Bridging Global Climate Models with Local Impact Simulations

🌍 Climate scientists often face a trade-off: Global Climate Models (GCMs) are essential for long-term climate projections — but they operate at coarse spatial resolution, making them too crude for regional or local decision-making. To get fine-scale data, researchers use Regional Climate Models (RCMs). These add crucial spatial detail, but come at a very high computational cost, often requiring supercomputers to run for months. ➡️ A new paper introduces EnScale — a machine learning framework that offers an efficient and accurate alternative to running full RCM simulations. Instead of solving the complex physics from scratch, EnScale "learns" the relationship between GCMs and RCMs by training on existing paired datasets. It then generates high-resolution, realistic, and diverse regional climate fields directly from GCM inputs. What makes EnScale stand out? ✅ It uses a generative ML model trained with a statistically principled loss (energy score), enabling probabilistic outputs that reflect natural variability and uncertainty ✅ It is multivariate – it learns to generate temperature, precipitation, radiation, and wind jointly, preserving spatial and cross-variable coherence ✅ It is computationally lightweight – training and inference are up to 10–20× faster than state-of-the-art generative approaches ✅ It includes an extension (EnScale-t) for generating temporally consistent time series – a must for studying events like heatwaves or prolonged droughts This approach opens the door to faster, more flexible generation of regional climate scenarios, essential for risk assessment, infrastructure planning, and climate adaptation — especially where computational resources are limited. 📄 Read the full paper: EnScale: Temporally-consistent multivariate generative downscaling via proper scoring rules ---> https://lnkd.in/dQr5rmWU (code: https://lnkd.in/dQk_Jv8g) 👏 Congrats to the authors — a strong step forward for ML-based climate modeling! #climateAI #downscaling #generativeAI #machinelearning #climatescience #EnScale #RCM #GCM #ETHZurich #climatescenarios

Every year, natural disasters hit harder and closer to home. But when city leaders ask, "How will rising heat or wildfire smoke impact my home in 5 years?"—our answers are often vague. Traditional climate models give sweeping predictions, but they fall short at the local level. It's like trying to navigate rush hour using a globe instead of a street map. That’s where generative AI comes in. This year, our team at Google Research built a new genAI method to project climate impacts—taking predictions from the size of a small state to the size of a small city. Our approach provides: - Unprecedented detail – in regional environmental risk assessments at a small fraction of the cost of existing techniques - Higher accuracy – reduced fine-scale errors by over 40% for critical weather variables and reduces error in extreme heat and precipitation projections by over 20% and 10% respectively - Better estimates of complex risks – Demonstrates remarkable skill in capturing complex environmental risks due to regional phenomena, such as wildfire risk from Santa Ana winds, which statistical methods often miss Dynamical-generative downscaling process works in two steps: 1) Physics-based first pass: First, a regional climate model downscales global Earth system data to an intermediate resolution (e.g., 50 km) – much cheaper computationally than going straight to very high resolution. 2) AI adds the fine details: Our AI-based Regional Residual Diffusion-based Downscaling model (“R2D2”) adds realistic, fine-scale details to bring it up to the target high resolution (typically less than 10 km), based on its training on high-resolution weather data. Why does this matter? Governments and utilities need these hyperlocal forecasts to prepare emergency response, invest in infrastructure, and protect vulnerable neighborhoods. And this is just one way AI is turbocharging climate resilience. Our teams at Google are already using AI to forecast floods, detect wildfires in real time, and help the UN respond faster after disasters. The next chapter of climate action means giving every city the tools to see—and shape—their own future. Congratulations Ignacio Lopez Gomez, Tyler Russell MBA, PMP, and teams on this important work! Discover the full details of this breakthrough: https://lnkd.in/g5u_WctW  PNAS Paper: https://lnkd.in/gr7Acz25

IBM & NASA - National Aeronautics and Space Administration just dropped an open-source AI model for weather & climate on Hugging Face that could revolutionize modeling in these fields. It’s far beyond your average forecasting tool—it handles everything from short-term weather to long-term climate projections. Some cool features: - Pre-trained on 40 years of NASA Earth observation data - Can be fine-tuned to global, regional, and local scales - Two fine-tuned versions available: climate and weather data downscaling, and gravity wave modeling Potential applications: - Creating targeted forecasts based on local observations, - Detecting and predicting severe weather patterns, - Improving the spatial resolution of global climate simulations, - Enhancing how physical processes are represented in numerical weather and climate models Oh, and they tested it on Hurricane Ida's formation, path, and intensification. The accuracy is wild! 🌪️ 👉 Grab the model here: Foundation model and the gravity wave parameterization model: https://lnkd.in/e-VpRcKw Downscaling model: https://lnkd.in/e-t7576k Who's planning to play with this? What other use cases can you think of? #NASA #WeatherAI #ClimateScience #OpenSource

How Machine Learning can help us addressing Climate Change: Climate downscaling is essential for transforming global climate model (GCM) projections into actionable, localized data. While GCMs provide broad climate trends, their coarse spatial resolutions overlook crucial details necessary for specific use cases. Downscaling addresses this gap by enhancing resolution, offering precise climate insights crucial for various applications. In agriculture, it aids in crop management and irrigation planning. For urban development, it informs infrastructure design and sustainability efforts. In disaster preparedness, it provides detailed forecasts for extreme weather events, enabling more effective response strategies. By bridging the gap between global models and local needs, downscaling ensures that climate projections are practical and applicable for targeted, regional planning. Recent research highlights the transformative potential of machine learning (ML) in climate downscaling. ML excels at capturing complex patterns and relationships in climate data, enhancing the accuracy of downscaled projections. It also addresses the challenge of applying learned relationships to future climate scenarios, crucial for robust climate planning. Moreover, ML can improve the representation of extreme weather events, essential for effective risk management. Integrating ML into climate downscaling is a game-changer, making high-resolution climate projections more accessible and reliable. This advancement is pivotal for developing effective climate adaptation and mitigation strategies, underscoring ML's critical role in tackling climate change. Read the full paper here: https://lnkd.in/d6BZRH-c #ClimateChange #MachineLearning #ClimateScience #Downscaling #Sustainability