Environment Climate Change Antarctica has a'gravity hole' The geological oddity has existed since dinosaurs roamed the Earth. Breakthroughs, discoveries, and DIY tips sent six days a week. A "gravity hole" beneath Antarctica sounds like the plot to a bad sci-fi movie, but it's a very real situation deep beneath the Earth's surface stretching back tens of millions of years. The phenomenon thankfully isn't as apocalyptic as it sounds, either. In fact, researchers say these complex interactions between rock densities, gravitational pull, and sea levels are actually helping them understand how the southernmost continent's ice sheets evolved, and what their influences mean for the planet's climate.

Artificial intelligence is transforming the sciences, yet general conversational AI systems often generate unverified "hallucinations" undermining scientific rigor. We present OceanAI, a conversational platform that integrates the natural-language fluency of open-source large language models (LLMs) with real-time, parameterized access to authoritative oceanographic data streams hosted by the National Oceanic and Atmospheric Administration (NOAA). Each query such as "What was Boston Harbor's highest water level in 2024?" triggers real-time API calls that identify, parse, and synthesize relevant datasets into reproducible natural-language responses and data visualizations. In a blind comparison with three widely used AI chat-interface products, only OceanAI produced NOAA-sourced values with original data references; others either declined to answer or provided unsupported results. Designed for extensibility, OceanAI connects to multiple NOAA data products and variables, supporting applications in marine hazard forecasting, ecosystem assessment, and water-quality monitoring. By grounding outputs and verifiable observations, OceanAI advances transparency, reproducibility, and trust, offering a scalable framework for AI-enabled decision support within the oceans. A public demonstration is available at https://oceanai.ai4ocean.xyz.

Doctor's husband'was watching X-rated videos in his house while daughter, 2, died in roasting car outside' Florida's housing market is flashing a warning for the rest of the US Now scientists redefine'obese' - and they've made up to 60% more people'fat' Bella Hadid's health battle takes dark turn: Loved ones reveal hellish new details about'missing' model... as ominous texts emerge America's saddest lost soul can no longer SPEAK and spends days hitting herself'after years of unspeakable abuse by gangs of men' Shocking moment brazen gunman opens fire at Michigan businessman's Land Rover in daylight attack'You will DIE if you do not remove your breasts', doctors screamed at me. I refused and tried a new experimental therapy instead... now I'm cancer-free The world's most powerful passport revealed - as UK and USA both drop to record lows Police say they have FOUND woman seen in viral'kidnapping' video and reveal what happened to her after harrowing footage emerged Will Trump's Gaza peace deal fail? Policy expert MARK DUBOWITZ breaks down all the forces at play... and how the president can actually pull this off America's most renowned'prophet' makes startling prediction about alien'mothership' Kim Kardashian says she wasn't'emotionally or financially safe' during'toxic' marriage to Kanye West as she claims rapper hasn't contacted their children for MONTHS and has destroyed her dating life Astonishing interactive map lays bare where MILLIONS of homes will be submerged by water within a few years... are YOU at risk? Outrageous reason LA County CEO was awarded $2m payout for'hurt feelings' that'll see her take months off taxpayer-funded $570,000-a-year job Ugly divorce war between Mitt Romney's wealthy brother and estranged wife before she was found dead Full horrors of torture suffered by Noa Argamani's commando boyfriend are revealed - including how 6ft 5in hostage was beaten and kept chained in 6ft cell for a year after he tried to escape from Hamas Mother, 52, and daughter, 21, die after eating'poisoned birthday cake delivered by relative who owed them money' in Brazil Astonishing interactive map lays bare where MILLIONS of homes will be submerged by water within a few years... are YOU at risk? Millions of buildings and even more Americans could be at risk of sinking underwater by the end of the century. Researchers from McGill University in Canada warned rising sea levels, resulting from continued greenhouse gas emissions, threaten to wipe out coastal cities worldwide. Sea level rise measures the ocean's surface height over time.

Neural operators are becoming the default tools to learn solutions to governing partial differential equations (PDEs) in weather and ocean forecasting applications. Despite early promising achievements, significant challenges remain, including long-term prediction stability and adherence to physical laws, particularly for high-frequency processes. In this paper, we take a step toward addressing these challenges in high-resolution ocean prediction by incorporating temporal Fourier modes, demonstrating how this modification enhances physical fidelity. This study compares the standard Fourier Neural Operator (FNO) with its variant, FNOtD, which has been modified to internalize the dispersion relation while learning the solution operator for ocean PDEs. The results demonstrate that entangling space and time in the training of integral kernels enables the model to capture multiscale wave propagation and effectively learn ocean dynamics. FNOtD substantially improves long-term prediction stability and consistency with underlying physical dynamics in challenging high-frequency settings compared to the standard FNO. It also provides competitive predictive skill relative to a state-of-the-art numerical ocean model, while requiring significantly lower computational cost.

Appropriate modelling of extreme skew surges is crucial, particularly for coastal risk management. Our study focuses on modelling extreme skew surges along the French Atlantic coast, with a particular emphasis on investigating the extremal dependence structure between stations. We employ the peak-over-threshold framework, where a multivariate extreme event is defined whenever at least one location records a large value, though not necessarily all stations simultaneously. A novel method for determining an appropriate level (threshold) above which observations can be classified as extreme is proposed. Two complementary approaches are explored. First, the multivariate generalized Pareto distribution is employed to model extremes, leveraging its properties to derive a generative model that predicts extreme skew surges at one station based on observed extremes at nearby stations. Second, a novel extreme regression framework is assessed for point predictions. This specific regression framework enables accurate point predictions using only the "angle" of input variables, i.e. input variables divided by their norms. The ultimate objective is to reconstruct historical skew surge time series at stations with limited data. This is achieved by integrating extreme skew surge data from stations with longer records, such as Brest and Saint-Nazaire, which provide over 150 years of observations.

Accurate wind speed prediction is crucial for designing and selecting sites for offshore wind farms. This paper investigates the effectiveness of various machine learning models in predicting offshore wind power for a site near the Gulf of Mexico by analyzing meteorological data. After collecting and preprocessing meteorological data, nine different input feature combinations were designed to assess their impact on wind power predictions at multiple heights. The results show that using wind speed as the output feature improves prediction accuracy by approximately 10% compared to using wind power as the output. In addition, the improvement of multi-feature input compared with single-feature input is not obvious mainly due to the poor correlation among key features and limited generalization ability of models. These findings underscore the importance of selecting appropriate output features and highlight considerations for using machine learning in wind power forecasting, offering insights that could guide future wind power prediction models and conversion techniques.

As greenhouse gas emissions continue to rise, scientists reveal many of the world's cities will be plunged underwater in just 75 years. In 2100, global sea levels will rise by a staggering 6.2ft (1.9 metres) if carbon dioxide (CO2) emissions continue to increase, say experts in Singapore. Now, artificial intelligence (AI) reveals exactly what this might look like. MailOnline turned to Google's AI image generator ImageFX to depict nine of the global cities that are particularly vulnerable to rising sea levels. For each city, we gave the command; 'Show me what it would look like in the year 2100 where sea levels have risen 6.2 feet.'

The idea of entire cities being plunged underwater might sound like the plot of the latest science fiction blockbuster. But it could become a reality in just 75 years, according to a terrifying new study. Scientists from Nanyang Technological University (NTU), Singapore, have predicted that global sea levels could rise by a staggering 6.2 feet (1.9 metres) by 2100 if carbon dioxide (CO2) emissions continue to increase. 'The high-end projection of 1.9 metres underscores the need for decision-makers to plan for critical infrastructure accordingly,' said Dr Benjamin Grandey, lead author of the study. If global sea levels were to rise by 6.2ft (1.9 metres), towns and cities around the world could be plunged underwater - including several in the UK.

A six-year investigation into the vast Thwaites glacier in Antarctica has concluded with a grim outlook on its future. Often dubbed the "doomsday glacier", this huge mass of ice is comparable in size to Britain or Florida and its collapse alone would raise sea levels by 65 centimetres. Worse still, this is expected to trigger a more widespread loss of the ice sheet covering West Antarctica, causing a calamitous sea level rise of 3.3 metres and threatening cities like New York, Kolkata and Shanghai. It is an extremely remote and difficult area to get to, but the International Thwaites Glacier Collaboration (ITGC), a joint UK-US research programme, has managed to deploy 100 scientists there over the past six years, using planes, ships and underwater robots to study the dynamics of this ice in detail. "It was a tremendous challenge, and yet we really learned a lot," says Ted Scambos at University of Colorado Boulder.

Projecting sea-level change in various climate-change scenarios typically involves running forward simulations of the Earth's gravitational, rotational and deformational (GRD) response to ice mass change, which requires high computational cost and time. Here we build neural-network emulators of sea-level change at 27 coastal locations, due to the GRD effects associated with future Antarctic Ice Sheet mass change over the 21st century. The emulators are based on datasets produced using a numerical solver for the static sea-level equation and published ISMIP6-2100 ice-sheet model simulations referenced in the IPCC AR6 report. We show that the neural-network emulators have an accuracy that is competitive with baseline machine learning emulators. In order to quantify uncertainty, we derive well-calibrated prediction intervals for simulated sea-level change via a linear regression postprocessing technique that uses (nonlinear) machine learning model outputs, a technique that has previously been applied to numerical climate models. We also demonstrate substantial gains in computational efficiency: a feedforward neural-network emulator exhibits on the order of 100 times speedup in comparison to the numerical sea-level equation solver that is used for training.