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El Niño events, the warm phase of the El Niño–Southern Oscillation (ENSO) phenomenon, amplify climate variability throughout the world. Uncertain climate model predictions limit our ability to assess whether these climatic events could become more extreme under anthropogenic greenhouse warming. Palaeoclimate records provide estimates of past changes, but it is unclear if they can constrain mechanisms underlying future predictions. Here we uncover a mechanism using numerical simulations that drives consistent changes in response to past and future forcings, allowing model validation against palaeoclimate data. The simulated mechanism is consistent with the dynamics of observed extreme El Niño events, which develop when western Pacific warm pool waters expand rapidly eastwards because of strongly coupled ocean currents and winds. These coupled interactions weaken under glacial conditions because of a deeper mixed layer driven by a stronger Walker circulation. The resulting decrease in ENSO variability and extreme El Niño occurrence is supported by a series of tropical Pacific palaeoceanographic records showing reduced glacial temperature variability within key ENSO-sensitive oceanic regions, including new data from the central equatorial Pacific. The model–data agreement on past variability, together with the consistent mechanism across climatic states, supports the prediction of a shallower mixed layer and weaker Walker circulation driving more frequent extreme El Niño genesis under greenhouse warming. 

Abstract Understanding El Niño-Southern Oscillation (ENSO) response to past climate forcings is hindered by conflicting paleoclimate evidence. Records from the eastern Pacific show an intensification of ENSO variability from early to late Holocene, while records from the central Pacific show highly variable ENSO throughout the Holocene without an obvious relation to insolation forcing, which is the main climate driver during this interval. Here, we show via climate model simulations that conflicting Holocene records can be reconciled by considering changes in the relative frequency of the three preferred spatial patterns in which El Niño events occur (Eastern Pacific, Central Pacific, and Coastal) and in the strength of their hydroclimatic impacts. The relationship between ENSO diversity and variance is not only crucial for interpreting paleo-ENSO records and understanding ENSO response to external forcings but can also be used across climate model simulations to help evaluate the realism of ENSO projections in a changing climate. 

Abstract 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. 

Abstract Analysis of observational data and a long control simulation of the Community Earth System Model, version 1 (CESM1), shows that El Niño events developing in boreal spring to early summer usually terminate after peaking in winter, whereas those developing after summer tend to persist into the second year. To test the predictability of El Niño duration based on the onset timing, perfect model predictions were conducted for three El Niño events developing in April or September in the CESM1 control simulation. For each event, 30-member ensemble simulations are initialized with the same oceanic conditions in the onset month but with slightly different atmospheric conditions and integrated for 2 years. The CESM1 successfully predicts the termination of El Niño after the peak in 95% of the April-initialized simulations and the continuation of El Niño into the second year in 83% of the September-initialized simulations. The predictable component of El Niño duration arises from the initial oceanic conditions that affect the timing and magnitude of negative feedback within the equatorial Pacific, as well as from the Indian and Atlantic Oceans. The ensemble spread of El Niño duration, on the other hand, originates from surface wind variability over the western equatorial Pacific in spring following the peak. The wind variability causes a larger spread in the September-initialized than the April-initialized ensemble simulations due to weaker negative feedback in spring. These results indicate potential predictability of El Niño events beyond the current operational forecasts by 1 year. 

Presently, the Indian Ocean (IO) resides in a climate state that prevents strong year-to-year climate variations. This may change under greenhouse warming, but the mechanisms remain uncertain, thus limiting our ability to predict future changes in climate extremes. Using climate model simulations, we uncover the emergence of a mode of climate variability capable of generating unprecedented sea surface temperature and rainfall fluctuations across the IO. This mode, which is inhibited under present-day conditions, becomes active in climate states with a shallow thermocline and vigorous upwelling, consistent with the predictions of continued greenhouse warming. These predictions are supported by modeling and proxy evidence of an active mode during glacial intervals that favored such a state. Because of its impact on hydrological variability, the emergence of such a mode would become a first-order source of climate-related risks for the densely populated IO rim. 

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.