17 papers in the archive · tier-1 watchlist.
AERIS is a 1.3 to 80B parameter Swin diffusion transformer reaching 10.2 ExaFLOPS on Aurora, stable to 90-day forecasts and outperforming IFS ENS.
Software, scaling & compressionAllen Institute for AI (Ai2, ACE)Argonne National LaboratoryUniversity of California, Los Angeles (UCLA)University of Chicago
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
ACE2 produces skilful winter seasonal forecasts from SST-persisted initialisation, reaching NAO correlation 0.47 over 1993–2016, comparable to a physics-based model.
Sub-seasonal to seasonalAllen Institute for AI (Ai2, ACE)Met Office
Couples the ACE2 emulator to a slab ocean and trains it across CO2 levels, reproducing equilibrium warming but violating energy conservation in transient runs.
Climate emulationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
ACE2 is a 450M-parameter emulator that conserves dry air and moisture, runs about 1500 simulated years per day, and reproduces El Niño responses over 80 years.
Climate emulationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
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
WeatherQA is a multimodal benchmark of 8,000+ image-text pairs for severe convective weather reasoning; GPT-4o shows a substantial gap to human forecasters.
LLMs & agentsAllen Institute for AI (Ai2, ACE)University of Washingtoncode
ACE is a 200M-parameter autoregressive emulator that stays stable for 100 years, reproduces a 100 km reference model's climate, and runs about 100x faster.
Climate emulationAllen Institute for AI (Ai2, ACE)NVIDIAUniversity of California, Berkeley
Commentary on reservoir-computing hybrid models coupling ML to a coarse climate model and SST predictor, arguing ML could disrupt conventional climate modeling.
A one-class SVM novelty detector disables ML corrections on out-of-sample inputs, stabilizing coarse climate runs and improving temperature and precipitation over standard ML corrections.
Hybrid physics-ML & parameterizationNVIDIAAllen Institute for AI (Ai2, ACE)Columbia University (LEAP, Lamont-Doherty)
Neural network corrections trained on a 3 km storm-resolving model reduce a 200 km climate model's land temperature and precipitation pattern errors by 6-25%.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
First neural emulator of a full cloud microphysics parameterization, including fast phase changes, performs well offline and online but shows Antarctic biases.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)
Machine-learned corrective tendencies from 25 km simulations improve a 200 km FV3GFS model's land precipitation pattern error by 8–28% across four SST-perturbed climates.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Machine-learned corrective tendencies from nudged 25 km runs improve land precipitation pattern errors by 17-30% in a coarse FV3GFS across four climates.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Machine-learned nudging tendencies correct a 200-km FV3GFS model toward 3-km X-SHiELD, gaining 18 hours of 3-7 day skill and 30% better precipitation.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Washington
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.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of WashingtonPrinceton University
Introduces tools to interpret neural convection parameterizations and diagnose gravity-wave instability when coupled to climate models, using SPCAM and cloud-resolving training data.
Hybrid physics-ML & parameterizationAllen Institute for AI (Ai2, ACE)Columbia University (LEAP, Lamont-Doherty)University of Washington
