In the face of sea ice loss, some of the bears on the Norwegian archipelago are gaining weight. Three polar bear cubs gather around their tranquilized mother. She had a litter of three cubs (an unusual brood size) and the smallest cub only weighed 11 pounds (five kilograms). Breakthroughs, discoveries, and DIY tips sent six days a week. The Arctic's polar bears () are often the poster species for the perils of climate change .

Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian attacks on Ukraine's southern Zaporizhia killed a 44-year-old man and wounded several others, Governor Ivan Fedorov said on Monday, as the death toll from other assaults on Sunday continued to rise. Ukrainian officials said the attacks on Sunday killed two people in the eastern Donetsk region and a 69-year-old man in the northern Sumy region.

Is Chicago the violent crime capital of the US? How did India-US relations decline so fast? A Ukrainian drone attack killed two women in the village of Golovchino in Russia's Belgorod region, Russia's state TASS news agency reports. A man who was seriously injured in a Ukrainian drone attack in Russia's Belgorod region in April has died in hospital, TASS reports. TASS also reported that Russian forces shot down 82 Ukrainian drones in a 24-hour period.

Russian military exercises near NATO borders follow the recent incursion of Russian drones into the airspace of Poland and Romania, further stoking tensions with the West. On Sunday, Russia released images of its launch of a 3M22 Zircon hypersonic missile from a frigate in the Barents Sea, in the Arctic Ocean, near NATO borders. The launch comes against a backdrop of rising tensions with the West, just days after several Russian drones violated the airspace of North Atlantic Treaty Organization member countries Poland and Romania. The Zircon test is part of the Zapad 2025 joint maneuvers with Belarus, a week of military exercises aimed at assessing defensive and coordination capabilities between the two allied countries. It also serves to show that Russia's military force has not lost its strength, despite heavy losses more than three years after the start of the invasion of Ukraine .

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? Russia and Ukraine have been engaging in major aerial battles, targeting energy and transportation infrastructure, as Moscow presses its fierce ground assault in the Ukrainian east in the war's fourth year and tests a type of hypersonic weapon. Russia's Ministry of Defence announced on Sunday that its air defences shot down 361 drones, four guided aerial bombs, and rockets from a US-made high mobility artillery rocket system (HIMARS) overnight.

How is Russia replenishing its military? What is a'coalition of the willing'? How China forgot promises and'debts' to Ukraine How are Europe, the US pulling apart on Ukraine? NATO fighter jets headed to eastern Europe under new'Eastern Sentry' Russian attacks on Ukraine killed at least three people in the Donetsk region and another in Kharkiv, the Kyiv Independent reported on Saturday, citing local officials. A drone breached Romanian airspace during a Russian attack on Ukrainian infrastructure, prompting Romania to scramble fighter jets, the country's defence minister, Ionut Mosteanu, said.

In recent years, diffusion models have emerged as powerful tools for generating ensemble members in meteorology. In this work, we demonstrate that a Denoising Diffusion Implicit Model (DDIM) can effectively control ensemble variance by varying the number of diffusion steps. Introducing a theoretical framework, we relate diffusion steps to the variance expressed by the reverse diffusion process. Focusing on reanalysis downscaling, we propose an ensemble diffusion model for the full ERA5-to-CERRA domain, generating variance-calibrated ensemble members for wind speed at full spatial and temporal resolution. Our method aligns global mean variance with a reference ensemble dataset and ensures spatial variance is distributed in accordance with observed meteorological variability. Additionally, we address the lack of ensemble information in the CARRA dataset, showcasing the utility of our approach for efficient, high-resolution ensemble generation.

Cloud cover plays a critical role in weather prediction and impacts several sectors, including agriculture, solar power generation, and aviation. Despite advancements in numerical weather prediction (NWP) models, forecasting total cloud cover remains challenging due to the small-scale nature of cloud formation processes. In this study, we introduce CloudCast, a convolutional neural network (CNN) based on the U-Net architecture, designed to predict total cloud cover (TCC) up to five hours ahead. Trained on five years of satellite data, CloudCast significantly outperforms traditional NWP models and optical flow methods. Compared to a reference NWP model, CloudCast achieves a 24% lower mean absolute error and reduces multi-category prediction errors by 46%. The model demonstrates strong performance, particularly in capturing the large-scale structure of cloud cover in the first few forecast hours, though later predictions are subject to blurring and underestimation of cloud formation. An ablation study identified the optimal input features and loss functions, with MAE-based models performing the best. CloudCast has been integrated into the Finnish Meteorological Institute's operational nowcasting system, where it improves cloud cover forecasts used by public and private sector clients. While CloudCast is limited by a relatively short skillful lead time of about three hours, future work aims to extend this through more complex network architectures and higher-resolution data. CloudCast code is available at https://github.com/fmidev/cloudcast.

The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach integrates advanced video prediction models, originally developed for vision tasks, with domain-specific data preprocessing and augmentation techniques tailored to the unique challenges of Arctic sea ice dynamics. Central to our methodology is the use of uncertainty quantification to assess the reliability of predictions, ensuring robust decision-making in safety-critical applications. Furthermore, we propose a confidence-based model mixture mechanism that enhances forecast accuracy and model robustness, crucial for reliable operations in volatile Arctic environments. Our results demonstrate substantial improvements over baseline approaches, underscoring the importance of uncertainty quantification and specialized data handling for effective and safe operations and reliable forecasting.

Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advances in artificial intelligence has made promising progress over numerical models. However, forecasting sea ice at higher resolutions is still under-explored. To bridge the gap, we propose a two-staged deep learning framework, IceDiff, to forecast sea ice concentration at finer scales. IceDiff first leverages an independently trained vision transformer to generate coarse yet superior forecasting over previous methods at a regular 25km x 25km grid. This high-quality sea ice forecasting can be utilized as reliable guidance for the next stage. Subsequently, an unconditional diffusion model pre-trained on sea ice concentration maps is utilized for sampling down-scaled sea ice forecasting via a zero-shot guided sampling strategy and a patch-based method. For the first time, IceDiff demonstrates sea ice forecasting with the 6.25km x 6.25km resolution. IceDiff extends the boundary of existing sea ice forecasting models and more importantly, its capability to generate high-resolution sea ice concentration data is vital for pragmatic usages and research.