Significant Winter Atlantic Niño effect on ENSO and its future projection

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However, the role of Winter Atlantic Niño in trans-basin interactions remains underexplored compared to its summer counterpart. Through analysis of observational reanalysis data since the mid-twentieth century, here we found that Winter Atlantic Niño significantly influences the development of El Niño–Southern Oscillation (ENSO), surpassing the impact of Summer Atlantic Niño with a longer lead time. This effect was reasonably captured in the historical simulation from the CMIP6, from a multi-model ensemble perspective. Further analysis with the global warming scenario projects that the influence of Winter Atlantic Niño on ENSO will persist into the future, contrasting with a reduced impact of Summer Atlantic Niño. Therefore, these findings underscore the importance of further investigating Winter Atlantic Niño for a comprehensive understanding of trans-basin interactions and their future change. Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Climate change Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Manifested by various air–sea coupled processes 1–7 , the Atlantic Niño is widely recognized as a leading climatic variability mode in the tropical Atlantic Ocean on interannual time scales 8–12 . It exhibits a strongest variability in boreal summer season (for convenience, seasons in this study follow those of the northern hemisphere) when climatological mean thermocline depth along the equatorial Atlantic becomes shallower, accompanied by intensified mean easterly winds. In its positive phase, sea surface temperature (SST) anomaly (SSTA) along the equatorial Atlantic becomes warm with westerly wind anomalies, and vice versa in its negative phase 3,10,12–16 . Atlantic Niño events also involve precipitation change around the Gulf of Guinea, western coast of equatorial Africa 17–23 , Northeast Brazil 24 , and development of Hurricane 25 . On seasonal timescales, it leads to displacement of the Intertropical convergence zone (ITCZ) from South America to Atlantic Ocean 13,26 . This shift in the ITCZ induces modifications in deep convection, giving rise to stationary Rossby waves that propagate into the mid-latitudes. Consequently, Atlantic Niño events influence the North Atlantic Oscillation 27 and rainfall over the Mediterranean Sea and Europe 11,28 . Simultaneously, by perturbing the Walker circulation 29 , the Atlantic Niño affects and/or is affected by other major climatic phenomena such as El Niño–Southern oscillation (ENSO) 30–39 and Indian ocean dipole (IOD) 40 , which are regarded as crucial drivers of climate variability in the Pacific and Indian Oceans, respectively. Given its substantial climatic and socioeconomic impacts 22,25,41 , Atlantic Niño has been extensively of interest. While a typical Atlantic Niño is commonly characterized as an SSTA loading center in the eastern equatorial Atlantic during summer, recent studies have revealed a variety of spatiotemporal evolution patterns for this phenomenon 42,43 . For example, unlike the consistent pattern previously assumed, the center of Atlantic Niño events is occasionally found in the central Atlantic region 43 . Furthermore, the onset and dissipation timings of these events display considerable diversity 42 . Particularly, Atlantic Niño events are frequently observed during winter 44,45 , coinciding with the secondly shallow thermocline with easterly wind in the season 46 . To differentiate this winter occurrence from the more commonly studied the Summer Atlantic Niño (a.k.a., Atlantic Niño I 46 ), in this study it is termed as the Winter Atlantic Niño (a.k.a., Atlantic Niño II 46 ). Previous studies have examined the lead-lagged relationship between Summer/Winter Atlantic Niño and following ENSO with a half- 30–39 and one-year lag 44,45 , respectively. For Summer Atlantic Niño, its effect on ENSO was found to be weak during the mid-twentieth century, but strengthened from the late twentieth to the early twenty-first century 30,44,45 . We found that their lagged correlation coefficient is -0.23 since mid-twentieth century (1950–2021), not significant at a 95% confidence level. Note that the change in their correlation coefficient is negligible (-0.22), when previous ENSO signals are excluded from the Summer Atlantic Niño index (Supplementary Table 1). In contrast to the Summer Atlantic Niño, the Winter Atlantic Niño effect on ENSO was strong during mid-twentieth century, which became weaker during the late twentieth to early twenty-first century 44,47 . During 1950–2021, their lagged correlation coefficient becomes − 0.41, significant at the 99% confidence level (when simultaneous ENSO signals are removed, it is -0.40). This result contrasts sharply with the Summer Atlantic Niño–ENSO relationship, which was found to be statistically insignificant over the same period. Notably, over the same period, the ENSO connection is most prominent with the Winter Atlantic Niño, rather than other primary climate variability mode such as Northern Tropical Atlantic (-0.32) 48–51 and Atlantic Warm Pool 52,53 (a.k.a., Western Hemisphere Warm Pool 54,55 , -0.22) (Supplementary Table 1). This implies that the most important climate variability in the Atlantic preceding ENSO events could be the Winter Atlantic Niño. This simple correlation analysis implies the significant role of the Winter Atlantic Niño in inter-basin interactions. Nevertheless, unfortunately the Winter Atlantic Niño effect on ENSO has not been well investigated so far. Similarly, there has been a lack of evaluation regarding the capability of current climate models to simulate the relationship between Winter Atlantic Niño and ENSO. In this view, in this study we examine the observed Winter Atlantic Niño effect on ENSO by analyzing reanalysis dataset over 1950–2021. Also, we aim to assess the fidelity of climate models participating in CMIP6 in capturing the lagged relationship between Winter Atlantic Niño and subsequent ENSO events. Based on the assessment, we will also address its future projection. Through our analysis, we propose that the significant Winter Atlantic Niño effect on ENSO will persist under global warming, in contrast to the weakened Summer Atlantic Niño–ENSO connection. Result Observed relationship between Atlantic Niño and ENSO (1950–2021) We first obtained the monthly indices for Atlantic Niño and ENSO by averaging SSTA in the eastern equatorial Atlantic (0–20°W and 5°S–5°N) and Niño3.4 regions (120–170°W, 5°S–5°N), respectively. To examine their relationship, we conducted a lead-lagged correlation analysis spanning from 1950 to 2021 (864-month). In Fig. 1 a, the negative (positive) on x-axis indicates that the ENSO (Atlantic Niño) leads the Atlantic Niño (ENSO) on monthly time scales, while the y-axis represents calendar months of the leading index. For example, the left bottom area in Fig. 1 a describes that the ENSO in October to December positively precedes the Atlantic Niño with a lag of 2–4 months. This result shows that ENSO influence appears relatively modest, consistent with findings from prior studies. When it comes to the Atlantic Niño-leading-ENSO signals, the upper red box in Fig. 1 a indicates that the Atlantic Niño in June to August negatively leads ENSO, roughly with a lag of 9month, of which signals are modest. Meanwhile, the Atlantic Niño from December to February significantly leads ENSO with a lag ranging from 5 to 15 months (bottom red box in Fig. 1 a). These results indicate that ENSO has a close lead-lagged relationship with Winter Atlantic Niño, rather than Summer Atlantic Niño. Building upon the aforementioned results, we investigated the spatiotemporal variability of Atlantic Niño during both summer and winter, along with their subsequent effects on ENSO in the following winter. To comprehend the variability of Summer Atlantic Niño, we applied the Rotating-Empirical Orthogonal Function (R-EOF) analysis to the SSTA in the tropical Atlantic in Summer 56 (Methods). As depicted in Fig. 1 b, the 1st R-EOF mode reveals a distinct spatial pattern characterized by strong SSTA amplitude extending from the eastern equatorial Atlantic Ocean to subtropical western coast of South Africa. This spatial pattern resembles that of the Summer Atlantic Niño, explaining 35.5% of the total variability. Its principal component (PC) time series are illustrated in Fig. 1 b with the Summer Atlantic Niño index which is obtained by averaging the monthly Atlantic Niño index over JJA season. Herein, their correlation coefficient is higher than 0.9, indicating a physical mode 57 . Figure 1 c illustrates anomalies of SST, low-level wind (850hPa), and precipitation regressed onto the Summer Atlantic Niño index. In JJA[ 1 ], SSTA warming is observed along the equatorial Atlantic with westerly wind and enhanced precipitation, consistent with the features of Summer Atlantic Niño (Fig. 1 b). Owing to the annual solar movement, precipitation over the northern hemisphere tends to be greater than that over the southern hemisphere. The modulation of the Walker circulation by Summer Atlantic Niño results in decreased precipitation over the equatorial Pacific Ocean, leading to low-level easterly winds and subsequent SSTA cooling in that region. Due to the Bjerknes feedback, the low-level easterly wind is still observed with SST cooling in the equatorial eastern Pacific in D[ 1 ]JF[ 1 ], despite the weak signals. Subsequently, we applied the R-EOF method to the SSTA in the tropical Atlantic, this time focusing on the winter. As illustrated in Fig. 1 d, the 1st R-EOF mode accounts for 23.3% of the total variability and exhibits a spatial pattern akin to that of the Summer Atlantic Niño, implying a similar underlying development mechanism. Its PC time series has a high correlation coefficient (> 0.9) with the winter Atlantic Niño index which is obtained by averaging the monthly Atlantic Niño index over DJF season (Fig. 1 d). This robust correlation underscores the physical relevance of the identified R-EOF mode in characterizing the winter Atlantic Niño phenomenon. To investigate how the Winter Atlantic Niño affect the development of ENSO, anomalies of SST, low-level wind (850hPa), and precipitation regressed onto the Winter Atlantic Niño index. In D[0]JF[ 1 ] (Fig. 1 e), SSTA warming is observed along the equatorial Atlantic with westerly wind anomalies and enhanced precipitation (Please refer to Supplementary Fig. 1 for other seasons). Concurrently, low-level cyclonic circulation is located near the Eastern Brazil (40°W) in both hemispheres, indicative of a Gill-type response to the equatorial atmospheric latent heat forcing. Since solar radiation faces southern hemisphere in this season, precipitation along the equatorial Atlantic is greater in southern hemisphere than in the northern hemisphere. Over the Pacific, northeasterly wind anomalies are observed over the off-equatorial North Pacific. In boreal spring, these easterly wind anomalies propagate toward the equator (Supplementary Fig. 1), facilitating the trade wind discharge 45,58,59 . With the Bjerkness feedback due to the easterly wind along the equator in following seasons, La Nina develops in the following winter (D[ 1 ]JF[ 2 ]). It is worth noting that these results are consistent when other reanalysis datasets are utilized (Supplementary Fig. 2). Winter Atlantic Niño effect on ENSO in the historical and global warming scenario Observational analysis during the last seven decades indicates that the Winter Atlantic Niño exerts a more significant effect on ENSO, compared to the Summer Atlantic Niño. Based on the results, we sought to investigate how current climate models participating the CMIP6 simulate the Winter Atlantic Niño effect on ENSO in the Historical and SSP585 simulations. To do this, we followed the same process as the observational analysis to obtain the Winer Atlantic Niño and ENSO. Then, the one-year lagged relationship between the winter Atlantic Niño (D[0]JF[ 1 ]) and ENSO (D[ 1 ]JF[ 2 ]) indices were examined. Figure 2 a indicates the lagged correlation coefficients between them in each climate model as well as their MME. For Historical simulations, the correlation coefficients across climate models exhibit a wide range, varying from − 0.47 for TaiESM1 to 0.17 for MPI-ESM1-2-LR, wherein approximately 4/5 of climate models (28 out of 35) show a negative correlation. As a result, the MME is -0.12, indicating a seemingly small, but statistically significant at the 99% confidence level based on the Student’s t-test with a large degree of freedom. For the SSP585 simulation, correlation coefficient tends to decrease in the majority of climate models, meaning enhanced negative relationship between them under global warming. Accordingly, compared to the Historical simulations, their MME is slightly enhanced, -0.14 (99% confidence level). In addition to the correlation coefficient, regression coefficients in each climate model were also analyzed to infer the actual change in ENSO amplitude regarding the Winter Atlantic Niño change. To this end, the original ENSO index (not normalized) is regressed onto the normalized winter Atlantic Niño index (y-axis in Fig. 2 b). In the figure, the x-axis represents the correlation coefficients in Fig. 2 a, wherein the R-Square is greater than 0.9, indicating a pronounced linear relationship between the correlation and regression coefficients. The slope is 3.952, implying that a correlation coefficient of 0.1 in a particular climate model corresponds to a change in the ENSO index of approximately − 0.4°C per standard deviation of Atlantic Niño. Similarly, the slope is about 4.55 for SSP585 simulations (Fig. 2 b), which is stronger than that of Historical simulation. Above results suggest that climate models in CMIP6 reasonably simulate the Winter Atlantic Niño-leading-ENSO relationship from an MME perspective. In order to examine the processes of how the Winter Atlantic Niño affects the following ENSO events, SST, low-level wind, and precipitation anomalies are regressed onto the Winter Atlantic-Niño in each climate model, and the results are averaged. Figure 3 a illustrate simultaneous oceanic and atmospheric states when Winter Atlantic Niño events occur in the Historical simulation. Over the Atlantic Ocean, SSTA warming occurs with westerly wind and enhanced precipitation, with stronger signals in the southern hemisphere than the northern hemisphere, mirroring observation. However, the overall structure of winter Atlantic Niño is meridionally and zonally wider than observed. Consequently, a pronounced modification of the Walker circulation is evident, characterized by pronounced low-level winds over the Pacific Ocean. From MAM to DJF, the signals of Atlantic Ocean gradually decay, while those over the Pacific Ocean grow due to Bjerknes feedback. In the case of the SSP585 simulations, the overall evolutionary processes associated with Winter Atlantic Niño are consistent to those in Historical simulations (Fig. 3 d-e). In short, these findings suggest that the Winter Atlantic Niño effect on ENSO is expected to persist during the global warming period. Atmospheric response to the Winter Atlantic Niño in the historical and SSP585 run Previous studies have suggested that the Summer Atlantic Niño effect on ENSO is likely to be weakened under greenhouse warming 60 . This weakening has been attributed to the reduced precipitation response over the equatorial Atlantic Ocean and the decreased variability of Summer Atlantic Niño 61 . In contrast, we showed that the Winter Atlantic Niño effect on ENSO is stronger than the Summer Atlantic Niño effect, and is expected to be maintained under global warming. In light of these results, we aimed to examine changes in the variability of the Winter Atlantic Niño under global warming. Additionally, to infer the change in the precipitation response, climatological mean state of precipitation over the equatorial Atlantic in winter and its variability were examined. The left box-whisker plot in Fig. 4 a illustrates the distribution of the standard deviation of Winter Atlantic Niño in climate models from historical and SSP585 simulations. It is evident that the distribution in climate models from SSP585 simulation is wider than that of historical simulation. However, the MMEs of historical and SSP585 simulations are 0.40 and 0.39, indicating insignificant difference between them. This suggests that the variability of the Winter Atlantic Niño is expected to be maintained under global warming. Meanwhile, the right box-whisker plot indicates the local precipitation response to the Winter Atlantic Niño. Similar to the standard deviation of winter Atlantic Niño, the distribution of precipitation response spreads widely in SSP585 simulation, compared to the historical simulation. However, their MME is close to each other (both 0.32). This suggests that the local precipitation response to the Winter Atlantic Niño under global warming is not significantly altered. Considering the sustained atmospheric response over the equatorial Atlantic, we examined the changes in the climatological winter precipitation mean state under global warming. Figure 4 b shows the difference in winter precipitation between SSP585 and Historical simulations, revealing intensified precipitation over the equatorial Atlantic and the South Atlantic convergence zone (SACZ) in the SSP585 simulation. This intensified mean precipitation accompanies an amplification in the standard deviation of precipitation over the equatorial Atlantic and SACZ area. Aligned with the enhanced mean precipitation and its variability, the precipitation response to the SST over the region is expected to be maintained, under enhanced atmospheric stability 62 . In summary, the sustained variability of the Winter Atlantic Niño and the precipitation response under global warming collectively contribute to the persistence of the Winter Atlantic Niño effect on ENSO. Conclusion and discussion The Atlantic Niño, recognized as the predominant climate variability mode in the equatorial Atlantic, has been gotten a considerable attention due to its profound impact on the climate of both nearby and distant regions. Previous research has primarily focused on the summer season when the its variability peaks. However, it is worth noting that significant variability also occurs during the winter season. Despite this, research on the Winter Atlantic Niño remains relatively scarce. In this context, we conducted an investigation into the influence of the Winter Atlantic Niño on subsequent ENSO events with a one-year lag over the past 70 years. By examining this relationship, we aimed to shed light on the importance of the Winter Atlantic Niño in shaping interannual climate variability and its implications for broader climate dynamics. We have demonstrated that the Winter Atlantic Niño exerts a significant and greater effect on ENSO compared to the Summer Atlantic Niño. Furthermore, our analysis of historical and SSP585 simulations from CMIP6 indicates that climate models are capable of reasonably simulating the relationship between the Winter Atlantic Niño and subsequent ENSO events from a multi-model ensemble (MME) perspective. Given that the variability of the Winter Atlantic Niño and the precipitation response to it are projected to be maintained under global warming, we anticipate that its influence on ENSO will persist in the future. This contrasts with the weakening influence of the Summer Atlantic Niño on ENSO under global warming. Therefore, we believe that further investigation of the Winter Atlantic Niño will lead to a better understanding of trans-basin interactions and their implications for future climate dynamics. As discussed above, the MME of climate models appears to reasonably capture the effect of winter Atlantic Niño on ENSO. However, it is important to acknowledge that most climate models struggle to accurately reproduce its spatiotemporal characteristics. For example, observations reveal that the Atlantic Niño exhibits peak phases during both summer and winter, yet climate models rarely simulate these double peaks, thus failing to replicate such phenomena from an MME perspective (see Supplementary Fig. 3). Furthermore, whereas the spatial variability of the winter Atlantic Niño in observation is well explained by Rotating EOF, climate models often fall short in adequately simulating this variability (see Supplementary Fig. 4). These discrepancies may be attributed, in part, to that observed re-intensification of the climatological low-level easterly wind and deepening of the thermocline along the equatorial Atlantic in boreal winter is not well reproduced in climate models. These issues regarding the simulation of the observed climatological mean state in climate models are well-documented in previous research 9,63 . Therefore, conducting an in-depth investigation to improve the climatological mean state in climate models would be beneficial for better simulating the Atlantic Niño and its influence. Methods Reanalysis Dataset. We utilized the ECMWF Reanalysis v5 (ERA5 64 ), combining vast amounts of historical observations into global estimates using advanced modelling and data assimilation systems. Additionally, to confirm the robustness of the results, we also use the monthly dataset of the U.S. NOAA National Center for Environmental Prediction Reanalysis 1 (NCEP-R1 65 ), which is an assimilated dataset that uses a state-of-the-art analysis and forecast system. For SSTs, NOAA’s Extended Reconstruction Sea Surface Temperature version 5 (ERSSTv5 66 ) are selected, which is a global monthly SST dataset derived from NOAA’s International Comprehensive Ocean-Atmosphere Dataset (ICOADS). The analysis period in this study was from 1950 to 2021. CMIP6 Dataset. For Historical simulations, we can reserve 35 climate models which have SST, SLP, wind, and precipitation. For SSP585 simulations, we can obtain 27 climate models which have SST, SLP, wind, precipitation. Table 1 List of climate models from CMIP6 utilized in the present study. Each climate model reserve SST, precipitation, low-level wind (U, V) dataset. Historical SSP585 ACCESS-CM2 ACCESS-CM2 ACCESS-ESM1-5 ACCESS-ESM1-5 AWI-CM-1-1-MR AWI-CM-1-1-MR BCC-CSM2-MR BCC-CSM2-MR BCC-ESM1 CanESM5 CAMS-CSM1-0 CESM2 CanESM5 CESM2-WACCM CESM2 CMCC-CM2-SR5 CESM2-FV2 E3SM-1-1 CESM2-WACCM EC-Earth3 CESM2-WACCM-FV2 EC-Earth3-Veg CMCC-CM2-SR5 EC-Earth3-Veg-LR E3SM-1-0 FGOALS-f3-L E3SM-1-1 FGOALS-g3 E3SM-1-1-ECA FIO-ESM-2-0 EC-Earth3-Veg GFDL-ESM4 EC-Earth3-Veg-LR INM-CM4-8 FGOALS-f3-L INM-CM5-0 FGOALS-g3 IPSL-CM6A-LR FIO-ESM-2-0 KACE-1-0-G GFDL-ESM4 MIROC6 INM-CM4-8 MPI-ESM1-2-HR INM-CM5-0 MPI-ESM1-2-LR IPSL-CM6A-LR MRI-ESM2-0 MCM-UA-1-0 NESM3 MIROC6 NorESM2-LM MPI-ESM1-2-HR NorESM2-MM MPI-ESM1-2-LR MRI-ESM2-0 NESM3 NorCPM1 NorESM2-LM NorESM2-MM SAM0-UNICON TaiESM1 Declarations Competing interests. The authors declare no competing interests. Author Contributions J.-H. Park and J.-S. Kug conceived the idea and shared it with other coauthors. J.-H. Park conducted the analyses and prepared the figures with H.-J. Park. All coauthors thoroughly revised the manuscript which was first written by J.-H. Park. All coauthors participated in the discussion on the results. Acknowledgments. J.-H. Park was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2023R1A2C1004083 and RS-2023-00219830). Data availability. All of observational and CMIP6 data 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 . ERSSTv5: https://www.esrl.noaa.gov/psd/data/data.php . CMIP6: https://aims2.llnl.gov/search/cmip6/ . Code availability. Codes used in the manuscript are available upon reasonable requests from J.-H. Park ( [email protected] ). References Zebiak, S. E. 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Distinct decadal modulation of Atlantic-Niño in fl uence on ENSO. doi: 10.1038/s41612-023-00429-9 Okumura, Y. & Xie, S.-P. Some Overlooked Features of Tropical Atlantic Climate Leading to a New Niño-Like Phenomenon. J. Clim. 19, 5859–5874 (2006). Park, J.-H. et al. Distinct decadal modulation of Atlantic-Niño influence on ENSO. npj Clim. Atmos. Sci. 6, 105 (2023). Ham, Y. G., Kug, J. S., Park, J. Y. & Jin, F. F. Sea surface temperature in the north tropical Atlantic as a trigger for El Niño/Southern Oscillation events. Nat. Geosci. 6, 112–116 (2013). Wang, L., Yu, J. Y. & Paek, H. Enhanced biennial variability in the Pacific due to Atlantic capacitor effect. Nat. Commun. 8, (2017). Park, J. H. et al. Two regimes of inter-basin interactions between the Atlantic and Pacific Oceans on interannual timescales. npj Clim. Atmos. Sci. 6, 1–8 (2023). Park, J.-H. et al. Role of the Climatological North Pacific High in the North Tropical Atlantic–ENSO Connection. J. Clim. 35, 3215–3226 (2022). Park, J. H., Kug, J. S., Li, T. & Behera, S. K. Predicting El Niño Beyond 1-year Lead: Effect of the Western Hemisphere Warm Pool. Sci. Rep. 8, 1–8 (2018). Park, J. H., Kug, J. S., An, S. Il & Li, T. Role of the western hemisphere warm pool in climate variability over the western North Pacific. Clim. Dyn. 53, 2743–2755 (2019). Wang, C., Enfield, D. B., Lee, S. K. & Landsea, C. W. Influences of the Atlantic warm pool on western hemisphere summer rainfall and Atlantic hurricanes. J. Clim. 19, 3011–3028 (2006). Enfield, D. B., Lee, S. K. & Wang, C. How are large western hemisphere warm pools formed? Prog. Oceanogr. 70, 346–365 (2006). Richman, M. B. Rotation of principal components. J. Climatol. 6, 293–335 (1986). Lian, T. & Chen, D. An evaluation of rotated eof analysis and its application to tropical pacific sst variability. J. Clim. 25, 5361–5373 (2012). Anderson, B. T. On the joint role of subtropical atmospheric variability and equatorial subsurface heat content anomalies in initiating the onset of ENSO events. J. Clim. 20, 1593–1599 (2007). Di Lorenzo, E. et al. ENSO and meridional modes: A null hypothesis for Pacific climate variability. Geophys. Res. Lett. 42, 9440–9448 (2015). Jia, F. et al. Weakening Atlantic Niño–Pacific connection under greenhouse warming. Sci. Adv. 5, eaax4111 (2019). Crespo, L. R. et al. Weakening of the Atlantic Niño variability under global warming. Nat. Clim. Chang. 12, 822–827 (2022). Chou, C., Wu, T.-C. & Tan, P.-H. Changes in gross moist stability in the tropics under global warming. Clim. Dyn. 41, 2481–2496 (2013). Wang, C., Zhang, L., Lee, S. K., Wu, L. & Mechoso, C. R. A global perspective on CMIP5 climate model biases. Nat. Clim. Chang. 4, 201–205 (2014). Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020). Kistler, R. et al. The NCEP-NCAR 50-year reanalysis: Monthly means CD-ROM and documentation. Bull. Am. Meteorol. Soc. 82, 247–267 (2001). Huang, B. et al. Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5): Upgrades, Validations, and Intercomparisons. J. Clim. 30, 8179–8205 (2017). Kaiser, H. F. The varimax criterion for analytic rotation in factor analysis. Psychometrika 23, 187–200 (1958). Hannachi, A. Tropospheric planetary wave dynamics and mixture modeling: Two preferred regimes and a regime shift. J. Atmos. Sci. 64, 3521–3541 (2007). Additional Declarations There is NO Competing Interest. Supplementary Files Supplementary20240605.docx Supplementary Info. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4531524","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":315301708,"identity":"bb8aa012-c55d-4a60-859e-116d2a5333c4","order_by":0,"name":"Jae-Heung Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYBACxhkMDAcSDCR4+EG8hALitDAeeFBhISfZANJiQIw1EgzMBx+cqTA2OADiEaOFeXbvgQOJbRKJm8+vTvzwwIBBnl/sAAGHzTmXANay7cbbzRJAhxnOnJ1AQMuMHAOolrMbQFoSDG4Tq2XzjLObfxCvJeGMhLEBf+82Ym3JSziQUCEhJ3GDd5sFMIII+8VwRu7hjz8M6nj4+89uvvmjwkaeX5qQlgYeKEsCrFICv3IQkGeAaeE/QFj1KBgFo2AUjEwAAPU4S/e+JkBQAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8556-2314","institution":"Seoul National University","correspondingAuthor":true,"prefix":"","firstName":"Jae-Heung","middleName":"","lastName":"Park","suffix":""},{"id":315301709,"identity":"6a9bb193-25fa-4b31-837c-fe92a1297fab","order_by":1,"name":"Young-Min Yang","email":"","orcid":"","institution":"Nanjing University of information science and technology","correspondingAuthor":false,"prefix":"","firstName":"Young-Min","middleName":"","lastName":"Yang","suffix":""},{"id":315301710,"identity":"9883f715-d627-481e-a9d9-c54031ab56bb","order_by":2,"name":"Yoo-Geun Ham","email":"","orcid":"https://orcid.org/0000-0002-0236-6968","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Yoo-Geun","middleName":"","lastName":"Ham","suffix":""},{"id":315301711,"identity":"a31c6610-c4ed-4761-aeb7-7e3e03049405","order_by":3,"name":"Hyun-Su Jo","email":"","orcid":"https://orcid.org/0000-0003-3696-068X","institution":"Chonnam National University","correspondingAuthor":false,"prefix":"","firstName":"Hyun-Su","middleName":"","lastName":"Jo","suffix":""},{"id":315301712,"identity":"a6099405-be29-46fe-a878-35406e7be810","order_by":4,"name":"Hyo-Jin Park","email":"","orcid":"","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Hyo-Jin","middleName":"","lastName":"Park","suffix":""},{"id":315301713,"identity":"15895b08-6cbe-480c-a6eb-ec9d786475de","order_by":5,"name":"So-Eun Park","email":"","orcid":"","institution":"Yonse University","correspondingAuthor":false,"prefix":"","firstName":"So-Eun","middleName":"","lastName":"Park","suffix":""},{"id":315301714,"identity":"de4c99f5-10fa-4fa5-ad12-14d8cc3e75a9","order_by":6,"name":"Chao Liu","email":"","orcid":"https://orcid.org/0000-0002-3509-5690","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Liu","suffix":""},{"id":315301715,"identity":"66c5dcec-2a92-4e08-a963-67217d0dc835","order_by":7,"name":"Gagan Mandal","email":"","orcid":"https://orcid.org/0000-0002-9586-0665","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Gagan","middleName":"","lastName":"Mandal","suffix":""},{"id":315301716,"identity":"d3ac5783-36ed-4de9-b00a-17d2b404f754","order_by":8,"name":"Soon-Il An","email":"","orcid":"https://orcid.org/0000-0002-0003-429X","institution":"Yonsei University","correspondingAuthor":false,"prefix":"","firstName":"Soon-Il","middleName":"","lastName":"An","suffix":""},{"id":315301717,"identity":"b1b5ed5b-c6c2-48ce-a069-b46d4eaf6760","order_by":9,"name":"Jong-Seong Kug","email":"","orcid":"https://orcid.org/0000-0003-2251-2579","institution":"Seoul National University","correspondingAuthor":false,"prefix":"","firstName":"Jong-Seong","middleName":"","lastName":"Kug","suffix":""}],"badges":[],"createdAt":"2024-06-05 05:25:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4531524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4531524/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41612-024-00790-3","type":"published","date":"2024-10-07T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60608690,"identity":"a49b4e9f-95c6-4e3a-a840-ade90d267a45","added_by":"auto","created_at":"2024-07-18 17:52:08","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":256087,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAtlantic Niño and its relationship with ENSO. \u003c/strong\u003e(a)\u003cstrong\u003e \u003c/strong\u003eLagged correlation between the monthly Atlantic Niño and ENSO indices for the period 1950–2021, where the x-axis indicates the number of months by which the Atlantic Niño index leads the Niño-3.4 index (for positive, Atlantic Niño leads ENSO, and vice versa); the y-axis indicates the period of October to the following September. The negative maximum is found at 5–15 (x-axis) during December to February (y-axis), indicating that the Atlantic Niño in boreal winter negatively leads ENSO with a lag of 5–15 months. Hatching indicates the 99% confidence level using a two-tailed Student’s t-test (degree of freedom: 50). (b) The 1\u003csup\u003est\u003c/sup\u003e Rotating-EOF mode of SSTA in tropical Atlantic Ocean in summer and its PC time series (black line in the bottom panel, normalized). The black box in the top panel indicates the area of Atlantic Niño (0-20°W, 5°S-5°N). The areal average of SSTA over the Atlantic Niño area in summer is illustrated by the red line in the bottom panel (normalized). (c) Anomalies of SST (shading, shading bar at right, 95%), low-level wind (850hPa, vector, 95%), and precipitation (green and brown dots for positive and negative, 95%) in JJA[1] (top) and D[1]JF[2] (bottom) regressed on to normalized Summer Atlantic Niño index (Previous winter ENSO signals are linearly removed). (d) Similar to (b), but for winter. (e) Anomalies of SST (shading, shading bar at right, 95%), low-level wind (850hPa, vector, 95%), and precipitation (green and brown dots for positive and negative, 95%) in D[0]JF[1] (top) and D[1]JF[2] (bottom) regressed on to normalized Winter Atlantic Niño index (simultaneous winter ENSO signals are linearly removed).\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/c8030b4e61e1266b05402645.jpg"},{"id":60609021,"identity":"32a4fc64-7a34-4460-99c5-c2b7a8b8fe3c","added_by":"auto","created_at":"2024-07-18 18:00:08","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe relationship between Winter Atlantic Niño and ENSO in historical and SSP585 simulations from CMIP6. \u003c/strong\u003e(a) Lagged correlation coefficients between the winter Atlantic Niño index (D[0]JF[1]) and ENSO index (D[1]JF[2]) in the 35 and 27 climate models from historical (pale blue, 1850–2014) and SSP585 (pale red, 2015–2100) simulations in CMIP6. Their MMEs are illustrated at the leftmost in blue and red color. The black border in each bar indicates significance at the 95% confidence level based on the Student’s t-test, wherein the number of degree of freedom is fixed at 120 and 70 for Historical and SSP585 simulations, respectively. Error bars in MME indicate 95% confidence level from bootstrap method (10000 times). (b) A scatter plot of correlation (x-axis) and regression (y-axis, °C) between Winter Atlantic Niño (D[0]JF[1], normalized) and ENSO (D[1]JF[2], not normalized) index. Pale blue and pale red dots indicate climate model result from historical and SSP585 simulations, and their MMEs are marked by blue and red dots, respectively. The regressed line is shown by the dotted pale blue and pale line.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/a5d73817a6d8c5c4022ac3df.jpg"},{"id":60608688,"identity":"0c582c7a-6fe7-46f5-ae27-4349d08b1c8a","added_by":"auto","created_at":"2024-07-18 17:52:08","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":267673,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatiotemporal evolution patterns of oceanic and atmospheric states regarding Winter Atlantic Niño in CMIP6.\u003c/strong\u003e (a) MME of anomalies of SST (°C, shading bar at right), low-level wind (vector, at 850 hPa), and precipitation (dots, green and brown for positive and negative) in D[0]JF[1] regressed onto the Winter Atlantic Niño index (D[0]JF[1]) in the Historical (1850-2014) simulation. (b)–(c) Same as (a), but in MAM[1] and D[1]JF[2], respectively. The marked SST, wind, and precipitation reflect a 95% confidence level by Student’s \u003cem\u003et\u003c/em\u003e-test. (d)-(f) Similar to (a)-(c), but with SSP585 simulations (2015-2100).\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/fef00364727f9d519a6ac905.jpg"},{"id":60609020,"identity":"847b8210-cb67-4c80-9ae4-e38557fd575e","added_by":"auto","created_at":"2024-07-18 18:00:08","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":97348,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariability of Winter Atlantic Niño and precipitation response to it with respect to the mean state changes between historical and SSP585 simulations. \u003c/strong\u003e(a) The box-whisker plot of standard deviation of winter Atlantic Niño (left) and precipitation response to the Winter Atlantic Niño (right) in historical (blue) and SSP585 (red) simulation. Herein, x and dot marks indicate mean and outlier. (b) Differences in climatological mean precipitation (shading) in the DJF season between SSP585 and historical simulations. (c) Differences in standard deviation of precipitation (shading) in the DJF season between SSP585 and historical simulations.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/23b683b292d94abef4123b43.jpg"},{"id":66148003,"identity":"5b2e029e-5b1b-4b6b-a09a-e2ee3a64de25","added_by":"auto","created_at":"2024-10-08 07:24:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1285319,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/05f136ec-3bd5-4669-9154-b73a62938e17.pdf"},{"id":60609194,"identity":"159ea33a-118f-4f93-9578-0041794885e4","added_by":"auto","created_at":"2024-07-18 18:08:08","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2139757,"visible":true,"origin":"","legend":"Supplementary Info.","description":"","filename":"Supplementary20240605.docx","url":"https://assets-eu.researchsquare.com/files/rs-4531524/v1/8eb1f0f529b6f63224fe2a49.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Significant Winter Atlantic Niño effect on ENSO and its future projection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eManifested by various air\u0026ndash;sea coupled processes\u003csup\u003e1\u0026ndash;7\u003c/sup\u003e, the Atlantic Ni\u0026ntilde;o is widely recognized as a leading climatic variability mode in the tropical Atlantic Ocean on interannual time scales\u003csup\u003e8\u0026ndash;12\u003c/sup\u003e. It exhibits a strongest variability in boreal summer season (for convenience, seasons in this study follow those of the northern hemisphere) when climatological mean thermocline depth along the equatorial Atlantic becomes shallower, accompanied by intensified mean easterly winds. In its positive phase, sea surface temperature (SST) anomaly (SSTA) along the equatorial Atlantic becomes warm with westerly wind anomalies, and vice versa in its negative phase\u003csup\u003e3,10,12\u0026ndash;16\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAtlantic Ni\u0026ntilde;o events also involve precipitation change around the Gulf of Guinea, western coast of equatorial Africa\u003csup\u003e17\u0026ndash;23\u003c/sup\u003e, Northeast Brazil\u003csup\u003e24\u003c/sup\u003e, and development of Hurricane\u003csup\u003e25\u003c/sup\u003e. On seasonal timescales, it leads to displacement of the Intertropical convergence zone (ITCZ) from South America to Atlantic Ocean\u003csup\u003e13,26\u003c/sup\u003e. This shift in the ITCZ induces modifications in deep convection, giving rise to stationary Rossby waves that propagate into the mid-latitudes. Consequently, Atlantic Ni\u0026ntilde;o events influence the North Atlantic Oscillation\u003csup\u003e27\u003c/sup\u003e and rainfall over the Mediterranean Sea and Europe\u003csup\u003e11,28\u003c/sup\u003e. Simultaneously, by perturbing the Walker circulation\u003csup\u003e29\u003c/sup\u003e, the Atlantic Ni\u0026ntilde;o affects and/or is affected by other major climatic phenomena such as El Ni\u0026ntilde;o\u0026ndash;Southern oscillation (ENSO)\u003csup\u003e30\u0026ndash;39\u003c/sup\u003e and Indian ocean dipole (IOD)\u003csup\u003e40\u003c/sup\u003e, which are regarded as crucial drivers of climate variability in the Pacific and Indian Oceans, respectively. Given its substantial climatic and socioeconomic impacts\u003csup\u003e22,25,41\u003c/sup\u003e, Atlantic Ni\u0026ntilde;o has been extensively of interest.\u003c/p\u003e \u003cp\u003eWhile a typical Atlantic Ni\u0026ntilde;o is commonly characterized as an SSTA loading center in the eastern equatorial Atlantic during summer, recent studies have revealed a variety of spatiotemporal evolution patterns for this phenomenon\u003csup\u003e42,43\u003c/sup\u003e. For example, unlike the consistent pattern previously assumed, the center of Atlantic Ni\u0026ntilde;o events is occasionally found in the central Atlantic region\u003csup\u003e43\u003c/sup\u003e. Furthermore, the onset and dissipation timings of these events display considerable diversity\u003csup\u003e42\u003c/sup\u003e. Particularly, Atlantic Ni\u0026ntilde;o events are frequently observed during winter\u003csup\u003e44,45\u003c/sup\u003e, coinciding with the secondly shallow thermocline with easterly wind in the season\u003csup\u003e46\u003c/sup\u003e. To differentiate this winter occurrence from the more commonly studied the Summer Atlantic Ni\u0026ntilde;o (a.k.a., Atlantic Ni\u0026ntilde;o I\u003csup\u003e46\u003c/sup\u003e), in this study it is termed as the Winter Atlantic Ni\u0026ntilde;o (a.k.a., Atlantic Ni\u0026ntilde;o II\u003csup\u003e46\u003c/sup\u003e).\u003c/p\u003e \u003cp\u003ePrevious studies have examined the lead-lagged relationship between Summer/Winter Atlantic Ni\u0026ntilde;o and following ENSO with a half-\u003csup\u003e30\u0026ndash;39\u003c/sup\u003e and one-year lag\u003csup\u003e44,45\u003c/sup\u003e, respectively. For Summer Atlantic Ni\u0026ntilde;o, its effect on ENSO was found to be weak during the mid-twentieth century, but strengthened from the late twentieth to the early twenty-first century\u003csup\u003e30,44,45\u003c/sup\u003e. We found that their lagged correlation coefficient is -0.23 since mid-twentieth century (1950\u0026ndash;2021), not significant at a 95% confidence level. Note that the change in their correlation coefficient is negligible (-0.22), when previous ENSO signals are excluded from the Summer Atlantic Ni\u0026ntilde;o index (Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eIn contrast to the Summer Atlantic Ni\u0026ntilde;o, the Winter Atlantic Ni\u0026ntilde;o effect on ENSO was strong during mid-twentieth century, which became weaker during the late twentieth to early twenty-first century\u003csup\u003e44,47\u003c/sup\u003e. During 1950\u0026ndash;2021, their lagged correlation coefficient becomes \u0026minus;\u0026thinsp;0.41, significant at the 99% confidence level (when simultaneous ENSO signals are removed, it is -0.40). This result contrasts sharply with the Summer Atlantic Ni\u0026ntilde;o\u0026ndash;ENSO relationship, which was found to be statistically insignificant over the same period. Notably, over the same period, the ENSO connection is most prominent with the Winter Atlantic Ni\u0026ntilde;o, rather than other primary climate variability mode such as Northern Tropical Atlantic (-0.32)\u003csup\u003e48\u0026ndash;51\u003c/sup\u003e and Atlantic Warm Pool\u003csup\u003e52,53\u003c/sup\u003e (a.k.a., Western Hemisphere Warm Pool\u003csup\u003e54,55\u003c/sup\u003e, -0.22) (Supplementary Table\u0026nbsp;1). This implies that the most important climate variability in the Atlantic preceding ENSO events could be the Winter Atlantic Ni\u0026ntilde;o.\u003c/p\u003e \u003cp\u003eThis simple correlation analysis implies the significant role of the Winter Atlantic Ni\u0026ntilde;o in inter-basin interactions. Nevertheless, unfortunately the Winter Atlantic Ni\u0026ntilde;o effect on ENSO has not been well investigated so far. Similarly, there has been a lack of evaluation regarding the capability of current climate models to simulate the relationship between Winter Atlantic Ni\u0026ntilde;o and ENSO.\u003c/p\u003e \u003cp\u003eIn this view, in this study we examine the observed Winter Atlantic Ni\u0026ntilde;o effect on ENSO by analyzing reanalysis dataset over 1950\u0026ndash;2021. Also, we aim to assess the fidelity of climate models participating in CMIP6 in capturing the lagged relationship between Winter Atlantic Ni\u0026ntilde;o and subsequent ENSO events. Based on the assessment, we will also address its future projection. Through our analysis, we propose that the significant Winter Atlantic Ni\u0026ntilde;o effect on ENSO will persist under global warming, in contrast to the weakened Summer Atlantic Ni\u0026ntilde;o\u0026ndash;ENSO connection.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eObserved relationship between Atlantic Ni\u0026ntilde;o and ENSO (1950\u0026ndash;2021)\u003c/h2\u003e \u003cp\u003eWe first obtained the monthly indices for Atlantic Ni\u0026ntilde;o and ENSO by averaging SSTA in the eastern equatorial Atlantic (0\u0026ndash;20\u0026deg;W and 5\u0026deg;S\u0026ndash;5\u0026deg;N) and Ni\u0026ntilde;o3.4 regions (120\u0026ndash;170\u0026deg;W, 5\u0026deg;S\u0026ndash;5\u0026deg;N), respectively. To examine their relationship, we conducted a lead-lagged correlation analysis spanning from 1950 to 2021 (864-month). In Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea, the negative (positive) on x-axis indicates that the ENSO (Atlantic Ni\u0026ntilde;o) leads the Atlantic Ni\u0026ntilde;o (ENSO) on monthly time scales, while the y-axis represents calendar months of the leading index. For example, the left bottom area in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea describes that the ENSO in October to December positively precedes the Atlantic Ni\u0026ntilde;o with a lag of 2\u0026ndash;4 months. This result shows that ENSO influence appears relatively modest, consistent with findings from prior studies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWhen it comes to the Atlantic Ni\u0026ntilde;o-leading-ENSO signals, the upper red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea indicates that the Atlantic Ni\u0026ntilde;o in June to August negatively leads ENSO, roughly with a lag of 9month, of which signals are modest. Meanwhile, the Atlantic Ni\u0026ntilde;o from December to February significantly leads ENSO with a lag ranging from 5 to 15 months (bottom red box in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). These results indicate that ENSO has a close lead-lagged relationship with Winter Atlantic Ni\u0026ntilde;o, rather than Summer Atlantic Ni\u0026ntilde;o.\u003c/p\u003e \u003cp\u003eBuilding upon the aforementioned results, we investigated the spatiotemporal variability of Atlantic Ni\u0026ntilde;o during both summer and winter, along with their subsequent effects on ENSO in the following winter. To comprehend the variability of Summer Atlantic Ni\u0026ntilde;o, we applied the Rotating-Empirical Orthogonal Function (R-EOF) analysis to the SSTA in the tropical Atlantic in Summer\u003csup\u003e56\u003c/sup\u003e (Methods). As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, the 1st R-EOF mode reveals a distinct spatial pattern characterized by strong SSTA amplitude extending from the eastern equatorial Atlantic Ocean to subtropical western coast of South Africa. This spatial pattern resembles that of the Summer Atlantic Ni\u0026ntilde;o, explaining 35.5% of the total variability. Its principal component (PC) time series are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb with the Summer Atlantic Ni\u0026ntilde;o index which is obtained by averaging the monthly Atlantic Ni\u0026ntilde;o index over JJA season. Herein, their correlation coefficient is higher than 0.9, indicating a physical mode\u003csup\u003e57\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec illustrates anomalies of SST, low-level wind (850hPa), and precipitation regressed onto the Summer Atlantic Ni\u0026ntilde;o index. In JJA[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], SSTA warming is observed along the equatorial Atlantic with westerly wind and enhanced precipitation, consistent with the features of Summer Atlantic Ni\u0026ntilde;o (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Owing to the annual solar movement, precipitation over the northern hemisphere tends to be greater than that over the southern hemisphere. The modulation of the Walker circulation by Summer Atlantic Ni\u0026ntilde;o results in decreased precipitation over the equatorial Pacific Ocean, leading to low-level easterly winds and subsequent SSTA cooling in that region. Due to the Bjerknes feedback, the low-level easterly wind is still observed with SST cooling in the equatorial eastern Pacific in D[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]JF[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], despite the weak signals.\u003c/p\u003e \u003cp\u003eSubsequently, we applied the R-EOF method to the SSTA in the tropical Atlantic, this time focusing on the winter. As illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, the 1st R-EOF mode accounts for 23.3% of the total variability and exhibits a spatial pattern akin to that of the Summer Atlantic Ni\u0026ntilde;o, implying a similar underlying development mechanism. Its PC time series has a high correlation coefficient (\u0026gt;\u0026thinsp;0.9) with the winter Atlantic Ni\u0026ntilde;o index which is obtained by averaging the monthly Atlantic Ni\u0026ntilde;o index over DJF season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). This robust correlation underscores the physical relevance of the identified R-EOF mode in characterizing the winter Atlantic Ni\u0026ntilde;o phenomenon.\u003c/p\u003e \u003cp\u003eTo investigate how the Winter Atlantic Ni\u0026ntilde;o affect the development of ENSO, anomalies of SST, low-level wind (850hPa), and precipitation regressed onto the Winter Atlantic Ni\u0026ntilde;o index. In D[0]JF[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), SSTA warming is observed along the equatorial Atlantic with westerly wind anomalies and enhanced precipitation (Please refer to Supplementary Fig.\u0026nbsp;1 for other seasons). Concurrently, low-level cyclonic circulation is located near the Eastern Brazil (40\u0026deg;W) in both hemispheres, indicative of a Gill-type response to the equatorial atmospheric latent heat forcing. Since solar radiation faces southern hemisphere in this season, precipitation along the equatorial Atlantic is greater in southern hemisphere than in the northern hemisphere. Over the Pacific, northeasterly wind anomalies are observed over the off-equatorial North Pacific. In boreal spring, these easterly wind anomalies propagate toward the equator (Supplementary Fig.\u0026nbsp;1), facilitating the trade wind discharge\u003csup\u003e45,58,59\u003c/sup\u003e. With the Bjerkness feedback due to the easterly wind along the equator in following seasons, La Nina develops in the following winter (D[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]JF[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). It is worth noting that these results are consistent when other reanalysis datasets are utilized (Supplementary Fig.\u0026nbsp;2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWinter Atlantic Niño effect on ENSO in the historical and global warming scenario\u003c/h3\u003e\n\u003cp\u003eObservational analysis during the last seven decades indicates that the Winter Atlantic Ni\u0026ntilde;o exerts a more significant effect on ENSO, compared to the Summer Atlantic Ni\u0026ntilde;o. Based on the results, we sought to investigate how current climate models participating the CMIP6 simulate the Winter Atlantic Ni\u0026ntilde;o effect on ENSO in the Historical and SSP585 simulations. To do this, we followed the same process as the observational analysis to obtain the Winer Atlantic Ni\u0026ntilde;o and ENSO. Then, the one-year lagged relationship between the winter Atlantic Ni\u0026ntilde;o (D[0]JF[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]) and ENSO (D[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]JF[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]) indices were examined.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea indicates the lagged correlation coefficients between them in each climate model as well as their MME. For Historical simulations, the correlation coefficients across climate models exhibit a wide range, varying from \u0026minus;\u0026thinsp;0.47 for TaiESM1 to 0.17 for MPI-ESM1-2-LR, wherein approximately 4/5 of climate models (28 out of 35) show a negative correlation. As a result, the MME is -0.12, indicating a seemingly small, but statistically significant at the 99% confidence level based on the Student\u0026rsquo;s t-test with a large degree of freedom. For the SSP585 simulation, correlation coefficient tends to decrease in the majority of climate models, meaning enhanced negative relationship between them under global warming. Accordingly, compared to the Historical simulations, their MME is slightly enhanced, -0.14 (99% confidence level).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn addition to the correlation coefficient, regression coefficients in each climate model were also analyzed to infer the actual change in ENSO amplitude regarding the Winter Atlantic Ni\u0026ntilde;o change. To this end, the original ENSO index (not normalized) is regressed onto the normalized winter Atlantic Ni\u0026ntilde;o index (y-axis in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In the figure, the x-axis represents the correlation coefficients in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, wherein the R-Square is greater than 0.9, indicating a pronounced linear relationship between the correlation and regression coefficients. The slope is 3.952, implying that a correlation coefficient of 0.1 in a particular climate model corresponds to a change in the ENSO index of approximately \u0026minus;\u0026thinsp;0.4\u0026deg;C per standard deviation of Atlantic Ni\u0026ntilde;o. Similarly, the slope is about 4.55 for SSP585 simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb), which is stronger than that of Historical simulation.\u003c/p\u003e \u003cp\u003eAbove results suggest that climate models in CMIP6 reasonably simulate the Winter Atlantic Ni\u0026ntilde;o-leading-ENSO relationship from an MME perspective. In order to examine the processes of how the Winter Atlantic Ni\u0026ntilde;o affects the following ENSO events, SST, low-level wind, and precipitation anomalies are regressed onto the Winter Atlantic-Ni\u0026ntilde;o in each climate model, and the results are averaged. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea illustrate simultaneous oceanic and atmospheric states when Winter Atlantic Ni\u0026ntilde;o events occur in the Historical simulation. Over the Atlantic Ocean, SSTA warming occurs with westerly wind and enhanced precipitation, with stronger signals in the southern hemisphere than the northern hemisphere, mirroring observation. However, the overall structure of winter Atlantic Ni\u0026ntilde;o is meridionally and zonally wider than observed. Consequently, a pronounced modification of the Walker circulation is evident, characterized by pronounced low-level winds over the Pacific Ocean. From MAM to DJF, the signals of Atlantic Ocean gradually decay, while those over the Pacific Ocean grow due to Bjerknes feedback. In the case of the SSP585 simulations, the overall evolutionary processes associated with Winter Atlantic Ni\u0026ntilde;o are consistent to those in Historical simulations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed-e). In short, these findings suggest that the Winter Atlantic Ni\u0026ntilde;o effect on ENSO is expected to persist during the global warming period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eAtmospheric response to the Winter Atlantic Niño in the historical and SSP585 run\u003c/h3\u003e\n\u003cp\u003ePrevious studies have suggested that the Summer Atlantic Ni\u0026ntilde;o effect on ENSO is likely to be weakened under greenhouse warming\u003csup\u003e60\u003c/sup\u003e. This weakening has been attributed to the reduced precipitation response over the equatorial Atlantic Ocean and the decreased variability of Summer Atlantic Ni\u0026ntilde;o\u003csup\u003e61\u003c/sup\u003e. In contrast, we showed that the Winter Atlantic Ni\u0026ntilde;o effect on ENSO is stronger than the Summer Atlantic Ni\u0026ntilde;o effect, and is expected to be maintained under global warming. In light of these results, we aimed to examine changes in the variability of the Winter Atlantic Ni\u0026ntilde;o under global warming. Additionally, to infer the change in the precipitation response, climatological mean state of precipitation over the equatorial Atlantic in winter and its variability were examined.\u003c/p\u003e \u003cp\u003eThe left box-whisker plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea illustrates the distribution of the standard deviation of Winter Atlantic Ni\u0026ntilde;o in climate models from historical and SSP585 simulations. It is evident that the distribution in climate models from SSP585 simulation is wider than that of historical simulation. However, the MMEs of historical and SSP585 simulations are 0.40 and 0.39, indicating insignificant difference between them. This suggests that the variability of the Winter Atlantic Ni\u0026ntilde;o is expected to be maintained under global warming.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMeanwhile, the right box-whisker plot indicates the local precipitation response to the Winter Atlantic Ni\u0026ntilde;o. Similar to the standard deviation of winter Atlantic Ni\u0026ntilde;o, the distribution of precipitation response spreads widely in SSP585 simulation, compared to the historical simulation. However, their MME is close to each other (both 0.32). This suggests that the local precipitation response to the Winter Atlantic Ni\u0026ntilde;o under global warming is not significantly altered.\u003c/p\u003e \u003cp\u003eConsidering the sustained atmospheric response over the equatorial Atlantic, we examined the changes in the climatological winter precipitation mean state under global warming. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb shows the difference in winter precipitation between SSP585 and Historical simulations, revealing intensified precipitation over the equatorial Atlantic and the South Atlantic convergence zone (SACZ) in the SSP585 simulation. This intensified mean precipitation accompanies an amplification in the standard deviation of precipitation over the equatorial Atlantic and SACZ area. Aligned with the enhanced mean precipitation and its variability, the precipitation response to the SST over the region is expected to be maintained, under enhanced atmospheric stability\u003csup\u003e62\u003c/sup\u003e. In summary, the sustained variability of the Winter Atlantic Ni\u0026ntilde;o and the precipitation response under global warming collectively contribute to the persistence of the Winter Atlantic Ni\u0026ntilde;o effect on ENSO.\u003c/p\u003e"},{"header":"Conclusion and discussion","content":"\u003cp\u003eThe Atlantic Ni\u0026ntilde;o, recognized as the predominant climate variability mode in the equatorial Atlantic, has been gotten a considerable attention due to its profound impact on the climate of both nearby and distant regions. Previous research has primarily focused on the summer season when the its variability peaks. However, it is worth noting that significant variability also occurs during the winter season. Despite this, research on the Winter Atlantic Ni\u0026ntilde;o remains relatively scarce. In this context, we conducted an investigation into the influence of the Winter Atlantic Ni\u0026ntilde;o on subsequent ENSO events with a one-year lag over the past 70 years. By examining this relationship, we aimed to shed light on the importance of the Winter Atlantic Ni\u0026ntilde;o in shaping interannual climate variability and its implications for broader climate dynamics.\u003c/p\u003e \u003cp\u003eWe have demonstrated that the Winter Atlantic Ni\u0026ntilde;o exerts a significant and greater effect on ENSO compared to the Summer Atlantic Ni\u0026ntilde;o. Furthermore, our analysis of historical and SSP585 simulations from CMIP6 indicates that climate models are capable of reasonably simulating the relationship between the Winter Atlantic Ni\u0026ntilde;o and subsequent ENSO events from a multi-model ensemble (MME) perspective. Given that the variability of the Winter Atlantic Ni\u0026ntilde;o and the precipitation response to it are projected to be maintained under global warming, we anticipate that its influence on ENSO will persist in the future. This contrasts with the weakening influence of the Summer Atlantic Ni\u0026ntilde;o on ENSO under global warming. Therefore, we believe that further investigation of the Winter Atlantic Ni\u0026ntilde;o will lead to a better understanding of trans-basin interactions and their implications for future climate dynamics.\u003c/p\u003e \u003cp\u003eAs discussed above, the MME of climate models appears to reasonably capture the effect of winter Atlantic Ni\u0026ntilde;o on ENSO. However, it is important to acknowledge that most climate models struggle to accurately reproduce its spatiotemporal characteristics. For example, observations reveal that the Atlantic Ni\u0026ntilde;o exhibits peak phases during both summer and winter, yet climate models rarely simulate these double peaks, thus failing to replicate such phenomena from an MME perspective (see Supplementary Fig.\u0026nbsp;3). Furthermore, whereas the spatial variability of the winter Atlantic Ni\u0026ntilde;o in observation is well explained by Rotating EOF, climate models often fall short in adequately simulating this variability (see Supplementary Fig.\u0026nbsp;4). These discrepancies may be attributed, in part, to that observed re-intensification of the climatological low-level easterly wind and deepening of the thermocline along the equatorial Atlantic in boreal winter is not well reproduced in climate models. These issues regarding the simulation of the observed climatological mean state in climate models are well-documented in previous research\u003csup\u003e9,63\u003c/sup\u003e. Therefore, conducting an in-depth investigation to improve the climatological mean state in climate models would be beneficial for better simulating the Atlantic Ni\u0026ntilde;o and its influence.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eReanalysis Dataset.\u003c/b\u003e We utilized the ECMWF Reanalysis v5 (ERA5\u003csup\u003e64\u003c/sup\u003e), combining vast amounts of historical observations into global estimates using advanced modelling and data assimilation systems. Additionally, to confirm the robustness of the results, we also use the monthly dataset of the U.S. NOAA National Center for Environmental Prediction Reanalysis 1 (NCEP-R1\u003csup\u003e65\u003c/sup\u003e), which is an assimilated dataset that uses a state-of-the-art analysis and forecast system. For SSTs, NOAA\u0026rsquo;s Extended Reconstruction Sea Surface Temperature version 5 (ERSSTv5\u003csup\u003e66\u003c/sup\u003e) are selected, which is a global monthly SST dataset derived from NOAA\u0026rsquo;s International Comprehensive Ocean-Atmosphere Dataset (ICOADS). The analysis period in this study was from 1950 to 2021.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCMIP6 Dataset.\u003c/b\u003e For Historical simulations, we can reserve 35 climate models which have SST, SLP, wind, and precipitation. For SSP585 simulations, we can obtain 27 climate models which have SST, SLP, wind, precipitation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of climate models from CMIP6 utilized in the present study. Each climate model reserve SST, precipitation, low-level wind (U, V) dataset.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistorical\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSSP585\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-CM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCESS-ESM1-5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAWI-CM-1-1-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAWI-CM-1-1-MR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBCC-CSM2-MR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBCC-ESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMS-CSM1-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCESM2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCanESM5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-FV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eE3SM-1-1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-WACCM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCESM2-WACCM-FV2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCMCC-CM2-SR5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGOALS-f3-L\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFGOALS-g3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3SM-1-1-ECA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFIO-ESM-2-0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-Veg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEC-Earth3-Veg-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM-CM4-8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOALS-f3-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eINM-CM5-0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFGOALS-g3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFIO-ESM-2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKACE-1-0-G\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGFDL-ESM4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINM-CM4-8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINM-CM5-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIPSL-CM6A-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCM-UA-1-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNESM3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMIROC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-LM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-HR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNorESM2-MM\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMPI-ESM1-2-LR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI-ESM2-0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNESM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorCPM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorESM2-LM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorESM2-MM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAM0-UNICON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTaiESM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \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 Contributions\u003c/h2\u003e \u003cp\u003eJ.-H. Park and J.-S. Kug conceived the idea and shared it with other coauthors. J.-H. Park conducted the analyses and prepared the figures with H.-J. Park. All coauthors thoroughly revised the manuscript which was first written by J.-H. Park. All coauthors participated in the discussion on the results.\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 Korea government (MSIT) (NRF-2023R1A2C1004083 and RS-2023-00219830).\u003c/p\u003e\u003ch2\u003eData availability.\u003c/h2\u003e \u003cp\u003eAll of observational and CMIP6 data 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. ERSSTv5: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.esrl.noaa.gov/psd/data/data.php\u003c/span\u003e\u003cspan address=\"https://www.esrl.noaa.gov/psd/data/data.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. CMIP6: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aims2.llnl.gov/search/cmip6/\u003c/span\u003e\u003cspan address=\"https://aims2.llnl.gov/search/cmip6/\" 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 J.-H. Park ([email protected]).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eZebiak, S. E. Air\u0026ndash;Sea Interaction in the Equatorial Atlantic Region. J. Clim. 6, 1567\u0026ndash;1586 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, P., Fang, Y., Saravanan, R., Ji, L. \u0026amp; Seidel, H. The cause of the fragile relationship between the Pacific El Nĩo and the Atlantic Nĩo. 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However, the role of Winter Atlantic Ni\u0026ntilde;o in trans-basin interactions remains underexplored compared to its summer counterpart. Through analysis of observational reanalysis data since the mid-twentieth century, here we found that Winter Atlantic Ni\u0026ntilde;o significantly influences the development of El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO), surpassing the impact of Summer Atlantic Ni\u0026ntilde;o with a longer lead time. This effect was reasonably captured in the historical simulation from the CMIP6, from a multi-model ensemble perspective. Further analysis with the global warming scenario projects that the influence of Winter Atlantic Ni\u0026ntilde;o on ENSO will persist into the future, contrasting with a reduced impact of Summer Atlantic Ni\u0026ntilde;o. Therefore, these findings underscore the importance of further investigating Winter Atlantic Ni\u0026ntilde;o for a comprehensive understanding of trans-basin interactions and their future change.\u003c/p\u003e","manuscriptTitle":"Significant Winter Atlantic Niño effect on ENSO and its future projection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 17:52:03","doi":"10.21203/rs.3.rs-4531524/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-climate-and-atmospheric-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjclimatsci","sideBox":"Learn more about [npj Climate and Atmospheric Science](http://www.nature.com/npjclimatsci/)","snPcode":"41612","submissionUrl":"https://submission.springernature.com/new-submission/41612/3","title":"npj Climate and Atmospheric Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"46e25942-cab8-4eb4-b447-a856424c7775","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":33339199,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":33339200,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2024-10-08T07:24:30+00:00","versionOfRecord":{"articleIdentity":"rs-4531524","link":"https://doi.org/10.1038/s41612-024-00790-3","journal":{"identity":"npj-climate-and-atmospheric-science","isVorOnly":false,"title":"npj Climate and Atmospheric Science"},"publishedOn":"2024-10-07 04:00:00","publishedOnDateReadable":"October 7th, 2024"},"versionCreatedAt":"2024-07-18 17:52:03","video":"","vorDoi":"10.1038/s41612-024-00790-3","vorDoiUrl":"https://doi.org/10.1038/s41612-024-00790-3","workflowStages":[]},"version":"v1","identity":"rs-4531524","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4531524","identity":"rs-4531524","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}