<div class="csl-bib-body">
<div class="csl-entry">Povey, A. C., Bulgin, C. E., & Gruber, A. (2025). A practical introduction to utilising uncertainty information in the analysis of essential climate variables. <i>Surveys in Geophysics</i>. https://doi.org/10.1007/s10712-025-09906-7</div>
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dc.identifier.issn
0169-3298
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/220252
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dc.description.abstract
An estimate of uncertainty is essential to understanding what information is conveyed by data and how it relates to the wider context of what one intended to measure. It can be difficult to know how to use uncertainty during the analysis of environmental data and the best way to present that information within a dataset. In many common uses, such as calculating statistical significance, it is easy to make mistakes due to incomplete or inappropriate use of the available uncertainty information. Uncertainty is itself uncertain, such that many practical or empirical solutions are available when a comprehensive uncertainty budget is impractical to produce. This manuscript collects actionable guidance on how uncertainty can be used, presented, and calculated when working with essential climate variables (ECVs). This includes qualitative discussions of the utility of uncertainties, explanations of common misconceptions, advice on presentation style, and plain descriptions of the essential equations. Selected worked examples are included on the propagation of uncertainties, particularly for data aggregation and merging. Uncertainty need not be off-putting as even incomplete uncertainty budgets add value to any observation. This paper aims to provide a starting point, or refresher, for researchers in the environmental sciences to make more complete use of uncertainty in their work.
en
dc.language.iso
en
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dc.publisher
SPRINGER
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dc.relation.ispartof
Surveys in Geophysics
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dc.rights.uri
http://creativecommons.org/licenses/by/4.0/
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dc.subject
Uncertainty
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dc.subject
Essential climate variables
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dc.subject
Dataset aggregation
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dc.subject
Representativeness
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dc.subject
Validation
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dc.subject
Data assimilation
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dc.title
A practical introduction to utilising uncertainty information in the analysis of essential climate variables
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dc.type
Article
en
dc.type
Artikel
de
dc.rights.license
Creative Commons Namensnennung 4.0 International
de
dc.rights.license
Creative Commons Attribution 4.0 International
en
dc.contributor.affiliation
University of Leicester, United Kingdom of Great Britain and Northern Ireland (the)
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dc.contributor.affiliation
University of Reading, United Kingdom of Great Britain and Northern Ireland (the)