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This study presents a description of the El Niño–Southern Oscillation (ENSO) and Pacific Decadal Variability (PDV) in a multicentury preindustrial simulation of the Community Earth System Model Version 2 (CESM2). The model simulates several aspects of ENSO relatively well, including dominant timescale, tropical and extratropical precursors, composite evolution of El Niño and La Niña events, and ENSO teleconnections. The good model representation of ENSO spectral characteristics is consistent with the spatial pattern of the anomalous equatorial zonal wind stress in the model, which results in the correct adjustment timescale of the equatorial thermocline according to the delayed/recharge oscillator paradigms, as also reflected in the realistic time evolution of the equatorial Warm Water Volume. PDV in the model exhibits a pattern that is very similar to the observed, with realistic tropical and South Pacific signatures which were much weaker in some of the CESM2 predecessor models. The tropical component of PDV also shows an association with ENSO decadal modulation which is similar to that found in observations. However, the ENSO amplitude is about 30% larger than observed in the preindustrial CESM2 simulation, and even larger in the historical ensemble, perhaps as a result of anthropogenic influences. In contrast to observations, the largest variability is found in the central Pacific rather than in the eastern Pacific, a discrepancy that somewhat hinders the model's ability to represent a full diversity in El Niño spatial patterns and appears to be associated with an unrealistic confinement of the precipitation anomalies to the western Pacific. Plain Language Summary The Community Earth System Model Version 2 (CESM2) is the latest version of the Earth System models developed at the National Center for Atmospheric Research in Boulder, CO. This study examines how well CESM2 simulates the El Niño–Southern Oscillation (ENSO), the leading mode of climate variability in the tropical Pacific at interannual timescales, with a large influence on the global climate and very important societal impacts. The modeled ENSO exhibits a larger amplitude than observed, with anomalies displaced further west than in observations, but with dominant timescale, temporal evolution, precursors, and teleconnections in good agreement with observations. This study also examines the model performance in simulating climate variability at decadal timescales in the Pacific sector. The spatial pattern of Pacific Decadal Variability and the relationship between decadal variations in the tropical Pacific and decadal ENSO modulation are well simulated by CESM2. Key Points El Niño–Southern Oscillation in the model exhibits realistic timescale, precursors, temporal evolution, and teleconnections The El Niño–Southern Oscillation amplitude is about 30% larger than observed, with anomalies displaced too far west than observed The model Pacific Decadal Variability has a realistic spatial pattern

In this study, we evaluate the intensity of the Central‐Pacific (CP) and Eastern‐Pacific (EP) types of El Niño‐Southern Oscillation (ENSO) simulated in the pre‐industrial, historical, and the Representative Concentration Pathways (RCP) 4.5 experiments of the Coupled Model Intercomparison Project Phase 5 (CMIP5). Compared to the CMIP3 models, the pre‐industrial simulations of the CMIP5 models are found to (1) better simulate the observed spatial patterns of the two types of ENSO and (2) have a significantly smaller inter‐model diversity in ENSO intensities. The decrease in the CMIP5 model discrepancies is particularly obvious in the simulation of the EP ENSO intensity, although it is still more difficult for the models to reproduce the observed EP ENSO intensity than the observed CP ENSO intensity. Ensemble means of the CMIP5 models indicate that the intensity of the CP ENSO increases steadily from the pre‐industrial to the historical and the RCP4.5 simulations, but the intensity of the EP ENSO increases from the pre‐industrial to the historical simulations and then decreases in the RCP4.5 projections. The CP‐to‐EP ENSO intensity ratio, as a result, is almost the same in the pre‐industrial and historical simulations but increases in the RCP4.5 simulation. Key Points Smaller inter‐model diversity of ENSO intensities in CMIP5 than in CMIP3 Decrease in the diversity is particularly significant for the simulated EP ENSO Different response of EP and CP ENSO to global warming

Recent violent and widespread tornado outbreaks in the US, such as occurred in the spring of 2011, have caused devastating societal impact with significant loss of life and property. At present, our capacity to predict US tornado and other severe weather risk does not extend beyond seven days. In an effort to advance our capability for developing a skillful long-range outlook for US tornado outbreaks, here we investigate the spring probability patterns of US regional tornado outbreaks during 1950-2014. We show that the four dominant springtime El Niño-Southern Oscillation (ENSO) phases (persistent versus early-terminating El Niño and resurgent versus transitioning La Niña) and the North Atlantic sea surface temperature tripole variability are linked to distinct and significant US regional patterns of outbreak probability. These changes in the probability of outbreaks are shown to be largely consistent with remotely forced regional changes in the large-scale atmospheric processes conducive to tornado outbreaks. An implication of these findings is that the springtime ENSO phases and the North Atlantic SST tripole variability may provide seasonal predictability of US regional tornado outbreaks.

Diverse characteristics of El Niño Southern Oscillation (ENSO) events challenge the traditional view of tropical coupled ocean-atmosphere systems. The probability of a transition from one type of event to another is influenced by multiple factors of which many are projected to change. Here we assess the likelihood of ENSO transitions in observations and climate models, including a distinction between events that peak in the Eastern Pacific (EP) and Central Pacific (CP). We find that the initial ENSO state influences the likelihood of certain transitions and that some transitions are not physically possible or stochastically likely. For example, transitions to CP events are more likely than EP events except from a neutral state. We also find that El Niños tend to occur as singular events compared to La Niñas. While consecutive El Niño and La Niña events of EP type are possible, opposing EP events do not occur in succession. We identify several transitions likely driven by internal dynamical processes including neutral conditions to El Niño, CP El Niño to another El Niño, EP El Niño to CP La Niña, CP La Niña to CP El Niño and La Niña, and EP La Niña to neutral and CP El Niño. Projections of future transitions show an increased probability of transitions to CP El Niño events while transitions to EP La Niña events become less frequent under a high-emissions scenario. Accordingly, transitions to these events become more and less likely, respectively. We also find changes in the likelihood of specific transitions in a warming world: consecutive CP El Niño events become more likely while EP El Niño events become less likely to transition into CP La Niña events. These changes are expected to occur as early as 2050 with some changes to be accelerated by the end of the 21st century.

The Warm Water Volume (WWV), a proxy for the equatorial Pacific heat content, is the most widely used oceanic precursor of the El Niño Southern Oscillation (ENSO). The standard interpretation of this lead relation in the context of the recharge oscillator theory is that anomalous easterlies during, e.g. La Niña, favour a slow recharge of the equatorial band that will later favour a transition to El Niño. Here we demonstrate that WWV only works as the best ENSO predictor during boreal spring, i.e. during ENSO onset, in both observations and CMIP5 models. At longer lead times, the heat content in the western Pacific (WWV W ) is the best ENSO predictor, as initially formulated in the recharge oscillator theory. Using idealised and realistic experiments with a linear continuously stratified ocean model, and a comprehensive wave decomposition method, we demonstrate that spring WWV mostly reflects the fast Kelvin wave response to wind anomalies early in the year, rather than the longer-term influence of winds during the previous year. WWV is hence not an adequate index of the slow recharge invoked in the recharge oscillator. The WWV W evolution before spring is dominated by forced Rossby waves, with a smaller contribution from the western boundary reflection. WWV W can be approximated from the integral of equatorial wind stress over the previous ~ 10 months, thus involving a longer-term time scale than WWV main time scale (~ 3 months). We hence recommend using WWV W rather than WWV as an index for the slow recharge before the spring predictability barrier.

La Niña persisted from 2020 to 2023, but its mechanisms are still unclear. In this study, atmosphere and ocean reanalysis and 100‐member initialized forecasts using a state‐of‐the‐art climate model were analyzed to identify factors contributing to the persistence of the first‐ to second‐year La Niña during 2020–2022. We found that North Pacific high pressure anomalies in the winter of 2020/2021 forced a negative phase of the Pacific meridional mode through the following spring, forming the broader structure of La Niña. The resultant broader La Niña pattern slowed down the recharge‐discharge process by Ekman transport, persisting La Niña. Ensemble forecast sensitivity analysis revealed that the meridional extent of La Niña explains its forecast spread, reaffirming the importance of La Niña spatial pattern. Advancing predictive understanding of 2020–2022 multi‐year La Niña can help to improve the extended seasonal forecast. Plain Language Summary Almost 3 years have passed since the sea surface temperature in the central‐eastern equatorial Pacific became a cooler‐than‐normal in 2020. This event, called La Niña, can influence our lives through causing anomalous weather conditions globally. Therefore, it is important to know why the La Niña event has lasted so long. Using the computer simulation of climate, we repeated La Niña forecasts started from November 2020 a hundred times. We found that when the shape of La Niña was broader, the forecast was successful 1 year ahead. The shape of La Niña in 2021 depended on the atmospheric condition in the North Pacific. This study suggests that checking La Niña shape informs whether La Niña continues or declines. This knowledge may improve future climate forecasts. Key Points The 100‐member ensemble forecasts initialized in November 2020 by MIROC6 captured prolonged La Niña in 2021/2022 The forecast sensitivity analysis to initial states of ensemble members revealed a critical role of the North Pacific high North Pacific high in winter 2020/2021, causing negative North Pacific Meridional Mode spring, formed the spatially broader La Niña, resulting in long persistence

The El Niño‐Southern Oscillation has two warm phase flavors of Eastern Pacific (EP) and Central Pacific (CP) El Niños that exhibit seemingly distinct global teleconnections, but the limited observational sample leaves open whether and where these differences are robust. Coupled climate models are conventionally used to more robustly sample these teleconnections and their differences, yet they have persistent tropical sea surface temperature (SST) biases. To better understand teleconnection differences between EP and CP El Niño, we use atmosphere‐only ensemble simulations with both observed and realistic synthetic SSTs generated from a Linear Inverse Model. With a sufficient number of El Niño events, the perceived teleconnection differences weaken due to broader sampling of event patterns, event strengths, and intrinsic atmospheric variability. However, biases in the atmospheric model's teleconnection caveat these conclusions. With CP event frequency projected to increase in the future, associated global impacts have the potential to become stronger, too.

We propose a unified statistical method based on deep learning and analysis to quantify the relative contributions of the global oceans to El Niño–Southern Oscillation (ENSO) predictability. By incorporating subsurface signals in the Indian Ocean and Atlantic, the forecast lead can be skillfully extended by about one season. This skill enhancement mainly originates from the tropical Indian Ocean, presumably related to signals of the Indian Ocean Dipole passing to the tropical Pacific through the Indonesian Throughflow. The sea surface temperature anomaly (SSTA) in the Indian Ocean accounts for nearly 50% of surface contributions to both El Niño and La Niña predictions at a 15‐month lead. The north tropical Atlantic SSTA has a moderate impact on La Niña at a 9‐month lead. The Pacific Meridional Mode plays a significant role in both ENSO phases at a 12‐month lead. Thus, our study suggests that trans‐basin effects for ENSO are more vigorous than previously thought. Plain Language Summary El Niño–Southern Oscillation (ENSO) can excite various modes of climate variability in the Indian Ocean, Atlantic, and extratropical Pacific. However, less understood and quantified than the ENSO‐driven influences is how these regions outside the tropical Pacific in turn affect ENSO. Here, we quantify the impacts of the Indian Ocean, Atlantic, and extratropical Pacific on the predictability of ENSO. The lead time can be skillfully extended by one season by incorporating subsurface signals from the Indian and Atlantic oceans. The extratropical Pacific SST can indeed provide some useful sources of information for ENSO predictability at long lead times. A heatmap analysis further indicates that the triggering precursors freely selected by the deep learning model link to our current understanding of physical pathways for ENSO prediction. The Indian Ocean Dipole, the north tropical Atlantic SSTA, and the Pacific Meridional Mode are the main trans‐basin factors that vigorously influence ENSO at different lead times. Our findings emphasize that the impacts of the global oceans on ENSO are significant, and their influences should be properly considered in the coupled models. Key Points Heat content in the Indian Ocean can provide extra signals for long‐term El Niño–Southern Oscillation (ENSO) forecasts and extend the lead time by about one season The Indian Ocean Dipole can significantly influence El Niño via both surface and subsurface physical pathways at a 15‐month lead The north tropical Atlantic warming and the Pacific Meridional Mode also vigorously affect ENSO at lead times of both 12 and 9 months

Hindcasts and real-time predictions of the east-central tropical Pacific sea surface temperature (SST) from the North American Multimodel Ensemble (NMME) system are verified for 1982–2015. Skill is examined using two deterministic verification measures: mean squared error skill score (MSESS) and anomaly correlation. Verification of eight individual models shows somewhat differing skills among them, with some models consistently producing more successful predictions than others. The skill levels of MME predictions are approximately the same as the two best performing individual models, and sometimes exceed both of them. A decomposition of the MSESS indicates the presence of calibration errors in some of the models. In particular, the amplitudes of some model predictions are too high when predictability is limited by the northern spring ENSO predictability barrier and/or when the interannual variability of the SST is near its seasonal minimum. The skill of the NMME system is compared to that of the MME from the IRI/CPC ENSO prediction plume, both for a comparable hindcast period and also for a set of real-time predictions spanning 2002–2011. Comparisons are made both between the MME predictions of each model group, and between the average of the skills of the respective individual models in each group. Acknowledging a hindcast versus real-time inconcsistency in the 2002–2012 skill comparison, the skill of the NMME is slightly higher than that of the prediction plume models in all cases. This result reflects well on the NMME system, with its large total ensemble size and opportunity for possible complementary contributions to skill.