RESUMO
The El Niño-Southern Oscillation (ENSO) provides most of the global seasonal climate forecast skill1-3, yet, quantifying the sources of skilful predictions is a long-standing challenge4-7. 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 possible8-10, 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.
RESUMO
El Niño-Southern Oscillation (ENSO) is the dominant mode of interannual climate variability in the tropical Pacific, whose nature nevertheless may change significantly in a warming climate. Here, we show that the predictability of ENSO may decrease in the future. Across the models in the Coupled Model Intercomparison Project Phase 6 (CMIP6), we find a robust decrease of the persistence and predictability for the Central Pacific (CP) ENSO under global warming, notably in passing through the boreal spring. The strength of spring predictability barrier will be increased by 25% in the future. The reduced predictability of CP ENSO is caused by the faster warming over surface ocean in tropical Pacific and, in turn, the enhanced thermodynamical damping rate on CP ENSO in response to global warming. In contrast, the predictability of Eastern Pacific ENSO will not change. Our results suggest that future greenhouse warming will make the prediction of CP ENSO more challenging, with far-reaching implications on future climate predictions.
RESUMO
In 2023, the development of El Niño is poised to drive a global upsurge in surface air temperatures (SAT), potentially resulting in unprecedented warming worldwide. Nevertheless, the regional patterns of SAT anomalies remain diverse, obscuring where historical warming records may be surpassed in the forthcoming year. Our study underscores the significant influence of El Niño and the persistence of climate signals on the inter-annual variability of regional SAT, both in amplitude and spatial distribution. The likelihood of global mean SAT exceeding historical records, calculated from July 2023 to June 2024, is estimated at 90%, contingent upon annual-mean sea surface temperature anomalies in the eastern equatorial Pacific exceeding 0.6 °C. Regions particularly susceptible to recording record-high SAT include coastal and adjacent areas in Asia such as the Bay of Bengal and the South China Sea, as well as Alaska, the Caribbean Sea, and the Amazon. This impending warmth heightens the risk of year-round marine heatwaves and escalates the threat of wildfires and other negative consequences in Alaska and the Amazon basin, necessitating strategic mitigation measures to minimize potential worst-case impacts.