Orals: Wed, 6 May, 08:30–12:30 | Room L2
Global mean surface temperature (GMST) reached a new record in 2024, exceeding pre-industrial levels by approximately 1.5 °C for the first time. The long-term rise in GMST is driven by Earth’s radiative imbalance at the top of the atmosphere, caused by human-induced increases in heat-trapping greenhouse gases. Superimposed on this long-term warming trend are natural variations like those associated with El Niño and La Niña. Following an unusual triple-dip La Niña from 2020 to 2023, a strong El Niño developed in boreal spring 2023 and persisted through mid-2024. From the final year of the La Niña (July 2022–June 2023) to the subsequent year encompassing the 2023–24 El Niño (July 2023–June 2024), GMST rose by an unprecedented 0.36 °C to 1.5 °C above the 1850-1900 average. This presentation demonstrates that the primary driver of this abrupt increase in GMST was the release of heat previously stored in the ocean during the prolonged La Niña, which was rapidly transferred to the atmosphere during the 2023–24 El Niño event.
How to cite: McPhaden, M., Jiang, N., Zhu, C., Lian, T., Hu, Z.-Z., Zhou, C., and Chen, D.: The 2023-24 El Niño Boosts Global Mean Surface Temperatures to a New Record High 1.5°C Above Preindustrial Levels, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2013, https://doi.org/10.5194/egusphere-egu26-2013, 2026.
The representation of El Niño-Southern Oscillation (ENSO) atmospheric teleconnections is investigated in a new set of multi-decadal simulations that combine an eddy-resolving ocean with a high-resolution atmosphere, both at an unprecedented horizontal resolution of ~10 km. The experiments were produced using three different models run under a coordinated protocol within the European Eddy-RIch Earth System Models (EERIE) project. The impact of high resolution on ENSO teleconnections is unsettled, and no assessment has been carried out so far using climate simulations at the 10-km scale employed here.
Model fidelity is evaluated using a set of diagnostics designed to capture key components of ENSO teleconnections, including the tropical atmospheric response, Rossby wave generation, extratropical tropospheric and stratospheric circulation anomalies, and associated surface signals. These diagnostics are further applied to atmosphere-only experiments at low (~30 km) and high (~10 km) resolution, which are used to assess the impact of atmospheric resolution alone.
The coupled EERIE simulations show heterogeneous results relative to previous models with coarser grids (maximum 25 km). While the overall performance is positive, it depends on the season, region, and model configuration. Consistent with this, the atmosphere-only experiments suggest only modest gains from enhanced atmospheric resolution. The results are placed in the context of uncertainty in the ENSO response associated with internal variability and sampling, which may hinder potential benefits.
How to cite: Mezzina, B., Roberts, C. D., Aengenheyster, M., Ghosh, R., Roberts, M. J., and Batlle Martin, M.: ENSO teleconnections in eddy-rich climate models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5711, https://doi.org/10.5194/egusphere-egu26-5711, 2026.
In this study we review the current knowledge of the impacts associated with the El Niño Southern Oscillation (ENSO) in Australia. Among the major large-scale modes of variability, ENSO is the dominant phenomenon influencing seasonal mean rainfall and temperature, producing a spatially coherent pattern across nearly two-thirds of Australia. Its influence typically amplifies during multi-year events and varies on multidecadal cycles. When co-occurring with other climate modes of variability, such as Indian Ocean Dipole, Southern Annular Mode and Madden–Julian Oscillation, ENSO’s combined influence can explain about 50% of seasonal rainfall variability in parts of eastern and northern Australia during spring.
The main large-scale mechanisms that explain ENSO’s influence in Australia are (1) via changes in tropical atmospheric circulation associated with the Southern Oscillation and its related changes in sea level pressure, (2) through the modulation of the Pacific South American (PSA) pattern, and (3) indirectly via changes in the Indian Ocean sea surface temperatures (SST), which trigger Rossby wave trains to the Australian extra-tropics. These mechanisms affect the intensity and persistence of weather systems that control rainfall, particularly in eastern Australia. Their impacts are further modulated by local SST and land-atmosphere processes that can alter evaporation, humidity and moisture advection inland, thereby modulating rainfall response during ENSO events.
Although most studies published in the literature have focused on addressing the El Niño impacts, it is the La Niña phase of ENSO that produces a more consistent and arguably more societally impactful change across Australia. The ENSO-Australian rainfall relationship is asymmetric and stronger for La Niña. It is also the Central Pacific-type of ENSO event that typically produces stronger impacts on Australia. We will discuss the links between ENSO diversity, weather patterns, and associated extreme events, such as droughts and floods.
In a warmer climate, the ENSO-Australian rainfall relationship is projected to intensify by about 10-20%, consistent with many other regions across the globe. ENSO-driven precipitation and surface temperature variability is projected to strengthen in September to November over southeastern and southern Australia, while the largest changes are projected to occur during the warm season from December to February over most of western and northern Australia.
Despite considerable improvements in ENSO predictability and seasonal outlooks over the past four decades, predicting its impacts remains challenging because of large internal atmospheric variability. In addition, the observed cooling trend in the Pacific Ocean directly challenges the accuracy of El Niño-like warming projections in a future warming climate. These evolving ENSO features highlight the need for strategic research, sustained in situ monitoring, reduced model biases, and improved understanding of the anthropogenically induced changes in Pacific temperatures to support adaptation strategies.
How to cite: Taschetto, A. S. and McGregor, S. and the great team: A review of the El Niño Southern Oscillation impacts on Australian climate, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19177, https://doi.org/10.5194/egusphere-egu26-19177, 2026.
The El Niño-Southern Oscillation (ENSO) is the main source of climate variability in the tropical Pacific, affecting global patterns and extreme events like heatwaves, with significant consequences. ENSO is marked by warmer (El Niño) and cooler (La Niña) sea surface temperature (SST) anomalies over the eastern Pacific, with a typical seasonal cycle: development in fall, a winter peak, and rapid decay in spring. However, some ENSO events last beyond this canonical cycle, extending into northern summer. A notable recent example occurred in 2018-2019, when El Niño conditions persisted until July 2019 and were implicated in driving a record-breaking marine heatwave in the North Pacific. Although prolonged El Niño events have occurred in the recent past and are projected to become more frequent under future climate conditions, their summer impacts, particularly their role in modulating heatwave characteristics, remain poorly understood.
This study compares the frequency, duration, and intensity of summer heatwaves in the Americas following summer-persistent El Niño events with those following normal El Niño events. We define a summer-persistent El Niño as an event in which SST anomalies in the Niño3.4 region remain above 0.5 °C through June of the decaying year. Heatwaves are defined as periods of at least three consecutive days with daily maximum temperatures above the 90th percentile of the 1961-1990 reference period. To assess these relationships, we analyse reanalysis datasets (ERSSTv5, NCEP20CR, ERA5) and perform AMIP-type simulations using the atmospheric component of the Earth System Model EC-Earth3. For ENSO-neutral, conventional El Niño, and summer-persistent El Niño conditions, monthly SST composites are generated that capture the annual cycle by averaging all historical events. Based on these composites, we construct a 10-member ensemble, each spanning a six-year simulation. To further isolate ENSO-related forcing, we perform sensitivity experiments by uniformly increasing SSTs across the ENSO-active region by 1 °C.
We identify six summer-persistent El Niño events since 1895. These events are associated with reduced heatwave activity over the western United States, characterised by less frequent, shorter, and cooler events, linked to a sustained but distorted Pacific-North American (PNA)-like pattern. In contrast, northeastern South America experiences pronounced positive heatwave anomalies, with more frequent, longer-lasting, and more intense heatwaves than those observed following conventional El Niño events. These regional differences are driven by a prolonged weakening and eastward shift of the Walker Circulation, accompanied by intensified descending motion over South America. Collectively, these findings underscore the extended influence of ENSO beyond its typical spring termination and highlight the importance of accounting for ENSO persistence in seasonal heatwave forecasts and climate adaptation strategies in ENSO-sensitive regions.
How to cite: Schultze, A., Lu, Z., Wang, Z., Karami, M. P., Zhang, Q., Zheng, M., and Pugh, T. A. M.: Prolonged El Niño conditions modulate heatwaves across the Americas, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12299, https://doi.org/10.5194/egusphere-egu26-12299, 2026.
The Peruvian upwelling system is one of the most productive coastal marine ecosystems, powered by persistent winds that bring cold, nutrient-rich water to the surface. However, it can be severely disrupted by rapid coastal warming episodes known as Coastal El Niño events, which alter marine ecosystems and regional climate. Although often linked to ENSO, evidence from events like 1925 and 2017 shows that some warmings arise without basin-wide equatorial warming or strong tropical Pacific coupling, prompting the need to assess how common they are and whether they represent a recurring climate mode.
Using an objective, pattern-based method rather than traditional Niño indices, this study identifies coastal warming events defined by an eastern-Pacific warming and central-Pacific cooling dipole while the canonical ENSO mode remains weak. This approach reveals that ENSO-independent coastal warmings are more frequent and diverse than previously thought, typically driven by subtropical atmospheric variability that weakens both far-eastern equatorial trade winds and alongshore coastal winds, reducing upwelling, deepening the nearshore thermocline, and amplifying surface warming; some events may later transition into full El Niño as equatorial feedbacks develop. Overall, coastal warming emerges as a distinct mode of tropical Pacific variability triggered by remote atmospheric forcing and strengthened by local ocean–atmosphere processes.
How to cite: Soto, J., Khodri, M., and Chamorro, A.: Coastal El Niño events off Peru associated with cold ENSO background conditions, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14196, https://doi.org/10.5194/egusphere-egu26-14196, 2026.
Despite substantial progress over the past four decades, accurately predicting the spatiotemporal structure of the El Niño–Southern Oscillation (ENSO) remains a persistent challenge for dynamical models. While deep learning models have demonstrated improved prediction skills, their performances are constrained by biases in climate models used for training and lack dynamic interpretability. Here we construct a novel hybrid model that integrates deep learning techniques into a dynamical model, enabling information exchanging during integration. Training on physical-informed data, the model continuously adapts and improves forecasts, achieves unprecedented ENSO prediction skills, particularly in El Niño diversity and the spring predictability barrier. Moreover, as the hybrid model requires only a small volume of data by training on observations, it circumvents biases in climate models. Enhanced prediction skills arise primarily from improved representation of the leading feedbacks associated with ENSO. Our results suggest that training models with physical-informed data is an effective approach for ENSO prediction.
How to cite: Feng, J., Lian, T., Liu, T., and Chen, D.: Achieving explainable ENSO prediction using small data training, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3457, https://doi.org/10.5194/egusphere-egu26-3457, 2026.
The El Niño–Southern Oscillation (ENSO) arises from ocean–atmosphere interactions in the tropical Pacific and is a major source of global seasonal climate predictability. Canonical theories describe ENSO as a cyclic phenomenon, with ocean dynamics favouring transitions between warm (El Niño) and cold (La Niña) phases, and atmospheric noise introducing irregularities. Here, we show that ocean dynamics rarely favour such transitions. Following La Niña and moderate El Niño events, opposing wave signals from the central and western Pacific weaken the ocean’s memory, inhibiting consistent phase reversals. In contrast, extreme El Niño events—such as those in 1982, 1997, and 2015—trigger strong, nonlinear atmospheric responses that generate distinctive ocean heat content anomalies and set up a robust transition to a two-year La Niña. We propose revising the canonical recharge oscillator framework to account for this behaviour and explain ENSO’s dominant 3–7 year timescale as emerging from transitions between extreme El Niño and multi-year La Niña events. Overall, these results indicate that extreme El Niño events uniquely provide two-year ENSO predictability, while in other cases, predictability stems from external forcing that generate imbalances between heat content anomalies in the central and western Pacific.
How to cite: Liu, F., Vialard, J., Han, S., Planton, Y., Lengaigne, M., Gangiredla, S., Zhao, S., Guilyardi, E., Ethé, C., Person, R., Voldoire, A., Jin, F.-F., V Fedorov, A., and J. McPhaden, M.: ENSO cycles mostly after extreme El Niño events, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2093, https://doi.org/10.5194/egusphere-egu26-2093, 2026.
El Niño-Southern Oscillation (ENSO) is the dominant atmosphere-ocean coupled mode of year-to-year variations in the tropical Pacific. It shows diverse spatiotemporal characteristics and casts major influences on seasonal predictions of global weather-climate extrema. Despite numerous dynamical and statistical models for ENSO prediction and predictability studies, they are commonly subjected to one-to-three issues among less skillful simulation of El Niño diversity, huge requirements of computational resources and a low robustness in statistics. Here, an efficient deep-learning model involving nonlinear coupling of multiple variables is independently developed to study the predictability of two types of El Niño events related to initial uncertainty, which is the first kind of predictability problem. The model can skillfully simulate statistically robust features of observed El Niño diversity in terms of periodicity, amplitude, and seasonal phase-locking. Using this model, we have revealed mathematically several new types of fastest-growing initial errors in two types of El Niño predictions based on a novel concept of conditional nonlinear optimal perturbation (CNOP), especially including one that can strengthen central Pacific types of events, which is rarely investigated before. Moreover, CNOPs are superimposed into a numerical model, GFDL CM2p1, for comprehensive validation and growth mechanism mining, which demonstrates the consistent dynamical evolution of initial errors in both numerical and AI models. Our study represents the first attempt to explore the first kind of ENSO predictability problem from perspectives of nonlinear error-evolving dynamics using a data-driven model. This is of great importance as it offers us sufficient confidence to perform ENSO-related (such as the Madden-Julian Oscillation, etc) mechanisms and predictability studies in the future without strongly relying on dynamical numerical models.
How to cite: Yang, Z., Ren, X., Wu, X., and Wang, G.: The First Kind of Predictability Study for El Niño Prediction in a Multivariate Coupled Data-Driven Model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6072, https://doi.org/10.5194/egusphere-egu26-6072, 2026.
Linear inverse models (LIMs) are widely used in climate science to diagnose dynamical relationships among climate variables and to predict large-scale variability such as El Niño-Southern Oscillation (ENSO). Recent extensions from stationary to cyclostationary LIMs (CS-LIMs) incorporate seasonally varying dynamics, but most formulations still assume white-noise forcing, which implicitly requires that the system state and the random forcing are well separated in frequency. This assumption limits their ability to represent realistic atmosphere-ocean interactions.
In this study, we further advance the cyclostationary LIM framework by introducing Ornstein-Uhlenbeck colored noise to represent persistent atmospheric stochastic forcing. We refer to this extension as CS-Colored-LIM. Incorporating persistent noise enables a more physically consistent representation of unresolved atmospheric variability and its cumulative influence on the coupled system.
We compare the newly developed CS-Colored-LIM and conventional LIMs in terms of their Niño 3.4 forecast skill and their ability to capture essential ENSO features and the influence of stochastic forcing. Our analysis demonstrates that CS-Colored-LIM accurately reproduces the seasonal cycle of ENSO variability, providing a framework for studying ENSO phase locking and the spring prediction barrier. Moreover, despite the submonthly characteristic timescale of persistent noise, its cumulative contribution to Niño 3.4 evolution exceeds 10%, revealing the non-trivial role of persistent stochastic forcing.
Forecast experiments show that cyclostationary formulation improves short-range prediction skill (≤ 12 months) through better representation of month-to-month variability, while colored noise enhances longer-lead performance (>12 months) by accounting for persistent atmospheric forcing. CS-Colored-LIM benefits from both effects, yielding statistically significant improvements in correlation skill, more reliable ensemble forecasts, and enhanced prediction of major ENSO events, compared to conventional LIMs. Consequently, CS-Colored-LIM provides a simple yet powerful framework for long-range ENSO diagnosis and prediction, offering new insights into the interaction between seasonally varying dynamics and persistent stochastic forcing.
How to cite: Lien, J., Ando, H., Richter, I., and Kido, S.: Diagnosing and predicting ENSO using cyclostationary linear inverse models with persistent stochastic forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2201, https://doi.org/10.5194/egusphere-egu26-2201, 2026.
The tropical Pacific Ocean plays an outsized role in global climate, affecting, for instance, temperature and precipitation globally, as well as cyclone genesis and location. The equatorial zonal gradient in tropical Pacific sea surface temperatures (SSTs) has strengthened toward a more La Niña-like state in observations over the 20th and 21st centuries. Confirming whether climate models are capable of matching the observed gradient strengthening through some combination of a forced response and internal variability is therefore an active topic of research. While some studies have demonstrated that models can skillfully reproduce observed trends in the tropical Pacific SST gradient, others have argued that models fail to simulate these trends. However, these prior papers have focused on different and specific periods in the observational record to perform their assessments, with implications for the nature of the trends identified and the characterization of correspondence between the models and observations. Moving beyond assessments over a single time interval, we perform a comprehensive analysis over all trends in intervals of twenty years or longer from 1870 to 2024. We compare the observed trends from 5 observational datasets with simulated trends from 14 CMIP6 large ensembles (337 ensemble members in total). We demonstrate that models are not able to match many long-term trends in the observed gradient, especially those that end more recently. Models that are able to match these trends do so through excessive internal variability that compensates for their gradient-weakening forced responses. We additionally find that trends in the observed gradient strengthen at an increasing rate with time, a forced response that is in contrast to the behavior of most models.
How to cite: Byrne, H., Seager, R., and Smerdon, J.: CMIP6 models cannot capture long-term forced changes in the tropical Pacific sea surface temperature gradient, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12841, https://doi.org/10.5194/egusphere-egu26-12841, 2026.
Most state-of-the-art climate models (CMIP6) struggle to simulate the non-linear asymmetry of the El Niño-Southern Oscillation (ENSO), specifically the positive skewness where El Niño events are stronger than La Niña. While previous studies have attributed this deficiency to atmospheric parameterizations or equatorial cold tongue biases, we identify a distinct oceanic driver originating in the Eastern Pacific. We show that a systematic coastal warm bias in the eastern tropical Pacific is actively transported westward by the South Equatorial Current, creating an "advective bridge" that warms the central Pacific cold tongue. This advected heat elevates the background state, saturating the deep convection threshold and effectively removing the thermodynamic "floor" required for non-linear atmospheric feedbacks. Critically, we demonstrate a physical dissociation: while central Pacific trade wind biases primarily control ENSO amplitude, it is the upstream advective warming that determines ENSO skewness. These results suggest that improving the representation of eastern boundary current dynamics is a prerequisite for capturing the non-linear character of future ENSO variability.
How to cite: Yu, D., Dommenget, D., and Müller, W.: The role of Eastern Pacific Coastal Warm Bias on the ENSO Nonlinearity Bias in CMIP6 Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9672, https://doi.org/10.5194/egusphere-egu26-9672, 2026.
The El Niño-Southern Oscillation (ENSO) is a dominant source of global climate variability. However, climate models exhibit persistent and substantial spread in simulating the ENSO-related cloud radiative feedback (CRFB), which directly limits the fidelity of ENSO amplitude and period projections. This study re-evaluates the representation of ENSO-related CRFB in CMIP6 models, which generally exhibit a weak amplitude and a westward shift of the negative feedback center in the tropical Pacific. To identify the sources of model uncertainty in ENSO CRFB, we analyzed experiments conducted with the atmospheric models Community Earth System Model (CESM) and a modified version of the Max Planck Institute for the Meteorology Earth System Model at low resolution (MPI-ESM-LR). Results show that compared to CESM, MPI-ESM-LR fails to accurately simulate mid-level cloud properties, which largely govern the cloud radiative effect. In contrast, biases in mean sea surface temperature (SST) and ENSO amplitude also considerably influence the simulation of CRFB. The CRFB bias in CMIP6 is strongly linked with that in the corresponding models from the Atmospheric Model Intercomparison Project (AMIP), further indicating the important role of atmosphere model (especially the cloud and convective parameterization) in simulating the CRFB on ENSO.
How to cite: Xu, W., Zheng, X.-T., and Hu, K.: Revisiting the modelled cloud radiative feedback on ENSO — the source of model uncertainty, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6165, https://doi.org/10.5194/egusphere-egu26-6165, 2026.
There is a long history of studies of potential interactions between the El Niño–Southern Oscillation (ENSO) and the quasi-biennial oscillation (QBO). Some suggested that ENSO may modulate QBO phase transitions or amplitude, although identifying a straightforward correlation of the two variability modes has been elusive. Recent studies showed some interesting connections between the surface composites of the two modes, sea surface temperature in particular. However, the observed record is short and noisy, raising the question whether such patterns reflect a robust dynamical coupling or a statistical artifact. In this talk, I will show the observed patterns from ERA5 show and therefore imply. Additionally, I will then discuss how the various high-top CMIP6 models produce (or do not produce) the observed signal. By comparing model output with observations, we assess the extent to which apparent correlations are reproducible by this physical mechanism or can be identified as an artifact.
How to cite: Koepnick, K., Harnik, N., Randall, D., and Tziperman, E.: ENSO-QBO correlations: a robust dynamical coupling or a coincidence due to the short record? , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3547, https://doi.org/10.5194/egusphere-egu26-3547, 2026.
While the Indian Ocean Dipole (IOD) plays a crucial role in the interannual variability of Indian Ocean climate, its co-variability with El Niño-Southern Oscillation (ENSO) makes it difficult to accurately quantify its independent contribution, with the scientific community struggling to find a consensus on the relationship between these two major tropical modes.
Recent modelling experiments that separate the independent roles of ENSO and IOD in tropical climate variability suggest a causal relation in which the Indian Ocean variability is energized by ENSO, while playing a damping role for Tropical Pacific variability. Here, we use reanalysis datasets and climate simulations to revisit fundamental null hypotheses regarding the relationship between these two modes, with the aim of quantifying the variability of IOD independent of ENSO and identifying the type of relation between these modes, ultimately proposing a novel improved null hypothesis for Indian Ocean variability.
To achieve this, we focus on a series of simple statistical models and assess their skill in representing Indian Ocean variability given the knowledge of the Pacific state. In particular, we divide our analysis between “one-way” models that assume that Indian Ocean predictability comes only from Pacific state variables, and “two-way” models where the two basins can influence each other. Lastly, we compare the results of these statistical models with Pacific Pacemaker experiments, revealing an inconsistency between these two approaches.
How to cite: Landi, N. and Liguori, G.: Revisiting null hypotheses for Indian Ocean interannual variability, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21356, https://doi.org/10.5194/egusphere-egu26-21356, 2026.
The springtime North Tropical Atlantic (NTA) sea surface temperature (SST) anomaly serves as a crucial extratropical precursor to the El Niño–Southern Oscillation (ENSO), helping to alleviate the spring predictability barrier of ENSO. While the influence of the NTA on ENSO has been gradually strengthening since the mid-20th century, this trend cannot be explained by global warming. This study reveals that the Victoria Mode (VM) in the North Pacific is the key driver of this intensification. Since the mid-20th century, the negative phase of the VM has progressively strengthened, which in turn has enhanced the coupled subtropical northeasterly trade winds. This enhancement has intensified air-sea coupling over the subtropical northeastern Pacific. Consequently, atmospheric anomalies excited by the spring NTA are now more likely to imprint significant SST anomalies onto this critical hub region in the subtropical northeastern Pacific. Through intensified local air-sea interactions, these anomalies are then further transmitted into the tropical Pacific, ultimately triggering ENSO events. Our findings demonstrate that the influence of the North Tropical Atlantic on the tropical Pacific is largely modulated by the background climatic state of the North Pacific.
How to cite: Zheng, Y.: Amplification of the NTA's Impact on ENSO: The Important Modulation by VM, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2033, https://doi.org/10.5194/egusphere-egu26-2033, 2026.
The El Niño-Southern Oscillation (ENSO) is among the most well understood climate phenomena. The Recharge Oscillator (RO) theory is widely used to conceptualize the key ENSO physics in observations and state-of-the-art models. It is well known that the ENSO-associated equatorial zonal wind anomalies shift southward in boreal winter, contributing to ENSO termination. Thus far, this effect has not been explicitly incorporated into the RO framework. Here we derive a new form of the RO, which incorporates the seasonal meridional migration of the zonal wind anomalies under the low-frequency limit. In our theory, wind stress centered off the equator forces equatorial waves and acts with a delayed effect on SST. Meanwhile, ENSO is stabilized as the central latitude of the zonal wind anomalies shift southward, owing to exponentially weakened thermocline feedback. A stochastic RO simulation with a prescribed observed southward wind shift reproduces ENSO seasonal synchronization and combination tones.
How to cite: Iwakiri, T., Stuecker, M., Jin, F.-F., and Zhao, S.: ENSO Recharge Oscillator Theory Integrating the Southward Wind Shift, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15517, https://doi.org/10.5194/egusphere-egu26-15517, 2026.
The dynamics of the El Niño Southern Oscillation (ENSO) vary from decade to decade in both observations and CMIP models. This complicates both ENSO prediction and the interpretation of any climate change signal. We aim to identify what processes are necessary to produce this decadal modulation, as quantified by ENSO growth rate and phase speed, using a set of recharge oscillator models with varying levels of nonlinearity.
We find that a linear recharge oscillator model with Gaussian white noise is sufficient to produce decadal modulation consistent with both observations and CMIP models. Although ENSO exhibits nonlinearity in other metrics, adding nonlinear terms to the recharge oscillator model does little to change the magnitude of decadal modulation.
We show that the white noise forcing in recharge oscillator models impacts both ENSO emergent characteristics (e.g. amplitude) and representations of ENSO dynamical behaviour (e.g. growth rate, phase speed). Furthermore, we demonstrate that CMIP-class models do not produce ENSO predictability beyond the timescale expected from a linear recharge oscillator model. Together, these findings suggest future research should focus on the shorter-timescale processes influencing ENSO, including the sub-monthly processes typically modelled as noise, and highlight that short-timescale processes play an underappreciated role in influencing ENSO on much longer decadal timescales.
How to cite: Jeffree, J., Maher, N., Quinn, C., and Dommenget, D.: Decadal modulation of ENSO dynamics emerges primarily from white-noise forcing, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8466, https://doi.org/10.5194/egusphere-egu26-8466, 2026.
The dynamics of the El Niño–Southern Oscillation (ENSO) can be succinctly represented by the Recharge Oscillator (RO) framework. In this presentation, we systematically explore how ENSO properties depend on RO parameters and assess the framework’s ability to diagnose both present-day variability and externally forced changes.
We first show that parameter regimes producing self-sustained oscillations yield unrealistically low Niño3.4 kurtosis compared to observations, indicating that ENSO is more consistently represented as a stochastically forced, linearly stable system. Rather than relying on standard fitting approaches, we conduct a broad exploration of RO parameter space to identify configurations that simultaneously reproduce observed ENSO amplitude, seasonality, skewness, and lagged autocorrelation. The simplest and most realistic configuration is a strongly damped oscillator, with a decay timescale shorter than the dominant ENSO period, forced by multiplicative white noise and modulated by weak deterministic nonlinearities.
These simulations generate interdecadal ENSO fluctuations comparable in magnitude to those observed, raising questions about the interpretability of slowly evolving RO parameters inferred from single realizations. Using idealized twin experiments, we show that fitting the RO to individual time series produces spurious interdecadal parameter shifts that appear to “explain” ENSO variability through changes in the Bjerknes–Wyrtki–Jin index, but do not reflect a forced response.
We then impose 20–40% linear trends in selected RO parameters over 200 years and test their recoverability using ensemble fits. About 50 ensemble members are sufficient to robustly detect linear parameter changes and most ENSO property trends over 40-year windows, while detecting changes in skewness and nonlinear parameters associated with extreme ENSO events requires 100 members or more. These idealized experiments demonstrate that ensemble simulations are essential for diagnosing externally forced changes in ENSO dynamics and provide a proof of concept for applying the RO framework to large-ensemble climate model experiments.
How to cite: Vialard, J., Han, S., and Fedorov, A.: Exploring present and future ENSO dynamics within the recharge oscillator framework, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9539, https://doi.org/10.5194/egusphere-egu26-9539, 2026.
Posters on site: Tue, 5 May, 08:30–10:15 | Hall X4
Climate change, in particular the rise in tropical sea surface temperature, is the greatest threat to coral reef ecosystems today with associated climatic extremes affecting the livelihood of tropical societies. The interaction between the tropical ocean basins plays a key role in modulating climate variability on interannual to decadal timescales. These timescales are of strong relevance to societies and ecosystems, because they control the time interval for recovery between extreme events. Throughout the tropical oceans, a key archive for reconstructions of temperature and hydrology are massive shallow-water corals. Annually to monthly resolved coral proxy records are critical for our understanding of tropical ocean-atmosphere interactions. The DFG Priority Programme “Tropical Climate Variability and Coral Reefs” (SPP 2299) aims to enhance our understanding of tropical marine climate variability and its impact on coral reef ecosystems in a warming world, by quantifying climatic and environmental changes during both the ongoing warming and past warm periods on timescales relevant for society. Ultra-high resolution (monthly to weekly) geochemistry of the coral skeleton is a valuable tool to understand the temporal response of corals to ongoing climate change. Developing reconstructions of past tropical climate and environmental variability, in conjunction with advanced statistical methods, earth system modelling and observed ecosystem responses allows improved projections of future changes in tropical climate and coral reef ecosystems. We present examples (1) for modes of tropical climate variability affecting coral reef ecosystems, such as interactions of the IOD and ENSO, (2) for thermal stress signatures in coral geochemical and isotopic records, and (3) highlight knowledge gaps and future directions in this emerging field, contributing to a better understanding of the response of coral reef ecosystems and tropical climate variability to ongoing and future climate change.
How to cite: Felis, T., Hargreaves, J. A., and Pfeiffer, M. and the SPP 2299 Team: Tropical Climate Variability and Coral Reefs, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20994, https://doi.org/10.5194/egusphere-egu26-20994, 2026.
Canonical El Niño (EN) events typically peak in boreal winter and then rapidly decay in the ensuing months, transitioning to a La Niña (LN) event or a neutral condition by the following winter. Strikingly, an EN event that peaked in 1986/87 boreal winter, unexpectedly persisted into the following year, generating a rare two-year consecutive EN event. However, the 1986/87~1987/88 two-year EN event receives few attention. This study reveals that this two-year EN event encompasses some critical yet commonly overlooked processes for the formation and development of EN event. Specifically, the high-frequency (HF) westerly wind anomalies, induced by the tropical cyclones (TCs) and Madden-Julian Oscillation (MJO) events, were the pivotal drivers of the unexpected re-ignition in the second year. During the 1986/87 winter, the unexpected emergence of four TCs induced vigorous westerly wind anomalies over the western equatorial Pacific (WEP), disrupting the anomalous anticyclone circulation over the western North Pacific (WNPAC) and the associated easterly wind anomalies over WEP that were anticipated during the 1986/87 winter. Such unexpected westerly wind anomalies helped maintain the EN warming through December 1986 to February 1987. Subsequently, a series of HF westerly wind anomalies, induced by TCs and MJO events in April, May and July 1987, reinvigorated the waning warming, pulling it back into a fledged EN event by the end of 1987. Gaining insights into the formation mechanism behind this unique two-year EN event can deepen our understanding of ENSO dynamics and provide implications for enhancing the accuracy of EN prediction.
How to cite: Chen, L., Song, M., Zhao, J., Lin, P., Jiang, L., Wang, L., and Zhi, H.: Impacts of Tropical Cyclones and MJO on El Niño Evolution: Revisiting the Formation Mechanism of the 1986/87~1987/88 Two-year El Niño, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19881, https://doi.org/10.5194/egusphere-egu26-19881, 2026.
The zonal gradient in tropical Pacific sea surface temperatures (ΔSSTwest-east) plays a major role in global climate, from modulating the rate of global warming and ocean-atmosphere CO2 fluxes, to influencing tropical cyclone genesis and regional precipitation patterns. While observations estimate that this gradient has strengthened over the historical record, most coupled climate models, from all generations up to the most recent (CMIP6), simulate a forced weakening of the zonal SST gradient over this period, a discrepancy that has been variously attributed to representations of internal variability, model mean-state biases and incorrect forced responses. In a previous study, we demonstrated that CMIP6 models fail to capture most recently-ending observed trends in ΔSSTwest-east and identified signs that the observed strengthening is consistent with a forced response to rising atmospheric greenhouse gases. We additionally classified the 14 analyzed CMIP6 models into two groups according to whether they simulate a forced weakening of the gradient or a more ambiguous response over the historical period. In this study, we characterize how the forced trends in these model groups evolve under different 21st-century Shared Socioeconomic Pathways and find that even models that simulate slight gradient strengthening over the historical period ultimately simulate weakening gradients under most projections, a transition that occurs roughly in the contemporary period. To better understand why the two model groups show contrasting gradient behavior over the historical period, and more consistent behavior under projected scenarios, we conduct an empirical orthogonal function analysis to investigate the contributions of greenhouse gas (GHG) and aerosol forcings to ΔSSTwest-east changes in the historical period. We further validate these analyses through use of single forcing large ensembles. We find that both greenhouse gas and aerosol modes are near ubiquitous within the model group, with these modes contributing in opposite senses to gradient changes between the model groups. A similar analysis on 4 observational products provides evidence for the influence of both GHG and aerosol modes on changes in observed ΔSSTwest-east. Taken together, these findings quantify contributions from both GHG and aerosol forcing to changes in observed and modeled ΔSSTwest-east, providing increased understanding of real-world ΔSSTwest-east changes and the origin of model responses that yield unrealistic gradient changes over the historical period.
How to cite: Byrne, H., Seager, R., and Smerdon, J.: Greenhouse gas and aerosol forcings contribute differently to changes in the tropical Pacific sea surface temperature gradient in models and observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12965, https://doi.org/10.5194/egusphere-egu26-12965, 2026.
The temperature contrast between the western and eastern equatorial Pacific Ocean (hereafter zonal temperature gradient) plays a central role in driving the Walker Circulation, an atmospheric circulation pattern that affects climate across the globe. Over the past forty years, observations show that this zonal temperature gradient has strengthened. However, fewer than 1% of simulations from the latest generation of climate models reproduce this observed trend.
Two possible explanations have been proposed for this discrepancy. First, the strengthening could be a response to human-driven climate change that models fail to represent accurately. Second, it could reflect natural, long-term fluctuations of the climate system that models underestimate. Here we examine the second possibility.
We show that climate models underestimate the magnitude of low-frequency natural variability in the tropical Pacific, partly because they rarely simulate extreme El Niño events. El Niño is a recurring climate phenomenon in which the zonal temperature gradient weakens. During extreme events, this gradient can nearly vanish, which substantially increases temperature variability on decadal timescales. When we statistically account for the rarity of such extreme events in models, agreement with observations improves only modestly: even after correction, only about 5% of simulations reproduce the observed strengthening.
These results indicate that it is very unlikely that the recent strengthening of the Pacific temperature contrast arises from natural variability alone. This finding instead points to potential deficiencies in how climate models represent the tropical Pacific’s response to global warming.
How to cite: Planton, Y., Vialard, J., Fedorov, A., Lengaigne, M., McGregor, S., and Stuecker, M.: Internal Variability Insufficient to Explain Recent Equatorial Pacific Trends, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20872, https://doi.org/10.5194/egusphere-egu26-20872, 2026.
Understanding changes in the pattern of tropical Pacific sea surface temperature (SST), especially its zonal contrast, in response to greenhouse gas forcing is essential for predicting future climate change. Among various mechanisms proposed, the ocean dynamical thermostat (ODT) associated with eastern Pacific upwelling may act to intensify the zonal SST contrast during the early stage of warming. However, its physical processes and efficiency in transient response remain controversial. Here we revisit the ODT mechanism by diagnosing the mixed-layer heat budget in both a simple coupled model (Zebiak–Cane model) and a complex GCM (MIROC6 abrupt 4xCO2 experiment). Following Clement et al. (1996), we decompose the ODT into two processes: ocean dynamical adjustment (ODA) due to mean upwelling and thermocline feedback (THF) due to anomalous upwelling, to investigate their roles in the SST pattern response to imposed surface heating. The SST pattern evolution is very different between the two models: initial eastern Pacific warming in the Zebiak–Cane model is quickly offset by ODA, enhancing the zonal SST contrast and triggering THF and horizontal advection that further cool the east, but MIROC6 exhibits a weakening of the zonal SST contrast from the beginning because the ODA cooling is overwhelmed by other processes. The contrast in the initial response is critical in their long-term response and it is explained mainly by differences in mean ocean currents and the spatial homogeneity of the radiative forcing.
How to cite: Li, W. and Watanabe, M.: Revisiting Ocean Dynamical Thermostat Mechanism for the Tropical Pacific SST response to Global Warming, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8356, https://doi.org/10.5194/egusphere-egu26-8356, 2026.
There is an emerging consensus that ENSO amplitude may change non-monotonically in response to external forcing, increasing over the course of the 20th century and decreasing in the 22nd and 23rd centuries. While the physical explanations for these asymptotic short-term and long-term cases are well-supported by climate model simulations, projections of ENSO’s 21st century changes vary widely between models. In this work, we investigate why ENSO amplitude begins to decline in the mid-21st century in the CESM2 model. We find that almost all of the amplitude decrease results from a weakening of the strongest El Niño events. While strong El Niños' intensity begins decreasing in ~2010, La Niñas’ intensity continues increasing for several decades afterwards. The net result of these opposing changes is a ~2030 maximum in overall ENSO amplitude and a reverse in ENSO asymmetry by the 21st century (La Niñas become stronger than El Niños, opposite to the 20th century). We show that this asymmetric change in ENSO intensity is consistent with a weakening of the zonal temperature gradient, which increases the zonal variability of the Walker circulation and limits the potential intensity of El Niños but not La Niñas. Overall, our analysis suggests that changes in asymmetry may have a leading-order effect on overall ENSO amplitude in the 21st century, and that El Niño intensity may not always be a useful upper bound on La Niña intensity.
How to cite: Carr, T., Gebbie, G., Gonzalez, A., Ummenhofer, C., and Vialard, J.: Towards understanding ENSO's non-monotonic projections in the CESM2 climate model, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15911, https://doi.org/10.5194/egusphere-egu26-15911, 2026.
Accurately predicting the El Niño-Southern Oscillation (ENSO) remains a central challenge in seasonal climate forecasting. Statistical approaches, such as the Linear Inverse Model (LIM) and the Model Analog (MA), have been widely applied to predict sea surface temperature and sea surface height anomalies in the tropical Indo-Pacific, but each approach has intrinsic limitations. Although their weighted combination, MA-LIM, improves forecast skill over LIM and MA individually, it is still affected by residual biases from both methods. To address this limitation, this study introduces a new statistical prediction framework, NEW MA-LIM, which more optimally unifies MA and LIM by explicitly modeling the temporal evolution of MA forecast errors within the LIM operator and applying dynamic corrections at each forecast lead. Hindcast experiments for the period 1961–2023, using observational datasets and 15 CMIP6 preindustrial control simulations, show that NEW MA-LIM consistently outperforms LIM, MA, and MA-LIM across 1–12 month leads. In particular, it substantially alleviates the spring predictability barrier. A key finding is that MA forecast errors exhibit significant linear predictability, and their spatiotemporal patterns can be effectively reproduced by LIM. This enables more reliable ENSO prediction within a low-dimensional dynamical framework.
How to cite: Kim, S. and Shin, J.: Error correction of model analog forecasts using a linear inverse model for improving statistical ENSO prediction skill, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16264, https://doi.org/10.5194/egusphere-egu26-16264, 2026.
Recently, we have developed two approaches (a climate network [1] and a complexity-based approach [2]) that allow forecasting the onset of El Niño events about 1 year in advance. The complexity-based approach additionally enables forecasting the magnitude of an upcoming El Niño event. These methods successfully forecasted the onset of an Eastern Pacific El Niño for 2023/24 and the subsequent record-breaking warming of 2024 [3]. Here, we propose the interannual relationship of the Oceanic Niño Index as an additional predictor for forecasting La Niña and neutral events. Combining the three approaches therefore enables probabilistic forecasting of all three phases of ENSO dynamics about 1 year in advance. Based on these approaches, in December 2024 we correctly forecasted with 91.4% probability the absence of an El Niño in 2025 [4]. With 69.6% probability, we predicted a neutral event as the most likely outcome for boreal winter 2025/26.
[1] Ludescher, J., Gozolchiani, A., Bogachev, M. I., Bunde, A., Havlin, S., Schellnhuber, H. J., (2013). Improved El Niño forecasting by cooperativity detection. Proc. Natl. Acad. Sci. U.S.A. 110(29), 11742.
[2] Meng, J., et al. (2020). Complexity-based approach for El Niño magnitude forecasting before the spring predictability barrier. Proc. Natl. Acad. Sci. U.S.A. 117(1), 177.
[3] Ludescher, J., Meng, J., Fan, J., Bunde, A., Schellnhuber, H. J., Very early warning of a moderate-to-strong El Niño in 2023, https://doi.org/10.48550/arXiv.2301.10763
[4] Ludescher, J., Meng, J., Fan, J., Bunde, A., Schellnhuber, H. J., Climate network and complexity approach predict neutral ENSO event for 2025, https://doi.org/10.48550/arXiv.2502.00643
How to cite: Ludescher, J., Meng, J., Fan, J., Bunde, A., and Schellnhuber, H. J.: Predicting ENSO dynamics with network & complexity analyses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14829, https://doi.org/10.5194/egusphere-egu26-14829, 2026.
El Niño prediction uncertainty is highly sensitive to initial-condition uncertainties. The present study explores the sources and dynamics of initial uncertainty using the Coupled Conditional Nonlinear Optimal Perturbation (C‑CNOP) method. By imposing physically consistent and rapidly growing coupled initial perturbations, a series of ensemble forecast experiments were conducted for El Niño events from 1982 to 2023, with initializations in different seasons. The resulting ensembles demonstrate high reliability for predictions initialized in January, April, and July, effectively characterizing prediction uncertainty. Conversely, October-initialized predictions show persistent under-dispersion, as perturbation growth is suppressed by an overly stable model background state, indicated by a low Bjerknes stability index. Building on the reliable framework, key sensitive regions were identified across the Pacific, Indian, and Atlantic Oceans, where initial uncertainties significantly contribute to prediction uncertainties in the tropical central‑eastern Pacific, with patterns that vary seasonally. Beyond reaffirming the significant impact of extratropical North Pacific initial uncertainties, the results also highlight the role of mid-latitude South Pacific regions. Cross‑basin remote effects are also identified. Specifically, interactions between the tropical Atlantic and Pacific are confirmed for January and July initializations, alongside a reaffirmed influence from the tropical Indian Ocean. Statistical evidence also suggests potential pathways originating from the mid-latitude South Atlantic in January-initialized predictions and the subtropical South Indian Ocean in April-initialized predictions. Validation experiments demonstrate that reducing initial errors in these identified regions enhances prediction performance and reduces overall prediction uncertainty. Moreover, utilizing perturbation information from these regions to select ensemble members improves both deterministic and probabilistic prediction performance. These findings clarify the initial sources of El Niño prediction uncertainty and provide a practical foundation for optimizing targeted observation strategies.
How to cite: Hou, G. and Duan, W.: Dominant Initial Uncertainty Sources for El Niño Forecasting, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3348, https://doi.org/10.5194/egusphere-egu26-3348, 2026.
The seasonal persistence barrier (PB) is a critical factor limiting the prediction skill of the El Niño-Southern Oscillation (ENSO). While previous studies have predominantly focused on the Niño3.4 region (Eastern Pacific El Niño), the increasing frequency of Central Pacific (CP) El Niño events since the 1990s necessitates a distinct examination of the Niño4 region, which exhibits unique predictability characteristics. Using long-term reanalysis datasets, this study investigates the PB of sea surface temperature (SST) in the Niño4 region. Climatologically, the PB for the Niño 4 index occurs during June–July, but it exhibits significant decadal variability. We identify that the Interdecadal Pacific Oscillation (IPO) plays a crucial role in modulating the timing of the PB (PB month). Our analysis reveals a robust relationship where the IPO phase regulates the seasonal locking of the prediction barrier. Specifically, during positive IPO phases, the PB tends to occur earlier, whereas distinct timing characteristics are observed during negative phases. Mechanistically, a diagnostic analysis based on the Bjerknes Stability Index (BJI) demonstrates that the IPO background state alters the seasonal cycle of the zonal advective feedback (ZA) in the equatorial central Pacific. This modulation shifts the seasonal peak of the total ENSO growth rate, thereby determining the timing of the steepest decline in prediction skill. These findings offer new insights into the decadal variability of CP-ENSO predictability and highlight the importance of background state modulation in ENSO forecasting.
How to cite: Hou, M.: Decadal Modulation of the Seasonal Persistence Barrier for Central Pacific El Niño by the Interdecadal Pacific Oscillation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10095, https://doi.org/10.5194/egusphere-egu26-10095, 2026.
Tropical land precipitation(TLP) plays a crucial role in tropical ecosystems, human activities and regional energy and hydrological cycles. To improve the predictability of TLP, it is essential to understand not only its variability but also its temporal persistence, which is a fundamental aspect of climate predictability. However, the persistence characteristics of TLP remain understudied. Using multiple precipitation datasets, this study reveals a rapidly declines in TLP persistence in June, regardless of the initial month. This phenomenon, termed the June persistence barrier (PB), is robust across datasets. Further analysis shows that sea surface temperature (SST) anomalies in the central and eastern tropical Pacific, associated with the El Niño–Southern Oscillation (ENSO), are the primary driver of the June TLP PB. ENSO-related SST exhibits a PB in May–June. When the linear influence of ENSO is removed, the TLP PB disappears, and persistence weakens significantly. Mechanistically, SST primarily affects the large-scale Walker circulation, which in turn causes quasi-consistent changes in vertical motion over tropical land. These changes directly influence local moisture transport, ultimately leading to TLP anomalies. The seasonal persistence of SST enables a sustained remote influence through the atmospheric bridge, linking oceanic variability to land precipitation. These findings not only deepen our understanding of the intrinsic variability of TLP but also provide a potential theoretical basis for the future seasonal prediction of TLP using its persistence.
How to cite: An, R., Li, J., and Feng, J.: June persistence barrier of tropical land precipitation and its relationship with ENSO, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8517, https://doi.org/10.5194/egusphere-egu26-8517, 2026.
The Warm Pool Dipole (WPD), a seesaw pattern of sea surface temperature anomalies (SSTAs) between the southeast Indian Ocean and the central-western Pacific, can strongly influence regional precipitation. This study explores its dynamical impact on South China spring rainfall and the decadal variability of this relationship. Observations and model simulations show that cold SSTAs in the central-western Pacific may excite a westward-propagating Rossby wave, while warm SSTAs in the southeast Indian Ocean induce low-pressure anomalies over the eastern Indian Ocean. These may both strengthen easterly anomalies over the western Pacific. The associated anticyclone further intensifies the western North Pacific subtropical high, increasing moisture transport to South China. Notably, the WPD’s influence weakened significantly after around 2000, becoming negligible compared to the period before. This shift is attributed to a diminished atmospheric response linked to the reduced intensity of the WPD itself in recent decades. This work identifies a new potential predictability source for seasonal forecasting of spring rainfall in South China.
How to cite: Xing, W.: Mechanisms and Decadal Variability of the Warm-Pool Dipole Mode’s Influence on South China Spring Rainfall, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3267, https://doi.org/10.5194/egusphere-egu26-3267, 2026.
The El Niño-Southern Oscillation (ENSO) is the leading source of predictable, internally generated, large-scale climate variability. Centered in the tropical Pacific, ENSO influences global precipitation, atmospheric circulation, and temperature patterns via atmospheric teleconnections. ENSO events can trigger climate extremes, in turn devastating communities and costing billions of dollars. Over recent decades, substantial progress has been made in early and accurate seasonal forecasting of ENSO events. However, relatively less is known about how ENSO teleconnections vary in space and time, so called nonstationarity, which limits our ability to confidently relate these forecasts to expected impacts.
Assessing ENSO teleconnection nonstationarity is challenging because the instrumental record is relatively short. Comprehensive physical climate models help to address these limitations, but intrinsic biases undermine their utility for this purpose. By contrast, statistical climate models are trained on observations and can therefore provide a valuable complementary perspective.
Linear Inverse Models (LIMs) are efficient, linear statistical climate models that are computationally inexpensive and straightforward to modify. Here we develop state-of-the-art LIMs that simulate ENSO asymmetry and diversity (Martinez-Villalobos et al., 2025), the seasonal cycle (Shin et al., 2021), and ENSO’s teleconnected impacts in the extratropics (Ault et al. 2018). Utilizing the LIMs we provide the most confident estimates to-date of intrinsic nonstationarity in ENSO teleconnections. Furthermore, by altering the LIM we assess the role for various processes in driving nonstationarity, with a particular focus on the influence of ENSO asymmetry, the seasonal cycle and phase locking, and inter-basin interactions. Our results have implications for seasonal forecasting, characterizing ENSO impacts in a changing climate, and validating the comprehensive physical climate models that are the basis of future projections.
How to cite: Coats, S., Heller, S., and Atwood, A.: Exploring nonstationarity of ENSO teleconnections using state-of-the-art Linear Inverse Models, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-15270, https://doi.org/10.5194/egusphere-egu26-15270, 2026.
Decadal variability in the Pacific plays an important role in shaping global climate and can modulate the expression of anthropogenic warming, as illustrated by its contribution to the early 21st-century global warming hiatus. The Interdecadal Pacific Oscillation (IPO) describes multi-decadal sea surface temperature variability across the Pacific, yet the processes driving its variability remain poorly understood. Although the Pacific Decadal Oscillation (PDO) and South Pacific Decadal Oscillation (SPDO) are often synchronized by tropical forcing, they are not always equal. Examining their covariance may provide critical insight into the mechanisms underlying IPO variability. In this study, we analyze a new paleo-based multivariate reanalysis of the Norwegian Climate Prediction Model (NorCPM) that assimilates annually resolved paleoclimate records from the past centuries while including transient external forcing. The dataset spans the 1500–2010 period, allowing us to overcome limitations of previous IPO studies associated with short observational records, especially in the Southern Hemisphere. The temporal evolution of PDO–SPDO covariance over the last five centuries is assessed using low-frequency component analysis to isolate the internal variability and examine how this relationship varies under differing backgrounds states. The results indicate that while the PDO and SPDO are predominantly in phase, their covariance is highly non-stationary, with periods of weakened or reversed coupling. Examining this covariance provides additional context for understanding how Pacific basin–scale interactions contribute to IPO variability.
How to cite: Richer, F., Svendsen, L., and Dalaiden, Q.: North–South Pacific Decadal Co-Variability over the last 500 years informed by a new paleo-reanalysis, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14504, https://doi.org/10.5194/egusphere-egu26-14504, 2026.
The Malacca and Singapore Straits (MSS) is one of the world’s most critical maritime corridors, supporting intensive navigation, port operations, and coastal activities. Mean ocean flow and volume transport in the MSS play a key role in environmental risk assessment, particularly for predicting the transport pathways of accidentally spilled oil or hazardous substances. The volume transport in the MSS is influenced by both local atmospheric forcing and remote drivers, including the South China Sea Throughflow (SCSTF), and exhibits pronounced variability across multiple time scales. In this study, a high-resolution dataset of ocean flow simulated by the NEMO ocean model over the Maritime Continent, forced by ORAS5 ocean reanalysis and ERA5 atmospheric data, is used to analyze volume transport variability in the MSS. The simulated volume transport in the MSS is investigated following comprehensive validation against observations in key passages, including the Luzon Strait, Taiwan Strait, and Karimata Strait. At interannual time scales, MSS volume transport shows moderate correlations with both the El Niño–Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). Strong seasonal variability is evident, driven by monsoon winds, with monthly climatology showing predominantly westward transport throughout the year, stronger in boreal winter and weaker in summer. Despite this climatological pattern, analysis of daily mean data reveals frequent eastward transport events during summer at sub-mesoscale time scales. These eastward transport events exhibit strong seasonality and show significant correlations with ENSO, with enhanced eastward transport occurring in summers following strong El Niño events. During these events, daily mean surface currents can reach magnitudes comparable to tidal currents in many parts of the strait. These results underscore the importance of accounting for short-term current variability when assessing pollutant transport and associated environmental impacts in the MSS.
How to cite: Ma, P., Wang, Z., Chow, J. H., and Tkalich, P.: Variability of Volume Transport in the Malacca and Singapore Straits and its Implications for Environmental Transport, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8939, https://doi.org/10.5194/egusphere-egu26-8939, 2026.
Posters virtual: Tue, 5 May, 14:00–18:00 | vPoster spot 1a
EGU26-12134 | Posters virtual | VPS20
Investigating decadal variations of the seasonal predictability limit of sea surface temperature in the tropical PacificTue, 05 May, 15:06–15:09 (CEST) vPoster spot 1a
El Niño and the Southern Oscillation (ENSO) have a worldwide impact on seasonal to yearly climate. However, there are decadal variations in the seasonal prediction skill of ENSO in dynamical and statistical models; in particular, ENSO prediction skill has declined since 2000. The shortcomings of models mean that it is very important to study ENSO seasonal predictability and its decadal variation using observational/reanalysis data. Here we quantitatively estimate the seasonal predictability limit (PL) of ENSO from 1900 to 2015 using Nonlinear local Lyapunov exponent (NLLE) theory with an observational/reanalysis dataset and explore its decadal variations. The mean PL of sea surface temperature (SST) is high in the central/eastern tropical Pacific and low in the western tropical Pacific, reaching 12–15 and 7–8 months, respectively. The PL in the tropical Pacific varies on a decadal timescale, with an interdecadal standard deviation of up to 2 months in the central tropical Pacific that has similar spatial structure to the mean PL. Taking the PL of SST in the Niño 3.4 region as representative of the PL in the central/eastern tropical Pacific, there are clearly higher values in the 1900s, mid-1930s, mid-1960s, and mid-1990s, and lower values in the 1920s, mid-1940s, and mid-2010s. Meanwhile, the PL of SST in the Niño 6 region—whose average value is 7 months—is in good agreement with the PL of most regions in the western tropical Pacific, with higher values in the 1910s, 1940s, and 1980s and lower values in the 1930s, 1950s, and mid-1990s.In the framework of NLLE theory, the PL is determined by the error growth rate (representing the dissipation rate of the predictable signal) and the saturation value of relative error (representing predictable signal intensity). We reveal that the spatial structure of the mean PL in the tropical Pacific is determined mainly by the error growth rate. The decadal variability of PL is affected more by the variation of the saturation value of relative error in the equatorial Pacific, whereas the error growth rate cannot be ignored in the PL of some regions. As an important source of predictability in ENSO dynamics, the relationship between warm water volume and SST in the Niño 3.4 region has a critical role in the decadal variability of PL in the tropical Pacific through the error growth rate and saturation value of relative error. This strong relationship reduces the error growth rate in the initial period and increases the saturated relative error, contributing to the high PL.
How to cite: Hou, Z. and Li, J.: Investigating decadal variations of the seasonal predictability limit of sea surface temperature in the tropical Pacific, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12134, https://doi.org/10.5194/egusphere-egu26-12134, 2026.
EGU26-6312 | Posters virtual | VPS20
Water flux from the Andaman Sea to the South China SeaTue, 05 May, 15:24–15:27 (CEST) vPoster spot 1a
Andam Sea to South China Sea (SCS) transient mesoscale water flux through the Singapore Strait, defined as reflux, reverses the annual SCS-Malacca Strait throughflow. Despite its dynamical significance, this process has received little attention. This study comprehensively examines its climatic features, driving factors and underlying mechanisms. Results indicate that reflux mainly occurs in summer and is rare in winter. Three types - WCE-, CE-, and E-type - are classified based on the extent of eastward intrusion across the Strait. The proposed physical mechanism is as follows: strong westerly winds drive surface water eastward, causing water accumulation along the western coasts of the Malay Peninsula and Sumatra and thereby elevating sea surface height (SSH) in the Malacca Strait. As SSH increases, the SSH gradient across the Strait reverses, initiating eastward flux. Meanwhile, strong westerly winds blocked by Sumatra deflect the southeastward flow northwestward around the Sunda Strait, intensifying the northward current at the eastern exit of the Singapore Strait, which enhances local Ekman transport and facilitates reflux. Although the same physical process operates in both seasons, the causes of strong westerly winds over the tropical eastern Indian Ocean differ. Summer reflux is favoured by the intensified southwest monsoon, whereas wintertime events are modulated by La Niña conditions, when warm waters and atmospheric heating near Sumatra induce a Gill-type low-level response to the equatorially symmetric heat source. Furthermore, while the considered three reflux types share the same fundamental mechanism, stronger atmospheric and oceanic forcing generates more intense and spatially extensive reflux events.
How to cite: Wang, Z., Ma, P., Xu, X., and Tkalich, P.: Water flux from the Andaman Sea to the South China Sea, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6312, https://doi.org/10.5194/egusphere-egu26-6312, 2026.
