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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.

An autonomous drone fleet overseen by Ocean Infinity has rediscovered the USS Stewart, the only US Navy destroyer ever captured by Japanese forces during World War II. The marine robotics company's trio of orange, 20-foot-long underwater robots found the historic vessel while mapping what is now the 1,286-square-mile Cordell Bank national marine sanctuary off the California coast. Also known as the "Ghost Ship of the Pacific," the 314-foot-long ship has spent the past 78 years resting roughly 3,500 feet below the ocean's surface, and appears to remain almost completely intact and upright. "This level of preservation is exceptional for a vessel of its age and makes it potentially one of the best-preserved examples of a US Navy'four-piper' destroyer known to exist," Maria Brown, superintendent for both Cordell Bank and Greater Farallones national marine sanctuaries, said in a statement to The New York Times on October 1. The USS Stewart's story is unique in US maritime history, making it one of the most sought-after wrecks for decades.

Seasonal climate forecasts are socioeconomically important for managing the impacts of extreme weather events and for planning in sectors like agriculture and energy. Climate predictability on seasonal timescales is tied to boundary effects of the ocean on the atmosphere and coupled interactions in the ocean-atmosphere system. We present the Ocean-linked-atmosphere (Ola) model, a high-resolution (0.25{\deg}) Artificial Intelligence/ Machine Learning (AI/ML) coupled earth-system model which separately models the ocean and atmosphere dynamics using an autoregressive Spherical Fourier Neural Operator architecture, with a view towards enabling fast, accurate, large ensemble forecasts on the seasonal timescale. We find that Ola exhibits learned characteristics of ocean-atmosphere coupled dynamics including tropical oceanic waves with appropriate phase speeds, and an internally generated El Ni\~no/Southern Oscillation (ENSO) having realistic amplitude, geographic structure, and vertical structure within the ocean mixed layer. We present initial evidence of skill in forecasting the ENSO which compares favorably to the SPEAR model of the Geophysical Fluid Dynamics Laboratory.

Large language models (LLMs) aligned through reinforcement learning from human feedback (RLHF) have quickly become one of the dominant paradigms for building intelligent conversational assistant agents. However, despite their strong performance across many benchmarks, LLM-based agents still lack conversational skills such as disambiguation: when generalized assistants are faced with ambiguity, they often overhedge or implicitly guess users' ground-truth intents rather than asking clarification questions, and under task-specific settings, high-quality conversation samples are often limited, affecting models' ability to learn optimal dialogue action policies. We propose Action-Based Contrastive Self-Training (henceforth ACT), a quasi-online preference optimization algorithm based on Direct Preference Optimization (DPO) which allows for sample-efficient dialogue policy learning in multi-turn conversation. We demonstrate ACT's efficacy under sample-efficient conditions in three difficult conversational tasks: tabular-grounded question-answering, machine reading comprehension, and AmbigSQL, a novel task for disambiguating information-seeking requests for text-to-SQL generation. Additionally, we propose evaluating LLMs' ability to function as conversational agents by examining whether they can implicitly recognize and reason about ambiguity in conversation. ACT demonstrates substantial conversation modeling improvements over standard approaches to supervised fine-tuning and DPO.