Climate Modeling and Simulation Tools

You might have seen news from our Google DeepMind colleagues lately on GenCast, which is changing the game of weather forecasting by building state-of-the-art weather models using AI. Some of our teams started to wonder – can we apply similar techniques to the notoriously compute-intensive challenge of climate modeling? General circulation models (GCMs) are a critical part of climate modeling, focused on the physical aspects of the climate system, such as temperature, pressure, wind, and ocean currents. Traditional GCMs, while powerful, can struggle with precipitation – and our teams wanted to see if AI could help. Our team released a paper and data on our AI-based GCM, building on our Nature paper from last year - specifically, now predicting precipitation with greater accuracy than prior state of the art. The new paper on NeuralGCM introduces 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗵𝗮𝘁 𝗹𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗱𝗮𝘁𝗮 𝘁𝗼 𝗽𝗿𝗼𝗱𝘂𝗰𝗲 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹𝗶𝘀𝘁𝗶𝗰 𝗿𝗮𝗶𝗻 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀. Kudos to Janni Yuval, Ian Langmore, Dmitrii Kochkov, and Stephan Hoyer! Here's why this is a big deal: 𝗟𝗲𝘀𝘀 𝗕𝗶𝗮𝘀, 𝗠𝗼𝗿𝗲 𝗔𝗰𝗰𝘂𝗿𝗮𝗰𝘆: These new models have less bias, meaning they align more closely with actual observations – and we see this both for forecasts up to 15 days, and also for 20-year projections (in which sea surface temperatures and sea ice were fixed at historical values, since we don’t yet have an ocean model). NeuralGCM forecasts are especially performant around extremes, which are especially important in understanding climate anomalies, and can predict rain patterns throughout the day with better precision. 𝗖𝗼𝗺𝗯𝗶𝗻𝗶𝗻𝗴 𝗔𝗜, 𝗦𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗜𝗺𝗮𝗴𝗲𝗿𝘆, 𝗮𝗻𝗱 𝗣𝗵𝘆𝘀𝗶𝗰𝘀: The model combines a learned physics model with a dynamic differentiable core to leverage both physics and AI methods, with the model trained directly on satellite-based precipitation observations. 𝗢𝗽𝗲𝗻 𝗔𝗰𝗰𝗲𝘀𝘀 𝗳𝗼𝗿 𝗘𝘃𝗲𝗿𝘆𝗼𝗻𝗲! This is perhaps the most exciting news! The team has made their pre-trained NeuralGCM model checkpoints (including their awesome new precipitation models) available under a CC BY-SA 4.0 license. Anyone can use and build upon this cutting-edge technology! https://lnkd.in/gfmAx_Ju 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: Accurate predictions of precipitation are crucial for everything from water resource management and flood mitigation to understanding the impacts of climate change on agriculture and ecosystems. Check out the paper to learn more:  https://lnkd.in/geqaNTRP

Coupled chemistry-climate models (CCMs) are essential tools for understanding chemical variability in the climate system, but they are extraordinarily expensive to run. Eric Mei's recent paper shows that linear inverse models (LIMs) can be used to emulate CCMs at a fraction of the computational cost (laptop vs HPC). This opens up new opportunities for strongly-coupled chemistry-climate data assimilation, large ensembles, hypothesis testing, and cost/benefit analysis for nonlinear machine learning emulators of CCMs. In constrast to ML emulators, LIMs have transparent explainability, illustrated by the figure below showing the coupled time-evolving relationship between sea-surface temperature, ozone, and hydroxyl radical for the El Nino mode in the model. Link to the paper: https://lnkd.in/d4DcJHVZ Supported by Schmidt Futures

Climate models have long struggled with coarse resolution, limiting precise climate risk insights. But AI-driven methods are now changing this, unlocking more detailed intelligence than traditional physics-based approaches. I recently spoke with a research scientist at Google Research who highlighted a promising new hybrid approach. This method combines physics-based General Circulation Models (GCMs) with AI refinement, significantly improving resolution. The process starts with Regional Climate Models (RCMs) anchoring physical consistency at ~45 km resolution. Then, it uses a diffusion model, R2-D2, to enhance output resolution to 9 km, making estimates more suitable for projecting extreme climate events. 🔥 About R2-D2 R2‑D2 (Regional Residual Diffusion-based Downscaling) is a diffusion model trained on residuals between RCM outputs and high-resolution targets. Conditioned on physical inputs like coarse climate fields and terrain, it rapidly generates high-res climate maps (~800 fields/hour on GPUs), complete with uncertainty estimates. ✅ Why this matters - Offers detailed projections of extreme climate events for precise risk quantification. - Delivers probabilistic forecasts, improving risk modeling and scenario planning. - Provides another high-resolution modeling approach, enriching ensemble strategies for climate risk projections. 👉 Read the full paper: https://lnkd.in/gU6qmZTR 👉 An excellent explainer blog: https://lnkd.in/gAEJFEV2 If your work involves climate risk assessment, adaptation planning, or quantitative modeling, how are you leveraging high-resolution risk projections?

𝗔𝗜 𝗳𝗼𝗿 𝗚𝗢𝗢𝗗: 𝗡𝗔𝗦𝗔 𝗮𝗻𝗱 𝗜𝗕𝗠 𝗹𝗮𝘂𝗻𝗰𝗵 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗺𝗼𝗱𝗲𝗹 𝗳𝗼𝗿 𝗺𝗼𝗿𝗲 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝘄𝗲𝗮𝘁𝗵𝗲𝗿 𝗮𝗻𝗱 𝗰𝗹𝗶𝗺𝗮𝘁𝗲 𝗳𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴! 🌍 (𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗮𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗴𝗲𝘁 𝗺𝗼𝗿𝗲 𝘀𝗽𝗼𝘁𝗹𝗶𝗴𝗵𝘁 𝗽𝗹𝗲𝗮𝘀𝗲 𝗮𝗻𝗱 𝗡𝗢𝗧 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗖𝗵𝗮𝘁𝗚𝗣𝗧 𝗪𝗿𝗮𝗽𝗽𝗲𝗿!) In collaboration with NASA, IBM just launched Prithvi WxC an open-source, general-purpose AI model for weather and climate-related applications. And the truly remarkable part is that this model can run on a desktop computer. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄: ⬇️ → The Prithvi WxC model (2.3-billion parameter) can create six-hour-ahead forecasts as a “zero-shot” skill – meaning it requires no tuning and runs on readily available data. → This AI model is designed to be customized for a variety of weather applications, from predicting local rainfall to tracking hurricanes or improving global climate simulations. → The model was trained using 40 years of NASA’s MERRA-2 data and can now be quickly tuned for specific use cases. And unlike traditional climate models that require massive supercomputers, this one operates on a desktop. Uniqueness lies in the ability to generalize from a small, high-quality sample of weather data to entire global forecasts. → This AI-powered model outperforms traditional numerical weather prediction methods in both accuracy and speed, producing global forecasts up to 10 days in advance within minutes instead of hours. → This model has immense potential for various applications, from downscaling high-resolution climate data to improving hurricane forecasts and capturing gravity waves. It could also help estimate the extent of past floods, forecast hurricanes, and infer the intensity of past wildfires from burn scars. It will be exciting to see what downstream apps, use cases, and potential applications emerge. What’s clear is that this AI foundation model joins a growing family of open-source tools designed to make NASA’s vast collection of satellite, geospatial, and Earth observational data faster and easier to analyze. With decades of observations, NASA holds a wealth of data, but its accessibility has been limited — until recently. This model is a big step toward democratizing data and making it more accessible to all. 𝗔𝗻𝗱 𝘁𝗵𝘀 𝗶𝘀 𝘆𝗲𝘁 𝗮𝗻𝗼𝘁𝗵𝗲𝗿 𝗽𝗿𝗼𝗼𝗳 𝘁𝗵𝗮𝘁 𝘁𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝘀 𝗼𝗽𝗲𝗻, 𝗱𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹𝗶𝘇𝗲𝗱, 𝗮𝗻𝗱 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝘁 𝘁𝗵𝗲 𝗲𝗱𝗴𝗲. 🌍 🔗 Resources: Download the models from the Hugging Face repository: https://lnkd.in/gp2zmkSq Blog post: https://ibm.co/3TDul9a Research paper: https://ibm.co/3TAILXG #AI #ClimateScience #WeatherForecasting #OpenSource #NASA #IBMResearch

AI has the potential to bring new waves of innovation, social and economic progress on a scale we’ve not seen before - including supercharging scientific progress. This week, Google published NeuralGCM: an openly available tool for fast, accurate climate modelling - critical to a changing global climate. We know that the Earth is getting warmer, but it’s hard to predict what that means for each different region. To figure this out, scientists use climate modelling. But current approaches have large uncertainty, including systematic errors - like forecasting extreme rain that is only half as intense as what scientists actually observe. That’s where NeuralGCM comes in. It combines physics-based modelling and AI to simulate the Earth’s atmosphere - making it faster and more accurate than existing climate models. For scientists exploring how to build better weather and climate models, it should make a huge difference in helping them understand the effects of the climate crisis on our world - and it could also be great for meteorologists making predictions about our daily weather! Interested in learning more? Read all about it here and watch the video below ⬇️ https://lnkd.in/e_bCuAhq 

Bridging the Gap Between Physics and AI with NVIDIA Modulus Are you ready to transform the way we solve physics problems with AI? NVIDIA Modulus is an open-source AI physics framework that seamlessly combines physics-based simulations with AI models, enabling groundbreaking applications in fields like fluid dynamics, climate modeling, materials science, and more. Here’s everything you need to know about NVIDIA Modulus and Modulus Sym: What Is NVIDIA Modulus? NVIDIA Modulus is a flexible and scalable framework for integrating, training, and deploying AI-driven physics models. It supports real-time inference, extensive model customization, and scaling for large-scale simulation challenges. What Is NVIDIA Modulus Sym? Modulus Sym combines partial differential equations (PDEs) with AI, supporting forward, data-driven, and hybrid approaches for physics analysis. With APIs for building and accelerating models, it empowers developers and researchers to tackle complex physics problems. --- Why Choose NVIDIA Modulus? 1️⃣ AI Toolkit for Physics: Model complex phenomena with high accuracy using AI-driven tools for data generation, training, and deployment. 2️⃣ Near-Real-Time Inference: Achieve instantaneous predictions for responsive and dynamic simulations. 3️⃣ Customization: Tailor neural network architectures, datasets, and workflows to your specific simulation needs. 4️⃣ Scalability: Handle large-scale systems like weather forecasting and aerospace engineering by leveraging NVIDIA AI technologies. --- Getting Started with NVIDIA Modulus The easiest way to start using Modulus is by installing the NVIDIA Modulus NGC Container. Step 1: Install Docker Engine Install the NVIDIA Docker toolkit with this command: sudo apt-get install nvidia-docker2 Step 2: Pull the Modulus Container Download the Modulus container via NGC: docker pull https://lnkd.in/d7Sc47Qj Step 3: Run the Modulus Container Start a session inside the container: docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \ --runtime nvidia -it --rm https://lnkd.in/d7Sc47Qj: bash To mount your current directory: docker run --shm-size=1g --ulimit memlock=-1 --ulimit stack=67108864 \ --runtime nvidia -v ${PWD}:/workspace \ -it --rm https://lnkd.in/d7Sc47Qj: bash --- NVIDIA Modulus is paving the way for integrating physics and AI, helping researchers, developers, and businesses tackle some of the most complex challenges. Whether you're working on weather forecasting, molecular dynamics, or design optimization, Modulus has the tools to make it possible. Start exploring NVIDIA Modulus today via the official GitHub repository. Let’s shape the future of AI-driven physics together! #AI #Physics #NVIDIA #Modulus #PINNs #Simulation #Technology

NVIDIA Revolutionizes Climate Tech with ‘Earth-2’: The World’s First Fully Open Accelerated AI Weather Stack In a move that democratizes climate science, NVIDIA unveiled 3 groundbreaking new models powered by novel architectures: Atlas, StormScope, and HealDA. These tools promise to accelerate forecasting speeds by orders of magnitude while delivering accuracy that rivals or exceeds traditional methods. The suite includes three new breakthrough models: Earth-2 Medium Range: High-accuracy 15-day forecasts across 70+ variables.  Earth-2 Nowcasting: Generative AI that delivers kilometer-scale storm predictions in minutes.  Earth-2 Global Data Assimilation: Real-time snapshots of global atmospheric conditions. Full analysis: https://lnkd.in/gt_BugDZ Model weight: https://lnkd.in/gkUVqH5E Paper [Earth-2 Medium Range]: https://lnkd.in/gTf-f_Gd Paper [Earth-2 Nowcasting]: https://lnkd.in/gQf7muqz Paper [Earth-2 Global Data Assimilation]: https://lnkd.in/gu_-eZsn Technical details: https://lnkd.in/gPQ66Me2 NVIDIA NVIDIA AI