11 papers in the archive.
SamudrACE is a coupled atmosphere-ocean ML emulator giving stable centuries-long runs at 1 degree with realistic ENSO variability absent in uncoupled mode.
Climate emulationAllen Institute for AI (Ai2, ACE)Columbia University (LEAP, Lamont-Doherty)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)New York University (M2LInES)Princeton University
FourCastNet trained without Category 3-5 cyclones cannot forecast Category 5 storms, showing AI weather models fail to extrapolate to unseen extremes.
Extremes & hazardsNew York University (M2LInES)University of Chicago
Nature Climate Change perspective on machine learning in climate modelling and analysis; abstract absent, judged from title and venue.
Climate emulationAllen Institute for AI (Ai2, ACE)California Institute of Technology (Caltech)Colorado State University (incl. CIRA)Columbia University (LEAP, Lamont-Doherty)Google ResearchInriaLawrence Berkeley National Laboratory / NERSCNSF NCAR / UCARNVIDIANew York University (M2LInES)University of California, BerkeleyUniversity of Oxford
Uses an interpretable neural network trained on Mars Planetary Climate Model outputs to predict Gale Crater relative humidity with R² of 0.92, emulating a local climate variable.
Neural networks emulate a non-orographic gravity wave drag scheme and generalize to reproduce the QBO and the climate response to increased CO2 when coupled online.
Hybrid physics-ML & parameterizationNASAStanford UniversityNew York University (M2LInES)
XGBoost reconstructs hurricane surface wind fields from storm and ERA-Interim features, with errors below climatological variance, for synthetic risk applications.
Extremes & hazardsNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)New York University (M2LInES)
VIIRS nightlight data and simulated hurricane weather feed machine learning that predicts Hurricane Maria power outages in Puerto Rico where outage records are missing.
Manifold learning characterizes the tropical Pacific climate attractor, revealing model biases that diverge between historical and warming-scenario climate model simulations.
Quantum-mechanics-inspired, kernel-based data assimilation using Koopman operators, applied to Indo-Pacific SST and Niño 3.4 probabilistic forecasts. Novel but early-stage.
Kernel analog forecasting, a Koopman-based nonparametric method, predicts Nino 3.4 out to 10 months, 3-7 months beyond a linear inverse model.
Position paper surveying how machine learning can help climate change mitigation and adaptation across sectors such as energy and disaster management; not focused on weather or climate science.
Applications & impactsETH ZurichGoogle DeepMindGoogle ResearchLawrence Berkeley National Laboratory / NERSCMassachusetts Institute of Technology (MIT)New York University (M2LInES)Stanford UniversityUniversity of California, Berkeley
