arXiv Machine Learning
Jun 2
Position: Neglecting the Sustainability of AI is Fuelling a Global AI Arms Race
arXiv:2502. 20016v2 Announce Type: replace Abstract: Sustainability encompasses three key facets: economic, environmental, and social.
The Rise of AI in Weather and Climate Information and its Impact on Global Inequality
arXiv:2603. 05710v2 Announce Type: replace-cross Abstract: AI development's current trajectory risks automating and amplifying the North-South divide in the global climate information system.
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arXiv Machine Learning
Jul 13
Enhancing AI and Dynamical Subseasonal Forecasts with Probabilistic Bias Correction
arXiv:2604. 16238v2 Announce Type: replace Abstract: Decision-makers rely on weather forecasts to plant crops, manage wildfires, allocate water and energy, and prepare for weather extremes.
DeepMind Blog
Nov 17, 2025
WeatherNext 2: Our most advanced weather forecasting model
The new AI model delivers more efficient, more accurate and higher-resolution global weather predictions.
arXiv Machine Learning
Jul 13
Bridging the Gap Between Climate Science and Machine Learning in Climate Model Emulation
arXiv:2603. 22320v2 Announce Type: replace Abstract: For decades, physics-based climate models have been used to provide insights for climate decision-making.
Hugging Face Trending Papers
Jun 4
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP). While these data-driven architectures demonstrate remarkable global skill, regional benchmarks in the Global South remain scarce, leaving their efficacy in complex, highly convective environments largely unverified.
arXiv Machine Learning
Jun 5
Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil
arXiv:2606. 06348v1 Announce Type: new Abstract: The paradigm of global weather forecasting is rapidly shifting with the emergence of Machine Learning Weather Prediction models (MLWP).
arXiv AI
Jun 10
GCA Framework: A GCC Countries-Grounded Dataset and Agentic Pipeline for Climate Decision Support
arXiv:2604. 12306v3 Announce Type: replace-cross Abstract: Climate decision-making in the GCC states increasingly demands systems that can translate heterogeneous scientific and policy evidence into actionable guidance, yet general-purpose large language models (LLMs) remain weak both in region-specific climate knowledge and grounded interaction with geospatial and forecasting tools.
arXiv AI
Jun 9
How Hyper-Datafication Impacts the Sustainability Costs in Frontier AI
arXiv:2602. 00056v4 Announce Type: replace-cross Abstract: Large-scale data has fuelled the success of frontier artificial intelligence (AI) models over the past decade.
arXiv AI
Jun 15
Position: AI Must Become Planet-Centered, Not Just Human-Centered
arXiv:2606. 13704v1 Announce Type: cross Abstract: This position paper argues that contemporary AI paradigms are insufficient for supporting complex global goals and introduces Planet-Centered AI (PCAI) as a design philosophy and research agenda that reorients AI toward planetary-scale socio-ecological systems and their long-term trajectories.
arXiv Machine Learning
Jun 30
NIVA: A Multimodal Foundation Model for Actionable Earth System Intelligence
arXiv:2606. 28546v1 Announce Type: new Abstract: Recent advances in AI-driven weather and climate modeling have improved forecast skill while reducing computational cost.
arXiv AI
Jul 7
A harmonised dataset for Earth system foundation models
arXiv:2607. 03298v1 Announce Type: cross Abstract: Foundation models for Earth systems have so far been trained primarily on physical climate and weather data, with limited representation of the human systems that both drive and respond to environmental change.
arXiv Machine Learning
Jul 31
Weather Emulators at the Frontier of Heat Extremes Predictability
arXiv:2607. 28220v1 Announce Type: cross Abstract: Atmospheric predictability declines rapidly beyond the next ten days, such that forecasts at longer lead times primarily convey large-scale trends rather than specific states.