Climate Modeling
Climate Modeling for the future of the planet
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, More Accurate 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.
Create Open-Source and 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 ensures 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 pproaches and to work with top young minds in this rapidly evolving field. To learn more, browse our Recent Updates and our extensive list of papers.
Recent Updates
Henn et al. ML subgrid cloud scheme for unbiased surface and TOA radiative fluxes
March 4, 2024We machine-learn cloud properties needed for radiative transfer - cloud cover and liquid and ice cloud condensate mixing ratios - as a function of…
ACE successfully emulates a second atmospheric model, DOE's EAMv2
February 22, 2024ACE, without modification, can be trained to emulate another major atmospheric model, EAMv2, run at a comparable grid spacing for at least ten years…
Watt-Meyer et al. 2024: ML of full physics has nearly the climate skill of corrective ML
February 4, 2024ML replaces the physical parameterizations of heating and moistening rates, but not wind tendencies, in a coarse-grid (200 km) global atmosphere…
ACE - updated, improved, Spotlight at NeurIPS Climate Change
December 7, 2023The AI2 Climate Emulator (ACE) was awarded a Spotlight presentation at the 2023 NeurIPS Climate Change workshop and will also be presented at the AGU…
ACE: A fast, skillful learned global atmospheric model for climate prediction
October 3, 2023Climate prediction requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter autoregressive…
Recent Papers
A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Brian Henn, Yakelyn R. Jauregui, Spencer K. Clark, Noah Brenowitz, Jeremy McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, Christopher S. BrethertonJAMES • 2024 Coarse-grid weather and climate models rely particularly on parameterizations of cloud fields, and coarse-grained cloud fields from a fine-grid reference model are a natural target for a machine-learned parameterization. We machine-learn the coarsened-fine…Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity
James P. C. Duncan, Elynn Wu, Jean-Christoph Golaz, Peter M. Caldwell, Oliver Watt-Meyer, Spencer K. Clark, Jeremy McGibbon, Gideon Dresdner, Karthik Kashinath, Boris Bonev, Michael S. Pritchard, and Christopher S. BrethertonAuthorea • 2024 Can the current successes of global machine learning-based weather simulators be generalized beyond two-week forecasts to stable and accurate multiyear runs? The recently developed AI2 Climate Emulator (ACE) suggests this is feasible, based upon 10-year…Global Precipitation Correction Across a Range of Climates Using CycleGAN
Jeremy McGibbon, S. K. Clark, Brian Henn, Anna Kwa, Oliver Watt‐Meyer, W. Perkins, Christopher S. Bretherton, S. K. ClarkGeophysical Research Letters • 2024 Accurate precipitation simulations for various climate scenarios are critical for understanding and predicting the impacts of climate change. This study employs a Cycle‐generative adversarial network (CycleGAN) to improve global 3‐hr‐average precipitation…Neural Network Parameterization of Subgrid‐Scale Physics From a Realistic Geography Global Storm‐Resolving Simulation
Oliver Watt‐Meyer, Noah D. Brenowitz, S. K. Clark, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Perkins, Lucas Harris, Christopher S. BrethertonJournal of Advances in Modeling Earth Systems • 2024 Parameterization of subgrid‐scale processes is a major source of uncertainty in global atmospheric model simulations. Global storm‐resolving simulations use a finer grid (less than 5 km) to reduce this uncertainty by explicitly resolving deep convection and…Tropical Cirrus Are Highly Sensitive to Ice Microphysics Within a Nudged Global Storm‐Resolving Model
R. Atlas, C. Bretherton, A. Sokol, P. Blossey, M. F. KhairoutdinovGeophysical Research Letters • 2024 Cirrus dominate the longwave radiative budget of the tropics. For the first time, the variability in cirrus properties and longwave cloud radiative effects (CREs) that arises from using different microphysical schemes within nudged global storm‐resolving…
Recent Press
AI churns out lightning-fast forecasts as good as the weather agencies’
November 14, 2023