AI for the environment - Climate modeling
Since the early days of climate modeling, software, hardware, and the ways that engineers and scientists collaborate have gone through incredible transformations. We’re creating fast, easy-to-use, accurate, open source AI-powered climate models to help scientists and the broader community plan for climate change and its consequences.
ACE
The Ai2 Climate Emulator (ACE) is a fast, accurate, state-of-the-art open-source AI emulator of weather and climate trained on observational reanalysis such as ERA5 or a physics-based climate model. Try it out on GitHub or Hugging Face.
What are climate models?
Planetary-scale Earth simulations known as global climate model projections are the primary sources of information on future climate change. Climate models are based on mathematical equations represented using a grid mesh that covers the globe: a finer grid mesh is more accurate but much more computationally expensive. Current global climate projections agree that a world with more greenhouse gases will be warmer everywhere, especially over land and at high latitudes. However, the current understanding of high-risk outcomes like rainfall extremes is more uncertain, and these changes have the potential to impact billions of people.
Refining climate predictions
The technology behind climate models was first created 50 years ago. Much has changed since then, and there is now an opportunity to make use of the latest advances in supercomputing, modern programming languages, and machine learning to improve climate models and enable more certain projections of local trends of average and extreme temperature and precipitation change in our rapidly warming climate. We're building modern machine learning (ML) into current climate models to improve their performance in key areas and ultimately to refine climate change predictions. Our ML is trained on ultra-realistic ‘digital twin’ simulations of the Earth’s atmosphere that exploit the world’s fastest supercomputers
Better climate models using finer grids
In the same way photos have become clearer because screens now pack in more pixels, fine grid ‘global storm resolving models’ (GSRMs) based on grids with less than 5 km (3 miles) horizontal spacing and 50 or more vertical grid levels spanning the depth of the atmosphere can now provide a detailed and actionable ‘digital twin’ of our world, enabling realistic simulation of airflow around mountain peaks and within thunderstorm systems that generate much of the world’s most intense rainfall.
Smarter simulations with machine learning
GSRMs are too costly to run for more than a few years, so they are not yet practical for climate modeling. But they can be run in a small selection of changed climates, and the simulations can be used to train a machine learning (ML) emulator that simulates similar climates and weather extremes, but 1000s of times faster, and is also accurate in intermediate climates. We partner with two leading climate modeling centers, NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) and the Department of Energy-funded Lawrence Livermore National Laboratory (LLNL), to design new GSRM simulations and use them for ML climate emulator training. Our group is a world leader in this area.
Creating open-source, collaborative solutions
We're developing open-source software so the broader climate modeling community can easily adopt our advances. Our partnerships with climate modeling centers ensure our work builds on their valuable experience and high-performance computing resources, and has the quickest impact. We also partner with NVIDIA and academic research groups to bring in the best new ML approaches and to work with top young minds in this rapidly evolving field.