The temporal evolution of El Niño and La Niña varies greatly from event to event. To understand the dynamical processes controlling the duration of El Niño and La Niña events, a suite of observational data and a long control simulation of the Community Earth System Model, version 1, are analyzed. Both observational and model analyses show that the duration of El Niño is strongly affected by the timing of onset. El Niño events that develop early tend to terminate quickly after the mature phase because of the early arrival of delayed negative oceanic feedback and fast adjustments of the tropical Atlantic and Indian Oceans to the tropical Pacific Ocean warming. The duration of La Niña events is, on the other hand, strongly influenced by the amplitude of preceding warm events. La Niña events preceded by a strong warm event tend to persist into the second year because of large initial discharge of the equatorial oceanic heat content and delayed adjustments of the tropical Atlantic and Indian Oceans to the tropical Pacific cooling. For both El Niño and La Niña, the interbasin sea surface temperature (SST) adjustments reduce the anomalous SST gradient toward the tropical Pacific and weaken surface wind anomalies over the western equatorial Pacific, hastening the event termination. Other factors external to the dynamics of El Niño–Southern Oscillation, such as coupled variability in the tropical Atlantic and Indian Oceans and atmospheric variability over the North Pacific, also contribute to the diversity of event duration.

Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century.

El Niño and La Niña events show a wide range of durations over the historical record. The predictability of event duration has remained largely unknown, although multiyear events could prolong their climate impacts. To explore the predictability of El Niño and La Niña event duration, multiyear ensemble forecasts are conducted with the Community Earth System Model, version 1 (CESM1). The 10–40-member forecasts are initialized with observed oceanic conditions on 1 March, 1 June, and 1 November of each year during 1954–2015; ensemble spread is created through slight perturbations to the atmospheric initial conditions. The CESM1 predicts the duration of individual El Niño and La Niña events with lead times ranging from 6 to 25 months. In particular, forecasts initialized in November, near the first peak of El Niño or La Niña, can skillfully predict whether the event continues through the second year with 1-yr lead time. The occurrence of multiyear La Niña events can be predicted even earlier with lead times up to 25 months, especially when they are preceded by strong El Niño. The predictability of event duration arises from initial thermocline depth anomalies in the equatorial Pacific, as well as sea surface temperature anomalies within and outside the tropical Pacific. The forecast error growth, on the other hand, originates mainly from atmospheric variability over the North Pacific in boreal winter. The high predictability of event duration indicates the potential for extending 12-month operational forecasts of El Niño and La Niña events by one additional year.

The potential for multiyear prediction of impactful Earth system change remains relatively underexplored compared to shorter (subseasonal to seasonal) and longer (decadal) timescales. In this study, we introduce a new initialized prediction system using the Community Earth System Model version 2 (CESM2) that is specifically designed to probe potential and actual prediction skill at lead times ranging from 1 month out to 2 years. The Seasonal-to-Multiyear Large Ensemble (SMYLE) consists of a collection of 2-year-long hindcast simulations, with four initializations per year from 1970 to 2019 and an ensemble size of 20. A full suite of output is available for exploring near-term predictability of all Earth system components represented in CESM2. We show that SMYLE skill for El Niño–Southern Oscillation is competitive with other prominent seasonal prediction systems, with correlations exceeding 0.5 beyond a lead time of 12 months. A broad overview of prediction skill reveals varying degrees of potential for useful multiyear predictions of seasonal anomalies in the atmosphere, ocean, land, and sea ice. The SMYLE dataset, experimental design, model, initial conditions, and associated analysis tools are all publicly available, providing a foundation for research on multiyear prediction of environmental change by the wider community.

El Niño–Southern Oscillation (ENSO) in a 1300-yr preindustrial control simulation of the Community Climate System Model, version 4 (CCSM4), exhibits distinct modulation in association with tropical Pacific decadal variability (TPDV). The frequency and duration of El Niño events modulate with changes in the interbasin sea surface temperature (SST) gradient related to the leading mode of TPDV, which resembles the interdecadal Pacific oscillation (IPO). La Niña shows similar changes with the IPO but is also controlled by changes in El Niño that often precedes La Niña, and these effects tend to cancel each other. The amplitude of ENSO, on the other hand, is closely related to the second leading mode of TPDV that affects the zonal and meridional contrast of tropical Pacific climate. Significant changes in the pattern and seasonal evolution related to this TPDV mode are found mainly for El Niño because of the nonlinear relation between the atmospheric deep convection and SSTs. The resultant changes in the amplitude of El Niño, in turn, affect the amplitude and duration of the following La Niña, as well as the asymmetry in their patterns and duration. The decadal ENSO modulation associated with both TPDV modes is not symmetrical between El Niño and La Niña and thus is not likely to occur solely as a result of random variability. The patterns of TPDV in CCSM4 have resemblance to those simulated by its atmospheric component coupled to a slab ocean model, suggesting that TPDV induced by stochastic atmospheric variability interacts with the ENSO dynamics.

The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.