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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

  • 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. BrethertonESSOAr2023 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. BrethertonESSOAr2023 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. NugentESSOAr2023 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. BrethertonESSOAr2023 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…
  • Pace v0.2: a Python-based performance-portable atmospheric model

    Johann Dahm, Eddie Davis, Florian Deconinck, Oliver Elbert, Rhea George, Jeremy McGibbon, Tobias Wicky,Elynn Wu,Christopher Kung, Tal Ben-Nun, Lucas Harris , Linus Groner, Oliver FuhrerGeoscientific Model Development2023 Progress in leveraging current and emerging high-performance computing infrastructures using traditional weather and climate models has been slow. This has become known more broadly as the software productivity gap. With the end of Moore's law driving forward…


  • 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-MeyerEngineering
  • personal photoElynn WuEngineering