We acknowledge the following for their contributions as active session participants: Steven Klein (LLNL), Scott Collis (ANL), Alex Cannon (Environment and Climate Change Canada), Nicola Maher (CIRES/CU Boulder), Da Yang (UC Davis, LBNL), John Allen (Central Michigan University), Manos Anagnostou (University of Connecticut), Jim Ang (PNNL), Marian Anghel (LANL), Kenneth Ball (Geometric Data Analytics), Karthik Balaguru (PNNL), Antara Banerjee (CIRES, NOAA/CSL), Elizabeth Barnes (Colorado State University), Carolyn Begeman (LANL), Emily Bercos-Hickey (LBNL), Celine Bonfils (LLNL), Andrew Bradley (Sandia National Laboratories), Antonietta Capotondi (CIRES, NOAA/PSL), William Chapman (Scripps Institution of Oceanography), Nan Chen (University of Wisconsin-Madison), Paul J. Durack (LLNL), Zhe Feng (PNNL), Andrew Geiss (PNNL), Carlo Grazian (ANL), Gary Geernaert (DOE), Huanping Huang (LBNL), Whitney Huang (Clemson University), Brian Hunt (University of Maryland), Robert Jacob (ANL), Renu Joseph (DOE), Karthik Kashinath (Nvidia), Grace E Kim (Booz Allen Hamilton), Gu Lianhong (ORNL), Jian Lu (PNNL), Valerio Lucarini (University of Reading), Hsi-Yen Ma (LLNL), Kirsten Mayer (Colorado State University), Amy McGovern (University of Oklahoma), Gerald Meehl (NCAR), Balu Nadiga (LANL), Christina Patricola (Iowa State University), Steve Penny (CIRES, NOAA/PSL), Stephen Po-Chedley (LLNL), Philip Rasch (PNNL), Deeksha Rastogi (ORNL), Adam Rupe (LANL), Christine Shields (NCAR), Jeff Stehr (DOE), Aneesh Subramanian (CU Boulder), Bob Vallario (DOE), Charuleka Varadharajan (LBNL), Hailong Wang (PNNL), Jiali Wang (ANL), Michael Wehner (LBNL), Brian White (UNC Chapel Hill, UC Berkeley), and Mark Zelinka (LLNL). This material is based upon work supported by the U.S. Department of Energy (DOE), Office of Science, Office of Biological and Environmental Research (BER), Regional and Global Model Analysis (RGMA) component of the Earth and Environmental System Modeling Program under Award DE-SC0022070 and National Science Foundation IA 1947282. The National Center for Atmospheric Research is sponsored by the National Science Foundation. A portion of this work was performed under the auspices of the U.S. DOE by Lawrence Livermore National Laboratory (LLNL) under Contract DE-AC52-07NA27344. Author Anderson was supported by LLNL Laboratory Directed Research and Development project 22-SI-008. This paper has been authored by UT-Battelle, LLC, under Contract DE-AC05-00OR22725 with the U.S. Department of Energy. The publisher, by accepting the article for publication, acknowledges that the U.S. government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of the paper, or allow others to do so, for U.S. government purposes. The DOE will provide public access to these results in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Abdar, M., and Coauthors, 2021: A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Inf. Fusion, 76, 243–297, https://doi.org/10.1016/j.inffus.2021.05.008.
Allen, J. T., and M. K. Tippett, 2015: The characteristics of United States hail reports: 1955–2014. Electron. J. Severe Storms Meteor., 10 (3), https://doi.org/10.55599/ejssm.v10i3.60.
American Meteorological Society, 2022: Climate variability. Glossary of Meteorology, https://glossary.ametsoc.org/wiki/Climate_variability.
Anderson, G. J., and D. D. Lucas, 2018: Machine learning predictions of a multiresolution climate model ensemble. Geophys. Res. Lett., 45, 4273–4280, https://doi.org/10.1029/2018GL077049.
Andersson, T. R., and Coauthors, 2022: Active learning with convolutional Gaussian neural processes for environmental sensor placement. arXiv, 2211.10381v1, https://doi.org/10.48550/arXiv.2211.10381.
Baño-Medina, J., R. Manzanas, and J. M. Gutierrez, 2020: Configuration and intercomparison of deep learning neural models for statistical downscaling. Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020.
Barnes, E. A., and R. J. Barnes, 2021a: Controlled abstention neural networks for identifying skillful predictions for classification problems. J. Adv. Model. Earth Syst., 13, e2021MS002573, https://doi.org/10.1029/2021MS002573.
Barnes, E. A., and R. J. Barnes, 2021b: Controlled abstention neural networks for identifying skillful predictions for regression problems. J. Adv. Model. Earth Syst., 13, e2021MS002575, https://doi.org/10.1029/2021MS002575.
Barnes, E. A., J. W. Hurrell, I. Ebert-Uphoff, C. Anderson, and D. Anderson, 2019: Viewing forced climate patterns through an AI lens. Geophys. Res. Lett., 46, 13 389–13 398, https://doi.org/10.1029/2019GL084944.
Bennett, K. E., and Coauthors, 2022: Spatial patterns of snow distribution in the sub-Arctic. Cryosphere, 16, 3269–3293, https://doi.org/10.5194/tc-16-3269-2022.
Bessenbacher, V., L. Gudmundsson, and S. I. Seneviratne, 2023: Optimizing soil moisture station networks for future climates. Geophys. Res. Lett., 50, e2022GL101667, https://doi.org/10.1029/2022GL101667.
Beucler, T., M. Pritchard, P. Gentine, and S. Rasp, 2020: Towards physically-consistent, data-driven models of convection. 2020 IEEE Int. Geoscience and Remote Sensing Symp., Waikoloa, HI, IEEE, 3987–3990, https://doi.org/10.1109/IGARSS39084.2020.9324569.
Bi, K., L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, 2022: Pangu-weather: A 3D high-resolution model for fast and accurate global weather forecast. arXiv, 2211.02556v1, https://doi.org/10.48550/arXiv.2211.02556.
Biard, J. C., and K. E. Kunkel, 2019: Automated detection of weather fronts using a deep learning neural network. Adv. Stat. Climatol. Meteor. Oceanogr., 5, 147–160, https://doi.org/10.5194/ascmo-5-147-2019.
Bird, L., M. Walker, G. Bodeker, I. Campbell, G. Liu, S. J. Sam, J. Lewis, and S. Rosier, 2023: Deep learning for stochastic precipitation generation—Deep SPG v1.0. Geosci. Model Dev., 16, 3785–3808, https://doi.org/10.5194/gmd-16-3785-2023.
Bocquet, M., J. Brajard, A. Carrassi, and L. Bertino, 2020: Bayesian inference of chaotic dynamics by merging data assimilation, machine learning and expectation-maximization. arXiv, 2001.06270v2, https://doi.org/10.48550/arXiv.2001.06270.
Brajard, J., A. Carrassi, M. Bocquet, and L. Bertino, 2021: Combining data assimilation and machine learning to infer unresolved scale parametrization. Philos. Trans. Roy. Soc., A379, 20200086, https://doi.org/10.1098/rsta.2020.0086.
Bretherton, C. S., and Coauthors, 2022: Correcting coarse-grid weather and climate models by machine learning from global storm-resolving simulations. J. Adv. Model. Earth Syst., 14, e2021MS002794, https://doi.org/10.1029/2021MS002794.
Brodu, N., and J. P. Crutchfield, 2022: Discovering causal structure with reproducing-kernel Hilbert space ϵ-machines. Chaos, 32, 023103, https://doi.org/10.1063/5.0062829.
Buizza, C., and Coauthors, 2022: Data learning: Integrating data assimilation and machine learning. J. Comput. Sci., 58, 101525, https://doi.org/10.1016/j.jocs.2021.101525.
Casey, S. P. F., and L. Cucurull, 2022: The impact of data latency on operational global weather forecasting. Wea. Forecasting, 37, 1211–1220, https://doi.org/10.1175/WAF-D-21-0149.1.
Celebi, M. E., and K. Aydin, 2016: Unsupervised Learning Algorithms. Springer, 558 pp.
Chantry, M., H. Christensen, P. Dueben, and T. Palmer, 2021: Opportunities and challenges for machine learning in weather and climate modelling: Hard, medium and soft AI. Philos. Trans. Roy. Soc., A379, 20200083, https://doi.org/10.1098/rsta.2020.0083.
Chattopadhyay, A., E. Nabizadeh, and P. Hassanzadeh, 2020: Analog forecasting of extreme-causing weather patterns using deep learning. J. Adv. Model. Earth Syst., 12, e2019MS001958, https://doi.org/10.1029/2019MS001958.
Clark, A. J., and E. D. Loken, 2022: Machine learning–derived severe weather probabilities from a warn-on-forecast system. Wea. Forecasting, 37, 1721–1740, https://doi.org/10.1175/WAF-D-22-0056.1.
Clark, S. K., and Coauthors, 2022: Correcting a 200 km resolution climate model in multiple climates by machine learning from 25 km resolution simulations. J. Adv. Model. Earth Syst., 14, e2022MS003219, https://doi.org/10.1029/2022MS003219.
Cleary, E., A. Garbuno-Inigo, S. Lan, T. Schneider, and A. M. Stuart, 2021: Calibrate, emulate, sample. J. Comput. Phys., 424, 109716, https://doi.org/10.1016/j.jcp.2020.109716.
, Cleary, E. , A. Garbuno-Inigo , S. Lan