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Climate Modeling

Climate Modeling Logo

Climate Modeling

Climate Modeling for the future of the planet

Since the early days of climate modeling, software, hardware, and the way that engineers and scientists collaborate have gone through incredible transformations. Better data and technologies will inform how we mitigate and adapt to global impacts, such as sea level rise, community destruction, and biodiversity loss.

Two pictures of the earth. One with the words 'Satellite Image' and one with the word 'Climate Model'

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 are 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.

Computer servers lit up with green and red lights.
Two images of a cumulonimbus cloud -- one is high resolution and the other is a pixelated version

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.

A satellite image of earth with swirly white clouds
A conference room with a bunch of folks having a meeting

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

  • The precipitation response to warming and CO2 increase: A comparison of a global storm resolving model and CMIP6 models.

    Ilai Guendelman, Timothy M. Merlis, Kai-Yuan Cheng, Lucas M. Harris, Christopher S. Bretherton, Max Bolot, Lin Zhou, Alex Kaltenbaugh, Spencer K. Clark, Stephan FueglistalerGeophysical Research Letters2024 Global storm-resolving models (GSRMs) can explicitly resolve some of deep convection are now being integrated for climate timescales. GSRMs are able to simulate more realistic precipitation distributions relative to traditional CMIP6 models. In this study, we…
  • Emulation of cloud microphysics in a climate model

    W. Andre Perkins, Noah D. Brenowitz, Christopher S. Bretherton, Jacqueline M. NugentJAMES2024 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…
  • 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. BrethertonJAMES2024 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. BrethertonAuthorea2024 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…
  • Improving Stratocumulus Cloud Amounts in a 200‐m Resolution Multi‐Scale Modeling Framework Through Tuning of Its Interior Physics

    Liran Peng, P. Blossey, W. Hannah, C. Bretherton, C. Terai, A. Jenney, M. PritchardJournal of Advances in Modeling Earth Systems2024 High‐Resolution Multi‐scale Modeling Frameworks (HR)—global climate models that embed separate, convection‐resolving models with high enough resolution to resolve boundary layer eddies—have exciting potential for investigating low cloud feedback dynamics due…

AI churns out lightning-fast forecasts as good as the weather agencies’

Science Magazine
November 14, 2023
Read the Article


  • personal photoChris BrethertonResearch
  • personal photoSpencer ClarkResearch & Engineering
  • personal photoGideon DresdnerResearch
  • personal photoBrian HennResearch & Engineering
  • personal photoAnna KwaEngineering
  • personal photoJeremy McGibbonResearch & Engineering
  • personal photoAndre PerkinsResearch & Engineering
  • personal photoOliver Watt-MeyerResearch
  • personal photoElynn WuEngineering