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Abstract Under anthropogenic warming, future changes to climate variability beyond specific modes such as the El Niño-Southern Oscillation (ENSO) have not been well-characterized. In the Community Earth System Model version 2 Large Ensemble (CESM2-LE) climate model, the future change to sea surface temperature (SST) variability (and correspondingly marine heatwave intensity) on monthly timescales and longer is spatially heterogeneous. We examined these projected changes (between 1960–2000 and 2060–2100) in the North Pacific using a local linear stochastic-deterministic model, which allowed us to quantify the effect of changes to three drivers on SST variability: ocean “memory” (the SST damping timescale), ENSO teleconnections, and stochastic noise forcing. The ocean memory declines in most areas, but lengthens in the central North Pacific. This change is primarily due to changes in air-sea feedbacks and ocean damping, with the shallowing mixed layer depth playing a secondary role. An eastward shift of the ENSO teleconnection pattern is primarily responsible for the pattern of SST variance change. 

Abstract The Pacific–South American (PSA) pattern is a key mode of climate variability in the mid-to-high latitudes of the Southern Hemisphere, impacting circulation and rainfall anomalies over South America. However, the effect of South American rainfall on the PSA has not been previously explored. This study focuses on the impact of rainfall over southeastern South American (SESA) during the austral summer (December–February). Observational analyses reveal that the PSA pattern remains confined to higher southern latitudes when SESA rainfall anomalies are weak. In contrast, strong SESA rainfall anomalies can generate a quasi-stationary Rossby wave train, which represents a cross-equatorial extension of the PSA. This wave train propagates along a southwest–northeast great circle path from higher latitudes, crosses the equator, and reaches the tropical Atlantic, southern Europe, and northern Africa, inducing a wet and cool weather condition over western and southern Europe. The observed wave train can be reproduced by the linear baroclinic model (LBM) simulations. Given the PSA’s connection to tropical forcing over the central Pacific, we examine differences in the wave response to central Pacific forcing alone versus combined central Pacific and SESA forcings. By incorporating SESA forcing, the wave train originally triggered by central Pacific forcing is amplified and extended. Our findings confirm the significant role of SESA rainfall anomalies in extending the PSA pattern to the Northern Hemisphere and highlight the South American continent as a land bridge that links circulation anomalies across the Pacific and Atlantic Oceans and the Southern and Northern Hemispheres. 

Removing CO₂from the atmosphere is emerging as a viable strategy to mitigate global warming, yet the responses of the climate system to CO₂reduction remain uncertain. One of the most uncertain aspects of El Niño behavior is the change in periodicity in response to CO₂forcing [O. Alizadeh,Earth-Sci. Rev.235, 104246 (2022)]. In this study, we show that climate models consistently project an abrupt shortening of El Niño periodicity once CO₂reductions commence in ramp-up and ramp-down CO₂experiments. Besides the contribution of slow mean state changes, this phenomenon is shown to be driven by a southward shift of the Intertropical Convergence Zone (ITCZ) [J.-S. Kug,et al.,Nat. Clim. Chang.12, 47–53 (2022)] and the consequent narrowing of El Niño’s spatial pattern, which enhances the effectiveness of ocean heat recharge/discharge processes, thereby shortening its periodicity. This suggests that the abrupt shift in El Niño periodicity results from a cascading reaction involving ITCZ dynamics and El Niño’s spatial configuration. These findings highlight the critical role of the global energy balance in shaping El Niño characteristics. 

The El Niño–Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill, yet, quantifying the sources of skilful predictions is a long-standing challenge. Different sources of predictability affect ENSO evolution, leading to distinct global effects. Artificial intelligence forecasts offer promising advancements but linking their skill to specific physical processes is not yet possible, limiting our understanding of the dynamics underpinning the advancements. Here we show that an extended nonlinear recharge oscillator (XRO) model shows skilful ENSO forecasts at lead times up to 16–18 months, better than global climate models and comparable to the most skilful artificial intelligence forecasts. The XRO parsimoniously incorporates the core ENSO dynamics and ENSO’s seasonally modulated interactions with other modes of variability in the global oceans. The intrinsic enhancement of ENSO’s long-range forecast skill is traceable to the initial conditions of other climate modes by means of their memory and interactions with ENSO and is quantifiable in terms of these modes’ contributions to ENSO amplitude. Reforecasts using the XRO trained on climate model output show that reduced biases in both model ENSO dynamics and in climate mode interactions can lead to more skilful ENSO forecasts. The XRO framework’s holistic treatment of ENSO’s global multi-timescale interactions highlights promising targets for improving ENSO simulations and forecasts. 

Abstract The El Niño‐Southern Oscillation (ENSO) influences ocean wave activity across the Pacific, but its effects on island shores are modulated by local weather and selective sheltering of multi‐modal seas. Utilizing 41 years of high‐resolution wave hindcasts, we decipher the season‐ and locality‐dependent connections between ENSO and wave patterns around the Hawaiian Islands. The north and west‐facing shores, exposed to energetic northwest swells during boreal winters, experience the most pronounced ENSO‐related variability, with increased high‐surf activity during El Niño years. While the year‐round trade wind waves exhibit moderate correlation with ENSO, the basin‐wide climate influence is masked by locally accelerated trade winds in channels and around large headlands. The remarkable global‐to‐local pathway through the high‐resolution hindcast enables development of an ENSO‐based semi‐empirical wave model to statistically describe and predict severe wave conditions on vulnerable shores with potential application in coastal risk management and hazard mitigation for Pacific Islands and beyond. 

Abstract This study examines historical simulations of ENSO in the E3SM-1-0, CESM2, and GFDL-CM4 climate models, provided by three leading U.S. modeling centers as part of the Coupled Model Intercomparison Project phase 6 (CMIP6). These new models have made substantial progress in simulating ENSO’s key features, including: amplitude; timescale; spatial patterns; phase-locking; spring persistence barrier; and recharge oscillator dynamics. However, some important features of ENSO are still a challenge to simulate. In the central and eastern equatorial Pacific, the models’ weaker-than-observed subsurface zonal current anomalies and zonal temperature gradient anomalies serve to weaken the nonlinear zonal advection of subsurface temperatures, leading to insufficient warm/cold asymmetry of ENSO’s sea surface temperature anomalies (SSTA). In the western equatorial Pacific, the models’ excessive simulated zonal SST gradients amplify their zonal temperature advection, causing their SSTA to extend farther west than observed. The models underestimate both ENSO’s positive dynamic feedbacks (due to insufficient zonal wind stress responses to SSTA) and its thermodynamic damping (due to insufficient convective cloud shading of eastern Pacific SSTA during warm events); compensation between these biases leads to realistic linear growth rates for ENSO, but for somewhat unrealistic reasons. The models also exhibit stronger-than-observed feedbacks onto eastern equatorial Pacific SSTAs from thermocline depth anomalies, which accelerates the transitions between events and shortens the simulated ENSO period relative to observations. Implications for diagnosing and simulating ENSO in climate models are discussed.