﻿<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD v1.3 20210610//EN" "JATS-archivearticle1-3.dtd">
<article xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" xml:lang="en">
  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">scholix</journal-id>
      <journal-title-group><journal-title>International Epidemiology and Public Health</journal-title></journal-title-group>
      <issn pub-type="epub">3144-7708</issn>
      <publisher><publisher-name>Scholix Publishers</publisher-name></publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">102</article-id>
      <article-id pub-id-type="doi">10.61148/IEPH/modeling-enso-driven-climate-variability</article-id>
      <title-group><article-title>Modeling ENSO-Driven Climate Variability and Extreme Weather: Hopf Bifurcations and Control of  Ocean–Atmosphere Oscillations</article-title></title-group>
      <contrib-group>
        <contrib contrib-type="author"><name><string-name>Lakshmi. N. Sridhar</string-name></name></contrib>
      </contrib-group>
      <pub-date pub-type="epub">
        <day>11</day><month>09</month><year>2026</year>
      </pub-date>
      <volume>1</volume>
      <issue>1</issue>
      <fpage>1</fpage>
      <abstract><p>Lakshmi. N. Sridhar
Chemical Engineering Department
University of Puerto Rico
Mayaguez, PR 00681

*Corresponding author: Lakshmi. N. Sridhar.

 Abstract
The El Niño–Southern Oscillation (ENSO) is a dominant driver of interannual climate variability, strongly influencing global weather patterns, tropical cyclone activity, and extreme climate events. This study develops and analyzes a reduced-order nonlinear ocean–atmosphere model that captures the essential mechanisms underlying ENSO dynamics, focusing on the interaction between sea surface temperature anomalies and thermocline depth anomalies in the tropical Pacific. The model incorporates positive feedback between ocean and atmosphere, delayed negative feedback through ocean adjustment processes, and nonlinear saturation effects that constrain system growth. These interactions give rise to self-sustained oscillations representing El Niño and La Niña cycles. A key feature of the system is the emergence of a Hopf bifurcation, marking the transition from a stable climate state to periodic climate variability. This transition is characterized through analytical and numerical Jacobian analysis, confirming the onset of oscillatory behavior. The resulting limit cycles provide a simplified representation of ENSO-driven climate oscillations that modulate global weather extremes, including variations in rainfall, drought patterns, and tropical cyclone activity across multiple ocean basins. The framework further highlights how small changes in ocean–atmosphere coupling strength can significantly alter climate stability, emphasizing the sensitivity of the tropical climate system. Overall, the model provides a physically interpretable and mathematically tractable description of ENSO dynamics, offering insight into the mechanisms governing climate variability and its far-reaching impacts on global weather and extreme events.
</p></abstract>
      <permissions>
        <copyright-statement>Copyright &#169; 2026 Scholix Publishers</copyright-statement>
        <license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/"><license-p>This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License.</license-p></license>
      </permissions>
    </article-meta>
  </front>
  <body><p>Keywords: ENSO dynamics, ocean–atmosphere interaction, Hopf bifurcation, climate variability, tropical cyclones

Introduction

The El Niño–Southern Oscillation (ENSO) is the dominant mode of interannual climate variability in the Earth system and arises from coupled interactions between the tropical Pacific Ocean and atmosphere. It is characterized by alternating warm (El Niño) and cold (La Niña) phases, associated with anomalous sea surface temperatures, thermocline displacements, and changes in large-scale atmospheric circulation. These coupled anomalies reorganize tropical convection and modify global atmospheric teleconnections, making ENSO a primary driver of year-to-year variability in weather and climate extremes worldwide. Because of its far-reaching influence, ENSO is not only a fundamental scientific problem in climate dynamics but also a major factor in disaster risk, particularly for droughts, floods, heatwaves, and tropical cyclone activity.

One of the most significant impacts of ENSO is its strong modulation of tropical cyclone activity, especially in the Atlantic basin. During El Niño events, enhanced convection over the central and eastern Pacific strengthens upper-level westerly winds over the Caribbean and tropical Atlantic, increasing vertical wind shear and suppressing hurricane formation. This dynamical environment inhibits cyclone development and reduces the intensity and frequency of Atlantic hurricanes. In contrast, La Niña conditions weaken vertical wind shear and promote warmer Atlantic sea surface temperatures, creating favorable conditions for enhanced hurricane activity. As a result, ENSO phases are strongly correlated with seasonal hurricane variability and are widely used in operational seasonal hurricane forecasts. This relationship highlights the importance of understanding ENSO dynamics not only for climate theory but also for practical forecasting and risk management.

Beyond the Atlantic basin, ENSO significantly influences tropical cyclone behavior across the Pacific and Indian Oceans. El Niño events tend to shift cyclone activity eastward into the central and eastern Pacific, increasing storm frequency in regions that are typically less active, while simultaneously suppressing cyclone formation in the western Pacific. This redistribution is linked to large-scale changes in the Walker circulation and shifts in the location of maximum sea surface temperature anomalies. Consequently, ENSO acts as a global organizer of tropical cyclone climatology, altering storm genesis regions, tracks, and intensities across multiple ocean basins. These teleconnections underscore the interconnected nature of the climate system, where perturbations in one region can have far-reaching impacts on weather extremes worldwide.

ENSO also plays a critical role in shaping global precipitation patterns and hydrological extremes. El Niño events are often associated with increased rainfall and flooding in parts of western South America, while simultaneously inducing drought conditions in Australia, Indonesia, and parts of Southeast Asia. La Niña events typically produce opposite anomalies, reinforcing the role of ENSO as a driver of alternating wet and dry climate extremes. These precipitation anomalies are mediated through atmospheric wave propagation, changes in convection, and shifts in jet stream positioning, all of which are directly linked to tropical Pacific sea surface temperature anomalies. The resulting impacts on agriculture, water resources, and infrastructure make ENSO one of the most economically and socially significant climate phenomena.

From a dynamical systems perspective, ENSO is often interpreted as a self-sustained oscillatory phenomenon arising from nonlinear ocean–atmosphere feedbacks. Key processes include positive feedback between sea surface temperature and atmospheric circulation, delayed negative feedback through thermocline adjustment, and nonlinear saturation mechanisms that limit amplitude growth. These interactions can generate oscillatory behavior even in the absence of external periodic forcing. In simplified conceptual models, these mechanisms are often represented using low-order dynamical systems that capture the essential structure of ENSO variability. Such models are particularly valuable because they allow analytical investigation of stability, oscillation onset, and sensitivity to parameter changes.

In this context, the transition from stable climate equilibrium to oscillatory ENSO behavior can be understood as a qualitative change in system dynamics driven by variations in ocean–atmosphere coupling strength. Near critical

thresholds, small perturbations in system parameters can lead to large-amplitude oscillations, indicating heightened climate sensitivity. This behavior is consistent with the presence of nonlinear instabilities in the coupled system, which can give rise to sustained periodic variability in the form of El </p></body>
  <back><ref-list><title>References</title><p>
 Timmermann, A. (2001). Changes in El Niño–Southern Oscillation stability due to greenhouse warming. Geophysical Research Letters, 28(10), 2061–2064.
 Wang, C. (2001). A unified oscillator model for the El Niño–Southern Oscillation. Journal of Climate, 14(1), 98–115.
 Philander, S. G. H., &amp; Fedorov, A. V. (2003). Role of ocean–atmosphere interaction in tropical climate variability. Journal of Climate, 16(19), 3143–3155.
 An, S. I., &amp; Jin, F. F. (2004). Nonlinearity and asymmetry of ENSO. Journal of Climate, 17(13), 2399–2414.
 Wang, C., &amp; Picaut, J. (2004). Understanding ENSO physics: A review. Earth’s Climate: The Ocean–Atmosphere Interaction, 21–48.
 Clarke, A. J. (2008). An introduction to ENSO dynamics. Journal of Climate, 21(14), 3430–3441.
 Ghil, M., Zaliapin, I., &amp; Thompson, S. (2008). A delay differential model of ENSO variability. Physica D: Nonlinear Phenomena, 237(14–17), 2411–2425.
 Collins, M., et al. (2010). The impact of global warming on the tropical Pacific Ocean and ENSO. Nature Geoscience, 3, 391–397.
 Latif, M., et al. (2010). ENSO prediction and predictability. Wiley Interdisciplinary Reviews: Climate Change, 1(5), 745–757.
 McPhaden, M. J. (2012). A 21st-century shift in ENSO research. Nature Climate Change, 2, 676–677.
 Power, S. B., Delage, F., Chung, C., Kociuba, G., &amp; Keay, K. (2013). Robust twenty-first-century projections of ENSO variability. Nature Climate Change, 3, 322–328.
 Cai, W., et al. (2014). Increasing frequency of extreme El Niño events due to greenhouse warming. Nature Climate Change, 4, 111–116.
 Stein, K., Timmermann, A., Jin, F. F. (2014). ENSO behavior under climate change. Journal of Climate, 27(1), 31–51.
 Yeh, S. W., et al. (2014). El Niño in a changing climate. Nature, 515, 406–409.
 Lengaigne, M., et al. (2015). Influence of climate change on ENSO diversity. Climate Dynamics, 45, 577–594.
 Wang, C. (2015). New theories of ENSO variability. Advances in Atmospheric Sciences, 32, 115–138.
 Timmermann, A., et al. (2018). El Niño–Southern Oscillation complexity. Nature, 559, 535–545.
 Capotondi, A., et al. (2015). Understanding ENSO diversity. Bulletin of the American Meteorological Society, 96(6), 921–938.
 Cai, W., et al. (2015). Increased ENSO variability under global warming. Nature Climate Change, 5, 849–859.
 Jin, F. F., &amp; Wang, C. (2017). ENSO recharge oscillator framework revisited. Climate Dynamics, 49, 1–17.
 Stein, K., &amp; Timmermann, A. (2017). ENSO dynamics in a warming climate. Geophysical Research Letters, 44, 1247–1256.
 Varotsos, C. A., &amp; Cracknell, A. P. (2017). Modelling ENSO coupling. arXiv preprint arXiv:1708.06114.
 Wang, C., Deser, C., Yu, J. Y., DiNezio, P., &amp; An, S. I. (2017). El Niño and Southern Oscillation. Annual Review of Marine Science, 9, 153–178.
 Timmermann, A. (2018). Tropical Pacific climate change and ENSO. Nature Reviews Earth &amp; Environment, 1, 1–15.
 Santoso, A., et al. (2019). ENSO extremes and climate impacts. Nature Reviews Earth &amp; Environment, 1, 1–12.
 McGregor, S., et al. (2020). ENSO response to greenhouse forcing. Nature Climate Change, 10, 441–447.
 Cai, W., et al. (2020). Pantropical climate changes and ENSO extremes. Nature Reviews Earth &amp; Environment, 1, 1–9.
 Chen, N., &amp; Zhang, Y. (2022). Rigorous derivation of stochastic conceptual models for ENSO. arXiv preprint arXiv:2212.14179.
 Taschetto, A. S., et al. (2021). ENSO teleconnections and climate variability. Journal of Climate, 34(15), 6123–6145.
 Dhooge, A., Govearts, W., and Kuznetsov, A. Y., MATCONT: “A Matlab package for numerical bifurcation analysis of ODEs”,   ACM transactions on Mathematical software 29(2) pp. 141-164, 2003.
 Kuznetsov,Y.A. “Elements of applied bifurcation theory” .Springer,NY, 1998.
 Govaerts, w. J. F., “Numerical Methods for Bifurcations of Dynamical Equilibria”, SIAM, 2000.
 Hart, William E., Carl D. Laird, Jean-Paul Watson, David L. Woodruff, Gabriel A. Hackebeil, Bethany L. Nicholson, and John D. Siirola. “Pyomo – Optimization Modeling in Python” Second Edition. Vol. 67.
 Wächter, A., Biegler, L. “On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming”. Math. Program. 106, 25–57 (2006). https://doi.org/10.1007/s10107-004-0559-y

</p></ref-list></back>
</article>