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This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.
Separating forced signal (due to anthropogenic climate change) from the "noise" due to natural climate variability has been a challenging task, given only one realization of observational record. Large ensemble simulations, where a given climate model is run multiple times with different initial conditions but identical radiative forcing, are one way of separating the anthropogenic signal from the total response (that is a combination of the natural and anthropogenic signals). ML methods provide another avenue for addressing this signal-to-noise problem, to aid with detecting the anthropogenic signal and attributing it to a given forcing. Statistical learning also allows detecting anthropogenic climate change from a single day.
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References[edit | edit source]
- "Detection and Attribution of Climate Change: from Global to Regional — IPCC". Retrieved 2021-01-25.
- Gillett, Nathan P.; Kirchmeier-Young, Megan; Ribes, Aurélien; Shiogama, Hideo; Hegerl, Gabriele C.; Knutti, Reto; Gastineau, Guillaume; John, Jasmin G.; Li, Lijuan; Nazarenko, Larissa; Rosenbloom, Nan (2021-01-18). "Constraining human contributions to observed warming since the pre-industrial period". Nature Climate Change: 1–6. doi:10.1038/s41558-020-00965-9. ISSN 1758-6798.
- "Multi-Model Large Ensemble Archive". www.cesm.ucar.edu. Retrieved 2021-01-25.
- Szekely,, Eniko; et al. (2020). "A direct approach to detection and attribution of climate change" (PDF). Explicit use of et al. in:
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- Barnes, Elizabeth A.; Hurrell, James W.; Ebert‐Uphoff, Imme; Anderson, Chuck; Anderson, David (2019). "Viewing Forced Climate Patterns Through an AI Lens". Geophysical Research Letters. 46 (22): 13389–13398. doi:10.1029/2019GL084944. ISSN 1944-8007.
- Sippel, Sebastian; Meinshausen, Nicolai; Fischer, Erich M.; Székely, Enikő; Knutti, Reto (2020). "Climate change now detectable from any single day of weather at global scale". Nature Climate Change. 10 (1): 35–41. doi:10.1038/s41558-019-0666-7. ISSN 1758-6798.