Climate spectra and detecting climate change View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1992-07

AUTHORS

Peter Bloomfield, Douglas Nychka

ABSTRACT

Part of the debate over possible climate changes centers on the possibility that the changes observed over the previous century are natural in origin. This raises the question of how large a change could be expected as a result of natural variability. If the climate measurement of interest is modelled as a stationary (or related) Gaussian time series, this question can be answered in terms of (a) the way in which change is estimated, and (b) the spectrum of the time series. These computations are illustrated for 128 years of global temperature data using some simple measures of change and for a variety of possible temperature spectra. The results highlight the time scales on which it is important to know the magnitude of natural variability. The uncertainties in estimates of trend are most sensitive to fluctuations in the temperature series with periods from approximately 50 to 500 years. For some of the temperature spectra, it was found that the standard error of the least squares trend estimate was 3 times the standard error derived under the naïve assumption that the temperature series was uncorrelated. The observed trend differs from zero by more than 3 times the largest of the calculated standard errors, however, and is therefore highly significant. More... »

PAGES

275-287

Journal

TITLE

Climatic Change

ISSUE

3

VOLUME

21

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf00139727

DOI

http://dx.doi.org/10.1007/bf00139727

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1024569108


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/2008/1403", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Econometrics", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/14", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Economics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "North Carolina State University", 
          "id": "https://www.grid.ac/institutes/grid.40803.3f", 
          "name": [
            "Department of Statistics, North Carolina State University, 27695-8203, Raleigh, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Bloomfield", 
        "givenName": "Peter", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "North Carolina State University", 
          "id": "https://www.grid.ac/institutes/grid.40803.3f", 
          "name": [
            "Department of Statistics, North Carolina State University, 27695-8203, Raleigh, NC, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nychka", 
        "givenName": "Douglas", 
        "id": "sg:person.07745505663.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07745505663.08"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/344324a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000092146", 
          "https://doi.org/10.1038/344324a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00143250", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016077135", 
          "https://doi.org/10.1007/bf00143250"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00143250", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016077135", 
          "https://doi.org/10.1007/bf00143250"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/330127a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016835893", 
          "https://doi.org/10.1038/330127a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/jd092id11p13345", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1025791400"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(1988)001<0654:hsatvr>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1027056055"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0469(1974)031<1958:vsohcf>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1028588453"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/322430a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1032895113", 
          "https://doi.org/10.1038/322430a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1029/gl015i004p00323", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1041393140"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1986)025<1213:shsatv>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042758895"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00134658", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049977494", 
          "https://doi.org/10.1007/bf00134658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00134658", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049977494", 
          "https://doi.org/10.1007/bf00134658"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1986)025<0161:nhsatv>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051729171"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1093/biomet/68.1.165", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1059419039"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "1992-07", 
    "datePublishedReg": "1992-07-01", 
    "description": "Part of the debate over possible climate changes centers on the possibility that the changes observed over the previous century are natural in origin. This raises the question of how large a change could be expected as a result of natural variability. If the climate measurement of interest is modelled as a stationary (or related) Gaussian time series, this question can be answered in terms of (a) the way in which change is estimated, and (b) the spectrum of the time series. These computations are illustrated for 128 years of global temperature data using some simple measures of change and for a variety of possible temperature spectra. The results highlight the time scales on which it is important to know the magnitude of natural variability. The uncertainties in estimates of trend are most sensitive to fluctuations in the temperature series with periods from approximately 50 to 500 years. For some of the temperature spectra, it was found that the standard error of the least squares trend estimate was 3 times the standard error derived under the na\u00efve assumption that the temperature series was uncorrelated. The observed trend differs from zero by more than 3 times the largest of the calculated standard errors, however, and is therefore highly significant.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf00139727", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1028211", 
        "issn": [
          "0165-0009", 
          "1573-1480"
        ], 
        "name": "Climatic Change", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "21"
      }
    ], 
    "name": "Climate spectra and detecting climate change", 
    "pagination": "275-287", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "750c1c8ddbf6a72d661f54bf43769d0023de3b4e3f09ba575d572ba7c002f06e"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf00139727"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1024569108"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf00139727", 
      "https://app.dimensions.ai/details/publication/pub.1024569108"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:48", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000371_0000000371/records_130793_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF00139727"
  }
]
 

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/bf00139727'

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/bf00139727'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf00139727'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf00139727'


 

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

108 TRIPLES      21 PREDICATES      39 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf00139727 schema:about anzsrc-for:14
2 anzsrc-for:1403
3 schema:author Nb36714ac601642b7a885f39cdceb5b51
4 schema:citation sg:pub.10.1007/bf00134658
5 sg:pub.10.1007/bf00143250
6 sg:pub.10.1038/322430a0
7 sg:pub.10.1038/330127a0
8 sg:pub.10.1038/344324a0
9 https://doi.org/10.1029/gl015i004p00323
10 https://doi.org/10.1029/jd092id11p13345
11 https://doi.org/10.1093/biomet/68.1.165
12 https://doi.org/10.1175/1520-0442(1988)001<0654:hsatvr>2.0.co;2
13 https://doi.org/10.1175/1520-0450(1986)025<0161:nhsatv>2.0.co;2
14 https://doi.org/10.1175/1520-0450(1986)025<1213:shsatv>2.0.co;2
15 https://doi.org/10.1175/1520-0469(1974)031<1958:vsohcf>2.0.co;2
16 schema:datePublished 1992-07
17 schema:datePublishedReg 1992-07-01
18 schema:description Part of the debate over possible climate changes centers on the possibility that the changes observed over the previous century are natural in origin. This raises the question of how large a change could be expected as a result of natural variability. If the climate measurement of interest is modelled as a stationary (or related) Gaussian time series, this question can be answered in terms of (a) the way in which change is estimated, and (b) the spectrum of the time series. These computations are illustrated for 128 years of global temperature data using some simple measures of change and for a variety of possible temperature spectra. The results highlight the time scales on which it is important to know the magnitude of natural variability. The uncertainties in estimates of trend are most sensitive to fluctuations in the temperature series with periods from approximately 50 to 500 years. For some of the temperature spectra, it was found that the standard error of the least squares trend estimate was 3 times the standard error derived under the naïve assumption that the temperature series was uncorrelated. The observed trend differs from zero by more than 3 times the largest of the calculated standard errors, however, and is therefore highly significant.
19 schema:genre research_article
20 schema:inLanguage en
21 schema:isAccessibleForFree false
22 schema:isPartOf N88c6475e37764ab0b71670b884e1f14f
23 Ne70202dc2ecb4504b8c0ce40ef4a9660
24 sg:journal.1028211
25 schema:name Climate spectra and detecting climate change
26 schema:pagination 275-287
27 schema:productId N2b24bdb6d83b43df8f0eb44a0a93541d
28 N5813abc07984407f8ce4a7b96793d842
29 N82aca3db1d6349449ebc5006323bd80a
30 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024569108
31 https://doi.org/10.1007/bf00139727
32 schema:sdDatePublished 2019-04-11T13:48
33 schema:sdLicense https://scigraph.springernature.com/explorer/license/
34 schema:sdPublisher Nbba0994f481c4fcf8cd92ea67e66b5f4
35 schema:url http://link.springer.com/10.1007/BF00139727
36 sgo:license sg:explorer/license/
37 sgo:sdDataset articles
38 rdf:type schema:ScholarlyArticle
39 N2b24bdb6d83b43df8f0eb44a0a93541d schema:name readcube_id
40 schema:value 750c1c8ddbf6a72d661f54bf43769d0023de3b4e3f09ba575d572ba7c002f06e
41 rdf:type schema:PropertyValue
42 N5813abc07984407f8ce4a7b96793d842 schema:name doi
43 schema:value 10.1007/bf00139727
44 rdf:type schema:PropertyValue
45 N82aca3db1d6349449ebc5006323bd80a schema:name dimensions_id
46 schema:value pub.1024569108
47 rdf:type schema:PropertyValue
48 N88c6475e37764ab0b71670b884e1f14f schema:volumeNumber 21
49 rdf:type schema:PublicationVolume
50 Na9b9ff7eeaa3435b90e6a43252bfbc6f rdf:first sg:person.07745505663.08
51 rdf:rest rdf:nil
52 Nb36714ac601642b7a885f39cdceb5b51 rdf:first Nf67ebaadb5aa42188a62d32d7cfce951
53 rdf:rest Na9b9ff7eeaa3435b90e6a43252bfbc6f
54 Nbba0994f481c4fcf8cd92ea67e66b5f4 schema:name Springer Nature - SN SciGraph project
55 rdf:type schema:Organization
56 Ne70202dc2ecb4504b8c0ce40ef4a9660 schema:issueNumber 3
57 rdf:type schema:PublicationIssue
58 Nf67ebaadb5aa42188a62d32d7cfce951 schema:affiliation https://www.grid.ac/institutes/grid.40803.3f
59 schema:familyName Bloomfield
60 schema:givenName Peter
61 rdf:type schema:Person
62 anzsrc-for:14 schema:inDefinedTermSet anzsrc-for:
63 schema:name Economics
64 rdf:type schema:DefinedTerm
65 anzsrc-for:1403 schema:inDefinedTermSet anzsrc-for:
66 schema:name Econometrics
67 rdf:type schema:DefinedTerm
68 sg:journal.1028211 schema:issn 0165-0009
69 1573-1480
70 schema:name Climatic Change
71 rdf:type schema:Periodical
72 sg:person.07745505663.08 schema:affiliation https://www.grid.ac/institutes/grid.40803.3f
73 schema:familyName Nychka
74 schema:givenName Douglas
75 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07745505663.08
76 rdf:type schema:Person
77 sg:pub.10.1007/bf00134658 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049977494
78 https://doi.org/10.1007/bf00134658
79 rdf:type schema:CreativeWork
80 sg:pub.10.1007/bf00143250 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016077135
81 https://doi.org/10.1007/bf00143250
82 rdf:type schema:CreativeWork
83 sg:pub.10.1038/322430a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032895113
84 https://doi.org/10.1038/322430a0
85 rdf:type schema:CreativeWork
86 sg:pub.10.1038/330127a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016835893
87 https://doi.org/10.1038/330127a0
88 rdf:type schema:CreativeWork
89 sg:pub.10.1038/344324a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000092146
90 https://doi.org/10.1038/344324a0
91 rdf:type schema:CreativeWork
92 https://doi.org/10.1029/gl015i004p00323 schema:sameAs https://app.dimensions.ai/details/publication/pub.1041393140
93 rdf:type schema:CreativeWork
94 https://doi.org/10.1029/jd092id11p13345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025791400
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1093/biomet/68.1.165 schema:sameAs https://app.dimensions.ai/details/publication/pub.1059419039
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1175/1520-0442(1988)001<0654:hsatvr>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027056055
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1175/1520-0450(1986)025<0161:nhsatv>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051729171
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1175/1520-0450(1986)025<1213:shsatv>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042758895
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1175/1520-0469(1974)031<1958:vsohcf>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028588453
105 rdf:type schema:CreativeWork
106 https://www.grid.ac/institutes/grid.40803.3f schema:alternateName North Carolina State University
107 schema:name Department of Statistics, North Carolina State University, 27695-8203, Raleigh, NC, USA
108 rdf:type schema:Organization
 




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


...