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Marine heatwaves (MHWs) have caused devasting ecological and socioeconomic impacts worldwide. Understanding the connection of regional events to large‐scale climatic drivers is key for enhancing predictability and mitigating MHW impacts. Despite the reported connection between MHWs globally and El Niño–Southern Oscillation (ENSO), establishing statistically significant links between different types of ENSO events and MHWs remains challenging due to the limited duration of observational data. Here, we use 10,000 years of simulations from a Linear Inverse Model (LIM) to address this issue. Our findings reveal distinct connections between MHWs and ENSO, with diverging influences from different flavors of El Niño and La Niña events. In addition, under long‐lasting El Niño conditions, the likelihood of MHWs increases by up to 12‐fold in the Indian and Pacific Oceans. This study highlights the global connections between ENSO diversity and variations in MHW events. Plain Language Summary: Marine heatwaves (MHWs) are periods of prolonged, extremely warm ocean temperatures that have caused widespread ecological and socioeconomic impacts worldwide. The predictability of these events can be improved if we can find connections between regional events and larger climatic drivers, such as El Niño‐Southern Oscillation (ENSO). Both the positive phase of ENSO, El Niño, and its negative phase, La Niña, have been linked to MHWs in various parts of the world. However, not all El Niño and La Niña events are the same, leading to uncertainty in the relationship between ENSO and MHWs. Due to the limited duration of the observational record, a major issue arises with the lack of examples of different types of El Niño and La Niña events in observations. To overcome this challenge, we utilized 10,000 years of simulated data from a near‐global linear inverse model to generate many more samples of possible global ocean temperature configurations. We find strong differences between regional MHWs and different types of El Niño and La Niña events. In some regions, the probability of MHWs is 12 times more likely under long‐lasting El Niño events. Key Points: Robust links between global marine heatwaves and El Niño–Southern Oscillation (ENSO) diversity are established using 10,000 years of samples from a linear inverse modelMarine heatwave (MHW) intensity and frequency in the Northeast Pacific increase significantly during Central Pacific El Niño eventsMHW intensity and frequency are significantly enhanced in Western Australia during Central Pacific La Niña events [ABSTRACT FROM AUTHOR]

Local meteorology over the Great Barrier Reef (GBR) can significantly influence ocean temperatures, which in turn impacts coral ecosystems. While El Niño–Southern Oscillation (ENSO) provides insight into the expected synoptic states, it lacks details of anticipated sub‐seasonal weather variability at local scales. This study explores the influence of the Madden‐Julian oscillation (MJO) on Australian tropical climate, both independently and in combination with ENSO, focusing on GBR impacts. We find that during El Niño periods, including the summer of 2009/10, faster propagating MJO patterns can disrupt background warm, dry conditions, and potentially provide cooling relief via increased cloud cover and stronger winds. In La Niña periods, such as the summer of 2021/22, the MJO tends to be prevented from passing the Maritime continent, forcing it to remain in a standing pattern in the Indian Ocean. This leads to decreased cloud cover and weaker winds over the GBR, generating warm ocean anomalies. Plain Language Summary: Bleaching is likely when tropical corals are exposed to ocean temperatures above a threshold for a prolonged period. In austral summer, tropical weather over the Great Barrier Reef (GBR) can vary from hot and sunny to stormy with rain and strong winds. During El Niño, summer weather over the GBR is typically warm, still, and dry, increasing the likelihood of coral bleaching due to increased exposure to solar radiation and decreased mixing. During La Niña, tropical storms, with cooling effects through increased rainfall and cloud cover, are more typical. The Madden‐Julian oscillation (MJO) is an eastward moving storm pattern near the equator that can also influence the background climate over the GBR. We find that the MJO can significantly influence the weather variability over the GBR, altering the expected states of El Niño and La Niña periods. Key Points: Composite maps show how the Madden‐Julian oscillation (MJO) can change meteorological patterns on the Great Barrier ReefCluster analysis is used to show the types of MJO propagation patterns likely to occur during El Niño and La Niña periodsOcean temperature variability is discussed with El Niño/La Niña phases as the background states and the MJO as a sub‐seasonal modulator [ABSTRACT FROM AUTHOR]

A stochastically forced linear inverse model (LIM) of the combined modes of variability from the tropical and South Pacific Oceans is used to investigate the linear growth of optimal initial perturbations and to identify the spatiotemporal features of the stochastic forcing associated with the atmospheric Pacific–South American patterns 1 and 2 (PSA1 and PSA2). Optimal initial perturbations are shown to project onto El Niño–Southern Oscillation (ENSO) and South Pacific decadal oscillation (SPDO), where the inclusion of subsurface South Pacific Ocean temperature variability significantly increases the multiyear linear predictability of the deterministic system. We show that the optimal extratropical sea surface temperature (SST) precursor is associated with the South Pacific meridional mode, which takes from 7 to 9 months to linearly evolve into the final ENSO and SPDO peaks in both the observations and as simulated in an atmosphere-forced ocean model. The optimal subsurface precursor resembles its peak phase, but with a weak amplitude, representing oceanic Rossby waves in the extratropical South Pacific. The stochastic forcing is estimated as the residual by removing the deterministic dynamics from the actual tendency under a centered difference approximation. The resulting stochastic forcing time series satisfies the Gaussian white noise assumption of the LIM. We show that the PSA-like variability is strongly associated with stochastic SST forcing in the tropical and South Pacific Oceans and contributes not only to excite the optimal initial perturbations associated with ENSO and the SPDO but in general to activate the entire stochastic SST forcing, especially in austral summer. [ABSTRACT FROM AUTHOR]

A multivariate linear inverse model (LIM) is developed to demonstrate the mechanisms and seasonal predictability of the dominant modes of variability from the tropical and South Pacific Oceans. We construct a LIM whose covariance matrix is a combination of principal components derived from tropical and extratropical sea surface temperature, and South Pacific Ocean vertically averaged temperature anomalies. Eigen-decomposition of the linear deterministic system yields stationary and/or propagating eigenmodes, of which the least damped modes resemble El Niño–Southern Oscillation (ENSO) and the South Pacific decadal oscillation (SPDO). We show that although the oscillatory periods of ENSO and SPDO are distinct, they have very close damping time scales, indicating that the predictive skill of the surface ENSO and SPDO is comparable. The most damped noise modes occur in the midlatitude South Pacific Ocean, reflecting atmospheric eastward-propagating Rossby wave train variability. We argue that these ocean wave trains occur due to the high-frequency atmospheric variability of the Pacific–South American pattern imprinting onto the surface ocean. The ENSO spring predictability barrier is apparent in LIM predictions initialized in March–May (MAM) but displays a significant correlation skill of up to ~3 months. For the SPDO, the predictability barrier tends to appear in June–September (JAS), indicating remote but delayed influences from the tropics. We demonstrate that subsurface processes in the South Pacific Ocean are the main source of decadal variability and further that by characterizing the upper ocean temperature contribution in the LIM, the seasonal predictability of both ENSO and the SPDO variability is increased. [ABSTRACT FROM AUTHOR]

The complex nature of the El Niño-Southern Oscillation (ENSO) is often simplified through the use of conceptual models, each of which offers a different perspective on the ocean-atmosphere feedbacks underpinning the ENSO cycle. One theory, the unified oscillator, combines a variety of conceptual frameworks in the form of a coupled system of delay differential equations. The system produces a self-sustained oscillation on interannual time scales. While the unified oscillator is assumed to provide a more complete conceptual framework of ENSO behaviors than the models it incorporates, its formulation and performance have not been systematically assessed. This paper investigates the accuracy of the unified oscillator through its ability to replicate the ENSO cycle modeled by flux-forced output from the Australian Community Climate and Earth-System Simulator Ocean Model (ACCESS-OM). The anomalous sea surface temperature equation reproduces the main features of the corresponding tendency modeled by ACCESS-OM reasonably well. However, the remaining equations for the thermocline depth anomaly and zonal wind stress anomalies are unable to accurately replicate the corresponding tendencies in ACCESS-OM. Modifications to the unified oscillator, including a diagnostic form of the zonal wind stress anomaly equations, improve its ability to emulate simulated ENSO tendencies. Despite these improvements, the unified oscillator model is less adept than the delayed oscillator model it incorporates in capturing ENSO behavior in ACCESS-OM, bringing into question its usefulness as a unifying ENSO framework. [ABSTRACT FROM AUTHOR]

El Niño-Southern Oscillation (ENSO) is regarded as the main driver of phytoplankton inter-annual variability. Remotely sensed surface chlorophyll-a (Chl-a), has made it possible to examine phytoplankton variability at a resolution and scale that allows for the investigation of climate signals such as ENSO. We provide empirical evidence of an immediate and lagged influence of ENSO on SeaWiFS and MODIS-Aqua derived global Chl-a concentrations. We use 13 years of Chl-a remotely sensed observations along with sea surface temperature (SST) observations across the Tropical and South Pacific to isolate and examine the spatial development of Chl-a anomalies during ENSO: its canonical or eastern Pacific (EP) mode, and El Niño Modoki or central Pacific (CP) mode, using the extended empirical orthogonal function (EEOF) technique. We describe how an EP ENSO phase transition affects Chl-a, and identify an interannual CP mode of variability induced spatial pattern. We argue that when ENSO is analysed as a propagating signal by the EEOF, CP ENSO is found to be more influential on Chl-a interannual to decadal variability than the canonical EP ENSO. Our results cannot confirm the independence of the two ENSO modes but clearly demonstrate that both ENSO flavors manifest a distinct OPEN ACCESS biological response. [ABSTRACT FROM AUTHOR]

A new and potentially skillful seasonal forecast model of tropical cyclone formation [tropical cyclogenesis (TCG)] is developed for the Australian region. The model is based on Poisson regression using the Bayesian approach. Predictor combinations are chosen using a step-by-step predictor selection. The three-predictor model based on derived indices of June-August average convective available potential energy, May-July average meridional winds at 850 hPa ( V850), and July-September geopotential height at 500 hPa produces the smallest standard error (se == 0.36) and root-mean-squared error (RMSE == 5.20) for the leave-one-out cross-validated TCG hindcasts over the 40-yr record between 1968/69-2007/08. The corresponding correlation coefficient between observed annual TCG totals and cross-validated model hindcasts is r == 0.73. Using fourfold cross validation, model hindcast skill is robust, with 85%% of the observed seasonal TCG totals hindcast within the model standard deviations. Seasonal TCG totals during ENSO events are typically well captured with RMSE == 5.14 during El Niño, and RMSE == 6.04 during La Niña years. The model is shown to be valuable in hindcasting seasonal TCG totals in the eastern Australian subregion ( r == 0.73) and also provides some skill for the western Australian region ( r == 0.42), while it not useful for the northern region. In summary, the authors find that the three-predictor Bayesian model provides substantial improvement over existing statistical TCG forecast models, with remarkably skillful hindcasts (forecasts) of Australian region and subregional seasonal TCG totals provided one month ahead of the TC season. [ABSTRACT FROM AUTHOR]