Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2004-04

AUTHORS

Qingxiang Li, Xiaoning Liu, Hongzheng Zhang, Peterson Thomas C., Easterling David R.

ABSTRACT

Adopting the Easterling-Peterson (EP) techniques and considering the reality of Chinese meteorological observations, this paper designed several tests and tested for inhomogeneities in all Chinese historical surface air temperature series from 1951 to 2001. The result shows that the time series have been widely impacted by inhomogeneities resulting from the relocation of stations and changes in local environment such as urbanization or some other factors. Among these factors, station relocations caused the largest magnitude of abrupt changes in the time series, and other factors also resulted in inhomogeneities to some extent. According to the amplitude of change of the difference series and the monthly distribution features of surface air temperatures, discontinuities identified by applying both the E-P technique and supported by China’s station history records, or by comparison with other approaches, have been adjusted. Based on the above processing, the most significant temporal inhomogeneities were eliminated, and China’s most homogeneous surface air temperature series has thus been created. Results show that the inhomogeneity testing captured well the most important change of the stations, and the adjusted dataset is more reliable than ever. This suggests that the adjusted temperature dataset has great value of decreasing the uncertaities in the study of observed climate change in China. More... »

PAGES

260

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/bf02915712

DOI

http://dx.doi.org/10.1007/bf02915712

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1028285792


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "China Meteorological Administration", 
          "id": "https://www.grid.ac/institutes/grid.8658.3", 
          "name": [
            "National Meteorological Center, China Meteorological Administration, 100081, Beijing"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Li", 
        "givenName": "Qingxiang", 
        "id": "sg:person.011622455536.28", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011622455536.28"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "China Meteorological Administration", 
          "id": "https://www.grid.ac/institutes/grid.8658.3", 
          "name": [
            "National Meteorological Center, China Meteorological Administration, 100081, Beijing"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Liu", 
        "givenName": "Xiaoning", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "China Meteorological Administration", 
          "id": "https://www.grid.ac/institutes/grid.8658.3", 
          "name": [
            "National Meteorological Center, China Meteorological Administration, 100081, Beijing"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Hongzheng", 
        "id": "sg:person.013225326454.44", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013225326454.44"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Centers for Environmental Information", 
          "id": "https://www.grid.ac/institutes/grid.454206.1", 
          "name": [
            "National Climatic Data Center, National Oceanic and Atmospheric Administration, NC28801, Asheville, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Thomas C.", 
        "givenName": "Peterson", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Centers for Environmental Information", 
          "id": "https://www.grid.ac/institutes/grid.454206.1", 
          "name": [
            "National Climatic Data Center, National Oceanic and Atmospheric Administration, NC28801, Asheville, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "David R.", 
        "givenName": "Easterling", 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "https://doi.org/10.1002/joc.3370150403", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1004474483"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0450(1987)026<1401:tfccaa>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1019933825"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0012-8252(91)90042-e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023316081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1016/0012-8252(91)90042-e", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023316081"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.3370140606", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023510738"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/joc.3370060607", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043043599"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02919312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045140840", 
          "https://doi.org/10.1007/bf02919312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02919312", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045140840", 
          "https://doi.org/10.1007/bf02919312"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1002/(sici)1097-0088(19981115)18:13<1493::aid-joc329>3.0.co;2-t", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049735928"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.1175/1520-0442(1996)009<0884:aoiirt>2.0.co;2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1053737353"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "https://doi.org/10.4159/harvard.9780674187856", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1099341989"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-04", 
    "datePublishedReg": "2004-04-01", 
    "description": "Adopting the Easterling-Peterson (EP) techniques and considering the reality of Chinese meteorological observations, this paper designed several tests and tested for inhomogeneities in all Chinese historical surface air temperature series from 1951 to 2001. The result shows that the time series have been widely impacted by inhomogeneities resulting from the relocation of stations and changes in local environment such as urbanization or some other factors. Among these factors, station relocations caused the largest magnitude of abrupt changes in the time series, and other factors also resulted in inhomogeneities to some extent. According to the amplitude of change of the difference series and the monthly distribution features of surface air temperatures, discontinuities identified by applying both the E-P technique and supported by China\u2019s station history records, or by comparison with other approaches, have been adjusted. Based on the above processing, the most significant temporal inhomogeneities were eliminated, and China\u2019s most homogeneous surface air temperature series has thus been created. Results show that the inhomogeneity testing captured well the most important change of the stations, and the adjusted dataset is more reliable than ever. This suggests that the adjusted temperature dataset has great value of decreasing the uncertaities in the study of observed climate change in China.", 
    "genre": "research_article", 
    "id": "sg:pub.10.1007/bf02915712", 
    "inLanguage": [
      "en"
    ], 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1135901", 
        "issn": [
          "0256-1530", 
          "1861-9533"
        ], 
        "name": "Advances in Atmospheric Sciences", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "21"
      }
    ], 
    "name": "Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data", 
    "pagination": "260", 
    "productId": [
      {
        "name": "readcube_id", 
        "type": "PropertyValue", 
        "value": [
          "86bf6822892eb40386a75233b791ea5183290c7177003ca7c6aa938b1becfe35"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/bf02915712"
        ]
      }, 
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1028285792"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/bf02915712", 
      "https://app.dimensions.ai/details/publication/pub.1028285792"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2019-04-11T13:59", 
    "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_130826_00000002.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "http://link.springer.com/10.1007/BF02915712"
  }
]
 

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/bf02915712'

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/bf02915712'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/bf02915712'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/bf02915712'


 

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

117 TRIPLES      21 PREDICATES      36 URIs      19 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/bf02915712 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N04b41d6499ce429d8fe0fb9ce694c971
4 schema:citation sg:pub.10.1007/bf02919312
5 https://doi.org/10.1002/(sici)1097-0088(19981115)18:13<1493::aid-joc329>3.0.co;2-t
6 https://doi.org/10.1002/joc.3370060607
7 https://doi.org/10.1002/joc.3370140606
8 https://doi.org/10.1002/joc.3370150403
9 https://doi.org/10.1016/0012-8252(91)90042-e
10 https://doi.org/10.1175/1520-0442(1996)009<0884:aoiirt>2.0.co;2
11 https://doi.org/10.1175/1520-0450(1987)026<1401:tfccaa>2.0.co;2
12 https://doi.org/10.4159/harvard.9780674187856
13 schema:datePublished 2004-04
14 schema:datePublishedReg 2004-04-01
15 schema:description Adopting the Easterling-Peterson (EP) techniques and considering the reality of Chinese meteorological observations, this paper designed several tests and tested for inhomogeneities in all Chinese historical surface air temperature series from 1951 to 2001. The result shows that the time series have been widely impacted by inhomogeneities resulting from the relocation of stations and changes in local environment such as urbanization or some other factors. Among these factors, station relocations caused the largest magnitude of abrupt changes in the time series, and other factors also resulted in inhomogeneities to some extent. According to the amplitude of change of the difference series and the monthly distribution features of surface air temperatures, discontinuities identified by applying both the E-P technique and supported by China’s station history records, or by comparison with other approaches, have been adjusted. Based on the above processing, the most significant temporal inhomogeneities were eliminated, and China’s most homogeneous surface air temperature series has thus been created. Results show that the inhomogeneity testing captured well the most important change of the stations, and the adjusted dataset is more reliable than ever. This suggests that the adjusted temperature dataset has great value of decreasing the uncertaities in the study of observed climate change in China.
16 schema:genre research_article
17 schema:inLanguage en
18 schema:isAccessibleForFree false
19 schema:isPartOf Ndeea16a384a644d4b30a6393509c8b1b
20 Nf0cc16767b8b4db69ad6ce68a6820434
21 sg:journal.1135901
22 schema:name Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data
23 schema:pagination 260
24 schema:productId N28ad158ebba54ec184be81e46ec7ce2b
25 N78ea1701489b4de6b0acb0b0e8e3147c
26 Nad749b1bab56431f954299fc6e400615
27 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028285792
28 https://doi.org/10.1007/bf02915712
29 schema:sdDatePublished 2019-04-11T13:59
30 schema:sdLicense https://scigraph.springernature.com/explorer/license/
31 schema:sdPublisher N7ab3c7fc49e5442694788b0d5e540a6e
32 schema:url http://link.springer.com/10.1007/BF02915712
33 sgo:license sg:explorer/license/
34 sgo:sdDataset articles
35 rdf:type schema:ScholarlyArticle
36 N04b41d6499ce429d8fe0fb9ce694c971 rdf:first sg:person.011622455536.28
37 rdf:rest N78b465f4f7d743d889cb7953b30a2346
38 N28ad158ebba54ec184be81e46ec7ce2b schema:name readcube_id
39 schema:value 86bf6822892eb40386a75233b791ea5183290c7177003ca7c6aa938b1becfe35
40 rdf:type schema:PropertyValue
41 N367b639933794f6a900beca450267021 rdf:first Na15c7713f7bf46df8aa8c5545df370bd
42 rdf:rest rdf:nil
43 N77edafcb6328449fa22ad6eb8e27cb1a rdf:first N9709d1c468ea45f98a6869f74c207bd5
44 rdf:rest N367b639933794f6a900beca450267021
45 N78b465f4f7d743d889cb7953b30a2346 rdf:first N7c990b66786b4d81a840b1234c0e0f74
46 rdf:rest Ne8d69b9520ca45638d5f6af36f54fd8c
47 N78ea1701489b4de6b0acb0b0e8e3147c schema:name dimensions_id
48 schema:value pub.1028285792
49 rdf:type schema:PropertyValue
50 N7ab3c7fc49e5442694788b0d5e540a6e schema:name Springer Nature - SN SciGraph project
51 rdf:type schema:Organization
52 N7c990b66786b4d81a840b1234c0e0f74 schema:affiliation https://www.grid.ac/institutes/grid.8658.3
53 schema:familyName Liu
54 schema:givenName Xiaoning
55 rdf:type schema:Person
56 N9709d1c468ea45f98a6869f74c207bd5 schema:affiliation https://www.grid.ac/institutes/grid.454206.1
57 schema:familyName Thomas C.
58 schema:givenName Peterson
59 rdf:type schema:Person
60 Na15c7713f7bf46df8aa8c5545df370bd schema:affiliation https://www.grid.ac/institutes/grid.454206.1
61 schema:familyName David R.
62 schema:givenName Easterling
63 rdf:type schema:Person
64 Nad749b1bab56431f954299fc6e400615 schema:name doi
65 schema:value 10.1007/bf02915712
66 rdf:type schema:PropertyValue
67 Ndeea16a384a644d4b30a6393509c8b1b schema:volumeNumber 21
68 rdf:type schema:PublicationVolume
69 Ne8d69b9520ca45638d5f6af36f54fd8c rdf:first sg:person.013225326454.44
70 rdf:rest N77edafcb6328449fa22ad6eb8e27cb1a
71 Nf0cc16767b8b4db69ad6ce68a6820434 schema:issueNumber 2
72 rdf:type schema:PublicationIssue
73 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
74 schema:name Earth Sciences
75 rdf:type schema:DefinedTerm
76 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
77 schema:name Atmospheric Sciences
78 rdf:type schema:DefinedTerm
79 sg:journal.1135901 schema:issn 0256-1530
80 1861-9533
81 schema:name Advances in Atmospheric Sciences
82 rdf:type schema:Periodical
83 sg:person.011622455536.28 schema:affiliation https://www.grid.ac/institutes/grid.8658.3
84 schema:familyName Li
85 schema:givenName Qingxiang
86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011622455536.28
87 rdf:type schema:Person
88 sg:person.013225326454.44 schema:affiliation https://www.grid.ac/institutes/grid.8658.3
89 schema:familyName Zhang
90 schema:givenName Hongzheng
91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013225326454.44
92 rdf:type schema:Person
93 sg:pub.10.1007/bf02919312 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045140840
94 https://doi.org/10.1007/bf02919312
95 rdf:type schema:CreativeWork
96 https://doi.org/10.1002/(sici)1097-0088(19981115)18:13<1493::aid-joc329>3.0.co;2-t schema:sameAs https://app.dimensions.ai/details/publication/pub.1049735928
97 rdf:type schema:CreativeWork
98 https://doi.org/10.1002/joc.3370060607 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043043599
99 rdf:type schema:CreativeWork
100 https://doi.org/10.1002/joc.3370140606 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023510738
101 rdf:type schema:CreativeWork
102 https://doi.org/10.1002/joc.3370150403 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004474483
103 rdf:type schema:CreativeWork
104 https://doi.org/10.1016/0012-8252(91)90042-e schema:sameAs https://app.dimensions.ai/details/publication/pub.1023316081
105 rdf:type schema:CreativeWork
106 https://doi.org/10.1175/1520-0442(1996)009<0884:aoiirt>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053737353
107 rdf:type schema:CreativeWork
108 https://doi.org/10.1175/1520-0450(1987)026<1401:tfccaa>2.0.co;2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019933825
109 rdf:type schema:CreativeWork
110 https://doi.org/10.4159/harvard.9780674187856 schema:sameAs https://app.dimensions.ai/details/publication/pub.1099341989
111 rdf:type schema:CreativeWork
112 https://www.grid.ac/institutes/grid.454206.1 schema:alternateName National Centers for Environmental Information
113 schema:name National Climatic Data Center, National Oceanic and Atmospheric Administration, NC28801, Asheville, USA
114 rdf:type schema:Organization
115 https://www.grid.ac/institutes/grid.8658.3 schema:alternateName China Meteorological Administration
116 schema:name National Meteorological Center, China Meteorological Administration, 100081, Beijing
117 rdf:type schema:Organization
 




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


...