38 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
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
Proposes memory-based neural network closures trained with a differentiable solver for Lorenz '96, giving more stable long-horizon forecasts than instantaneous parameterizations.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Physically constrained neural network with a 2D latent space predicts boundary-layer turbulent fluxes from LES data, outperforming the eddy-diffusivity mass-flux scheme.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Neural networks classify US West Coast atmospheric river landfalls back to 1916, and tree-ring regression extends annual AR counts to 1400 CE.
Climate analysis & attributionColumbia University (LEAP, Lamont-Doherty)University of California, Los Angeles (UCLA)
Mixture density networks model monthly wildfire frequency and area burned across the western US, matching annual area burned with r = 0.85 observed.
Extremes & hazardsColumbia University (LEAP, Lamont-Doherty)University of California, Los Angeles (UCLA)
A neural-network precipitation parameterization fails at extremes unless informed by a novel sub-grid organization metric, which raises variance R² from about 0.4 to 0.95.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Symbolic regression yields an 11-parameter interpretable cloud cover equation matching neural network skill (R²=0.94), beating Xu-Randall and transferring better to ERA5.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
WMO prize challenge winners used AI to post-process ECMWF S2S forecasts, beating bias-corrected references for weeks 3-6 precipitation and temperature.
Sub-seasonal to seasonalColumbia University (LEAP, Lamont-Doherty)Environment and Climate Change Canada (ECCC)ECMWFMax Planck Institute for MeteorologySwiss Data Science Center (SDSC)
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)
Reviews 2021 extreme events and barriers to deploying ML models on Earth observation imagery for disaster damage detection, highlighting InSAR as a tool.
Extremes & hazardsColumbia University (LEAP, Lamont-Doherty)
Unsupervised ML on tree-ring PDSI drought atlases finds outlier drought patterns in 80% of 21st-century years, showing unusual hydroclimate states.
Climate analysis & attributionNASAColumbia University (LEAP, Lamont-Doherty)
Neural networks learn FV3-GFS systematic error from data assimilation analysis increments and apply online corrections, improving 10-day forecast skill at all lead times.
Hybrid physics-ML & parameterizationNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)
Presents XCast, an open Python toolkit that applies scikit-learn-style methods gridpoint-by-gridpoint to gridded climate data, simplifying statistical and ML climate forecasting workflows.
Software, scaling & compressionNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Columbia University (LEAP, Lamont-Doherty)
Deep-learning cloud-type classifier trained on MODIS and CloudSat labels is applied to coarse satellite data to enable process-oriented evaluation of clouds in climate models.
Observations & remote sensingColumbia University (LEAP, Lamont-Doherty)
Variational encoder-decoder compresses superparameterized aquaplanet convection into five latent nodes that separate convective regimes, offering an interpretable subgrid parameterization.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Applies time-evolving self-organizing maps to a CESM pre-industrial run, tracing a 10-year transition from positive NAO to AMV and weak negative NAO in North Atlantic SST.
Climate analysis & attributionColumbia University (LEAP, Lamont-Doherty)
Trains feedforward and deep neural networks on CFD simulations to downscale mesoscale wind and temperature to urban microscale, reaching R2 of 0.91 and 0.94.
Downscaling & super-resolutionColumbia University (LEAP, Lamont-Doherty)
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)
CNN with residual and attention blocks estimates tropical cyclone intensity from Himawari-8 cloud products, reaching RMSE 4.06 m/s on independent tests.
Extremes & hazardsColumbia University (LEAP, Lamont-Doherty)
Neural network cloud cover parameterizations trained on ICON storm-resolving simulations generalise across regional and global data, with SHAP used to explain their feature use.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)Max Planck Institute for Meteorology
Climate-invariant ML embeds physical knowledge into parameterization emulators so they generalize across climate regimes, maintaining offline accuracy in three atmospheric models.
Explainability, uncertainty & physicsColumbia University (LEAP, Lamont-Doherty)Google ResearchMassachusetts Institute of Technology (MIT)NVIDIAUniversity of California, Los Angeles (UCLA)
Causal-predictive ML links tropical cyclone exposure to mortality and hospitalization in Medicare recipients, finding substantial respiratory hospitalization increases with wind speed as main driver.
Applications & impactsColumbia University (LEAP, Lamont-Doherty)Colorado State University (incl. CIRA)
Online sequential-learning combinations of NWP and statistical extended-range forecasts improve probabilistic skill by 5-15% over standard multimodel methods up to lead week 5.
Post-processing & calibrationColumbia University (LEAP, Lamont-Doherty)University of Reading
XGBoost model estimates hourly 1-km air temperature across 13 northeastern US states, with RMSE about 1.6 K versus 2.5 K for NLDAS-2.
Observations & remote sensingColumbia University (LEAP, Lamont-Doherty)
A machine-learning bias correction for a 25-km regional climate model raises simulated violent typhoon frequency from zero to match observations and projects increases under warming.
Post-processing & calibrationColumbia University (LEAP, Lamont-Doherty)
Combines an emergent constraint with machine learning to narrow CMIP5 end-of-century GPP to 171 ± 12 Gt C/yr and reduce gridded projection uncertainty.
Climate analysis & attributionColumbia University (LEAP, Lamont-Doherty)Max Planck Institute for Biogeochemistry
Feed-forward DNNs emulate cloud superparameterization with real geography, explaining over 70% of mid-to-upper tropospheric temporal variance at 15-minute scale.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Machine learning identifies three-dimensional drought events in tree-ring reconstructions and CMIP simulations, suggesting models underestimate the range of future hydroclimate states.
Climate analysis & attributionNSF NCAR / UCARColumbia University (LEAP, Lamont-Doherty)
Nonhomogeneous hidden Markov model identifies five ENSO regimes from 1856 to 2019 and gives out-of-sample probabilistic Niño forecasts comparable to existing models.
Sub-seasonal to seasonalColumbia University (LEAP, Lamont-Doherty)
Artificial neural networks classify 500 hPa circulation patterns, showing the 2019 Greenland melt season was about five standard deviations anomalous in atmospheric persistence.
Climate analysis & attributionNASAColumbia University (LEAP, Lamont-Doherty)
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
Deep neural networks learn subgrid stresses from DNS of the atmospheric boundary layer, achieving higher correlation than Smagorinsky-based models across stability regimes and resolutions.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
Architectural and loss-based constraints make neural emulators of convection conserve physical laws to machine precision without degrading accuracy.
Explainability, uncertainty & physicsColumbia University (LEAP, Lamont-Doherty)Technical University of Munich (TUM)
Self-organizing maps applied to reanalysis identify four circulation pattern types behind extreme US floods, including tropical moisture exports and tropical cyclones.
Extremes & hazardsColumbia University (LEAP, Lamont-Doherty)
CBRAIN, a deep network trained on superparameterization output, skillfully emulates convective heating and moistening in an aquaplanet, a proof of concept for ML convection schemes.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
A neural network trained on cloud-resolving output replaces subgrid convection in a global climate model, giving stable multi-year runs that capture precipitation extremes and approximately conserve energy.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
CBRAIN, a neural network trained on superparameterized aquaplanet data, replaces convection parameterization and reproduces its features with reduced stochasticity.
Hybrid physics-ML & parameterizationColumbia University (LEAP, Lamont-Doherty)
