James P. C. Duncan, Elynn Wu, Surya Dheeshjith et al.
· Geophysical Research Letters
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SamudrACE is a coupled atmosphere-ocean ML emulator giving stable centuries-long runs at 1 degree with realistic ENSO variability absent in uncoupled mode.
U-Net predicts flow-dependent covariances for an Ensemble Kalman Filter in a quasi-geostrophic model, matching 3DVar and EnKF for ensemble sizes up to 80.
Labe. Zachary Michael, Barnes. Elizabeth A, Hurrell. James W.
· Environmental Research Letters
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Logistic regression and neural networks show stratospheric aerosol injection signals in regional temperature emerge within a decade, while precipitation signals are harder to detect.
Firas Gerges, Michel C. Boufadel, Elie R. Bou-Zeid et al.
· World Scientific Annual Review of Artificial Intelligence
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An overview of ML downscaling of GCM output, with a deep-learning case study producing local weekly temperature projections for a New Jersey watershed.
Mingfeng Huang, Qing Wang, Maofeng Liu et al.
· Scientific Reports
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Convection-permitting simulations with ML damage models project 128% higher typhoon losses for Southeast China under RCP8.5 warming, with 50-year losses up 20–170%.
Hiroyuki Murakami, Thomas L. Delworth, William F. Cooke et al.
· Earth s Future
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A deep autoencoder detects anomalous heavy precipitation events in Japan and attributes their increase since 1977 partly to anthropogenic forcing, with more expected this century.
Oliver Watt‐Meyer, Noah Brenowitz, Spencer Koncius Clark et al.
· Geophysical Research Letters
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A random forest learns nudging tendencies and is coupled to FV3GFS, reducing 500 hPa height forecast error and annual-mean precipitation RMSE by about 20% in year-long runs.
Christopher Irrgang, Niklas Boers, Maike Sonnewald et al.
· Nature Machine Intelligence
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Perspective proposing Neural Earth System Modelling, which fuses deep networks and Earth system models into interpretable hybrids that might eventually replace them.
V. Balaji
· Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences
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Prospectus arguing that the end of Dennard scaling will push climate science toward combining machine learning pattern recognition with physical equations, reshaping how weather and climate are simulated.