Relative detectability of greenhouse-gas and aerosol climate change signals View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1998-09

AUTHORS

T. M. L. Wigley, P. J. Jaumann, B. D. Santer, K. E. Taylor

ABSTRACT

The use of pattern correlations to compare observed temperature changes with predicted anthropogenic effects has greatly increased our confidence in the reality of these effects. Here we use synthetic observed data to determine the expected behavior of the pattern correlation statistic, R(t), and hence clarify some results obtained in previous studies. We show that, for the specific case considered here (near-surface temperature changes), even with a perfectly-known signal, expected values of R(t) currently should be only of order 0.3–0.5, as observed; that R(t) may show markedly non-linear variations in time; that the CO2-alone signal pattern should be difficult to detect today primarily because of data coverage deficiencies; and why the signal due to combined CO2-aerosol forcing is easier to detect than either the CO2-alone or aerosol-alone signals. Finally, we show that little is to be gained at present by searching for a time-dependent signal compared with a representative constant signal pattern. More... »

PAGES

781-790

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s003820050254

DOI

http://dx.doi.org/10.1007/s003820050254

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1052517600


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/2008/04", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Earth Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geology", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US", 
          "id": "http://www.grid.ac/institutes/grid.57828.30", 
          "name": [
            "National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wigley", 
        "givenName": "T. M. L.", 
        "id": "sg:person.016171504677.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171504677.21"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US", 
          "id": "http://www.grid.ac/institutes/grid.57828.30", 
          "name": [
            "National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jaumann", 
        "givenName": "P. J.", 
        "id": "sg:person.010154530306.63", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010154530306.63"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US", 
          "id": "http://www.grid.ac/institutes/grid.250008.f", 
          "name": [
            "Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Santer", 
        "givenName": "B. D.", 
        "id": "sg:person.01234767320.60", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234767320.60"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US", 
          "id": "http://www.grid.ac/institutes/grid.250008.f", 
          "name": [
            "Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Taylor", 
        "givenName": "K. E.", 
        "id": "sg:person.01041300062.84", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041300062.84"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1998-09", 
    "datePublishedReg": "1998-09-01", 
    "description": "Abstract\u2002The use of pattern correlations to compare observed temperature changes with predicted anthropogenic effects has greatly increased our confidence in the reality of these effects. Here we use synthetic observed data to determine the expected behavior of the pattern correlation statistic, R(t), and hence clarify some results obtained in previous studies. We show that, for the specific case considered here (near-surface temperature changes), even with a perfectly-known signal, expected values of R(t) currently should be only of order 0.3\u20130.5, as observed; that R(t) may show markedly non-linear variations in time; that the CO2-alone signal pattern should be difficult to detect today primarily because of data coverage deficiencies; and why the signal due to combined CO2-aerosol forcing is easier to detect than either the CO2-alone or aerosol-alone signals. Finally, we show that little is to be gained at present by searching for a time-dependent signal compared with a representative constant signal pattern.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s003820050254", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "11", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "14"
      }
    ], 
    "keywords": [
      "climate change signal", 
      "observed temperature changes", 
      "pattern correlation statistic", 
      "change signal", 
      "pattern correlation", 
      "anthropogenic effects", 
      "observed data", 
      "temperature changes", 
      "order 0.3", 
      "forcing", 
      "correlation statistics", 
      "non-linear variation", 
      "previous studies", 
      "patterns", 
      "variation", 
      "time-dependent signals", 
      "changes", 
      "signals", 
      "data", 
      "relative detectability", 
      "correlation", 
      "today", 
      "confidence", 
      "values", 
      "detectability", 
      "specific case", 
      "time", 
      "statistics", 
      "results", 
      "effect", 
      "study", 
      "use", 
      "behavior", 
      "signal patterns", 
      "cases", 
      "deficiency", 
      "reality", 
      "coverage deficiency"
    ], 
    "name": "Relative detectability of greenhouse-gas and aerosol climate change signals", 
    "pagination": "781-790", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1052517600"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s003820050254"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s003820050254", 
      "https://app.dimensions.ai/details/publication/pub.1052517600"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_271.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s003820050254"
  }
]
 

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/s003820050254'

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/s003820050254'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s003820050254'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s003820050254'


 

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

119 TRIPLES      20 PREDICATES      63 URIs      55 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s003820050254 schema:about anzsrc-for:04
2 anzsrc-for:0403
3 schema:author N4b60019afb544ff9b8e7fe29bb3337e2
4 schema:datePublished 1998-09
5 schema:datePublishedReg 1998-09-01
6 schema:description Abstract The use of pattern correlations to compare observed temperature changes with predicted anthropogenic effects has greatly increased our confidence in the reality of these effects. Here we use synthetic observed data to determine the expected behavior of the pattern correlation statistic, R(t), and hence clarify some results obtained in previous studies. We show that, for the specific case considered here (near-surface temperature changes), even with a perfectly-known signal, expected values of R(t) currently should be only of order 0.3–0.5, as observed; that R(t) may show markedly non-linear variations in time; that the CO2-alone signal pattern should be difficult to detect today primarily because of data coverage deficiencies; and why the signal due to combined CO2-aerosol forcing is easier to detect than either the CO2-alone or aerosol-alone signals. Finally, we show that little is to be gained at present by searching for a time-dependent signal compared with a representative constant signal pattern.
7 schema:genre article
8 schema:isAccessibleForFree false
9 schema:isPartOf N20eb75252ed34b63837f1ee71490610b
10 N9b0507969fe74eed8b7b542ca008396b
11 sg:journal.1049631
12 schema:keywords anthropogenic effects
13 behavior
14 cases
15 change signal
16 changes
17 climate change signal
18 confidence
19 correlation
20 correlation statistics
21 coverage deficiency
22 data
23 deficiency
24 detectability
25 effect
26 forcing
27 non-linear variation
28 observed data
29 observed temperature changes
30 order 0.3
31 pattern correlation
32 pattern correlation statistic
33 patterns
34 previous studies
35 reality
36 relative detectability
37 results
38 signal patterns
39 signals
40 specific case
41 statistics
42 study
43 temperature changes
44 time
45 time-dependent signals
46 today
47 use
48 values
49 variation
50 schema:name Relative detectability of greenhouse-gas and aerosol climate change signals
51 schema:pagination 781-790
52 schema:productId N15ac9e2c2a7b41e7a022b32e4000ea5a
53 N84a74d5bb88a46699f60088e9d088d1f
54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052517600
55 https://doi.org/10.1007/s003820050254
56 schema:sdDatePublished 2022-12-01T06:21
57 schema:sdLicense https://scigraph.springernature.com/explorer/license/
58 schema:sdPublisher N84f9382a290746ab9c4120076c679614
59 schema:url https://doi.org/10.1007/s003820050254
60 sgo:license sg:explorer/license/
61 sgo:sdDataset articles
62 rdf:type schema:ScholarlyArticle
63 N15ac9e2c2a7b41e7a022b32e4000ea5a schema:name doi
64 schema:value 10.1007/s003820050254
65 rdf:type schema:PropertyValue
66 N20eb75252ed34b63837f1ee71490610b schema:volumeNumber 14
67 rdf:type schema:PublicationVolume
68 N4b60019afb544ff9b8e7fe29bb3337e2 rdf:first sg:person.016171504677.21
69 rdf:rest Nfcde015cb1734a0299c9cd89845915c2
70 N5b2beb228fd54c55ad3c0dc0dc48d370 rdf:first sg:person.01041300062.84
71 rdf:rest rdf:nil
72 N84a74d5bb88a46699f60088e9d088d1f schema:name dimensions_id
73 schema:value pub.1052517600
74 rdf:type schema:PropertyValue
75 N84f9382a290746ab9c4120076c679614 schema:name Springer Nature - SN SciGraph project
76 rdf:type schema:Organization
77 N9b0507969fe74eed8b7b542ca008396b schema:issueNumber 11
78 rdf:type schema:PublicationIssue
79 Nd00d689052c8493e998539712eecd51b rdf:first sg:person.01234767320.60
80 rdf:rest N5b2beb228fd54c55ad3c0dc0dc48d370
81 Nfcde015cb1734a0299c9cd89845915c2 rdf:first sg:person.010154530306.63
82 rdf:rest Nd00d689052c8493e998539712eecd51b
83 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
84 schema:name Earth Sciences
85 rdf:type schema:DefinedTerm
86 anzsrc-for:0403 schema:inDefinedTermSet anzsrc-for:
87 schema:name Geology
88 rdf:type schema:DefinedTerm
89 sg:journal.1049631 schema:issn 0930-7575
90 1432-0894
91 schema:name Climate Dynamics
92 schema:publisher Springer Nature
93 rdf:type schema:Periodical
94 sg:person.010154530306.63 schema:affiliation grid-institutes:grid.57828.30
95 schema:familyName Jaumann
96 schema:givenName P. J.
97 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010154530306.63
98 rdf:type schema:Person
99 sg:person.01041300062.84 schema:affiliation grid-institutes:grid.250008.f
100 schema:familyName Taylor
101 schema:givenName K. E.
102 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01041300062.84
103 rdf:type schema:Person
104 sg:person.01234767320.60 schema:affiliation grid-institutes:grid.250008.f
105 schema:familyName Santer
106 schema:givenName B. D.
107 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01234767320.60
108 rdf:type schema:Person
109 sg:person.016171504677.21 schema:affiliation grid-institutes:grid.57828.30
110 schema:familyName Wigley
111 schema:givenName T. M. L.
112 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171504677.21
113 rdf:type schema:Person
114 grid-institutes:grid.250008.f schema:alternateName Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US
115 schema:name Program for Climate Model Diagnosis and Intercomparison, Lawrence Livermore National Laboratory, Livermore, CA 94550, USA, US
116 rdf:type schema:Organization
117 grid-institutes:grid.57828.30 schema:alternateName National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US
118 schema:name National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO 80307-3000, USA, US
119 rdf:type schema:Organization
 




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


...