Published
- 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 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]
- 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]
- 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 Editor's Highlight]
- 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]
- Brenowitz, N.D. et al. (2020), Machine learning climate model dynamics: Offline versus online performance. arXiv [link]
- 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]
- 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]
- 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]
- 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]