You're viewing the US edition You can switch to the UK, AU or IE homepage at any time using this menu. Leaked Noah Presgrove filing makes explosive claims about alleged lover's plot to kill Oklahoma teen hours before his naked body was found on remote road next to his teeth Prince and Princess of Wales dazzle as they join King hosting Sultan of Oman at glittering State Banquet - with royals bidding to put'distraction' of Harry's latest interview behind them Robert F Kennedy's granddaughter Kyle dies at New England home aged just 42 'Cornell 7' frat boy's powerful family dynasty and web of connections to global elite revealed... as his own sister's fight against university rape emerges My fisherman friend, 71, was killed by an alligator after he'stole' its dinner. He saw it coming and tried to run... then slipped at the worst possible moment America's most popular baby names for 2026 revealed So many single men are taking this new drug cocktail before dates. The results in the bedroom are startling... as I discovered during one marathon session: JANA HOCKING Christina Applegate tearfully announces she does not have MS and was'misdiagnosed' for years I'm a Christian leader who attended a secret meeting with Anthropic. The crisis of faith will be so earth-shattering that we prayed here's my warning to anyone using AI Kylie Jenner causes catfight backstage at Victoria's Secret show: Supermodels who couldn't hide their rage over her $50m Fantasy Bra... as Kris Jenner's secret deal to get her on runway is exposed The pro-cellulite brigade thundered down the runway and ruined Victoria's Secret... here's what ordinary women really think of the brand's reboot: CAROLINE BULLOCK Tensions boil over at ESPN as commentator Troy Aikman fears for career: After awkward on-air incident, insiders reveal he's digging in heels despite seeing'writing on the wall' More humiliation for Kimberly Guilfoyle as ugly gossip among the A-list'Athens set' is revealed and even her pop star confidant goes silent... as her designer dresses are seen out on the street Prince Harry says he'wants it to work out' with his family... but takes a jab at those who'don't want to' Devastated family of airman who died in rappelling stunt reveal their shock over his'extremely traumatic and public' death Menopause left me with thinning, wispy hair - and it shattered my confidence.

Let's Talk About the Super El Niño and Climate Change Here's what we know about the record-setting El Niño on a record-hot planet, and the most important questions scientists are trying to answer. The tropical Pacific has seen heat build for months as a "super" El Niño takes shape. A climate phenomenon that forms every few years, this year's iteration of El Niño has been on a tear of daily records and is forecast to reach unprecedented strength this winter. While it's characterized by hotter-than-normal waters in the eastern tropical Pacific, El Niño is a global problem. It's fair to wonder how another global problem is interacting with El Niño: climate change, driven by burning fossil fuels.

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The World Meteorological Organization is warning that the climate phenomenon that has the Pacific running a fever will last until at least February, with dire consequences for weather worldwide. The world's leading weather organization is sounding the alarm about this year's super El Niño, issuing its most certain forecast in the history of the organization. In an update issued early Thursday, the World Meteorological Organization predicted a "nearly 100% likelihood" that the El Niño will last through February. The strong language from the World Meteorological Organization--the United Nations' weather and climate agency--underlies just how historic this El Niño is shaping up to be. "This is the first time that a WMO El Niño/La Niña Update has been so unequivocal," the agency says, noting that certainty reflects "the strong agreement within WMO's extensive network."

This paper introduces a distillation framework for an ensemble of entropy-optimal Sparse Probabilistic Approximation (eSPA) models, trained exclusively on satellite-era observational and reanalysis data to predict ENSO phase up to 24 months in advance. While eSPA ensembles yield state-of-the-art forecast skill, they are harder to interpret than individual eSPA models. We show how to compress the ensemble into a compact set of "distilled" models by aggregating the structure of only those ensemble members that make correct predictions. This process yields a single, diagnostically tractable model for each forecast lead time that preserves forecast performance while also enabling diagnostics that are impractical to implement on the full ensemble. An analysis of the regime persistence of the distilled model "superclusters", as well as cross-lead clustering consistency, shows that the discretised system accurately captures the spatiotemporal dynamics of ENSO. By considering the effective dimension of the feature importance vectors, the complexity of the input space required for correct ENSO phase prediction is shown to peak when forecasts must cross the boreal spring predictability barrier. Spatial importance maps derived from the feature importance vectors are introduced to identify where predictive information resides in each field and are shown to include known physical precursors at certain lead times. Case studies of key events are also presented, showing how fields reconstructed from distilled model centroids trace the evolution from extratropical and inter-basin precursors to the mature ENSO state. Overall, the distillation framework enables a rigorous investigation of long-range ENSO predictability that complements real-time data-driven operational forecasts.

These rare sea creatures live where the sun don't shine. Breakthroughs, discoveries, and DIY tips sent every weekday. Like a scene out of a Jules Verne novel, scientists from Schmidt Ocean Institute recently encountered a giant phantom jelly (). The enormous deep-sea jellyfish was spotted about 830 feet below the surface of the Pacific Ocean by a Remotely Operated Vehicle (ROV) exploring the Colorado-Rawson submarine canyon wall off the coast of Argentina. ROV pilots filmed this giant phantom jelly, or Stygiomedusa gigantea, at 253 meters during an ROV descent to explore the Colorado-Rawson submarine canyon wall.

Neither President Franklin Delano Roosevelt nor British Prime Minister Winston Churchill, nor any of their senior military or political advisors, saw the Japanese attacks of late 1941 coming. The forces of Imperial Japan achieved total surprise across the Pacific. The intelligence failures in the U.S. leading up to Pearl Harbor were catastrophic. So was Great Britain's general underestimation of the threat from Imperial Japan. The U.K.'s fortress outpost in the Pacific at Singapore was thought to be, if not impregnable, than as close to it as possible.

Machine learning (ML) models are successful with weather forecasting and have shown progress in climate simulations, yet leveraging them for useful climate predictions needs exploration. Here we show this feasibility using Neural General Circulation Model (NeuralGCM), a hybrid ML-physics atmospheric model developed by Google, for seasonal predictions of large-scale atmospheric variability and Northern Hemisphere tropical cyclone (TC) activity. Inspired by physical model studies, we simplify boundary conditions, assuming sea surface temperature (SST) and sea ice follow their climatological cycle but persist anomalies present at the initialization time. With such forcings, NeuralGCM can generate 100 simulation days in ~8 minutes with a single Graphics Processing Unit (GPU), while simulating realistic atmospheric circulation and TC climatology patterns. This configuration yields useful seasonal predictions (July to November) for the tropical atmosphere and various TC activity metrics. Notably, the predicted and observed TC frequency in the North Atlantic and East Pacific basins are significantly correlated during 1990 to 2023 (r=~0.7), suggesting prediction skill comparable to existing physical GCMs. Despite challenges associated with model resolution and simplified boundary forcings, the model-predicted interannual variations demonstrate significant correlations with the observation, including the sub-basin TC tracks (p<0.1) and basin-wide accumulated cyclone energy (p<0.01) of the North Atlantic and North Pacific basins. These findings highlight the promise of leveraging ML models with physical insights to model TC risks and deliver seamless weather-climate predictions.

Its evolution is governed by intricate air-sea interactions, posing significant challenges for long-term prediction. In this study, we introduce CTEFNet, a multivariate deep learning model that synergizes convolutional neural networks and transformers to enhance ENSO forecasting. By integrating multiple oceanic and atmospheric predictors, CTEFNet extends the effective forecast lead time to 20 months while mitigating the impact of the spring predictability barrier, outperforming both dynamical models and state-of-the-art deep learning approaches. Furthermore, CTEFNet offers physically meaningful and statistically significant insights through gradient-based sensitivity analysis, revealing the key precursor signals that govern ENSO dynamics, which align with well-established theories and reveal new insights about inter-basin interactions among the Pacific, Atlantic, and Indian Oceans. The CTEFNet's superior predictive skill and interpretable sensitivity assessments underscore its potential for advancing climate prediction. Our findings highlight the importance of multivariate coupling in ENSO evolution and demonstrate the promise of deep learning in capturing complex climate dynamics with enhanced interpretability. 1 Introduction El Ni no-Southern Oscillation (ENSO) is one of the most prominent modes of inter-annual climate variability, characterized by shifts in sea surface temperatures (SST) across the tropical Pacific Ocean and the weakening of equatorial trade winds.