Asymmetric Response of Sri Lanka Northeast Monsoon Rainfall to El Niño/La Niña

Asymmetric Response of Sri Lanka Northeast Monsoon Rainfall to El Niño/La Niña | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Asymmetric Response of Sri Lanka Northeast Monsoon Rainfall to El Niño/La Niña Pathmarasa Kajakokulan, Agus Santoso, Sen Zhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4874154/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2025 Read the published version in Climate Dynamics → Version 1 posted 5 You are reading this latest preprint version Abstract Many studies have indicated that an asymmetry in rainfall anomalies over India exists between the warm and cold phases of the El Niño-Southern Oscillation (ENSO). However, the extent to which the distinctive phases of ENSO influence the asymmetry pattern of Sri Lanka rainfall remains unclear. In this study, utilizing observational/reanalysis datasets for the period 1981–2022, we found that Sri Lanka's winter rainfall response to El Niño/La Niña is asymmetric, with a significant response during El Niño. During the El Niño peak winter, the presence of PSAC (Philippine Sea anomalous anticyclone) results in the prevailing anticyclone over the Bay of Bengal (BoB), suppressing moisture convergence and rainfall over Sri Lanka. On the other hand, the PSCC (Philippine Sea anomalous cyclone), which has shifted westward during the La Niña. This shift enhances cyclone over the BoB, resulting in enhanced moisture convergence and rainfall over Sri Lanka, with a magnitude that is weaker than that of the El Niño-induced PSAC. This results in the emergence of asymmetric rainfall anomaly patterns in Sri Lanka in the El Niño and La Niña peak phases. Thus, this study highlights that the asymmetric circulation of PSAC/PSCC during the ENSO phenomenon contributes to the observed asymmetry in rainfall anomalies between El Niño and La Niña events and has important implications for seasonal forecasting. El Niño-Southern Oscillation (ENSO) Rainfall Sri Lanka Philippine Sea anomalous anticyclone Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1 Introduction Sri Lanka, situated between the Arabian Sea (AS) and the Bay of Bengal (BoB) in the equatorial Indian Ocean (Fig. 1 a), experiences significant weather and climate impacts due to the dynamical interaction between these two bodies of water (Bandurathna et al. 2021 ). Therefore, several factors influence Sri Lanka's rainfall variability, including monsoon systems, the Intertropical Convergence Zone (ITCZ), oceanic currents, depression, and tropical cyclones (Kajakokulan et al. 2023b ; Jinadasa et al. 2020 ). Among these, the monsoon system is a vital factor that controls the seasonal rainfall in Sri Lanka (Deoras et al. 2023 ) and can be characterized by two principal monsoon rainfall seasons and two inter-monsoon rainfall seasons (Malmgren et al. 2003 ). The Southwest Monsoon, one of the most significant monsoon regimes in the region, originates from the Indian Ocean and affects Sri Lanka's wet zone from May to September (Abeysekera et al. 2021 ). On the other hand, the Northeast Monsoon, also known as the winter monsoon, originates from the Bay of Bengal and impacts the northern and eastern parts of the island from December to February (Fig. 1 b Alahacoon and Edirisinghe 2021 ). Furthermore, the primary agricultural season is also associated with the occurrence of the Northeast Monsoon (Kajakokulan et al. 2023a ). The two inter-monsoon seasons that occur between the two monsoon periods, the first monsoon from March to April and the second from October to November, are associated with the movement of the intertropical convergence zone (ITCZ) over Sri Lanka (Naveendrakumar et al. 2018 ). The variability of the Northeast Monsoon can significantly impact agriculture, irrigation, and livelihood of people in Sri Lanka (Hapuarachchi and Jayawardena 2015 ; Burt and Weerasinghe 2014 ). The monsoon system in Sri Lanka is significantly influenced by various tropical climate oscillations, such as the El Niño Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD; Ranaweera and Kamae 2024 ; Huang et al. 2024 ). Thus, these climate variability modes have a crucial impact on Sri Lankan rainfall, making it essential to understand their impact on the island's climate. The El Niño–Southern Oscillation (ENSO), which significantly influences climate in the tropical Pacific Ocean and beyond (Gillett et al. 2023 ; McPhaden et al. 2020 ; Pathirana et al. 2023 ), exhibits an asymmetry between its warm (El Niño) and cold (La Niña) phases, leading to distinct and unequal impacts on global weather patterns (Lin et al. 2023 ). During the El Niño peak winter, the most prominent and persistent low-level atmospheric circulation anomalies over the tropical western Pacific are the anomalous west North Pacific anticyclone (WNPAC; Wang et al. 2022 ), also known as the Philippine Sea anomalous anticyclone (PSAC; Wang and Zhang 2002 ); during the La Niña peak winter, the anomalous west North Pacific cyclone (WNPC; Yuan and Yang 2012 ), also referred to as the Philippine Sea anomalous cyclone (PSCC; Bagtasa 2020 ), is the dominant anomalous circulation. It has previously been demonstrated that ENSO-related anomalous anti-cyclonic and cyclonic anomalies can induce complex precipitation anomaly patterns, particularly over the tropical ocean (Zhang et al. 1999 ; Trenberth and Shea 2005 ). During El Niño events, the PSAC is known to result in a reduction in both convection and precipitation over the maritime continent and the western Pacific (Jiang et al. 2023 ). Conversely, La Niña is characterized by an increase in precipitation, the primary driver of which is the PSCC (Wang and Zhang 2002 ). However, there exists asymmetry in the impact of ENSO on regional climate. For example, Lin et al. ( 2023 ) identified that during El Niño peak winter, the prevailing south-westerly winds are deflected from the southeast coast of China, significantly decreasing winter precipitation in southern and eastern central China, which is linked to an anomalous anticyclone over the western North Pacific (WNP); changes in La Niña are uncertain. It is well documented that there is a correlation between the rainfall patterns observed in Sri Lanka and the El Niño phenomenon, significantly impacting the country's seasonal rainfall patterns (Kane 1998 ; Zubair 2002 ; Zubair et al. 2008 ). The results of several studies have produced conflicting findings regarding the impact of ENSO phases on Sri Lankan precipitation patterns on a seasonal scale. Suppiah ( 1996 ) analyzed Sri Lanka's rainfall response to El Niño, noting decreased (increased) rainfall during the Southwest monsoon (second inter-monsoon) season, with uncertain impacts during the Northeast monsoon. However, it has been documented that El Niño (La Niña) has a suppressive (enhancing) influence on rainfall in Sri Lanka during Northeast monsoon periods (Kane 1998 ). Furthermore, Vialard et al. ( 2011 ) observed the alterations in wind circulation patterns associated with moisture transport towards Sri Lanka during the northeast monsoon. Over the past few years, numerous studies have been conducted on the variability and trends of rainfall in Sri Lanka (Amarasinghe 2020 ; Jayawardena et al. 2020; Abeysekera et al. 2021 ; Alahacoon and Edirisinghe 2021 ; Tillekaratne et al. 2022 ; Kajakokulan et al. 2023a ). Only a few studies focus on the impact of ENSO on Sri Lanka's rainfall in recent years (Kajakokulan et al. 2024;Koralegedara et al. 2019 ). Given the asymmetric sea surface temperature (SST) anomalies between El Niño and La Niña, it is pertinent to investigate whether the Sri Lanka rainfall to El Niño and La Niña is also asymmetric in recent decades. However, an apparent lack of research exists on asymmetric responses of the Sri Lankan rainfall to ENSO and the associated mechanisms. Thus, this study aims to explore the differential influence of El Niño and La Niña on winter precipitation in Sri Lanka, particularly during periods of heightened ENSO influence, and to examine potential underlying physical mechanisms. The remainder of the study is organized as follows. Section 2 provides an overview of the datasets and methods utilized. Section 3 then presents the results, and Section 4 show a summary, and discussion. 2 Data and methods 2.1 Data This study used the monthly extended reconstructed SST (ERSSTv5) dataset at a resolution of 2 ° × 2 ° from the National Oceanographic and Atmospheric Administration (NOAA, Huang et al. 2017 ). The monthly mean atmospheric ERA5 data at a resolution of 0.25 ◦ × 0.25 ◦ , including specific humidity, sea level pressure, total cloud condition, rainfall, and wind, is derived from the fifth-generation European Centre for Medium-Range Forecasts (ECMWF) reanalysis (Dee et al. 2011 ). In addition, the vertical velocity at a resolution of 2.5 ◦ × 2.5 ◦ was sourced from the National Centers for Environment Prediction–National Center for Atmospheric Research (NCEP–NCAR) reanalysis data (Kalnay et al. 1996 ), while the daily precipitation data at a horizontal resolution of 0.05 ◦ × 0.05 ◦ was derived from the Climate Hazards Group InfraRed Precipitation (CHIRPS, Funk et al. 2015 ). 2.2 Methods Table 1 The following indices were utilized in the present study Index Definition Reference Niño3.4 The Niño 3.4 index is computed by the SST anomalies averaged in the central to eastern equatorial Pacific (5°S-5°N and 120°W-170°W). (Trenbeth et al. 2002 ) PSAC PSAC index is calculated as the area-averaged MSP anomalies in the equatorial Indo-Pacific Ocean (0°-20°N and 100°E-140°E). (Wang and Zhang 2002 ) Observational rainfall data were purchased from the Sri Lanka Meteorological Department to verify the reanalysis of CHIRPS and ERA5 rainfall data (Fig. S1 ). The methodology employed to define the PSAC and ENSO index is elucidated in detail in Table 1 . In the present study, the strong El Niño (La Niña) events are classified as occurring when the normalized December–February (DJF) Niño-3.4 index exceeds the 1 (-1) standard deviation (Fig. S2). Thus, we have identified seven strong El Niño events, which include 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16, as well as eight La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22. A composite analysis is employed in the study to understand the asymmetry pattern of rainfall and associated mechanisms. It should be noted that the anomalies presented in this study, relative to the climatological annual cycle from 1981–2022, were calculated by removing both the seasonal climatology and a long-term linear trend, and the statistical significance is evaluated by the two-tailed Student’s t-test. The vertically integrated moisture flux convergence (VIMFC) has been determined from the following equation. \(\:\text{V}\text{I}\text{M}\text{F}\text{C}=-\frac{1}{g}{\int\:}_{1000hPa}^{300hPa}\left(\frac{\partial\:uq\:\:\:}{\partial\:x}+\frac{\partial\:vq\:\:\:}{\partial\:y}\right)dp\) ………..…Eq. 1 Where: q is the specific humidity (kg/kg) u and v are zonal and meridional wind (m s -1 ) g is the gravitational acceleration (m s -2 ) p is the pressure (Pa) 3 Results 3.1 The seasonal Sri Lanka rainfall response to the El Niño-Southern Oscillation El Niño, a coupled air-sea phenomenon, exhibits a primary peak during the winter months of December, January, and February (DJF), profoundly influencing Sri Lanka's climate variability (Koralegedara et al. 2023 ). Therefore, we calculated the correlation pattern between the Niño 3.4 index in winter and rainfall anomalies from the El Niño developing autumn to decaying summer, which is presented in Fig. 2 a. The El Niño El Niño developing autumn correlation is statistically significant, likely due to the competing effect of a positive Indian Ocean Dipole (Zubair et al. 2003 ). On the other hand, the correlation during the El Niño peak winter for both data sets is negative (r~-0.41) and statistically significant (p-value = 0.081), indicating the higher ENSO impact during this season over other seasons, with El Niño condition (positive Niño3.4) corresponding with reduced rainfall; and La Niña with increased rainfall. The corresponding regression pattern over Sri Lanka (Fig. 2 b) shows significant negative rainfall anomalies throughout the country, with strongest impact over the eastern part of Sri Lanka. 3.2 The asymmetric responses of Sri Lanka rainfall to El Niño and La Niña Figure 3 presents the composite precipitation anomalies for El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22 winters in Sri Lanka. During El Niño peak winter, there is a notable decrease in rainfall across Sri Lanka, particularly over the eastern part of the island (Fig. 3 a). On the other hand, rainfall tends to increase when a La Niña event occurs (Fig. 3 b). To elucidate the asymmetric component in Sri Lanka, we examine the summation of the precipitation anomalies for the El Niño and La Niña composites (Fig. 3 c). The below-normal precipitation anomaly in Sri Lanka is still evident, with broad regions that are statistically significant above 90% significance level, thereby revealing the greater effect induced by El Niño compared to La Niña. Furthermore, it is observed that the eastern region of Sri Lanka exhibits a more pronounced difference than other parts of the island. We also analyzed the composite precipitation anomalies using the ERA5 data and found similar results to CHIRPS data (Fig. S3). Thus, our study identified that El Niño has a higher impact on Sri Lanka rainfall than La Niña. Therefore, it is important to understand the factors that may play a more significant role in the asymmetry. 3.3 The physical mechanism for asymmetry rainfall response of Sri Lanka To illustrate the mechanism for the evidence for asymmetric Northeast Monsoon precipitation over Sri Lanka, we show the composite anomalous SST and OLR during the El Niño and La Niña events (Fig. 4 ). During the El Niño event, a tropical SST anomalies dipole pattern is observed, with the positive pole over the eastern Pacific Ocean and the negative pole over the tropical western Pacific Ocean and strong basin-wide warming over the entire Indian Ocean (Fig. 4 a). In contrast, during La Niña, the dipole pattern reversed and extend westward, with the positive pole over the western Pacific Ocean and the negative pole over the tropical eastern Pacific Ocean and strong cooling over the Indian Ocean (Fig. 4 b). The summation of El Niño and La Niña events demonstrates a notable prevalence of robust positive SST anomalies over the Indian Ocean, which further substantiates the asymmetric responses exhibited by atmospheric circulation in response to El Niño than La Niña (Fig. 4 c). We also analyzed the Walker circulation and consistent with SST and OLR results (Fig.S4). The Strong upward motion during El Niño, while downward motion over the Indian Ocean in La Niña. These results suggest that the precursor of asymmetry in rainfall variability over Sri Lanka cannot be obtained through changes in the Walker circulation. Besides the spatial asymmetries of ENSO, we also show the asymmetries of the temporal evolution of ENSO intensity and duration (Fig. 5 ). The SST intensity of El Niño is higher than that of La Niña from the onset of summer to the peak of winter, and decaying from the following spring, showing that the decay rate of El Niño is significantly stronger than that of La Niña. This implies that the intensity of ENSO may have a major influence on asymmetric rainfall patterns over Sri Lanka. Therefore, given the asymmetrical modulation of the SST, we conducted the composite wind at 850 hPa and mean sea level pressure anomalies to understand the mechanism. During the El Niño peak winter event, PSAC is observed with its central position at approximately (10 o N, 130 o E), which leads to the prevailing anti-cyclonic condition over the BoB (Chowdary et al. 2017 ; Fig. 6 a), and suppresses the rainfall over Sri Lanka. In contrast, a PSCC shifted westward with its central position at approximately (10 o N, 120 o E), leading to prevailing cyclonic conditions over the BoB (Wang and Wang 2023 ; Fig. 6 b) and enhancing the rainfall over Sri Lanka. In addition, the asymmetric component of wind exhibits an evident anticyclone, indicating that the amplitude of PSAC is considerably greater than that of PSCC (Fig. 6 c). It is well consistent with previous studies showing that PSCC is weaker and moved westward relative to the PSAC (Guo et al. 2017 ). This leads to the conclusion that there is a greater suppression of rainfall in Sri Lanka during an El Niño event compared to enhancement of rainfall during a La Niña event through the asymmetry in PSAC/PSCC associated circulations. The large-scale convergence of atmospheric moisture is the primary driver of tropical precipitation patterns rather than locally enhanced evaporation (Darand and Pazhoh 2019 ). Given the pivotal role of moisture supply, it is crucial to comprehend the underlying factors responsible for the precipitation enhancement and suppression over Sri Lanka during El Niño and La Niña events, respectively, during the winter season. Therefore, we analyzed the composite analysis of TCC, VIMF, and VIMFC over the IO. The presence of positive values of VIMFC and TCC indicates convergence, while negative values indicate divergence. Figure 7 a presents the moisture flux transport indicated by vectors and the associated flux convergence indicated by shading for the El Niño peak winter season. During the El Niño peak winter period, there is observed anti-cyclonic conditions oriented vertically-integrated moisture flux over BoB, which carries moisture divergence and suppression of rainfall to Sri Lanka (Fig. 7 a). Conversely, during the La Niña peak winter, cyclonic conditions oriented vertically integrated moisture flux indicates moisture convergence from the BoB to Sri Lanka, leading to above normal rainfall (Fig. 7 b). The sum of the vertically integrated moisture flux and convergence for the El Niño and La Niña peak winters is used to estimate the asymmetry component of ENSO-induced moisture transport, with a pattern resembling that seen in the El Niño-induced vertically integrated moisture flux (Fig. 7 c). It is noted that response of vertically integrated moisture flux is similar to 850 hPa wind anomalies. Moreover, a negative value is observed for the total cloud condition during the El Niño peak winter (Fig. 7 d), whereas a positive value is observed during the La Niña peak winter (Fig. 7 e). This is consistent with findings indicating that moisture divergence occurs during the El Niño peak winter and moisture convergence occurs during the La Niña peak winter. Thus, our result confirms that the influence of ENSO on PSAC/PSCC circulation, and vertically integrated moisture flux convergence are vital factors that control Sri Lanka's rainfall variability. Figure 8 presents a scatter diagram of the Niño3.4 index versus the anomalous PSAC index, VIMFC index, and Sri Lanka rainfall index over Sri Lanka to elucidate the asymmetry of El Niño on precipitation. We initially investigated the potential correlation between the Niño3.4 and anomalous PSAC indexes. It is noted that the warm phase of the Niño3.4 index is positively correlated with the PSAC index (r = 0.63), indicating a significant PSAC induced by the warm phase, while the positive correlation coefficient is 0.67 for the cold phase with the PSSC index, reveals that the cold phase induces a PSCC (Fig. 8 a). On the other hand, the warm phase of Niño-3.4 index displays a negative correlation (r=-0.50) with the VIMFC index, suggesting that the warm phase of the Niño-3.4 index is associated with a moisture divergence anomaly in Sri Lanka. Conversely, there is a weaker negative correlation (r=-0.33) between the cold phase Niño-3.4 and the VIMFC index, indicating that the cold phase of the Niño-3.4 index is associated with a moisture convergence anomaly in Sri Lanka (Fig. 8 b). Furthermore, correlation pattern between El Niño and Sri Lanka rainfall index is also similar with the moisture results (Fig. 8 c). Thus, our study found that El Niño (La Niña) is associated with PSAC (PSCC) lead to moisture divergence (convergence) over Sri Lanka, as a result, rainfall suppression (enhancement). To affirm the asymmetric influence of the cold and warm phases on winter precipitation over Sri Lanka, we have calculated the correlations between the winter precipitation anomalies and the winter Niño3.4 index. It can be observed that the negative correlations in the warm and cold phases of the El Niño-Southern Oscillation (ENSO), indicate the exhibiting clear asymmetries (Fig. 9 ). In years with positive SST anomalies (i.e., Niño3.4 index > 0), negative strong correlations are observed to be more prevalent entire part of Sri Lanka. On the other hand, in years when the SST anomalies are negative (i.e., Niño3.4 index < 0), the negative moderate correlation is observed to extend into Sri Lanka. This suggests that the impact of El Niño peak years on winter precipitation in Sri Lanka is greater than during La Niña peak years. Thus, our study confirms that the warm phase and cold phase of ENSO lead to an asymmetric pattern of rainfall in Sri Lanka via the longitudinal shift of PSAC and PSCC. 4 Summary and Discussion In this study, the impact of the warm and cold phases of ENSO on rainfall over Sri Lanka during winter was investigated, utilizing observational and reanalysis data from 1981 to 2022. Our study identified seven El Niño events in 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16, and eight La Niña events in 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22. Based on composite analysis, our results reveal an asymmetry in the influence of El Niño and La Niña on Sri Lanka's winter precipitation. The findings align with previous research by Vialard et al. ( 2011 ), indicating that the warm phase of ENSO is associated with a decrease in rainfall, while the cold phase has been associated with a decrease in rainfall. The physical mechanisms responsible for the observed asymmetry are investigated and summarized by analyzing SST, wind, MSLP, TCC, moisture flux, and convergence. In years with a particularly strong El Niño, a development of PSAC generated predominant anti-cyclonic conditions over BoB. This suppresses moisture convergence and precipitation over Sri Lanka during winter (Fig. 10 a). In contrast, a strong La Niña with a presence of westward PSCC compared to PSAC, leads to cyclonic conditions over BoB, promotes moisture convergence, and increases rainfall over Sri Lanka (Fig. 10 b). This gives rise to in the asymmetry in the rainfall anomalies between El Niño and La Niña. Our study confirms the role of PSAC/PSCC in strengthening the asymmetry of winter precipitation in SL. Previous studies suggest that PSAC (PSCC) linked with CP (Central Pacific) El Niño (La Niña) exhibit less asymmetry than those associated with EP (Eastern Pacific) El Niño and La Niña, owing to the more symmetric distribution and intensity of tropical SST anomalies between CP El Niño and CP La Niña events (Wang and Zhang 2002 ; Feng et al. 2018 ). For example, both the El Niño and La Niña phases of the El Niño–Southern Oscillation (ENSO) phenomenon exhibit asymmetric impacts on the summer precipitation patterns in the Tibetan Plateau (Liu et al. 2023 ). Although the above analysis explored the asymmetric effects of El Niño and La Niña on Sri Lanka's rainfall, it did not differentiate between the effects of EP and CP El Niño and La Niña events, nor did it consider the interplay with the Indian Ocean Dipole which is known to influence rainfall in the region (Zubair et al. 2003 ). Future research should investigate precipitation patterns in Sri Lanka associated with EP and CP ENSO events and their interactions with the IOD, utilizing observational data and climate models that are able to capture the asymmetry of ENSO and IOD, and ENSO-IOD interactions (McKenna et al. 2020). Nevertheless, the present study highlighted the significant asymmetric impact of El Niño and La Niña on winter rainfall in Sri Lanka. Declarations Competing Interests The authors have no relevant financial or non-financial interests to disclose. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contributions Pathmarasa Kajakokulan: Data analysis, Investigation, Software, Methodology, First draft preparation. Agus Santoso: Supervision and Writing-review & editing. Sen Zhao- Writing-review & editing. Data Availability The ERSSTV5 dataset is available at http://apdrc.soest.hawaii.edu/las/v6/ . 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Wiley, pp 1–19. https://doi.org/10.1002/9781119548164.ch1 Naveendrakumar G, Vithanage M, Kwon HH et al (2018) Five decadal trends in averages and extremes of rainfall and temperature in Sri Lanka. https://doi.org/10.1155/2018/4217917 . Adv Meteorol 2018: Pathirana G, Oh JH, Cai W et al (2023) Increase in convective extreme El Niño events in a CO2 removal scenario. Sci Adv 9:eadh2412. https://doi.org/10.1126/sciadv.adh2412 Ranaweera KRKDN, Kamae Y (2024) Impact of El Niño Southern Oscillation on the first inter-monsoon rainfall over Sri Lanka in the post-El Niño years. https://doi.org/10.3389/fclim.2024.1361322 Shiromani Priyanthika Jayawardena IM, Wheeler MC, Sumathipala WL, Basnayake BRSB (2020) Impacts of the Madden-Julian oscillation (Mjo) on rainfall in Sri Lanka. Mausam 71:405–422 Suppiah R (1996) Spatial and temporal variations in the relationships between the Southern Oscillation phenomenon and the rainfall of Sri Lanka. Int J Climatol 16:1391–1407. https://doi.org/10.1002/(sici)1097-0088(199612)16:123.0.co;2-x Tillekaratne HI, Werellagama I, Madduma-Bandara CM et al (2022) Hydro-Meteorological Incident and Disaster Response in Sri Lanka. Case Study: 2016 May Rain Events. Earth (Switzerland) 3:1–17. https://doi.org/10.3390/earth3010001 Trenberth KE, Shea DJ (2005) Relationships between precipitation and surface temperature. Geophys Res Lett 32:1–4. https://doi.org/10.1029/2005GL022760 Trenbeth KE, Caron JM, Stepaniak DP, Worley S (2002) Evolution of El Niño-Southern Oscillation and global atmospheric surface temperatures. J Geophys Res D Atmos 107:5–1. https://doi.org/10.1029/2000jd000298 Vialard J, Terray P, Duvel JP et al (2011) Factors controlling January-April rainfall over southern India and Sri Lanka. Clim Dyn 37:493–507. https://doi.org/10.1007/s00382-010-0970-4 Wang B, Zhang Q (2002) Pacific-East Asian teleconnection. Part II: How the Philippine Sea anomalous anticyclone is established during El Niño development. J Clim 15:3252–3265. https://doi.org/10.1175/1520-0442(2002)0152.0.CO;2 Wang C-Y, Zheng X-T, Xie S-P (2022) Enhanced ENSO-Unrelated Summer Variability in the Indo–Western Pacific under Global Warming. J Clim 36:1749–1765. https://doi.org/10.1175/jcli-d-22-0450.1 Wang H, Wang C (2023) Large-Scale Anomalous Cyclone in the Western North Pacific. J Clim 36:5895–5906. https://doi.org/10.1175/JCLI-D-22-0920.1 Yuan Y, Yang S (2012) Impacts of different types of El NiñO on the East Asian climate: Focus on ENSO cycles. J Clim 25:7702–7722. https://doi.org/10.1175/JCLI-D-11-00576.1 Zhang R, Akimasa S, Masahide K (1999) A Diagnostic Study of the Impact of El Niño on the Precipitation in China. Adv Atmos Sci 16:229–241. https://doi.org/10.1007/BF02973084 Zubair L (2002) El Niño-Southern oscillation influences on rice production in Sri Lanka. Int J Climatol 22:249–260. https://doi.org/10.1002/joc.714 Zubair L, Rao SA, Yamagata T (2003) Modulation of Sri Lankan Maha rainfall by the Indian Ocean Dipole. Geophys Res Lett 30. https://doi.org/10.1029/2002GL015639 Zubair L, Siriwardhana M, Chandimala J, Yahiya Z (2008) Predictability of Sri Lankan rainfall based on ENSO. Int J Climatol 28:91–101. https://doi.org/10.1002/joc.1514 Supplementary Files AsymmrtrySupp.docx Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2025 Read the published version in Climate Dynamics → Version 1 posted Editorial decision: Major Revision 16 Oct, 2024 Reviewers agreed at journal 09 Aug, 2024 Reviewers invited by journal 08 Aug, 2024 Editor assigned by journal 08 Aug, 2024 First submitted to journal 07 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4874154","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":337666579,"identity":"c11f502f-a8c2-49a5-bfdc-6f1ceea1b788","order_by":0,"name":"Pathmarasa Kajakokulan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIiWNgGAWjYBACCQaGBMYGhgQGPiDH+EcFkGRmbiBOCxuQU8xwBqSFkaAWBriWz4xtDBA+PiDZfuDhxxkVaYlt7GcPbi6cVxvN3w7U8qNiG04t0jwJyZIbzuQktvHkJRvP3HY8d8ZhxgbGnjO3cWqRY0hIkHzYVpHYxpBjZsC77VhuA1ALM2MbHi38D5J/grXwvzH/wTvnWO58QlqkJRLSJDe2AR0mkWNgzNtQk7uBkBbJGQ/SLGecSTNuk3hjYDjj2IHcjUAtB/H5ReJ8TvLNnopk2X7+HAODDzV1ufPOHz744EcFbi0MDDwJyLzDYPIAHvVAwI4iX4df8SgYBaNgFIxIAADE+WBKB0nlygAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-7361-9620","institution":"University of Ruhuna","correspondingAuthor":true,"prefix":"","firstName":"Pathmarasa","middleName":"","lastName":"Kajakokulan","suffix":""},{"id":337666580,"identity":"6d36087b-e17a-433b-aaac-9ae966369619","order_by":1,"name":"Agus Santoso","email":"","orcid":"","institution":"ARC Centre of Excellence for Climate System Science: Australian Research Council","correspondingAuthor":false,"prefix":"","firstName":"Agus","middleName":"","lastName":"Santoso","suffix":""},{"id":337666581,"identity":"f4a7ad35-8b44-444a-834f-09a70a7541f1","order_by":2,"name":"Sen Zhao","email":"","orcid":"","institution":"University of Hawai'i at Mānoa: University of Hawai'i at Manoa","correspondingAuthor":false,"prefix":"","firstName":"Sen","middleName":"","lastName":"Zhao","suffix":""}],"badges":[],"createdAt":"2024-08-07 11:00:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4874154/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4874154/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00382-025-07590-2","type":"published","date":"2025-01-28T15:57:59+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":63999470,"identity":"89b6afdc-00de-4c26-89a1-3505d030a709","added_by":"auto","created_at":"2024-09-04 18:31:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":180707,"visible":true,"origin":"","legend":"\u003cp\u003ea) Location map of Sri Lanka in the IO with a topography (meters), b) Climatology of rainfall (colour shading), and low-level wind (850 hPa, vector) \u0026nbsp;in winter from 1940 to 2021 and black box denotes Sri Lanka.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/353046171e6d9f44585267ba.jpg"},{"id":63999472,"identity":"38249fbd-670e-4cf6-8242-82b006928bf8","added_by":"auto","created_at":"2024-09-04 18:31:53","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":113173,"visible":true,"origin":"","legend":"\u003cp\u003ea) Correlation between rainfall anomalies of Sri Lanka from ERA5 (red) and CHIRPS (blue) and Niño 3.4 index during ENSO peak winter for the period of 1981 to 2022. The green horizontal dashed line denotes statistical significance at the 90% confidence level. b) Regression of rainfall anomalies onto Niño 3.4 index during El Niño peak winter from CHIRPS data. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots. In addition, (0) denotes the year in which El Niño developed, and (+1) denotes the following year.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/a8a2865c17e16a51a78acc2c.jpg"},{"id":63999466,"identity":"dd636456-0201-487f-b86a-097a3c0e2117","added_by":"auto","created_at":"2024-09-04 18:31:44","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":212555,"visible":true,"origin":"","legend":"\u003cp\u003eComposite of rainfall anomalies from 1981 to 2022 during December-February for a) El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and b) La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22, c) asymmetric part was calculated by summation of El Niño and La Niña composites. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/8331afa1c881e7e4da4d2fae.jpg"},{"id":63999432,"identity":"61e636a8-c1c6-4ef4-aaaa-d977fc55109b","added_by":"auto","created_at":"2024-09-04 18:31:29","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":367666,"visible":true,"origin":"","legend":"\u003cp\u003eComposite of SST anomalies for the period of 1981 to 2022 (El Niño peak winter) for a) El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and b) La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22, c) asymmetric part was calculated by summation of El Niño and La Niña. d) as a), e) as b), f) as c) but for OLR. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/6fb701e9442d52ee12879797.jpg"},{"id":63999431,"identity":"49acbea2-328a-4d9c-86c1-2abab9ea4aea","added_by":"auto","created_at":"2024-09-04 18:31:29","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":152162,"visible":true,"origin":"","legend":"\u003cp\u003eTemporal SST anomalies for the period of 1981 to 2022 of El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22. In addition, La Niña is multiplied by a minus one factor for comparison purposes.\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/970d777ea169d32ec73ec8e2.jpg"},{"id":63999441,"identity":"c5b7be66-6fdc-4fdb-ae59-5ef4f7525240","added_by":"auto","created_at":"2024-09-04 18:31:36","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":697150,"visible":true,"origin":"","legend":"\u003cp\u003eComposite of MSP (color shading) and low-level wind (850 hPa, vector) \u0026nbsp;anomalies for the period of 1981 to 2022 (ENSO peak winter) for a) El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and b) La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22, c) asymmetric part was calculated by summation of El Niño and La Niña. In addition, the contour line denotes where MSP exceeds 1 Mb. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots.\u003c/p\u003e","description":"","filename":"Picture6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/da0de177c2b1b981dd7387fc.jpg"},{"id":63999435,"identity":"da9f1a23-4a7b-4bc7-ac25-715fa7927c60","added_by":"auto","created_at":"2024-09-04 18:31:30","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":460729,"visible":true,"origin":"","legend":"\u003cp\u003eComposite of VIMFC (color shading), and VIMF (vector) \u0026nbsp;anomalies for the period of 1981 to 2022 (El Niño peak winter) for a) El Niño events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and b) La Niña events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22, c) asymmetric part was calculated by summation of El Niño and La Niña. d) as a), e) as b), f) as c) but for TCC. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots.\u003c/p\u003e","description":"","filename":"Picture7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/c8372f1ec87bf288a09872d3.jpg"},{"id":63999467,"identity":"54aabf96-9355-489d-9406-598e427c44a2","added_by":"auto","created_at":"2024-09-04 18:31:45","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":113041,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation of Niño3.4 index with a) PSAC index, b) VIMFC index, c) Sri Lanka rainfall index from 1981 to 2022 during El Niño peak winter. The blue and red dots represent the cold and warm phases of the Niño3.4 index, respectively. In addition, blue, red, and black lines denote the linear regression for the cold, warm, and all phases of the Niño3.4 index, respectively.\u003c/p\u003e","description":"","filename":"Picture8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/c1444a6efd4ae4d38ee6e85f.jpg"},{"id":63999439,"identity":"91b85ba3-9641-42b3-ab32-61668c867940","added_by":"auto","created_at":"2024-09-04 18:31:35","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":187696,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial correlation coefficient of Sri Lanka rainfall index with a) warm phase, b) cold phase from 1981 to 2022 during El Niño peak winter. The map denotes regions of statistical significance at the 90% confidence level, represented by white dots.\u003c/p\u003e","description":"","filename":"Picture9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/e9c5eac09ae61fd35ee73d07.jpg"},{"id":63999477,"identity":"00af7911-95a8-40ab-b8f6-fb95b1cc2fd0","added_by":"auto","created_at":"2024-09-04 18:31:54","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":166731,"visible":true,"origin":"","legend":"\u003cp\u003eThe schematic diagram visually represents the mechanism for rainfall responses during a) El Niño and b) La Niña. SST is shading, positive and negative sign shows the convergence, divergence respectively, and the black vector represent the Walker cell.\u003c/p\u003e","description":"","filename":"Picture10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/87d03be0ec17991712beb89d.jpg"},{"id":75351459,"identity":"310b3cf8-203e-46c2-8cd1-a2045a7e8f84","added_by":"auto","created_at":"2025-02-03 16:11:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3214602,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/cc035058-98d3-4f73-8a39-6ce5ee6e3113.pdf"},{"id":63999487,"identity":"5629d424-1557-494f-b0b5-10dafb948d43","added_by":"auto","created_at":"2024-09-04 18:31:57","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":839598,"visible":true,"origin":"","legend":"","description":"","filename":"AsymmrtrySupp.docx","url":"https://assets-eu.researchsquare.com/files/rs-4874154/v1/f899d8dcc7f7384fff37391b.docx"}],"financialInterests":"","formattedTitle":"Asymmetric Response of Sri Lanka Northeast Monsoon Rainfall to El Niño/La Niña","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eSri Lanka, situated between the Arabian Sea (AS) and the Bay of Bengal (BoB) in the equatorial Indian Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea), experiences significant weather and climate impacts due to the dynamical interaction between these two bodies of water (Bandurathna et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, several factors influence Sri Lanka's rainfall variability, including monsoon systems, the Intertropical Convergence Zone (ITCZ), oceanic currents, depression, and tropical cyclones (Kajakokulan et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023b\u003c/span\u003e; Jinadasa et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Among these, the monsoon system is a vital factor that controls the seasonal rainfall in Sri Lanka (Deoras et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and can be characterized by two principal monsoon rainfall seasons and two inter-monsoon rainfall seasons (Malmgren et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The Southwest Monsoon, one of the most significant monsoon regimes in the region, originates from the Indian Ocean and affects Sri Lanka's wet zone from May to September (Abeysekera et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). On the other hand, the Northeast Monsoon, also known as the winter monsoon, originates from the Bay of Bengal and impacts the northern and eastern parts of the island from December to February (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb Alahacoon and Edirisinghe \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Furthermore, the primary agricultural season is also associated with the occurrence of the Northeast Monsoon (Kajakokulan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). The two inter-monsoon seasons that occur between the two monsoon periods, the first monsoon from March to April and the second from October to November, are associated with the movement of the intertropical convergence zone (ITCZ) over Sri Lanka (Naveendrakumar et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The variability of the Northeast Monsoon can significantly impact agriculture, irrigation, and livelihood of people in Sri Lanka (Hapuarachchi and Jayawardena \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Burt and Weerasinghe \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The monsoon system in Sri Lanka is significantly influenced by various tropical climate oscillations, such as the El Ni\u0026ntilde;o Southern Oscillation (ENSO), the Madden-Julian Oscillation (MJO), and the Indian Ocean Dipole (IOD; Ranaweera and Kamae \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Huang et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Thus, these climate variability modes have a crucial impact on Sri Lankan rainfall, making it essential to understand their impact on the island's climate.\u003c/p\u003e \u003cp\u003eThe El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO), which significantly influences climate in the tropical Pacific Ocean and beyond (Gillett et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; McPhaden et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pathirana et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), exhibits an asymmetry between its warm (El Ni\u0026ntilde;o) and cold (La Ni\u0026ntilde;a) phases, leading to distinct and unequal impacts on global weather patterns (Lin et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). During the El Ni\u0026ntilde;o peak winter, the most prominent and persistent low-level atmospheric circulation anomalies over the tropical western Pacific are the anomalous west North Pacific anticyclone (WNPAC; Wang et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), also known as the Philippine Sea anomalous anticyclone (PSAC; Wang and Zhang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e); during the La Ni\u0026ntilde;a peak winter, the anomalous west North Pacific cyclone (WNPC; Yuan and Yang \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), also referred to as the Philippine Sea anomalous cyclone (PSCC; Bagtasa \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), is the dominant anomalous circulation. It has previously been demonstrated that ENSO-related anomalous anti-cyclonic and cyclonic anomalies can induce complex precipitation anomaly patterns, particularly over the tropical ocean (Zhang et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Trenberth and Shea \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). During El Ni\u0026ntilde;o events, the PSAC is known to result in a reduction in both convection and precipitation over the maritime continent and the western Pacific (Jiang et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, La Ni\u0026ntilde;a is characterized by an increase in precipitation, the primary driver of which is the PSCC (Wang and Zhang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). However, there exists asymmetry in the impact of ENSO on regional climate. For example, Lin et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified that during El Ni\u0026ntilde;o peak winter, the prevailing south-westerly winds are deflected from the southeast coast of China, significantly decreasing winter precipitation in southern and eastern central China, which is linked to an anomalous anticyclone over the western North Pacific (WNP); changes in La Ni\u0026ntilde;a are uncertain.\u003c/p\u003e \u003cp\u003eIt is well documented that there is a correlation between the rainfall patterns observed in Sri Lanka and the El Ni\u0026ntilde;o phenomenon, significantly impacting the country's seasonal rainfall patterns (Kane \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Zubair \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Zubair et al. \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The results of several studies have produced conflicting findings regarding the impact of ENSO phases on Sri Lankan precipitation patterns on a seasonal scale. Suppiah (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) analyzed Sri Lanka's rainfall response to El Ni\u0026ntilde;o, noting decreased (increased) rainfall during the Southwest monsoon (second inter-monsoon) season, with uncertain impacts during the Northeast monsoon. However, it has been documented that El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) has a suppressive (enhancing) influence on rainfall in Sri Lanka during Northeast monsoon periods (Kane \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Furthermore, Vialard et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) observed the alterations in wind circulation patterns associated with moisture transport towards Sri Lanka during the northeast monsoon.\u003c/p\u003e \u003cp\u003eOver the past few years, numerous studies have been conducted on the variability and trends of rainfall in Sri Lanka (Amarasinghe \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Jayawardena et al. 2020; Abeysekera et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Alahacoon and Edirisinghe \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Tillekaratne et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kajakokulan et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023a\u003c/span\u003e). Only a few studies focus on the impact of ENSO on Sri Lanka's rainfall in recent years (Kajakokulan et al. 2024;Koralegedara et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the asymmetric sea surface temperature (SST) anomalies between El Ni\u0026ntilde;o and La Ni\u0026ntilde;a, it is pertinent to investigate whether the Sri Lanka rainfall to El Ni\u0026ntilde;o and La Ni\u0026ntilde;a is also asymmetric in recent decades. However, an apparent lack of research exists on asymmetric responses of the Sri Lankan rainfall to ENSO and the associated mechanisms. Thus, this study aims to explore the differential influence of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a on winter precipitation in Sri Lanka, particularly during periods of heightened ENSO influence, and to examine potential underlying physical mechanisms. The remainder of the study is organized as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provides an overview of the datasets and methods utilized. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e then presents the results, and Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e show a summary, and discussion.\u003c/p\u003e"},{"header":"2 Data and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data\u003c/h2\u003e \u003cp\u003eThis study used the monthly extended reconstructed SST (ERSSTv5) dataset at a resolution of 2\u003csup\u003e\u0026deg;\u003c/sup\u003e \u0026times; 2\u003csup\u003e\u0026deg;\u003c/sup\u003e from the National Oceanographic and Atmospheric Administration (NOAA, Huang et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The monthly mean atmospheric ERA5 data at a resolution of 0.25\u003csup\u003e◦\u003c/sup\u003e \u0026times; 0.25\u003csup\u003e◦\u003c/sup\u003e, including specific humidity, sea level pressure, total cloud condition, rainfall, and wind, is derived from the fifth-generation European Centre for Medium-Range Forecasts (ECMWF) reanalysis (Dee et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). In addition, the vertical velocity at a resolution of 2.5\u003csup\u003e◦\u003c/sup\u003e \u0026times; 2.5\u003csup\u003e◦\u003c/sup\u003e was sourced from the National Centers for Environment Prediction\u0026ndash;National Center for Atmospheric Research (NCEP\u0026ndash;NCAR) reanalysis data (Kalnay et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e), while the daily precipitation data at a horizontal resolution of 0.05\u003csup\u003e◦\u003c/sup\u003e \u0026times; 0.05\u003csup\u003e◦\u003c/sup\u003e was derived from the Climate Hazards Group InfraRed Precipitation (CHIRPS, Funk et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Methods\u003c/h2\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\u003eThe following indices were utilized in the present study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNi\u0026ntilde;o3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Ni\u0026ntilde;o 3.4 index is computed by the SST anomalies averaged in the central to eastern equatorial Pacific (5\u0026deg;S-5\u0026deg;N and 120\u0026deg;W-170\u0026deg;W).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Trenbeth et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePSAC index is calculated as the area-averaged MSP anomalies in the equatorial Indo-Pacific Ocean (0\u0026deg;-20\u0026deg;N and 100\u0026deg;E-140\u0026deg;E).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e(Wang and Zhang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eObservational rainfall data were purchased from the Sri Lanka Meteorological Department to verify the reanalysis of CHIRPS and ERA5 rainfall data (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The methodology employed to define the PSAC and ENSO index is elucidated in detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. In the present study, the strong El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) events are classified as occurring when the normalized December\u0026ndash;February (DJF) Ni\u0026ntilde;o-3.4 index exceeds the 1 (-1) standard deviation (Fig. S2). Thus, we have identified seven strong El Ni\u0026ntilde;o events, which include 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16, as well as eight La Ni\u0026ntilde;a events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22. A composite analysis is employed in the study to understand the asymmetry pattern of rainfall and associated mechanisms. It should be noted that the anomalies presented in this study, relative to the climatological annual cycle from 1981\u0026ndash;2022, were calculated by removing both the seasonal climatology and a long-term linear trend, and the statistical significance is evaluated by the two-tailed Student\u0026rsquo;s t-test. The vertically integrated moisture flux convergence (VIMFC) has been determined from the following equation.\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\text{V}\\text{I}\\text{M}\\text{F}\\text{C}=-\\frac{1}{g}{\\int\\:}_{1000hPa}^{300hPa}\\left(\\frac{\\partial\\:uq\\:\\:\\:}{\\partial\\:x}+\\frac{\\partial\\:vq\\:\\:\\:}{\\partial\\:y}\\right)dp\\)\u003c/span\u003e \u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;..\u0026hellip;Eq.\u0026nbsp;1\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eq is the specific humidity (kg/kg)\u003c/p\u003e \u003cp\u003eu and v are zonal and meridional wind (m s\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003eg is the gravitational acceleration (m s\u003csup\u003e-2\u003c/sup\u003e)\u003c/p\u003e \u003cp\u003ep is the pressure (Pa)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The seasonal Sri Lanka rainfall response to the El Ni\u0026ntilde;o-Southern Oscillation\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEl Ni\u0026ntilde;o, a coupled air-sea phenomenon, exhibits a primary peak during the winter months of December, January, and February (DJF), profoundly influencing Sri Lanka's climate variability (Koralegedara et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Therefore, we calculated the correlation pattern between the Ni\u0026ntilde;o 3.4 index in winter and rainfall anomalies from the El Ni\u0026ntilde;o developing autumn to decaying summer, which is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea. The El Ni\u0026ntilde;o El Ni\u0026ntilde;o developing autumn correlation is statistically significant, likely due to the competing effect of a positive Indian Ocean Dipole (Zubair et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). On the other hand, the correlation during the El Ni\u0026ntilde;o peak winter for both data sets is negative (r~-0.41) and statistically significant (p-value\u0026thinsp;=\u0026thinsp;0.081), indicating the higher ENSO impact during this season over other seasons, with El Ni\u0026ntilde;o condition (positive Ni\u0026ntilde;o3.4) corresponding with reduced rainfall; and La Ni\u0026ntilde;a with increased rainfall. The corresponding regression pattern over Sri Lanka (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) shows significant negative rainfall anomalies throughout the country, with strongest impact over the eastern part of Sri Lanka.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The asymmetric responses of Sri Lanka rainfall to El Ni\u0026ntilde;o and La Ni\u0026ntilde;a\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the composite precipitation anomalies for El Ni\u0026ntilde;o events of 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16 and La Ni\u0026ntilde;a events of 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22 winters in Sri Lanka. During El Ni\u0026ntilde;o peak winter, there is a notable decrease in rainfall across Sri Lanka, particularly over the eastern part of the island (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). On the other hand, rainfall tends to increase when a La Ni\u0026ntilde;a event occurs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). To elucidate the asymmetric component in Sri Lanka, we examine the summation of the precipitation anomalies for the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). The below-normal precipitation anomaly in Sri Lanka is still evident, with broad regions that are statistically significant above 90% significance level, thereby revealing the greater effect induced by El Ni\u0026ntilde;o compared to La Ni\u0026ntilde;a. Furthermore, it is observed that the eastern region of Sri Lanka exhibits a more pronounced difference than other parts of the island. We also analyzed the composite precipitation anomalies using the ERA5 data and found similar results to CHIRPS data (Fig. S3). Thus, our study identified that El Ni\u0026ntilde;o has a higher impact on Sri Lanka rainfall than La Ni\u0026ntilde;a. Therefore, it is important to understand the factors that may play a more significant role in the asymmetry.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The physical mechanism for asymmetry rainfall response of Sri Lanka\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo illustrate the mechanism for the evidence for asymmetric Northeast Monsoon precipitation over Sri Lanka, we show the composite anomalous SST and OLR during the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). During the El Ni\u0026ntilde;o event, a tropical SST anomalies dipole pattern is observed, with the positive pole over the eastern Pacific Ocean and the negative pole over the tropical western Pacific Ocean and strong basin-wide warming over the entire Indian Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). In contrast, during La Ni\u0026ntilde;a, the dipole pattern reversed and extend westward, with the positive pole over the western Pacific Ocean and the negative pole over the tropical eastern Pacific Ocean and strong cooling over the Indian Ocean (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The summation of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events demonstrates a notable prevalence of robust positive SST anomalies over the Indian Ocean, which further substantiates the asymmetric responses exhibited by atmospheric circulation in response to El Ni\u0026ntilde;o than La Ni\u0026ntilde;a (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). We also analyzed the Walker circulation and consistent with SST and OLR results (Fig.S4). The Strong upward motion during El Ni\u0026ntilde;o, while downward motion over the Indian Ocean in La Ni\u0026ntilde;a. These results suggest that the precursor of asymmetry in rainfall variability over Sri Lanka cannot be obtained through changes in the Walker circulation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBesides the spatial asymmetries of ENSO, we also show the asymmetries of the temporal evolution of ENSO intensity and duration (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The SST intensity of El Ni\u0026ntilde;o is higher than that of La Ni\u0026ntilde;a from the onset of summer to the peak of winter, and decaying from the following spring, showing that the decay rate of El Ni\u0026ntilde;o is significantly stronger than that of La Ni\u0026ntilde;a. This implies that the intensity of ENSO may have a major influence on asymmetric rainfall patterns over Sri Lanka. Therefore, given the asymmetrical modulation of the SST, we conducted the composite wind at 850 hPa and mean sea level pressure anomalies to understand the mechanism. During the El Ni\u0026ntilde;o peak winter event, PSAC is observed with its central position at approximately (10\u003csup\u003eo\u003c/sup\u003eN, 130\u003csup\u003eo\u003c/sup\u003eE), which leads to the prevailing anti-cyclonic condition over the BoB (Chowdary et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea), and suppresses the rainfall over Sri Lanka. In contrast, a PSCC shifted westward with its central position at approximately (10\u003csup\u003eo\u003c/sup\u003eN, 120\u003csup\u003eo\u003c/sup\u003eE), leading to prevailing cyclonic conditions over the BoB (Wang and Wang \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb) and enhancing the rainfall over Sri Lanka. In addition, the asymmetric component of wind exhibits an evident anticyclone, indicating that the amplitude of PSAC is considerably greater than that of PSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). It is well consistent with previous studies showing that PSCC is weaker and moved westward relative to the PSAC (Guo et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This leads to the conclusion that there is a greater suppression of rainfall in Sri Lanka during an El Ni\u0026ntilde;o event compared to enhancement of rainfall during a La Ni\u0026ntilde;a event through the asymmetry in PSAC/PSCC associated circulations.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe large-scale convergence of atmospheric moisture is the primary driver of tropical precipitation patterns rather than locally enhanced evaporation (Darand and Pazhoh \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Given the pivotal role of moisture supply, it is crucial to comprehend the underlying factors responsible for the precipitation enhancement and suppression over Sri Lanka during El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events, respectively, during the winter season. Therefore, we analyzed the composite analysis of TCC, VIMF, and VIMFC over the IO. The presence of positive values of VIMFC and TCC indicates convergence, while negative values indicate divergence. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea presents the moisture flux transport indicated by vectors and the associated flux convergence indicated by shading for the El Ni\u0026ntilde;o peak winter season. During the El Ni\u0026ntilde;o peak winter period, there is observed anti-cyclonic conditions oriented vertically-integrated moisture flux over BoB, which carries moisture divergence and suppression of rainfall to Sri Lanka (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea). Conversely, during the La Ni\u0026ntilde;a peak winter, cyclonic conditions oriented vertically integrated moisture flux indicates moisture convergence from the BoB to Sri Lanka, leading to above normal rainfall (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). The sum of the vertically integrated moisture flux and convergence for the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a peak winters is used to estimate the asymmetry component of ENSO-induced moisture transport, with a pattern resembling that seen in the El Ni\u0026ntilde;o-induced vertically integrated moisture flux (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). It is noted that response of vertically integrated moisture flux is similar to 850 hPa wind anomalies. Moreover, a negative value is observed for the total cloud condition during the El Ni\u0026ntilde;o peak winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed), whereas a positive value is observed during the La Ni\u0026ntilde;a peak winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). This is consistent with findings indicating that moisture divergence occurs during the El Ni\u0026ntilde;o peak winter and moisture convergence occurs during the La Ni\u0026ntilde;a peak winter. Thus, our result confirms that the influence of ENSO on PSAC/PSCC circulation, and vertically integrated moisture flux convergence are vital factors that control Sri Lanka's rainfall variability.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e presents a scatter diagram of the Ni\u0026ntilde;o3.4 index versus the anomalous PSAC index, VIMFC index, and Sri Lanka rainfall index over Sri Lanka to elucidate the asymmetry of El Ni\u0026ntilde;o on precipitation. We initially investigated the potential correlation between the Ni\u0026ntilde;o3.4 and anomalous PSAC indexes. It is noted that the warm phase of the Ni\u0026ntilde;o3.4 index is positively correlated with the PSAC index (r\u0026thinsp;=\u0026thinsp;0.63), indicating a significant PSAC induced by the warm phase, while the positive correlation coefficient is 0.67 for the cold phase with the PSSC index, reveals that the cold phase induces a PSCC (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ea). On the other hand, the warm phase of Ni\u0026ntilde;o-3.4 index displays a negative correlation (r=-0.50) with the VIMFC index, suggesting that the warm phase of the Ni\u0026ntilde;o-3.4 index is associated with a moisture divergence anomaly in Sri Lanka. Conversely, there is a weaker negative correlation (r=-0.33) between the cold phase Ni\u0026ntilde;o-3.4 and the VIMFC index, indicating that the cold phase of the Ni\u0026ntilde;o-3.4 index is associated with a moisture convergence anomaly in Sri Lanka (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003eb). Furthermore, correlation pattern between El Ni\u0026ntilde;o and Sri Lanka rainfall index is also similar with the moisture results (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003ec). Thus, our study found that El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) is associated with PSAC (PSCC) lead to moisture divergence (convergence) over Sri Lanka, as a result, rainfall suppression (enhancement).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo affirm the asymmetric influence of the cold and warm phases on winter precipitation over Sri Lanka, we have calculated the correlations between the winter precipitation anomalies and the winter Ni\u0026ntilde;o3.4 index. It can be observed that the negative correlations in the warm and cold phases of the El Ni\u0026ntilde;o-Southern Oscillation (ENSO), indicate the exhibiting clear asymmetries (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). In years with positive SST anomalies (i.e., Ni\u0026ntilde;o3.4 index\u0026thinsp;\u0026gt;\u0026thinsp;0), negative strong correlations are observed to be more prevalent entire part of Sri Lanka. On the other hand, in years when the SST anomalies are negative (i.e., Ni\u0026ntilde;o3.4 index\u0026thinsp;\u0026lt;\u0026thinsp;0), the negative moderate correlation is observed to extend into Sri Lanka. This suggests that the impact of El Ni\u0026ntilde;o peak years on winter precipitation in Sri Lanka is greater than during La Ni\u0026ntilde;a peak years. Thus, our study confirms that the warm phase and cold phase of ENSO lead to an asymmetric pattern of rainfall in Sri Lanka via the longitudinal shift of PSAC and PSCC.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Summary and Discussion","content":"\u003cp\u003eIn this study, the impact of the warm and cold phases of ENSO on rainfall over Sri Lanka during winter was investigated, utilizing observational and reanalysis data from 1981 to 2022. Our study identified seven El Ni\u0026ntilde;o events in 1982/83, 1986/87, 1991/92, 1994/95, 1997/98, 2009/10, and 2015/16, and eight La Ni\u0026ntilde;a events in 1984/85, 1988/89, 1998/99, 1999/00, 2007/08, 2010/11, 2020/21, and 2021/22. Based on composite analysis, our results reveal an asymmetry in the influence of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a on Sri Lanka's winter precipitation. The findings align with previous research by Vialard et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), indicating that the warm phase of ENSO is associated with a decrease in rainfall, while the cold phase has been associated with a decrease in rainfall. The physical mechanisms responsible for the observed asymmetry are investigated and summarized by analyzing SST, wind, MSLP, TCC, moisture flux, and convergence. In years with a particularly strong El Ni\u0026ntilde;o, a development of PSAC generated predominant anti-cyclonic conditions over BoB. This suppresses moisture convergence and precipitation over Sri Lanka during winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003ea). In contrast, a strong La Ni\u0026ntilde;a with a presence of westward PSCC compared to PSAC, leads to cyclonic conditions over BoB, promotes moisture convergence, and increases rainfall over Sri Lanka (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003eb). This gives rise to in the asymmetry in the rainfall anomalies between El Ni\u0026ntilde;o and La Ni\u0026ntilde;a. Our study confirms the role of PSAC/PSCC in strengthening the asymmetry of winter precipitation in SL.\u003c/p\u003e \u003cp\u003ePrevious studies suggest that PSAC (PSCC) linked with CP (Central Pacific) El Ni\u0026ntilde;o (La Ni\u0026ntilde;a) exhibit less asymmetry than those associated with EP (Eastern Pacific) El Ni\u0026ntilde;o and La Ni\u0026ntilde;a, owing to the more symmetric distribution and intensity of tropical SST anomalies between CP El Ni\u0026ntilde;o and CP La Ni\u0026ntilde;a events (Wang and Zhang \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Feng et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, both the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a phases of the El Ni\u0026ntilde;o\u0026ndash;Southern Oscillation (ENSO) phenomenon exhibit asymmetric impacts on the summer precipitation patterns in the Tibetan Plateau (Liu et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Although the above analysis explored the asymmetric effects of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a on Sri Lanka's rainfall, it did not differentiate between the effects of EP and CP El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events, nor did it consider the interplay with the Indian Ocean Dipole which is known to influence rainfall in the region (Zubair et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Future research should investigate precipitation patterns in Sri Lanka associated with EP and CP ENSO events and their interactions with the IOD, utilizing observational data and climate models that are able to capture the asymmetry of ENSO and IOD, and ENSO-IOD interactions (McKenna et al. 2020). Nevertheless, the present study highlighted the significant asymmetric impact of El Ni\u0026ntilde;o and La Ni\u0026ntilde;a on winter rainfall in Sri Lanka.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interests\u003c/h2\u003e \u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003ePathmarasa Kajakokulan: Data analysis, Investigation, Software, Methodology, First draft preparation. Agus Santoso: Supervision and Writing-review \u0026amp; editing. Sen Zhao- Writing-review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003eThe ERSSTV5 dataset is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://apdrc.soest.hawaii.edu/las/v6/\u003c/span\u003e\u003cspan address=\"http://apdrc.soest.hawaii.edu/las/v6/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. NCEP/NCAR dataset is from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html\u003c/span\u003e\u003cspan address=\"https://psl.noaa.gov/data/gridded/data.ncep.reanalysis.pressure.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The ERA5 data is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/#!/\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/#!/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. CHIRPS data is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.chc.ucsb.edu/products/CHIRPS-2.0/\u003c/span\u003e\u003cspan address=\"https://data.chc.ucsb.edu/products/CHIRPS-2.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The in-situ observation data is available from the Department of Meteorology, Sri Lanka, and the data can be accessed upon request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbeysekera AB, Punyawardena BVR, Marambe B et al (2021) Effect of Indian Ocean Dipole (IOD) Events on the Second Inter-monsoonal Rainfall in the Wet Zone of Sri Lanka. Trop Agric Res 32:287. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4038/tar.v32i3.8492\u003c/span\u003e\u003cspan address=\"10.4038/tar.v32i3.8492\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlahacoon N, Edirisinghe M (2021) Spatial variability of rainfall trends in sri lanka from 1989 to 2019 as an indication of climate change. 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Int J Climatol 28:91\u0026ndash;101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/joc.1514\u003c/span\u003e\u003cspan address=\"10.1002/joc.1514\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \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":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"El Niño-Southern Oscillation (ENSO), Rainfall, Sri Lanka, Philippine Sea anomalous anticyclone","lastPublishedDoi":"10.21203/rs.3.rs-4874154/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4874154/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMany studies have indicated that an asymmetry in rainfall anomalies over India exists between the warm and cold phases of the El Ni\u0026ntilde;o-Southern Oscillation (ENSO). However, the extent to which the distinctive phases of ENSO influence the asymmetry pattern of Sri Lanka rainfall remains unclear. In this study, utilizing observational/reanalysis datasets for the period 1981\u0026ndash;2022, we found that Sri Lanka's winter rainfall response to El Ni\u0026ntilde;o/La Ni\u0026ntilde;a is asymmetric, with a significant response during El Ni\u0026ntilde;o. During the El Ni\u0026ntilde;o peak winter, the presence of PSAC (Philippine Sea anomalous anticyclone) results in the prevailing anticyclone over the Bay of Bengal (BoB), suppressing moisture convergence and rainfall over Sri Lanka. On the other hand, the PSCC (Philippine Sea anomalous cyclone), which has shifted westward during the La Ni\u0026ntilde;a. This shift enhances cyclone over the BoB, resulting in enhanced moisture convergence and rainfall over Sri Lanka, with a magnitude that is weaker than that of the El Ni\u0026ntilde;o-induced PSAC. This results in the emergence of asymmetric rainfall anomaly patterns in Sri Lanka in the El Ni\u0026ntilde;o and La Ni\u0026ntilde;a peak phases. Thus, this study highlights that the asymmetric circulation of PSAC/PSCC during the ENSO phenomenon contributes to the observed asymmetry in rainfall anomalies between El Ni\u0026ntilde;o and La Ni\u0026ntilde;a events and has important implications for seasonal forecasting.\u003c/p\u003e","manuscriptTitle":"Asymmetric Response of Sri Lanka Northeast Monsoon Rainfall to El Niño/La Niña","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-04 17:41:26","doi":"10.21203/rs.3.rs-4874154/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revision","date":"2024-10-16T09:01:51+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-08-09T06:01:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-08T11:32:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-08T07:49:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climate Dynamics","date":"2024-08-07T06:59:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climate-dynamics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cldy","sideBox":"Learn more about [Climate Dynamics](https://www.springer.com/journal/382)","snPcode":"382","submissionUrl":"https://submission.nature.com/new-submission/382/3","title":"Climate Dynamics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"0ef04714-802a-4671-b19d-4124a6e63415","owner":[],"postedDate":"September 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-03T16:06:35+00:00","versionOfRecord":{"articleIdentity":"rs-4874154","link":"https://doi.org/10.1007/s00382-025-07590-2","journal":{"identity":"climate-dynamics","isVorOnly":false,"title":"Climate Dynamics"},"publishedOn":"2025-01-28 15:57:59","publishedOnDateReadable":"January 28th, 2025"},"versionCreatedAt":"2024-09-04 17:41:26","video":"","vorDoi":"10.1007/s00382-025-07590-2","vorDoiUrl":"https://doi.org/10.1007/s00382-025-07590-2","workflowStages":[]},"version":"v1","identity":"rs-4874154","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4874154","identity":"rs-4874154","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}