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dc.contributor.author
Zumwald, Marius
dc.contributor.author
Knüsel, Benedikt
dc.contributor.author
Baumberger, Christoph
dc.contributor.author
Hirsch Hadorn, Gertrude
dc.contributor.author
Bresch, David N.
dc.contributor.author
Knutti, Reto
dc.date.accessioned
2020-09-15T11:00:56Z
dc.date.available
2020-06-13T05:18:32Z
dc.date.available
2020-06-16T11:00:57Z
dc.date.available
2020-09-15T11:00:56Z
dc.date.issued
2020-09
dc.identifier.issn
1757-7780
dc.identifier.issn
1757-7799
dc.identifier.other
10.1002/wcc.654
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/420193
dc.identifier.doi
10.3929/ethz-b-000420193
dc.description.abstract
In climate science, observational gridded climate datasets that are based on in situ measurements serve as evidence for scientific claims and they are used to both calibrate and evaluate models. However, datasets only represent selected aspects of the real world, so when they are used for a specific purpose they can be a source of uncertainty. Here, we present a framework for understanding this uncertainty of observational datasets which distinguishes three general sources of uncertainty: (1) uncertainty that arises during the generation of the dataset; (2) uncertainty due to biased samples; and (3) uncertainty that arises due to the choice of abstract properties, such as resolution and metric. Based on this framework, we identify four different types of dataset ensembles—parametric, structural, resampling, and property ensembles—as tools to understand and assess uncertainties arising from the use of datasets for a specific purpose. We advocate for a more systematic generation of dataset ensembles by using these sorts of tools. Finally, we discuss the use of dataset ensembles in climate model evaluation. We argue that a more systematic understanding and assessment of dataset uncertainty is needed to allow for a more reliable uncertainty assessment in the context of model evaluation. The more systematic use of such a framework would be beneficial for both scientific reasoning and scientific policy advice based on climate datasets.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
Wiley
en_US
dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
dc.subject
climate datasets
en_US
dc.subject
dataset ensembles
en_US
dc.subject
framework
en_US
dc.subject
model evaluation
en_US
dc.subject
uncertainty
en_US
dc.title
Understanding and assessing uncertainty of observational climate datasets for model evaluation using ensembles
en_US
dc.type
Review Article
dc.rights.license
Creative Commons Attribution 4.0 International
dc.date.published
2020-05-31
ethz.journal.title
WIREs Climate Change
ethz.journal.volume
11
en_US
ethz.journal.issue
5
en_US
ethz.pages.start
e654
en_US
ethz.size
19 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.identifier.wos
ethz.identifier.scopus
ethz.publication.place
Hoboken, NJ
en_US
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::02723 - Institut für Umweltentscheidungen / Institute for Environmental Decisions::09576 - Bresch, David Niklaus / Bresch, David Niklaus
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.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02350 - Dep. Umweltsystemwissenschaften / Dep. of Environmental Systems Science::02723 - Institut für Umweltentscheidungen / Institute for Environmental Decisions::09576 - Bresch, David Niklaus / Bresch, David Niklaus
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
ethz.grant.agreementno
167215
ethz.grant.fundername
SNF
ethz.grant.funderDoi
10.13039/501100001711
ethz.grant.program
NFP 75: Gesuch
ethz.date.deposited
2020-06-13T05:18:36Z
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2020-09-15T11:01:08Z
ethz.rosetta.lastUpdated
2021-02-15T17:15:11Z
ethz.rosetta.versionExported
true
ethz.COinS
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