The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Common Framework for Approaches to Extreme Event Attribution
Open accessAbstract
The extent to which a given extreme weather or climate event is attributable to anthropogenic climate change is a question of considerable public interest. From a scientific perspective, the question can be framed in various ways, and the answer depends very much on the framing. One such framing is a risk-based approach, which answers the question probabilistically, in terms of a change in likelihood of a class of event similar to the one in question, and natural variability is treated as noise. A rather different framing is a storyline approach, which examines the role of the various factors contributing to the event as it unfolded, including the anomalous aspects of natural variability, and answers the question deterministically. It is argued that these two apparently irreconcilable approaches can be viewed within a common framework, where the most useful level of conditioning will depend on the question being asked and the uncertainties involved.
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Cites
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BackgroundResultInfluentialAttribution of climate extreme events
Trenberth, K. E., Fasullo, J. T., & Shepherd, T. G. (2015) · Nature Climate Change · doi:10.1038/nclimate2657 · Cited by 982
Self-citationClimate model genealogy: Generation CMIP5 and how we got there
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How climate change affects extreme weather events
Stott, P. (2016) · Science · doi:10.1126/science.aaf7271 · Cited by 940
Machine learning and artificial intelligence to aid climate change research and preparedness
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Heat Waves: Physical Understanding and Scientific Challenges
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Self-citationA protocol for probabilistic extreme event attribution analyses
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Metadata
- Authors
- Shepherd, T. G.
- Venue
- Current Climate Change Reports
- Published
- 2016-02-18
- Volume
- 2
- Issue
- 1
- Pages
- 28-38
- ISSN
- 2198-6061 (ISSN-L)
- OpenAlex
- W2286268161
- Cited by
- 273
- References
- 6
- FWCI
- 16.08
- Type
- journal article
- Language
- en
- Open access
- hybrid
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
