Papers

Learn more about AI2's Lasting Impact Award
Viewing 1-10 of 31 papers
  • Kilometer-scale global warming simulations and active sensors reveal changes in tropical deep convection

    Maximilien Bolot, Lucas M. Harris, Kai-Yuan Cheng, Timothy M. Merlis, Peter N. Blossey, Christopher S. Bretherton, Spencer K. Clark, Alex Kaltenbaugh, Linjiong Zhou & Stephan Fueglistaler NPJ Climate and Atmospheric Science2023 Changes in tropical deep convection with global warming are a leading source of uncertainty for future climate projections. A comparison of the responses of active sensor measurements of cloud ice to interannual variability and next-generation global storm…
  • ACE: A fast, skillful learned global atmospheric model for climate prediction

    Oliver Watt‐Meyer, Gideon Dresdner, J. McGibbon, Spencer K. Clark, Brian Henn, James Duncan, Noah Brenowitz, K. Kashinath, Michael S. Pritchard, B. Bonev, Matthew E. Peters, Christopher S. BrethertonNeurIPS • Tackling Climate Change with Machine Learning2023 Existing ML-based atmospheric models are not suitable for climate prediction, which requires long-term stability and physical consistency. We present ACE (AI2 Climate Emulator), a 200M-parameter, autoregressive machine learning emulator of an existing…
  • Probabilistic Precipitation Downscaling with Optical Flow-Guided Diffusion

    Prakhar Srivastava, Ruihan Yang, Gavin Kerrigan, Gideon Dresdner, Jeremy McGibbon, Christopher Bretherton, S. MandtarXiv2023 In climate science and meteorology, local precipitation predictions are limited by the immense computational costs induced by the high spatial resolution that simulation methods require. A common workaround is statistical downscaling (aka superresolution…
  • 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. BrethertonJAMES (Journal of Advances in Modeling Earth Systems)2023 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…
  • 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, Christopher S. BrethertonJournal of Advances in Modeling Earth Systems2023 One approach to improving the accuracy of a coarse-grid global climate model is to add machine-learned (ML) state-dependent corrections to the prognosed model tendencies, such that the climate model evolves more like a reference fine-grid global storm…
  • Old dog, new trick: Reservoir computing advances machine learning for climate modeling

    Christopher S. BrethertonGeophysical Research Letters2023 Physics-informed machine learning (ML) applied to geophysical simulation is developing explosively. Recently, graph neural net and vision transformer architectures have shown 1-7 day global weather forecast skill superior to any conventional model with…