28 papers in the archive.
Five recommendations for standardized, independent evaluation of ML-based Earth system models, stressing physical consistency for climate projections without historical analogues.
Benchmarks, datasets & evaluationColorado State University (incl. CIRA)Lawrence Berkeley National Laboratory / NERSCNSF NCAR / UCAROak Ridge National LaboratoryUniversity of California, Berkeley
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
Review of ML across climate physics, covering observation reconstruction, subgrid parameterization, causal inference and climate and weather prediction.
Hybrid physics-ML & parameterizationECMWFInriaLawrence Berkeley National Laboratory / NERSCUniversity of Chicago
A 1.1-billion-parameter SFNO ensemble with bred vectors and perturbed parameters passes spectral tests and is calibrated, setting up ML huge ensembles of 1,000+ members.
Probabilistic & ensemble forecastingLawrence Berkeley National Laboratory / NERSCNVIDIAUniversity of California, Berkeley
A minimally modified SwinV2 trained on ERA5 beats IFS, and ablations on loss, model size and multi-step fine-tuning show which training choices matter.
Software, scaling & compressionLawrence Berkeley National Laboratory / NERSCUniversity of California, Berkeley
Lagged ensembles give a parameter-free probabilistic comparison showing GraphCast and Pangu tie on CRPS, and that multi-step losses hurt probabilistic skill.
Probabilistic & ensemble forecastingLawrence Berkeley National Laboratory / NERSCNVIDIAUniversity of California, Berkeley
Hybrid model uses an ANN trained on analysis increments to correct errors in a coupled ocean-atmosphere model (MAOOAM), improving prediction skill across lead times.
Hybrid physics-ML & parameterizationLawrence Berkeley National Laboratory / NERSC
Review of ML for climate variability modes, extreme weather precursors, observation-model integration, downscaling and bias correction, aimed at domain scientists.
Climate analysis & attributionNSF NCAR / UCARUniversity of California, BerkeleyLawrence Berkeley National Laboratory / NERSCOak Ridge National Laboratory
Machine learning emulates the systematic error of a reduced-order coupled ocean-atmosphere model, and the resulting hybrid model beats the dynamical model alone in twin experiments, best when correcting both components.
Hybrid physics-ML & parameterizationLawrence Berkeley National Laboratory / NERSC
Interpretable attention-based fire model for African and South American burned area reveals lagged climate-wetness controls and projects South American burned-area recovery under high emissions.
Extremes & hazardsLawrence Berkeley National Laboratory / NERSC
A GAN-enhanced FourCastNet better captures fine-scale structure and extreme global precipitation percentiles, matching NWP skill at 1-2 day leads.
Global medium-range NWPUniversity of California, BerkeleyLawrence Berkeley National Laboratory / NERSC
FourCastNet uses adaptive Fourier neural operators to produce global medium-range forecasts about 80,000 times faster than NWP, scaling to thousands of GPUs.
Software, scaling & compressionLawrence Berkeley National Laboratory / NERSCNVIDIA
A convolutional VAE pretrained on imperfect quasi-geostrophic simulations and transfer-learned on noisy observations gives stable stochastic long-term rollouts with accurate climatology.
Climate emulationLawrence Berkeley National Laboratory / NERSCNVIDIA
ML and causal inference reduce CMIP6 land model evaporative fraction biases at 64 FLUXNET sites, raising correlation from 0.47 to 0.80.
Climate analysis & attributionLawrence Berkeley National Laboratory / NERSC
A DNN surrogate replaces the process-based wildfire model in E3SM, matching observed burned area with R2 0.93 and far lower parameterization cost.
Hybrid physics-ML & parameterizationLawrence Berkeley National Laboratory / NERSC
FourCastNet uses adaptive Fourier neural operators to forecast global 0.25 degree weather in under 2 seconds, matching IFS at short leads and beating it for precipitation.
Global medium-range NWPCalifornia Institute of Technology (Caltech)Lawrence Berkeley National Laboratory / NERSCNVIDIA
Equivariance-preserving U-STN networks improve data-driven Z500 forecasts by 45% over U-Nets, and coupling them with ensemble Kalman data assimilation gives stable cycles under high observation noise.
Data assimilationLawrence Berkeley National Laboratory / NERSC
Surveys physics-informed ML approaches with ten case studies for emulating, downscaling and forecasting weather and climate, reporting greater consistency and data efficiency.
Explainability, uncertainty & physicsCalifornia Institute of Technology (Caltech)Lawrence Berkeley National Laboratory / NERSCNVIDIAStanford UniversityUniversity of California, Berkeley
ClimateNet releases an expert-labeled dataset of tropical cyclones and atmospheric rivers in CAM5.1 output, and a deep learning segmentation model that enables pixel-level extreme event analysis.
Extremes & hazardsColorado State University (incl. CIRA)ETH ZurichLawrence Berkeley National Laboratory / NERSCNSF NCAR / UCARNVIDIAUniversity of California, Berkeley
Fusing rain radar images with NWP wind forecasts in a deep network improves 30-minute rain nowcasts, scoring 8% higher F1 than optical flow and 7% higher than radar-only.
Ensemble Kalman filter estimates model error from sparse noisy observations to train a neural parametrisation, yielding hybrid forecasts with better skill on Lorenz and a coupled ocean-atmosphere model.
Data assimilationLawrence Berkeley National Laboratory / NERSCUniversity of Reading
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
Covariance-constrained GAN emulates Rayleigh-Bénard convection, an idealized atmosphere-like turbulent flow, as a closure model and cuts training time by up to 80%.
Hybrid physics-ML & parameterizationLawrence Berkeley National Laboratory / NERSC
Nature review on deep learning for data-driven Earth system science, covering weather, climate and process understanding; abstract absent, judged from title and venue.
Climate analysis & attributionLawrence Berkeley National Laboratory / NERSCMax Planck Institute for BiogeochemistryMax Planck Institute for Meteorology
Physics-informed generative learning is proposed to emulate unresolved climate-model physics; the abstract is missing, so details are limited.
Hybrid physics-ML & parameterizationLawrence Berkeley National Laboratory / NERSC
Tiramisu and DeepLabv3+ segment extreme weather patterns and scale to 27,360 V100 GPUs at 325.8 PF/s sustained throughput.
Software, scaling & compressionLawrence Berkeley National Laboratory / NERSCNVIDIAOak Ridge National Laboratory
Abstract missing; applies topological data analysis with machine learning to classify atmospheric river patterns in large climate datasets.
Climate analysis & attributionLawrence Berkeley National Laboratory / NERSC
Introduces ExtremeWeather, a labeled climate dataset and semi-supervised spatiotemporal CNN for localizing hurricanes, extratropical cyclones and fronts in simulations.
Extremes & hazardsLawrence Berkeley National Laboratory / NERSCcode
