. J. Randel, J. R. Holton, M. J. Alexander, I. Hirota, T. Horinouchi, D. B. A. Jones, J. S. Kinnersley, C. Marquardt, K. Sato, and M. Takahashi, 2001: The quasi-biennial oscillation. Rev. Geophys., 39, 179\u0026ndash;229, doi:10.1029/1999RG000073.\u003c/li\u003e\n \u003cli\u003eDee, D. P., and coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553\u0026ndash;597.\u003c/li\u003e\n \u003cli\u003eDunkerton, T. J., and D. P. Delisi, 1985: Climatology of the equatorial lower stratosphere. J. Atmos. Sci., 42, 376\u0026ndash;396.\u003c/li\u003e\n \u003cli\u003eDurre, I., X. Yin, R. S. Vose, S. Applequist, J. Arnfield, B. Korzeniewski, and B. Hundermark, 2016: Integrated Global Radiosonde Archive (IGRA), Version 2. NOAA National Centers for Environmental Information, doi:10.7289/V5X63K0Q.\u003c/li\u003e\n \u003cli\u003eGelaro, R., and coauthors, 2017: The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). J. Climate, 30, 5419\u0026ndash;5454, doi:10.1175/JCLI-D-16-0758.1.\u003c/li\u003e\n \u003cli\u003eHamilton, K., A. Hertzog, F. Vial, and G. Stenchikov, 2004: Longitudinal variation of the stratospheric quasi-biennial oscillation. J. Atmos. Sci., 61, 383\u0026ndash;402.\u003c/li\u003e\n \u003cli\u003eHersbach, H., and coauthors, 2020: The ERA5 global reanalysis. Q. J. R. Meteorol. Soc., 146, 1999\u0026ndash;2049, https://doi.org/10.1002/qj.3803.\u003c/li\u003e\n \u003cli\u003eHo, C., H. Kim, J. Jeong, and S. Son, 2009: Influence of stratospheric quasi-biennial oscillation on tropical cyclone tracks in the western North Pacific. Geophys. Res. Lett., 36, L06702, https://doi.org/10.1029/2009GL037163.\u003c/li\u003e\n \u003cli\u003eKawatani, Y., and K. Hamilton, 2013: Weakened stratospheric quasibiennial oscillation driven by increased tropical mean upwelling. Nature, 497, 478\u0026ndash;481, doi:10.1038/nature12140.\u003c/li\u003e\n \u003cli\u003eKawatani, Y., K. Hamilton, K. Miyazaki, M. Fujiwara, and J. A. Anstey, 2016: Representation of the tropical stratospheric zonal wind in global atmospheric reanalyses. Atmos. Chem. Phys., 16, 6681\u0026ndash;6699.\u003c/li\u003e\n \u003cli\u003eKobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 reanalysis: General specifications and basic characteristics. J. Meteor. Soc. Japan, 93, 5\u0026ndash;48, https://doi.org/10.2151/jmsj.2015-001.\u003c/li\u003e\n \u003cli\u003eNaujokat, B., 1986: An update of the observed quasi-biennial oscillation of the stratospheric winds over the tropics. J. Atmos. Sci., 43, 1873-1877.\u003c/li\u003e\n \u003cli\u003eNewman, P. A., L. Coy, S. Pawson, and L. R. Lait, 2016: The anomalous change in the QBO in 2015\u0026ndash;2016. Geophys. Res. Lett., 43, 8791\u0026ndash;8797.\u003c/li\u003e\n \u003cli\u003eOsprey, S., N. Butchart, J. Knight, A. Scaife, K. Hamilton, J. Anstey, V. Schenzinger, and C. Zhang, 2016: An unexpected disruption of the atmospheric quasi-biennial oscillation. Science, 353, 1424\u0026ndash;1427.\u003c/li\u003e\n \u003cli\u003eRienecker, M. 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Luo, 2023: A revisit and comparison of the quasi-biennial oscillation disruption events in 2015/16 and 2019/20. Atmos. Res., 294, 106970, doi:10.1016/j.atmosres.2023.106970.\u003c/li\u003e\n \u003cli\u003eYoo, C., and S.-W. Son, 2016: Modulation of the boreal wintertime Madden\u0026ndash;Julian oscillation by the stratospheric quasi-biennial oscillation. Geophys. Res. Lett., 43, 1392\u0026ndash;1398, doi:10.1002/2016GL067762.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"sola","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [SOLA](https://link.springer.com/journal/44393)","snPcode":"44393","submissionUrl":"https://submission.springernature.com/new-submission/44393/3?_gl=1*3gm7xl*_gcl_au*MTQxNDAxMjI3My4xNzYwNjE1NTM2*_ga*MjAwMTA4NDA0NS4xNzE2OTAwNjg2*_ga_B3E4QL2TPR*czE3NjA2MTU1MzckbzkyJGcwJHQxNzYwNjE1NTM3JGo2MCRsMCRoMTYwMDQ5Nzk0NA..","title":"SOLA","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7877638/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7877638/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eZonal asymmetry in the amplitude of the Quasi-Biennial Oscillation (QBO) is investigated using JRA-55 reanalysis data and in-situ IGRA radiosonde observations. In addition to the previously reported asymmetry near 10 hPa, a significant longitudinal variation with the largest amplitudes over the western and central Pacific is found around 70 hPa. Unlike the dominant wavenumber-1 pattern near 10 hPa, the structure at 70 hPa exhibits more localized variations. Longitudinal differences in the QBO zonal wind at 70 hPa reach up to 30%, indicating that the descent of the QBO into the lower stratosphere and upper troposphere exhibits pronounced longitudinal variation. The asymmetry results were confirmed in five additional reanalysis datasets. Similar zonal variations of QBO amplitudes are also identified in radiosonde observations. These findings suggest that such significant longitudinal variations in QBO amplitude could play a role in interactions with the tropical tropopause and associated teleconnections.\u003c/p\u003e","manuscriptTitle":"Zonal asymmetry of the Quasi Biennial Oscillation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 16:51:42","doi":"10.21203/rs.3.rs-7877638/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-09T05:32:26+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-09T05:20:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"37756286949898167959209319277669246426","date":"2025-11-09T05:16:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-20T07:12:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"90995149093670948682772638646439508004","date":"2025-10-15T04:32:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-03T10:34:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-30T10:38:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-30T10:36:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"SOLA","date":"2025-09-29T07:43:49+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"sola","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [SOLA](https://link.springer.com/journal/44393)","snPcode":"44393","submissionUrl":"https://submission.springernature.com/new-submission/44393/3?_gl=1*3gm7xl*_gcl_au*MTQxNDAxMjI3My4xNzYwNjE1NTM2*_ga*MjAwMTA4NDA0NS4xNzE2OTAwNjg2*_ga_B3E4QL2TPR*czE3NjA2MTU1MzckbzkyJGcwJHQxNzYwNjE1NTM3JGo2MCRsMCRoMTYwMDQ5Nzk0NA..","title":"SOLA","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e1a7f934-ebd4-41b1-be8a-749110d47eea","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-31T01:53:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 16:51:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7877638","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7877638","identity":"rs-7877638","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}