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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.

 

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.

 

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 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. 

In this study, we investigate the relative contributions of dynamical forcings, particularly the eastern and central‐western Pacific winds, and thermodynamical forcings to the evolution of the 2017 extreme coastal El Niño using observations and modeling experiments. We show that the competing effects of anomalous eastern Pacific westerlies and central‐western Pacific easterlies and their resulting downwelling and upwelling equatorial Kelvin waves are essential for the evolution of the event, together with alongshore anomalous northerlies which suppress coastal upwelling and reduce latent heat release as discussed in previous studies. We find that eastern Pacific zonal wind anomalies are about twice as effective in generating a coastal response as central‐western Pacific anomalies, thus compensating for their usually smaller magnitude. While the 2017 event exemplified these competing effects, they were also found to be important in other coastal and basin‐scale El Niño events, thus contributing to the mechanisms responsible for El Niño diversity.

 

Abstract This study assesses the predictive skill of eight North American Multimodel Ensemble (NMME) models in predicting the Indian Ocean dipole (IOD). We find that the forecasted ensemble-mean IOD–El Niño–Southern Oscillation (ENSO) relationship deteriorates away from the observed relationship with increasing lead time, which might be one reason that limits the IOD predictive skill in coupled models. We are able to improve the IOD predictive skill using a recently developed stochastic dynamical model (SDM) forced by forecasted ENSO conditions. The results are consistent with the previous result that operational IOD predictability beyond persistence at lead times beyond one season is mostly controlled by ENSO predictability and the signal-to-noise ratio of the Indo-Pacific climate system. The multimodel ensemble (MME) investigated here is found to be of superior skill compared to each individual model at most lead times. Importantly, the skill of the SDM IOD predictions forced with forecasted ENSO conditions were either similar or better than those of the MME IOD forecasts. Moreover, the SDM forced with observed ENSO conditions exhibits significantly higher IOD prediction skill than the MME at longer lead times, suggesting the large potential skill increase that could be achieved by improving operational ENSO forecasts. We find that both cold and warm biases of the predicted Niño-3.4 index may cause false alarms of negative and positive IOD events, respectively, in NMME models. Many false alarms for IOD forecasts at lead times longer than one season in the original forecasts disappear or are significantly reduced in the SDM forced by forecasted ENSO conditions.