United States President Donald Trump will undertake a three-day tour of the Gulf for his first state visit since retaking office in January. The trip begins in Saudi Arabia, followed by Qatar and the United Arab Emirates. It marks Trump's second foreign visit as president after he attended Pope Francis's funeral in Rome in April. Trump will fly out of the US on Monday and start his trip in the Saudi capital, Riyadh, on Tuesday. He is expected to attend a Gulf summit in the city on Wednesday, visit Qatar later that day and conclude his visit in the UAE on Thursday.

Israel said the airport attack was in response to a Houthi ballistic missile strike near Ben-Gurion International Airport, outside Tel Aviv, on Sunday. Multiple airlines have temporarily suspended flights in response to the attack, which wounded at least six people. For more than a year, the Houthis, who rule much of northwestern Yemen, have fired rockets and drones at Israel and ships in the Red Sea in what they call a solidarity campaign with Palestinians in Gaza. The United States has lent its military support to Israel in the conflict, launching missile strikes against Yemen and deploying its aircraft carriers to protect shipping. The efforts began under the Biden administration but were stepped up in mid-March, when Mr. Trump sharply escalated attacks and vowed that the Houthis would be "annihilated."

Explosions have been heard at the Port of Sudan, a critical lifeline and entry point for aid, as attacks on the city continued for a fourth day in the latest confrontation between Sudanese Armed Forces (SAF) and the paramilitary Rapid Support Forces (RSF) in the country's brutal two-year civil war. The attacks have been blamed on the RSF by Sudan's army and by residents. On Wednesday morning, an army source told the AFP news agency on condition of anonymity that the explosion was due to a drone attack that was met with "anti-aircraft missiles". The Port of Sudan on the Red Sea coast had been a haven city hosting hundreds of thousands of displaced people since the war began and serves as an interim seat for Sudan's military-allied government, which has been at war with the RSF since 2023. The attacks on Port Sudan have increased fears of disruptions to desperately needed aid deliveries in the country suffering one of the world's most dire humanitarian crises, and where famine has been declared in some areas.

Multiple explosions have been heard and a huge fire seen in Port Sudan, though the exact locations and causes were unclear, as Sudan's civil war rocks the previously quiet city for the third day. Dark plumes of smoke could be seen emerging from the vicinity of the country's main maritime port in the city, where hundreds of thousands of displaced people have sought refuge. Al Jazeera's Hiba Morgan, reporting from the Sudanese capital, Khartoum, said residents in the port city reported that attack drones launched by the paramilitary Rapid Support Forces (RSF) hit a fuel depot and other targets. "According to the residents, they believe that it was drone strikes by the paramilitary Rapid Support Forces – once again. They targeted a fuel depot in the city but also around the port and the air base," Morgan said.

Climate change alters ocean conditions, notably temperature and sea level. In the Bay of Bengal, these changes influence monsoon precipitation and marine productivity, critical to the Indian economy. In Phase 6 of the Coupled Model Intercomparison Project (CMIP6), Global Climate Models (GCMs) use different shared socioeconomic pathways (SSPs) to obtain future climate projections. However, significant discrepancies are observed between these models and the reanalysis data in the Bay of Bengal for 2015-2024. Specifically, the root mean square error (RMSE) between the climate model output and the Ocean Reanalysis System (ORAS5) is 1.2C for the sea surface temperature (SST) and 1.1 m for the dynamic sea level (DSL). We introduce a new data-driven deep learning model to correct for this bias. The deep neural model for each variable is trained using pairs of climatology-removed monthly climate projections as input and the corresponding month's ORAS5 as output. This model is trained with historical data (1950 to 2014), validated with future projection data from 2015 to 2020, and tested with future projections from 2021 to 2023. Compared to the conventional EquiDistant Cumulative Distribution Function (EDCDF) statistical method for bias correction in climate models, our approach decreases RMSE by 0.15C for SST and 0.3 m for DSL. The trained model subsequently corrects the projections for 2024-2100. A detailed analysis of the monthly, seasonal, and decadal means and variability is performed to underscore the implications of the novel dynamics uncovered in our corrected projections.

A'flame in the sky,' eerie red glowing objects and swarms of UFOs over military bases are just some of the many sightings that have gravely concerned the US government. There are dozens of unsolved cases going back to the 1960s that occurred over nuclear missile installations, Navy ships and a desert in New Mexico. The FBI, CIA, and other government branches have spent years looking into these reports, but have yet to determine what the objects were and where they came from. One report in 2019 detailed how'drones' appeared over Colorado, Nebraska, Wyoming, and Kansas as locals reported spying a mothership hanging in the sky. In just the last few months, the skies over New Jersey were filled with unidentified aircraft and drones that required a formal response from both the Biden and Trump presidencies.

Most operational climate services providers base their seasonal predictions on initialised general circulation models (GCMs) or statistical techniques that fit past observations. GCMs require substantial computational resources, which limits their capacity. In contrast, statistical methods often lack robustness due to short historical records. Recent works propose machine learning methods trained on climate model output, leveraging larger sample sizes and simulated scenarios. Yet, many of these studies focus on prediction tasks that might be restricted in spatial extent or temporal coverage, opening a gap with existing operational predictions. Thus, the present study evaluates the effectiveness of a methodology that combines variational inference with transformer models to predict fields of seasonal anomalies. The predictions cover all four seasons and are initialised one month before the start of each season. The model was trained on climate model output from CMIP6 and tested using ERA5 reanalysis data. We analyse the method's performance in predicting interannual anomalies beyond the climate change-induced trend. We also test the proposed methodology in a regional context with a use case focused on Europe. While climate change trends dominate the skill of temperature predictions, the method presents additional skill over the climatological forecast in regions influenced by known teleconnections. We reach similar conclusions based on the validation of precipitation predictions. Despite underperforming SEAS5 in most tropics, our model offers added value in numerous extratropical inland regions. This work demonstrates the effectiveness of training generative models on climate model output for seasonal predictions, providing skilful predictions beyond the induced climate change trend at time scales and lead times relevant for user applications.

Prior work has shown that large language models (LLMs) can predict human attitudes based on other attitudes, but this work has largely focused on predictions from highly similar and interrelated attitudes. In contrast, human attitudes are often strongly associated even across disparate and dissimilar topics. Using a novel dataset of human responses toward diverse attitude statements, we found that a frontier language model (GPT-4o) was able to recreate the pairwise correlations among individual attitudes and to predict individuals' attitudes from one another. Crucially, in an advance over prior work, we tested GPT-4o's ability to predict in the absence of surface-similarity between attitudes, finding that while surface similarity improves prediction accuracy, the model was still highly-capable of generating meaningful social inferences between dissimilar attitudes. Altogether, our findings indicate that LLMs capture crucial aspects of the deeper, latent structure of human belief systems.

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.

Coral reefs are declining worldwide due to climate change and local stressors. To inform effective conservation or restoration, monitoring at the highest possible spatial and temporal resolution is necessary. Conventional coral reef surveying methods are limited in scalability due to their reliance on expert labor time, motivating the use of computer vision tools to automate the identification and abundance estimation of live corals from images. However, the design and evaluation of such tools has been impeded by the lack of large high quality datasets. We release the Coralscapes dataset, the first general-purpose dense semantic segmentation dataset for coral reefs, covering 2075 images, 39 benthic classes, and 174k segmentation masks annotated by experts. Coralscapes has a similar scope and the same structure as the widely used Cityscapes dataset for urban scene segmentation, allowing benchmarking of semantic segmentation models in a new challenging domain which requires expert knowledge to annotate. We benchmark a wide range of semantic segmentation models, and find that transfer learning from Coralscapes to existing smaller datasets consistently leads to state-of-the-art performance. Coralscapes will catalyze research on efficient, scalable, and standardized coral reef surveying methods based on computer vision, and holds the potential to streamline the development of underwater ecological robotics.