Breakthroughs, discoveries, and DIY tips sent every weekday. At the US Army Corps of Engineers' research facility in central Alaska, a unique tunnel descends underground. They were hunting for something much smaller--and smellier. "The first thing you notice when you walk in there is that it smells really bad. It smells like a musty basement that's been left to sit for way too long," geological scientist Tristan Caro recounted in a statement .

Arctic warming threatens over $100 billion in permafrost-dependent infrastructure across Northern territories, yet existing risk assessment frameworks lack spatiotemporal validation, uncertainty quantification, and operational decision-support capabilities. W e present a hybrid physics-machine learning framework integrating 2.9 million observations from 171,605 locations (2005-2021) combining permafrost fraction data with climate reanalysis. Our stacked ensemble model (Random F orest + Histogram Gradient Boosting + Elastic Net) achieves R=0.980 (RMSE=5.01 pp) with rigorous spatiotemporal cross-validation preventing data leakage. T o address machine learning limitations in extrapolative climate scenarios, we develop a hybrid approach combining learned climate-permafrost relationships (60%) with physical permafrost sensitivity models (40%, -10 pp/C). Under RCP8.5 forcing (+5C over 10 years), we project mean permafrost fraction decline of -20.3 pp (median: -20.0 pp), with 51.5% of Arctic Russia experiencing over 20 percentage point loss. Infrastructure risk classification identifies 15% high-risk zones (25% medium-risk) with spatially explicit uncertainty maps. Our framework represents the largest validated permafrost ML dataset globally, provides the first operational hybrid physics-ML forecasting system for Arctic infrastructure, and delivers open-source tools enabling probabilistic permafrost projections for engineering design codes and climate adaptation planning. The methodology is generalizable to other permafrost regions and demonstrates how hybrid approaches can overcome pure data-driven limitations in climate change applications.

Plus: America's first AI law is here Scientists can see Earth's permafrost thawing from space Something is rotten in the city of Nunapitchuk. In recent years, sewage has leached into the earth. The ground can feel squishy, sodden. This small town in northern Alaska is experiencing a sometimes overlooked consequence of climate change: thawing permafrost. And Nunapitchuk is far from the only Arctic town to find itself in such a predicament. Now scientists think they may be able to use satellite data to delve deep beneath the ground's surface and get a better understanding of how the permafrost thaws, and which areas might be most severely affected.

Fine-scale soil mapping in Alaska, traditionally relying on fieldwork and localized simulations, remains a critical yet underdeveloped task, despite the region's ecological importance and extensive permafrost coverage. As permafrost thaw accelerates due to climate change, it threatens infrastructure stability and key ecosystem services, such as soil carbon storage. High-resolution soil maps are essential for characterizing permafrost distribution, identifying vulnerable areas, and informing adaptation strategies. We present MISO, a vision-based machine learning (ML) model to produce statewide fine-scale soil maps for near-surface permafrost and soil taxonomy. The model integrates a geospatial foundation model for visual feature extraction, implicit neural representations for continuous spatial prediction, and contrastive learning for multimodal alignment and geo-location awareness. We compare MISO with Random Forest (RF), a traditional ML model that has been widely used in soil mapping applications. Spatial cross-validation and regional analysis across Permafrost Zones and Major Land Resource Areas (MLRAs) show that MISO generalizes better to remote, unseen locations and achieves higher recall than RF, which is critical for monitoring permafrost thaw and related environmental processes. These findings demonstrate the potential of advanced ML approaches for fine-scale soil mapping and provide practical guidance for future soil sampling and infrastructure planning in permafrost-affected landscapes. The project will be released at https://github.com/knowledge-computing/Peatland-permafrost.

A 200-acre wide, nearly 300-foot deep pit in the Yana highlands of Siberia, known as the'Batagaika Crater,' is expanding faster than expected due to climate change. Sometimes called the'Gateway to Hell,' the Batagaika Crater first formed when melting'permafrost' soil within the Siberian tundra began to release tons of previously frozen methane, a powerful greenhouse gas, into Earth's atmosphere. Now, new research has discovered that the rate of methane and other carbon gases released as the crater deepens has reached between 4000 and 5000 tons per year. The findings, according to the study's lead author, 'demonstrate how quickly permafrost degradation occurs.' He warns the crater is soon likely to leak all the remaining greenhouse gas it has left.

Scientists fear'time-traveling' pathogens could be leaked into the world as their icy prison in permafrost is melting - and they could spark the next planet and destroy the environment. Ancient viruses, sealed in permafrost for thousands of years, could survive and evolve to become the dominant free-living species- killing up to one-third of bacteria-like hosts. The stark revelation was made by researchers at the European Commission Joint Research Center, who used computer simulations to find about three percent of virus-like pathogens became dominant after being released from the ice. The new findings suggest that the risks posed by time-traveling pathogens – so far confined to science fiction stories – could be powerful drivers of ecological change and threats to human health. Scientists fear'time-traveling' pathogens could be leaked into the world as their icy prison in permafrost is melting - and their escape would be detrimental to the environment.

Scientists used torpedo-shaped robots to map the Arctic seafloor with sonar, revealing massive sinkholes of thawed permafrost. This story was originally published by Wired and is reproduced here as part of the Climate Desk collaboration. Around 20,000 years ago, the world was so frigid that massive glaciers sucked up enough water to lower sea levels by 400 feet. As the sea pulled back, newly exposed land froze to form permafrost, a mixture of earth and ice that today sprawls across the far north. But as the world warmed into the climate we enjoy today (for the time being), sea levels rose again, submerging the coastal edges of that permafrost, which remain frozen below the water. It's a huge, hidden climate variable that scientists are racing to understand.