The audio, etched onto a plastic disc in 1949, predates the era when researchers could even recognize whale calls. Breakthroughs, discoveries, and DIY tips sent six days a week. In 1970, a single record would change history.

Noisy ships and industry are impacting critically endangered Kemp's ridley sea turtles. Breakthroughs, discoveries, and DIY tips sent six days a week. For the world's smallest sea turtles, life in the ocean is getting pretty noisy. These relatively little turtles (on average they're still 75 to 100 pounds) mostly found in the Gulf of Mexico already face fishing gear accidents, seacraft collisions, plastic pollution, and habitat deterioration, and now excess noise may be harming the critically endangered and rare Kemp's ridley sea turtles (). We say because even though these sea turtles share waters with extremely busy shipping lanes, scientists know very little about their underwater hearing.

That time Oregon blew up a whale with dynamite. And why we should never do it again. Breakthroughs, discoveries, and DIY tips sent every weekday. When a whale dies in the ocean, an ecosystem grows around its sunken carcass. It's an epic burial at sea, something researchers call a whale fall .

Today’s global coral bleaching events are the worst kind of climate warning.

Environment Conservation Ocean Underwater robot survives voyage to'never-accessed region of the planet' An Argo float delivered unprecedented data after eight months beneath Antarctic ice. Breakthroughs, discoveries, and DIY tips sent every weekday. Thanks to an underwater survey robot, oceanographers are getting the first-ever readings collected from underneath East Antarctic's vast ice shelves . But for a moment, it wasn't clear when(or if) the bright yellow float would return to the ocean's surface after it dove underneath the ice. "We got lucky," Steve Rintoul, an oceanographer with Australia's Commonwealth Scientific and Industrial Research Organization (CSIRO), said in a statement .

Sensorium Arc (AI reflects on climate) is a real-time multimodal interactive AI agent system that personifies the ocean as a poetic speaker and guides users through immersive explorations of complex marine data. Built on a modular multi-agent system and retrieval-augmented large language model (LLM) framework, Sensorium enables natural spoken conversations with AI agents that embodies the ocean's perspective, generating responses that blend scientific insight with ecological poetics. Through keyword detection and semantic parsing, the system dynamically triggers data visualizations and audiovisual playback based on time, location, and thematic cues drawn from the dialogue. Developed in collaboration with the Center for the Study of the Force Majeure and inspired by the eco-aesthetic philosophy of Newton Harrison, Sensorium Arc reimagines ocean data not as an abstract dataset but as a living narrative. The project demonstrates the potential of conversational AI agents to mediate affective, intuitive access to high-dimensional environmental data and proposes a new paradigm for human-machine-ecosystem.

Environment Conservation Ocean Dolphins may be getting an Alzheimer's-like disease due to this neurotoxin The neurotoxins, found in algal blooms, primarily affect the body's nervous system. Breakthroughs, discoveries, and DIY tips sent every weekday. For marine biologists, dolphins are often viewed as sentinel species, or animals that shed light on the health of the ocean . Along with whales, porpoises, and other cetacean species, dolphins are one way that researchers know to sound the alarm about environmental hazards that might affect the ocean as a whole and potentially humans. In this context, researchers have connected neurotoxins from algal blooms to brain changes associated with an Alzheimer's-like disease in dolphins in Florida.

Interactions between different components of the Earth System (e.g. ocean, atmosphere, land and cryosphere) are a crucial driver of global weather patterns. Modern Numerical Weather Prediction (NWP) systems typically run separate models of the different components, explicitly coupled across their interfaces to additionally model exchanges between the different components. Accurately representing these coupled interactions remains a major scientific and technical challenge of weather forecasting. GraphDOP is a graph-based machine learning model that learns to forecast weather directly from raw satellite and in-situ observations, without reliance on reanalysis products or traditional physics-based NWP models. GraphDOP simultaneously embeds information from diverse observation sources spanning the full Earth system into a shared latent space. This enables predictions that implicitly capture cross-domain interactions in a single model without the need for any explicit coupling. Here we present a selection of case studies which illustrate the capability of GraphDOP to forecast events where coupled processes play a particularly key role. These include rapid sea-ice freezing in the Arctic, mixing-induced ocean surface cooling during Hurricane Ian and the severe European heat wave of 2022. The results suggest that learning directly from Earth System observations can successfully characterise and propagate cross-component interactions, offering a promising path towards physically consistent end-to-end data-driven Earth System prediction with a single model.

Environment Animals Wildlife Fish Self-piloting submarine set to begin historic mission to circle Earth's oceans Breakthroughs, discoveries, and DIY tips sent every weekday. An autonomous submersible named Redwing is heading out on a truly historic voyage. If successful, it will achieve the first around-the-world ocean trip made by an unpiloted underwater vehicle . Marine engineering company Teledyne Marine and researchers at Rutgers University in New Jersey are planning to launch the nearly nine-foot-long, specially outfitted Slocum Sentinel Glider on October 11 from Woods Hole Oceanographic Institution off the coast of Martha's Vineyard in Massachusetts. A livestream of the launch will be broadcast here, beginning at about 8:15 a.m. EDT on Saturday October 11.

Traditional numerical global climate models simulate the full Earth system by exchanging boundary conditions between separate simulators of the atmosphere, ocean, sea ice, land surface, and other geophysical processes. This paradigm allows for distributed development of individual components within a common framework, unified by a coupler that handles translation between realms via spatial or temporal alignment and flux exchange. Following a similar approach adapted for machine learning-based emulators, we present SamudrACE: a coupled global climate model emulator which produces centuries-long simulations at 1-degree horizontal, 6-hourly atmospheric, and 5-daily oceanic resolution, with 145 2D fields spanning 8 atmospheric and 19 oceanic vertical levels, plus sea ice, surface, and top-of-atmosphere variables. SamudrACE is highly stable and has low climate biases comparable to those of its components with prescribed boundary forcing, with realistic variability in coupled climate phenomena such as ENSO that is not possible to simulate in uncoupled mode.