Oliver Watt-Meyer

I lead the Climate Modeling team at the Allen Institute for AI. We develop deep learning to improve the fidelity, speed, and accessibility of climate models. My research interests are in atmospheric dynamics, climate change, climate modeling and machine learning.

I am an Associate Editor at the Journal of Advances in Modeling Earth Systems focused on handling manuscripts related to the use of machine learning in Earth system modeling. We welcome your submissions!

I also hold an Affiliate Associate Professor position in the Department of Atmospheric and Climate Science at the University of Washington.

Contact me at oliverwm@allenai.org.

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Publications

Under Review

  • Gregory, W. et al. (2026), FloeNet: A mass-conserving global sea ice emulator that generalizes across climates. [preprint]
  • Mahesh, A. et al. (2026), Examining Fast Radiative Feedbacks Using Machine-Learning Weather Emulators. [preprint]
  • Perkins, W. A. et al. (2025), HiRO-ACE: Fast and skillful AI emulation and downscaling trained on a 3 km global storm-resolving model. [preprint]
  • Duncan, J. P. C. et al. (2025), SamudrACE: Fast and Accurate Coupled Climate Modeling with 3D Ocean and Atmosphere Emulators. [preprint]

2025

  • Clark, S.K. et al. (2025), ACE2-SOM: Coupling an ML atmospheric emulator to a slab ocean and learning the sensitivity of climate to changed CO2. J. Geophys. Res. Machine Learning and Computation, 2, e2024JH000575. [link] [pdf] [EOS Highlight]
  • Kent, C. et al. (2025), Skilful global seasonal predictions from a machine learning weather model trained on reanalysis data. npj Climate and Atmospheric Science, 8, 314. [link] [pdf]
  • Watt-Meyer, O. et al. (2025), ACE2: Accurately learning subseasonal to decadal atmospheric variability and forced responses. npj Climate and Atmospheric Science, 8, 205. [link] [pdf]

2024

  • Rühling Cachay, S. et al. (2024), Probabilistic Emulation of a Global Climate Model with Spherical DYffusion. [link] [NeurIPS 2024 spotlight] [ICML Best Paper]
  • Duncan, J. P. C. et al. (2024), Application of the AI2 Climate Emulator to E3SMv2's global atmosphere model, with a focus on precipitation fidelity. J. Geophys. Res. Machine Learning and Computation, 1, e2024JH000136. [link] [pdf]
  • Eyring, V. et al. (2024), Pushing the frontiers in climate modelling and analysis with machine learning. Nat. Clim. Chang., 14, 916-928. [link] [pdf]
  • Pendergrass, A. G. et al. (2024), Impact of ITCZ width on global climate: ITCZ-MIP. Geosci. Model Dev., 17, 6365-6378. [link] [pdf]
  • Henn, B. et al. (2024), A machine learning parameterization of clouds in a coarse-resolution climate model for unbiased radiation. JAMES, 16, e2023MS003949. [link] [pdf]
  • McGibbon, J. et al. (2024), Global precipitation correction across a range of climates using CycleGAN. Geophys. Res. Lett., 51, e2023GL105131. [link] [pdf]
  • Watt-Meyer, O. et al. (2024), Neural network parameterization of subgrid-scale physics from a realistic geography global storm-resolving simulation. JAMES, 16, e2023MS003668. [link] [pdf]

2023

  • Watt-Meyer, O. et al. (2023), ACE: A fast, skillful learned global atmospheric model for climate prediction. NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning. [link] [pdf] [spotlight talk]
  • Sanford, C. et al. (2023), Improving the reliability of ML-corrected climate models with novelty detection. JAMES, 15, e2023MS003809. [link] [pdf]
  • Kwa, A. et al. (2023), Machine-learned climate model corrections from a global storm-resolving model: Performance across the annual cycle. JAMES, 15, e2022MS003400. [link] [pdf]

2022

  • Clark, S.K. et al. (2022), Correcting a 200 km Resolution Climate Model in Multiple Climates by Machine Learning From 25 km Resolution Simulations. JAMES, 14, e2022MS003219. [link] [pdf]
  • Bretherton, C. S. et al. (2022), Correcting Coarse-Grid Weather and Climate Models by Machine Learning From Global Storm-Resolving Simulations. JAMES, 14, e2021MS002794. [link] [pdf] [EOS Highlight]

2021

  • Watt-Meyer, O. et al. (2021), Correcting weather and climate models by machine learning nudged historical simulations. Geophys. Res. Lett., 48, e2021GL092555. [link] [pdf]
  • McGibbon, J. et al. (2021), fv3gfs-wrapper: a Python wrapper of the FV3GFS atmospheric model. Geosci. Model Dev., 14, 4401-4409. [link] [pdf]

2020

  • Brenowitz, N.D. et al. (2020), Machine learning climate model dynamics: Offline versus online performance. arXiv [link]

2019

  • Watt-Meyer, O., D.M.W. Frierson and Q. Fu (2019), Hemispheric asymmetry of tropical expansion under CO2 forcing. Geophys. Res. Lett., 46, 9231-9240. [link] [pdf] [US CLIVAR Highlight]
  • Watt-Meyer, O. and D.M.W. Frierson (2019), ITCZ width controls on Hadley cell extent and eddy-driven jet position and their response to warming. J. Clim., 32, 1151-1166. [link] [pdf] [PCC Highlight]

2018

  • Watt-Meyer, O. and P.J. Kushner (2018), Why are temperature and upward wave activity flux positively skewed in the polar stratosphere? J. Clim., 31, 115-130. [link] [pdf]

2017

  • Watt-Meyer, O. and D.M.W. Frierson (2017), Local and remote impacts of atmospheric cloud radiative effects onto the eddy-driven jet. Geophys. Res. Lett., 44, 10,036-10,044. [link] [pdf] [supplementary]

2015

  • Watt-Meyer, O. and P.J. Kushner (2015), The role of standing waves in driving persistent anomalies of upward wave activity flux. J. Clim., 28, 9941-9954. [link] [pdf]
  • Watt-Meyer, O. and P.J. Kushner (2015), Decomposition of atmospheric disturbances into standing and travelling components, with application to Northern Hemisphere planetary waves and stratosphere-troposphere coupling. J. Atmos. Sci., 72, 787-802. [link] [pdf]

Other

  • Watt-Meyer, O. (2024), Weather and climate predicted by hybrid model. Nature News and Views. [link] [pdf]
  • Watt-Meyer, O. (2016), The role of standing and travelling waves in stratosphere-troposphere coupling. Ph.D. Thesis, University of Toronto. [pdf]