Detection and attribution of anthropogenic climate change
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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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- ↑ "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.