Climate Physics: Insights from Theory, Models, and AI

FOCUS · MAR-C52 · ID: MAR-C52

Presentations

  • Invited-In-person · Invited

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    Publication: Dagon, K., B.M. Sanderson, R.A. Fisher, D.M. Lawrence (2020), A machine learning approach to emulation and biophysical parameter estimation with the Community Land Model, version 5, Advances in Statistical Climatology, Meteorology and Oceanography, 6, 223-244, https://doi.org/10.5194/ascmo-6-223-2020.

    Dagon, K., J. Truesdale, J.C. Biard, K.E. Kunkel, G.A. Meehl, and M.J. Molina (2022), Machine learning-based detection of weather fronts and associated extreme precipitation in historical and future climates, Journal of Geophysical Research: Atmospheres, 127, e2022JD037038, https://doi.org/10.1029/2022JD037038.

    Kennedy, D., K. Dagon, D.M. Lawrence, R.A. Fisher, B.M. Sanderson, N. Collier, et al. (2025), One-at-a-time parameter perturbation ensemble of the Community Land Model, version 5.1, Journal of Advances in Modeling Earth Systems, 17, e2024MS004715, https://doi.org/10.1029/2024MS004715.

    Presenters

    • Katie Dagon

      • NSF National Center for Atmospheric Research

    Authors

    • Katie Dagon

      • NSF National Center for Atmospheric Research

    View abstract →

  • Oral-In-person

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    Publication: The abstract reports on work extending
    C. Taylor, "Separating Greenhouse-Gas Driven Forcing from Natural Fluctuations in the Time Series for Global Mean Temperatures", submitted to PRL, manuscript LD19563, revised version currently under review.

    A new paper detailing the work reported on in the submitted abstract is currently in preparation.

    Presenters

    • Cyrus Taylor

      • Case Western Reserve University

    Authors

    • Cyrus Taylor

      • Case Western Reserve University

    View abstract →

  • Oral-In-person

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    Publication: A. Mahesh, W. D. Collins, B. Bonev, N. Brenowitz, Y. Cohen, J. Elms, P. Harrington,
    K. Kashinath, T. Kurth, J. North, T. O'Brien, M. Pritchard, D. Pruitt, M. Risser, S. Sub-
    ramanian, and J. Willard. Huge ensembles—Part 1: Design of ensemble weather forecasts
    using spherical Fourier neural operators. Geoscientific Model Development, 18(17):5575–
    5603, 2025, doi:10.5194/gmd-18-5575-2025.

    A. Mahesh, W. D. Collins, B. Bonev, N. Brenowitz, Y. Cohen, P. Harrington, K. Kashinath,
    T. Kurth, J. North, T. A. O'Brien, M. Pritchard, D. Pruitt, M. Risser, S. Subramanian, and
    J. Willard. Huge ensembles—Part 2: Properties of a huge ensemble of hindcasts generated
    with spherical Fourier neural operators. Geoscientific Model Development, 18(17):5605–
    5633, 2025, doi:10.5194/gmd-18-5605-2025.

    Bonev, B., T. Kurth, A. Mahesh, M. Bisson, J. Kossaifi, K. Kashinath, A. Anandkumar, W.D. Collins, M. Pritchard, and A. Keller, 2025: FourCastNet 3: A principled approach to probabilistic machine-learning weather forecast at scale. Submitted to arXiv.org,
    doi:10.48550/arXiv.2507.12144.

    Presenters

    • William Collins

      • Lawrence Berkeley National Laboratory

    Authors

    • William Collins

      • Lawrence Berkeley National Laboratory

    • Ankur Mahesh

    • Travis O'Brien

    • Paul Goddard

    • Sinclair Zebaze

    • Shashank Subramanian

    • James Duncan

    • Oliver Watt-Meyer

    • Boris Bonev

    • Thorsten Kurth

    • Karthik Kashinath

    • Michael Pritchard

      • NVIDIA Research & University of California, Irvine

    View abstract →

  • Oral-In-person

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    Publication: C. C. Maiocchi, V. Lucarini, A. Gritsun. Y. Sato, Heterogeneity of the attractor of the Lorenz '96 model: Lyapunov analysis, unstable periodic orbits, and shadowing properties, Physica D 457, 133970 (2024) https://doi.org/10.1016/j.physd.2023.133970

    Presenters

    • Valerio Lucarini

      • University of Leicester

    Authors

    • Valerio Lucarini

      • University of Leicester

    • Yuzuru Sato

      • Hokkaido University

    • Chiara Maiocchi

    • Andrey Gritsun

    View abstract →