Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 19 papers
  • 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…
  • 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…
  • Hallett‐Mossop Rime Splintering Dims Cumulus Clouds Over the Southern Ocean: New Insight From Nudged Global Storm‐Resolving Simulations

    R. Atlas, C. Bretherton, M. Khairoutdinov, P. BlosseyAGU Advances2022 In clouds containing both liquid and ice with temperatures between −3°C and −8°C, liquid droplets collide with large ice crystals, freeze, and shatter, producing a plethora of small ice splinters. This process, known as Hallett‐Mossop rime splintering, and…