Is the choice of statistical paradigm critical in extreme event attribution studies? View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2017-08-28

AUTHORS

Peter A. Stott, David J. Karoly, Francis W. Zwiers

ABSTRACT

The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between “conventional” approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence. More... »

PAGES

143-150

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10584-017-2049-2

DOI

http://dx.doi.org/10.1007/s10584-017-2049-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1091409451


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0104", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Statistics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Met Office Hadley Centre, Fitzroy Road, EX1 3PB, Exeter, UK", 
          "id": "http://www.grid.ac/institutes/grid.17100.37", 
          "name": [
            "College of Engineering, Mathematics and Physical Sciences, University of Exeter, North Park Road, EX4 4QF, Exeter, UK", 
            "Met Office Hadley Centre, Fitzroy Road, EX1 3PB, Exeter, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stott", 
        "givenName": "Peter A.", 
        "id": "sg:person.015667030077.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015667030077.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "School of Earth Sciences and ARC Centre of Excellence for Climate System Science, University of Melbourne, 3010, Melbourne, Australia", 
          "id": "http://www.grid.ac/institutes/grid.1008.9", 
          "name": [
            "Environmental Change Institute, Oxford Martin School, Oxford, UK", 
            "School of Earth Sciences and ARC Centre of Excellence for Climate System Science, University of Melbourne, 3010, Melbourne, Australia"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Karoly", 
        "givenName": "David J.", 
        "id": "sg:person.01134215130.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134215130.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Pacific Climate Impacts Consortium, University of Victoria, University House, Stn CSC, 1700, V8W 2Y2, Victoria, British Columbia, Canada", 
          "id": "http://www.grid.ac/institutes/grid.143640.4", 
          "name": [
            "Pacific Climate Impacts Consortium, University of Victoria, University House, Stn CSC, 1700, V8W 2Y2, Victoria, British Columbia, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zwiers", 
        "givenName": "Francis W.", 
        "id": "sg:person.0603650500.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603650500.31"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00382-018-4183-6", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1103475514", 
          "https://doi.org/10.1007/s00382-018-4183-6"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-017-2048-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1091408365", 
          "https://doi.org/10.1007/s10584-017-2048-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/421891a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1003018692", 
          "https://doi.org/10.1038/421891a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate3287", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085411043", 
          "https://doi.org/10.1038/nclimate3287"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate3089", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016220395", 
          "https://doi.org/10.1038/nclimate3089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate2657", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021001021", 
          "https://doi.org/10.1038/nclimate2657"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ngeo2201", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024084910", 
          "https://doi.org/10.1038/ngeo2201"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03089", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028450143", 
          "https://doi.org/10.1038/nature03089"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s40641-016-0033-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1017429140", 
          "https://doi.org/10.1007/s40641-016-0033-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature09762", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043530920", 
          "https://doi.org/10.1038/nature09762"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/978-1-4899-2887-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1109705820", 
          "https://doi.org/10.1007/978-1-4899-2887-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-013-0705-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038008793", 
          "https://doi.org/10.1007/s10584-013-0705-8"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2017-08-28", 
    "datePublishedReg": "2017-08-28", 
    "description": "The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between \u201cconventional\u201d approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10584-017-2049-2", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7037241", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1028211", 
        "issn": [
          "0165-0009", 
          "1573-1480"
        ], 
        "name": "Climatic Change", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "144"
      }
    ], 
    "keywords": [
      "event attribution studies", 
      "statistical paradigm", 
      "usual null hypothesis", 
      "Bayesian paradigm", 
      "estimation problem", 
      "null hypothesis", 
      "event attribution", 
      "hypothesis testing", 
      "frequentist", 
      "extreme events", 
      "attribution studies", 
      "such approaches", 
      "different approaches", 
      "approach", 
      "problem", 
      "choice", 
      "types of events", 
      "such choices", 
      "point", 
      "likelihood", 
      "paradigm", 
      "science", 
      "link", 
      "demand", 
      "alternative", 
      "information", 
      "utility", 
      "users", 
      "values", 
      "context", 
      "types", 
      "dichotomy", 
      "timely information", 
      "conditioning", 
      "preferable alternative", 
      "contribution", 
      "hypothesis", 
      "magnitude", 
      "progress", 
      "floods", 
      "spectra", 
      "influence", 
      "events", 
      "study", 
      "different places", 
      "climate change", 
      "testing", 
      "human influence", 
      "place", 
      "changes", 
      "criticism", 
      "alternative framing", 
      "attribution", 
      "such criticism", 
      "human-induced climate change", 
      "heatwaves", 
      "framing", 
      "false dichotomy", 
      "drought", 
      "individual extreme events", 
      "new alternative framings", 
      "conditioning spectrum", 
      "choice of conditioning", 
      "extreme event attribution studies"
    ], 
    "name": "Is the choice of statistical paradigm critical in extreme event attribution studies?", 
    "pagination": "143-150", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1091409451"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10584-017-2049-2"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10584-017-2049-2", 
      "https://app.dimensions.ai/details/publication/pub.1091409451"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-11-01T18:30", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/article/article_736.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10584-017-2049-2"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10584-017-2049-2'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10584-017-2049-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10584-017-2049-2'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10584-017-2049-2'


 

This table displays all metadata directly associated to this object as RDF triples.

194 TRIPLES      22 PREDICATES      101 URIs      81 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10584-017-2049-2 schema:about anzsrc-for:01
2 anzsrc-for:0104
3 schema:author Ndfefe5a8f84948b1b3e1952012b4976d
4 schema:citation sg:pub.10.1007/978-1-4899-2887-0
5 sg:pub.10.1007/s00382-018-4183-6
6 sg:pub.10.1007/s10584-013-0705-8
7 sg:pub.10.1007/s10584-017-2048-3
8 sg:pub.10.1007/s40641-016-0033-y
9 sg:pub.10.1038/421891a
10 sg:pub.10.1038/nature03089
11 sg:pub.10.1038/nature09762
12 sg:pub.10.1038/nclimate2657
13 sg:pub.10.1038/nclimate3089
14 sg:pub.10.1038/nclimate3287
15 sg:pub.10.1038/ngeo2201
16 schema:datePublished 2017-08-28
17 schema:datePublishedReg 2017-08-28
18 schema:description The science of event attribution meets a mounting demand for reliable and timely information about the links between climate change and individual extreme events. Studies have estimated the contribution of human-induced climate change to the magnitude of an event as well as its likelihood, and many types of event have been investigated including heatwaves, floods, and droughts. Despite this progress, such approaches have been criticised for being unreliable and for being overly conservative. We argue that such criticisms are misplaced. Rather, a false dichotomy has arisen between “conventional” approaches and new alternative framings. We have three points to make about the choice of statistical paradigm for event attribution studies. First, different approaches to event attribution may choose to occupy different places on the conditioning spectrum. Providing this choice of conditioning is communicated clearly, the value of such choices depends ultimately on their utility to the user concerned. Second, event attribution is an estimation problem for which either frequentist or Bayesian paradigms can be used. Third, for hypothesis testing, the choice of null hypothesis is context specific. Thus, the null hypothesis of human influence is not inherently a preferable alternative to the usual null hypothesis of no human influence.
19 schema:genre article
20 schema:inLanguage en
21 schema:isAccessibleForFree true
22 schema:isPartOf N5a6cedcc4f8d47f585402f30abd73ccd
23 Nb157bfea082b497fb09b9e83f1125890
24 sg:journal.1028211
25 schema:keywords Bayesian paradigm
26 alternative
27 alternative framing
28 approach
29 attribution
30 attribution studies
31 changes
32 choice
33 choice of conditioning
34 climate change
35 conditioning
36 conditioning spectrum
37 context
38 contribution
39 criticism
40 demand
41 dichotomy
42 different approaches
43 different places
44 drought
45 estimation problem
46 event attribution
47 event attribution studies
48 events
49 extreme event attribution studies
50 extreme events
51 false dichotomy
52 floods
53 framing
54 frequentist
55 heatwaves
56 human influence
57 human-induced climate change
58 hypothesis
59 hypothesis testing
60 individual extreme events
61 influence
62 information
63 likelihood
64 link
65 magnitude
66 new alternative framings
67 null hypothesis
68 paradigm
69 place
70 point
71 preferable alternative
72 problem
73 progress
74 science
75 spectra
76 statistical paradigm
77 study
78 such approaches
79 such choices
80 such criticism
81 testing
82 timely information
83 types
84 types of events
85 users
86 usual null hypothesis
87 utility
88 values
89 schema:name Is the choice of statistical paradigm critical in extreme event attribution studies?
90 schema:pagination 143-150
91 schema:productId N81c91d745f5b46c68d75c361b3201d49
92 Nb201beb006194b6890f4870c77d83eb0
93 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091409451
94 https://doi.org/10.1007/s10584-017-2049-2
95 schema:sdDatePublished 2021-11-01T18:30
96 schema:sdLicense https://scigraph.springernature.com/explorer/license/
97 schema:sdPublisher N92ba16e106284c7bbbf7bd7cecd2a2b9
98 schema:url https://doi.org/10.1007/s10584-017-2049-2
99 sgo:license sg:explorer/license/
100 sgo:sdDataset articles
101 rdf:type schema:ScholarlyArticle
102 N0ccb8ca9b8594fcab2464eda420ecda6 rdf:first sg:person.0603650500.31
103 rdf:rest rdf:nil
104 N5a6cedcc4f8d47f585402f30abd73ccd schema:volumeNumber 144
105 rdf:type schema:PublicationVolume
106 N6c87224138f34720b560ed1988fe476f rdf:first sg:person.01134215130.17
107 rdf:rest N0ccb8ca9b8594fcab2464eda420ecda6
108 N81c91d745f5b46c68d75c361b3201d49 schema:name doi
109 schema:value 10.1007/s10584-017-2049-2
110 rdf:type schema:PropertyValue
111 N92ba16e106284c7bbbf7bd7cecd2a2b9 schema:name Springer Nature - SN SciGraph project
112 rdf:type schema:Organization
113 Nb157bfea082b497fb09b9e83f1125890 schema:issueNumber 2
114 rdf:type schema:PublicationIssue
115 Nb201beb006194b6890f4870c77d83eb0 schema:name dimensions_id
116 schema:value pub.1091409451
117 rdf:type schema:PropertyValue
118 Ndfefe5a8f84948b1b3e1952012b4976d rdf:first sg:person.015667030077.29
119 rdf:rest N6c87224138f34720b560ed1988fe476f
120 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
121 schema:name Mathematical Sciences
122 rdf:type schema:DefinedTerm
123 anzsrc-for:0104 schema:inDefinedTermSet anzsrc-for:
124 schema:name Statistics
125 rdf:type schema:DefinedTerm
126 sg:grant.7037241 http://pending.schema.org/fundedItem sg:pub.10.1007/s10584-017-2049-2
127 rdf:type schema:MonetaryGrant
128 sg:journal.1028211 schema:issn 0165-0009
129 1573-1480
130 schema:name Climatic Change
131 schema:publisher Springer Nature
132 rdf:type schema:Periodical
133 sg:person.01134215130.17 schema:affiliation grid-institutes:grid.1008.9
134 schema:familyName Karoly
135 schema:givenName David J.
136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01134215130.17
137 rdf:type schema:Person
138 sg:person.015667030077.29 schema:affiliation grid-institutes:grid.17100.37
139 schema:familyName Stott
140 schema:givenName Peter A.
141 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015667030077.29
142 rdf:type schema:Person
143 sg:person.0603650500.31 schema:affiliation grid-institutes:grid.143640.4
144 schema:familyName Zwiers
145 schema:givenName Francis W.
146 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0603650500.31
147 rdf:type schema:Person
148 sg:pub.10.1007/978-1-4899-2887-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1109705820
149 https://doi.org/10.1007/978-1-4899-2887-0
150 rdf:type schema:CreativeWork
151 sg:pub.10.1007/s00382-018-4183-6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103475514
152 https://doi.org/10.1007/s00382-018-4183-6
153 rdf:type schema:CreativeWork
154 sg:pub.10.1007/s10584-013-0705-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038008793
155 https://doi.org/10.1007/s10584-013-0705-8
156 rdf:type schema:CreativeWork
157 sg:pub.10.1007/s10584-017-2048-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091408365
158 https://doi.org/10.1007/s10584-017-2048-3
159 rdf:type schema:CreativeWork
160 sg:pub.10.1007/s40641-016-0033-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1017429140
161 https://doi.org/10.1007/s40641-016-0033-y
162 rdf:type schema:CreativeWork
163 sg:pub.10.1038/421891a schema:sameAs https://app.dimensions.ai/details/publication/pub.1003018692
164 https://doi.org/10.1038/421891a
165 rdf:type schema:CreativeWork
166 sg:pub.10.1038/nature03089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028450143
167 https://doi.org/10.1038/nature03089
168 rdf:type schema:CreativeWork
169 sg:pub.10.1038/nature09762 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043530920
170 https://doi.org/10.1038/nature09762
171 rdf:type schema:CreativeWork
172 sg:pub.10.1038/nclimate2657 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021001021
173 https://doi.org/10.1038/nclimate2657
174 rdf:type schema:CreativeWork
175 sg:pub.10.1038/nclimate3089 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016220395
176 https://doi.org/10.1038/nclimate3089
177 rdf:type schema:CreativeWork
178 sg:pub.10.1038/nclimate3287 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085411043
179 https://doi.org/10.1038/nclimate3287
180 rdf:type schema:CreativeWork
181 sg:pub.10.1038/ngeo2201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024084910
182 https://doi.org/10.1038/ngeo2201
183 rdf:type schema:CreativeWork
184 grid-institutes:grid.1008.9 schema:alternateName School of Earth Sciences and ARC Centre of Excellence for Climate System Science, University of Melbourne, 3010, Melbourne, Australia
185 schema:name Environmental Change Institute, Oxford Martin School, Oxford, UK
186 School of Earth Sciences and ARC Centre of Excellence for Climate System Science, University of Melbourne, 3010, Melbourne, Australia
187 rdf:type schema:Organization
188 grid-institutes:grid.143640.4 schema:alternateName Pacific Climate Impacts Consortium, University of Victoria, University House, Stn CSC, 1700, V8W 2Y2, Victoria, British Columbia, Canada
189 schema:name Pacific Climate Impacts Consortium, University of Victoria, University House, Stn CSC, 1700, V8W 2Y2, Victoria, British Columbia, Canada
190 rdf:type schema:Organization
191 grid-institutes:grid.17100.37 schema:alternateName Met Office Hadley Centre, Fitzroy Road, EX1 3PB, Exeter, UK
192 schema:name College of Engineering, Mathematics and Physical Sciences, University of Exeter, North Park Road, EX4 4QF, Exeter, UK
193 Met Office Hadley Centre, Fitzroy Road, EX1 3PB, Exeter, UK
194 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...