18 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
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
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
NowcastNet unifies advection physics with learned conditional generation for extreme precipitation nowcasts up to 3 hours, ranked first by meteorologists in 71% of cases.
NowcastingChina Meteorological Administration (CMA)Tsinghua University (NowcastNet)University of California, Berkeley
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
A deep learning atmospheric river tracker is applied to a high-resolution large-ensemble climate dataset and checked for consistency against established algorithms over the US West Coast.
Extremes & hazardsNVIDIANSF NCAR / UCARETH ZurichUniversity of OxfordUniversity of California, Berkeley
U-Net CNN post-processes West-WRF precipitation over the US West Coast, cutting RMSE 12.9–15.9% versus raw model at 1–4 day leads.
Post-processing & calibrationUniversity of California, Berkeley
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
A ConvLSTM treats GOES satellite-based solar irradiance nowcasting as next-frame prediction, reaching 120 W/m2 RMSE for North America up to 3 hours ahead without a GPU.
Benchmarks ESRGAN, EDSR and PhIREGAN for super-resolving NWP wind and solar fields, and releases an ML-ready dataset for renewable forecasting.
Downscaling & super-resolutionMassachusetts Institute of Technology (MIT)California Institute of Technology (Caltech)University of California, Berkeley
Generic overview of neural networks and big data for climate resilience, covering modeling, disaster response and resource management, with no new technical results.
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
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
Random forests show that climate seasonality metrics of precipitation and temperature predict 66-87% of global multi-year fire activity variability.
Climate analysis & attributionUniversity of California, Berkeley
