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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.

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.

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 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

  • Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics

    Liran Peng, Michael Pritchard, Peter N. Blossey, Walter M. Hannah, Christopher S. Bretherton, Christopher R. Terai, and Andrea M. JenneyESSOAr (submitted to the American Geophysical Union journal JAMES)2023 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…
  • Emulating Fast Processes in Climate Models

    Noah Brenowitz, W. Perkins, J. M. Nugent, Oliver Watt‐Meyer, S. Clark, Anna Kwa, B. Henn, J. McGibbon, C. BrethertonNeurIPS•Machine Learning and Physical Sciences2022 Cloud microphysical parameterizations in atmospheric models describe the formation and evolution of clouds and precipitation, a central weather and climate process. Cloud-associated latent heating is a primary driver of large and small-scale circulations…
  • Improving the predictions of ML-corrected climate models with novelty detection

    Clayton Sanford, Anna Kwa, Oliver Watt‐Meyer, S. Clark, Noah Brenowitz, J. McGibbon, C. BrethertonNeurIPS•Climate Change AI2022 While previous works have shown that machine learning (ML) can improve the prediction accuracy of coarse-grid climate models, these ML-augmented methods are more vulnerable to irregular inputs than the traditional physics-based models they rely on. Because ML…
  • Machine-learned climate model corrections from a global storm-resolving model

    Anna Kwa, S. Clark, B. Henn, Noah Brenowitz, J. McGibbon, W. Perkins, Oliver Watt‐Meyer, L. Harris, C. BrethertonNeurIPS•Machine Learning and Physical Sciences2022 Due to computational constraints, running global climate models (GCMs) for many years requires a lower spatial grid resolution ( (cid:38) 50 km) than is optimal for accurately resolving important physical processes. Such processes are approximated in GCMs via…
  • Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle

    Anna Kwa, Spencer. K. Clark, Brian Henn, Noah D. Brenowitz, Jeremy McGibbon, Oliver Watt-Meyer, W. Andre Perkins, Lucas Harris, and Christopher S. BrethertonESSOAr2022 One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm-resolving…


  • personal photoChris BrethertonResearch
  • personal photoSpencer ClarkResearch & Engineering
  • personal photoJohann DahmEngineering
  • personal photoBrian HennResearch & Engineering
  • personal photoAnna KwaEngineering
  • personal photoJeremy McGibbonResearch & Engineering
  • personal photoAndre PerkinsResearch & Engineering
  • personal photoOliver Watt-MeyerEngineering
  • personal photoElynn WuEngineering