Robust detection of forced warming in the presence of potentially large climate variability
dc.contributor.author
Sippel, Sebastian
dc.contributor.author
Meinshausen, Nicolai
dc.contributor.author
Székely, Enikő
dc.contributor.author
Fischer, Erich
dc.contributor.author
Pendergrass, Angeline G.
dc.contributor.author
Lehner, Flavio
dc.contributor.author
Knutti, Reto
dc.date.accessioned
2021-11-03T06:48:38Z
dc.date.available
2021-11-02T16:42:59Z
dc.date.available
2021-11-03T06:48:38Z
dc.date.issued
2021-10
dc.identifier.issn
2375-2548
dc.identifier.other
10.1126/sciadv.abh4429
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/513307
dc.identifier.doi
10.3929/ethz-b-000513307
dc.description.abstract
Climate warming is unequivocal and exceeds internal climate variability. However, estimates of the magnitude of decadal-scale variability from models and observations are uncertain, limiting determination of the fraction of warming attributable to external forcing. Here, we use statistical learning to extract a fingerprint of climate change that is robust to different model representations and magnitudes of internal variability. We find a best estimate forced warming trend of 0.8°C over the past 40 years, slightly larger than observed. It is extremely likely that at least 85% is attributable to external forcing based on the median variability across climate models. Detection remains robust even when evaluated against models with high variability and if decadal-scale variability were doubled. This work addresses a long-standing limitation in attributing warming to external forcing and opens up opportunities even in the case of large model differences in decadal-scale variability, model structural uncertainty, and limited observational records.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
AAAS
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.title
Robust detection of forced warming in the presence of potentially large climate variability
en_US
dc.type
Journal Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2021-10-22
ethz.journal.title
Science Advances
ethz.journal.volume
7
en_US
ethz.journal.issue
43
en_US
ethz.journal.abbreviated
Sci Adv
ethz.pages.start
eabh4429
en_US
ethz.size
17 p.
en_US
ethz.version.deposit
publishedVersion
en_US
ethz.grant
Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts
en_US
ethz.grant
Extreme Events: Artificial Intelligence for Detection and Attribution
en_US
ethz.grant
Constraining dynamic and thermodynamic drivers of mid-term regional climate change projections for Northern mid-latitudes
en_US
ethz.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Washington, DC
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03777 - Knutti, Reto / Knutti, Reto
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03990 - Meinshausen, Nicolai / Meinshausen, Nicolai
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02717 - Institut für Atmosphäre und Klima / Inst. Atmospheric and Climate Science::03777 - Knutti, Reto / Knutti, Reto
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02000 - Dep. Mathematik / Dep. of Mathematics::02537 - Seminar für Statistik (SfS) / Seminar for Statistics (SfS)::03990 - Meinshausen, Nicolai / Meinshausen, Nicolai
ethz.grant.agreementno
167215
ethz.grant.agreementno
101003469
ethz.grant.agreementno
174128
ethz.grant.fundername
SNF
ethz.grant.fundername
EC
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.funderDoi
10.13039/501100000780
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NFP 75: Gesuch
ethz.grant.program
H2020
ethz.grant.program
Ambizione
ethz.date.deposited
2021-11-02T16:43:08Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2021-11-03T06:48:45Z
ethz.rosetta.lastUpdated
2024-02-02T15:18:11Z
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true
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