Pushing the frontiers in climate modelling and analysis with machine learning
Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fideli...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2024
|
| _version_ | 1811140926290001920 |
|---|---|
| author | Eyring, V Collins, WD Gentine, P Barnes, EA Barreiro, M Beucler, T Bocquet, M Bretherton, CS Christensen, HM Dagon, K Gagne, DJ Hall, D Hammerling, D Hoyer, S Iglesias-Suarez, F Lopez-Gomez, I McGraw, MC Meehl, GA Molina, MJ Monteleoni, C Mueller, J Pritchard, MS Rolnick, D Runge, J Stier, P Watt-Meyer, O Weigel, K Yu, R Zanna, L |
| author_facet | Eyring, V Collins, WD Gentine, P Barnes, EA Barreiro, M Beucler, T Bocquet, M Bretherton, CS Christensen, HM Dagon, K Gagne, DJ Hall, D Hammerling, D Hoyer, S Iglesias-Suarez, F Lopez-Gomez, I McGraw, MC Meehl, GA Molina, MJ Monteleoni, C Mueller, J Pritchard, MS Rolnick, D Runge, J Stier, P Watt-Meyer, O Weigel, K Yu, R Zanna, L |
| author_sort | Eyring, V |
| collection | OXFORD |
| description | Climate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science. |
| first_indexed | 2024-09-25T04:29:44Z |
| format | Journal article |
| id | oxford-uuid:425a0032-025d-4e47-89bc-51a829cdccf8 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:29:44Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:425a0032-025d-4e47-89bc-51a829cdccf82024-08-27T11:06:31ZPushing the frontiers in climate modelling and analysis with machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:425a0032-025d-4e47-89bc-51a829cdccf8EnglishSymplectic ElementsSpringer Nature2024Eyring, VCollins, WDGentine, PBarnes, EABarreiro, MBeucler, TBocquet, MBretherton, CSChristensen, HMDagon, KGagne, DJHall, DHammerling, DHoyer, SIglesias-Suarez, FLopez-Gomez, IMcGraw, MCMeehl, GAMolina, MJMonteleoni, CMueller, JPritchard, MSRolnick, DRunge, JStier, PWatt-Meyer, OWeigel, KYu, RZanna, LClimate modelling and analysis are facing new demands to enhance projections and climate information. Here we argue that now is the time to push the frontiers of machine learning beyond state-of-the-art approaches, not only by developing machine-learning-based Earth system models with greater fidelity, but also by providing new capabilities through emulators for extreme event projections with large ensembles, enhanced detection and attribution methods for extreme events, and advanced climate model analysis and benchmarking. Utilizing this potential requires key machine learning challenges to be addressed, in particular generalization, uncertainty quantification, explainable artificial intelligence and causality. This interdisciplinary effort requires bringing together machine learning and climate scientists, while also leveraging the private sector, to accelerate progress towards actionable climate science. |
| spellingShingle | Eyring, V Collins, WD Gentine, P Barnes, EA Barreiro, M Beucler, T Bocquet, M Bretherton, CS Christensen, HM Dagon, K Gagne, DJ Hall, D Hammerling, D Hoyer, S Iglesias-Suarez, F Lopez-Gomez, I McGraw, MC Meehl, GA Molina, MJ Monteleoni, C Mueller, J Pritchard, MS Rolnick, D Runge, J Stier, P Watt-Meyer, O Weigel, K Yu, R Zanna, L Pushing the frontiers in climate modelling and analysis with machine learning |
| title | Pushing the frontiers in climate modelling and analysis with machine learning |
| title_full | Pushing the frontiers in climate modelling and analysis with machine learning |
| title_fullStr | Pushing the frontiers in climate modelling and analysis with machine learning |
| title_full_unstemmed | Pushing the frontiers in climate modelling and analysis with machine learning |
| title_short | Pushing the frontiers in climate modelling and analysis with machine learning |
| title_sort | pushing the frontiers in climate modelling and analysis with machine learning |
| work_keys_str_mv | AT eyringv pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT collinswd pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT gentinep pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT barnesea pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT barreirom pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT beuclert pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT bocquetm pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT brethertoncs pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT christensenhm pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT dagonk pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT gagnedj pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT halld pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT hammerlingd pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT hoyers pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT iglesiassuarezf pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT lopezgomezi pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT mcgrawmc pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT meehlga pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT molinamj pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT monteleonic pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT muellerj pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT pritchardms pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT rolnickd pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT rungej pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT stierp pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT wattmeyero pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT weigelk pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT yur pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning AT zannal pushingthefrontiersinclimatemodellingandanalysiswithmachinelearning |