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.
GSRMs are currently too costly to run for more than a year or two, so they are not yet practical for climate modeling. An earlier AI2 Climate Modeling project team pioneered the use of a domain-specific language (DSL) for making a GSRM run more efficiently on modern high-performance computers using diverse hardware accelerators such as GPUs, while still presenting developers with elegant, understandable, maintainable code. This project was transitioned to NOAA and NASA partners at the end of 2022.
Smarter, More Accurate Simulations with Machine Learning
Present-day GSRM simulations can be leveraged to increase the accuracy of currently affordable global climate models by training machine learning (ML) to replace their less accurate components—something that has never been done in operational climate models before. We partner with a leading climate modeling center, NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) using their X-SHiELD GSRM, a modified version of the U.S. global weather forecasting model, to design new GSRM simulations and create ML training datasets across multiple climates. 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 partnership with GFDL ensures our work builds on their valuable experience and has the quickest impact. This collaboration brings our team’s innovation together with GFDL’s climate modeling experience and computing resources to achieve quicker impacts that can set an example for other climate modeling centers.
Recent Updates
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. We've updated our arxiv post with a…
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…
Bretherton commentary for GRL on reservoir computing for AGCM emulation
August 30, 2023Geophysical Research Letters invited Chris Bretherton to write a Commentary about a recent paper by Arcomano et al. 2023 applying reservoir computing…
Bias-correction of GCM precipitation fields using a CycleGAN
July 8, 2023A CycleGAN is used to improve global 3-hour-average precipitation fields predicted by a coarse grid (200 km) atmospheric model across a range of…
First ML emulation of a human-designed cloud microphysics parameterization
June 7, 2023This project to emulate the Zhao-Carr cloud microphysics parameterization operational for years in NOAA's Global Forecast System was started by…
Recent Papers
ACE: A fast, skillful learned global atmospheric model for climate prediction
Oliver Watt‐Meyer, Gideon Dresdner, J. McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah Brenowitz, K. Kashinath, Michael S. Pritchard, B. Bonev, Matthew E. Peters, Christopher S. BrethertonNeurIPS • Tackling Climate Change with Machine Learning • 2023 Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing…A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation
Brian Henn, Y. R. Jauregui, Spencer K. Clark, Noah Brenowitz, J. McGibbon, Oliver Watt‐Meyer, Andrew G. Pauling, C. BrethertonESSOAr • 2023 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…Global Precipitation Correction Across a Range of Climates Using CycleGAN
Jeremy J McGibbon, Spencer K. Clark, Brian Henn, Anna Kwa, Oliver Watt-Meyer, W. Andre Perkins, Christopher S. BrethertonESSOAr • 2023 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-hour-average precipitation…Emulation of cloud microphysics in a climate model
W. Andre Perkins, Noah D. Brenowitz, Christopher S. Bretherton, Jacqueline M. NugentESSOAr • 2023 We present a machine learning based emulator of a microphysics scheme for condensation and precipitation processes (Zhao-Carr) used operationally in a global atmospheric forecast model (FV3GFS). Our tailored emulator architecture achieves high skill (≥94%) in…Improving the reliability of ML-corrected climate models with novelty detection
Clayton Sanford, Anna Kwa, Oliver Watt-Meyer, Spencer K. Clark, Noah D. Brenowitz, Jeremy McGibbon, and Christopher S. BrethertonJAMES (Journal of Advances in Modeling Earth Systems) • 2023 The use of machine learning (ML) for the online correction of coarse-resolution atmospheric models has proven effective in reducing biases in near-surface temperature and precipitation rate. However, this often introduces biases in the upper atmosphere and…
Recent Press
AI churns out lightning-fast forecasts as good as the weather agencies’
November 14, 2023