10 papers in the archive.
Nature Reviews review of deep learning's impact on weather and climate prediction; abstract absent, judged from title and venue.
Global medium-range NWPECMWFETH ZurichMax Planck Institute for MeteorologyUniversity of Oklahoma (AI2ES)
Position paper on Earth Virtualization Engines that pair high-resolution physics-based climate models with machine learning and federated access to exabyte-scale climate data.
Software, scaling & compressionCSCS - Swiss National Supercomputing CentreETH ZurichMeteoSwissMassachusetts Institute of Technology (MIT)Max Planck Institute for MeteorologyNSF NCAR / UCARNVIDIAUniversity of Reading
Explainable neural network on 100 MPI Grand Ensemble members identifies spring tripolar North Atlantic SST patterns as precursors of European summer heatwaves.
Climate analysis & attributionMax Planck Institute for MeteorologyColorado State University (incl. CIRA)
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 statistical GAM emulator predicts local surface temperature responses to tree cover change from ESM output, offering a cheap complement to greenhouse-gas emulators.
Climate emulationETH ZurichKNMI (Royal Netherlands Meteorological Institute)Max Planck Institute for BiogeochemistryMax Planck Institute for Meteorology
Supervised classifier trained on 18 observation and reanalysis datasets labels Central and Eastern Pacific El Niño types, then shows the observed CP increase lies within internal variability in large ensembles.
Climate analysis & attributionNSF NCAR / UCARMax Planck Institute for Meteorology
Self-organizing maps link North Atlantic spring SST to summer jet-stream teleconnections, identifying windows of opportunity for seasonal European summer predictions with MPI-ESM hindcasts.
Sub-seasonal to seasonalMax Planck Institute for Meteorology
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
Crowd-labelled about 50,000 shallow cloud clusters in satellite images to train deep learning that maps four organization patterns and builds global climatologies.
Observations & remote sensingMax Planck Institute for MeteorologyTechnical University of Munich (TUM)
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
