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Climate Modeling

Climate Modeling Logo

Climate Modeling

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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.

Faster Climate Models

Since the early days of climate modeling inception, software, hardware, and the way that engineers and scientists collaborate have gone through incredible transformations. We are redesigning a climate model that will run on the world’s largest supercomputers. A new programming language specifically designed for climate modeling helps scientists work more efficiently, allowing fine-grid weather and climate models to run up to tenfold faster and longer.

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Two images of a cumulonimbus cloud -- one is high resolution and the other is a pixelated version

Better Climate Models

In the same way photos have become clearer because screens now pack in more pixels, high resolution climate models now provide a detailed and actionable view of our world. High resolution models are very costly to run, but they can be leveraged to increase the accuracy of currently affordable models by replacing their less accurate components with machine learning (ML) -- something that has never been done in operational climate models before.

Smarter, More Accurate Simulations with Machine Learning

The technology behind climate models was first created 50 years ago. Much has changed in technology since then, and there is now opportunity to make use of the latest advances in supercomputing, modern programming languages, and machine learning to improve climate models. We're building modern machine learning into current climate models to improve their performance in key areas and ultimately to refine climate change predictions. Our goal is to accelerate climate science by building models that exploit the world’s fastest supercomputers, modern programming languages, and coding best practices.

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Create Open-Source and Collaborative Solutions

We're developing open-source software so the broader climate modeling community can easily adopt our advances. We partner with a leading climate modeling center, NOAA’s Geophysical Fluid Dynamics Laboratory, to ensure our work builds on their valuable experience and has the quickest impact. Our work is focused on improving their experimental fine-grid model, SHiELD, which shares components with the U. S. global weather forecasting model. This collaboration brings our team’s innovation together with GFDL’s climate modeling experience and computing resources to achieve quicker impact that can set an example for other climate modeling centers to follow.

Learn more about NOAA’s Geophysical Fluid Dynamics Laboratory

  • Domain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Comp

    Gysi, T., C. Müller, T. Grosser, T. Hoefler, O. Zinenko, O. Fuhrer, E. Davis, and T. WickyACM Transactions on Architecture and Code Optimization2021 Most compilers have a single core intermediate representation (IR) (e.g., LLVM) sometimes complemented with vaguely defined IR-like data structures. This IR is commonly low-level and close to machine instructions. As a result, optimizations relying on domain…
  • Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations.

    Bretherton, C. S., B. Henn, A. Kwa, N. D. Brenowitz, O. Watt-Meyer, J. McGibbon, W. A. Perkins, S. K. Clark, and L. HarrisEarth and Space Science Open Archive2021 Global atmospheric `storm-resolving' models with horizontal grid spacing of less than 5~km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse-grid…
  • fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model

    McGibbon, J., N. Brenowitz, M. Cheeseman, S. K. Clark, J. Dahm, E. Davis, O. D. Elbert, R. C. George, L. M. Harris, B. Henn, A. Kwa, W. A. Perkins, O. Watt-Meyer, T. Wicky, C. S. Bretherton, and O. Fuhrer,Geoscientific Model Development2021 Simulation software in geophysics is traditionally written in Fortran or C++ due to the stringent performance requirements these codes have to satisfy. As a result, researchers who use high-productivity languages for exploratory work often find these codes…
  • Correcting weather and climate models by machine learning nudged historical simulations

    Watt-Meyer, O., N. Brenowitz, S. K. Clark, B. Henn, A. Kwa, J. McGibbon, W. A. Perkins, and C. S. BrethertonGeophysical Research Letters2021 Due to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective…
  • GridTools: A framework for portable weather and climate applications

    Afanasyev, A., M. Bianco, L. Mosimann, C. Osuna, F. Thaler, H. Vogt, O. Fuhrer, J. VandeVondele, and T. C. SchulthessElsevier2021 Weather forecasts and climate projections are of tremendous importance for economical and societal reasons. Software implementing weather and climate models is complex to develop and hard to maintain, and requires a large range of different competencies…


  • personal photoChris BrethertonResearch
  • personal photoOliver FuhrerResearch
  • personal photoNoah BrenowitzResearch
  • personal photoSpencer ClarkResearch & Engineering
  • personal photoJohann DahmEngineering
  • personal photoEddie DavisEngineering
  • personal photoFlorian DeconinckEngineering
  • personal photoOliver ElbertEngineering
  • personal photoRhea GeorgeEngineering
  • personal photoBrian HennResearch & Engineering
  • personal photoAnna KwaEngineering
  • personal photoJeremy McGibbonResearch & Engineering
  • personal photoAndre PerkinsResearch & Engineering
  • personal photoOliver Watt-MeyerEngineering
  • personal photoTobias WickyEngineering
  • personal photoElynn WuEngineering