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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.

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.

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

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.

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. 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

Recent Updates

  • Pace v0.1: A python-based performance-portable implementation of the FV3 dynamical core

    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, and Oliver FuhrerEGUsphere2022 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…
  • Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations

    S. Clark, Noah Brenowitz, B. Henn, Anna Kwa, J. McGibbon, W. Perkins, Oliver Watt‐Meyer, C. Bretherton, L. HarrisJournal of Advances in Modeling Earth Systems2022 Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse‐resolution global atmosphere model with real geography (a ∼200 km version of NOAA's FV3GFS) evolve more like a…
  • Impact of Warmer Sea Surface Temperature on the Global Pattern of Intense Convection: Insights From a Global Storm Resolving Model

    K. Cheng, L. Harris, C. Bretherton, T. Merlis, M. Bolot, Linjiong Zhou, Alex Kaltenbaugh, S. Clark, S. FueglistalerGeophysical Research Letters2022 Intense convection (updrafts exceeding 10 m s−1) plays an essential role in severe weather and Earth's energy balance. Despite its importance, how the global pattern of intense convection changes in response to warmed climates remains unclear, as simulations…
  • Correcting a coarse-grid climate model in multiple climates by machine learning from global 25-km resolution simulations

    Spencer K. Clark, Noah D. Brenowitz, Brian Henn, Anna Kwa, Jeremy McGibbon, W. Andre Perkins, Oliver Watt-Meyer, Christopher S. Bretherton, Lucas M. Harris Earth and Space Science Open Archive2022 Bretherton et al. (2022, https://doi.org/10.1029/2021MS002794) demonstrated a successful approach for using machine learning (ML) to help a coarse-resolution global atmosphere model with real geography (a ~200 km version of NOAA’s FV3GFS) evolve more like a…
  • Productive Performance Engineering for Weather and Climate Modeling with Python

    Tal Ben-Nun, Linus Groner, Florian Deconinck, Tobias Wicky, Eddie Davis, Johann P. S. Dahm, Oliver D. Elbert, Rhea George, Jeremy McGibbon, Lukas Trümper, Elynn Wu, Oliver Fuhrer, Thomas Schulthess, Torsten HoeflerarXiv2022 Earth system models are developed with a tight coupling to target hardware, often containing highly-specialized code predicated on processor characteristics. This coupling stems from using imperative languages that hard-code computation schedules and layout…

Team

  • personal photoChris BrethertonResearch
  • personal photoOliver FuhrerResearch
  • personal photoSpencer ClarkResearch & Engineering
  • personal photoJohann DahmEngineering
  • personal photoFlorian DeconinckEngineering
  • personal photoOliver ElbertEngineering
  • 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