An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-04-06

AUTHORS

Yoshimitsu Chikamoto, Masahide Kimoto, Masayoshi Ishii, Takashi Mochizuki, Takashi T. Sakamoto, Hiroaki Tatebe, Yoshiki Komuro, Masahiro Watanabe, Toru Nozawa, Hideo Shiogama, Masato Mori, Sayaka Yasunaka, Yukiko Imada

ABSTRACT

Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble. More... »

PAGES

1201-1222

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-012-1351-y

DOI

http://dx.doi.org/10.1007/s00382-012-1351-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023684109


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/2008/0401", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Atmospheric Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0405", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oceanography", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chikamoto", 
        "givenName": "Yoshimitsu", 
        "id": "sg:person.016311203527.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311203527.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Kimoto", 
        "givenName": "Masahide", 
        "id": "sg:person.0575555075.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0575555075.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Meteorological Research Institute, Tsukuba, Japan", 
            "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ishii", 
        "givenName": "Masayoshi", 
        "id": "sg:person.01372606615.94", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372606615.94"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mochizuki", 
        "givenName": "Takashi", 
        "id": "sg:person.01324473415.61", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324473415.61"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sakamoto", 
        "givenName": "Takashi T.", 
        "id": "sg:person.011051736466.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011051736466.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Tatebe", 
        "givenName": "Hiroaki", 
        "id": "sg:person.0601416175.95", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601416175.95"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan", 
          "id": "http://www.grid.ac/institutes/grid.410588.0", 
          "name": [
            "Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Komuro", 
        "givenName": "Yoshiki", 
        "id": "sg:person.013306720352.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013306720352.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Watanabe", 
        "givenName": "Masahiro", 
        "id": "sg:person.016316106377.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016316106377.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institute for Environmental Studies, Tsukuba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "National Institute for Environmental Studies, Tsukuba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Nozawa", 
        "givenName": "Toru", 
        "id": "sg:person.011033746441.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011033746441.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institute for Environmental Studies, Tsukuba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "National Institute for Environmental Studies, Tsukuba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shiogama", 
        "givenName": "Hideo", 
        "id": "sg:person.011356656533.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mori", 
        "givenName": "Masato", 
        "id": "sg:person.013374646715.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013374646715.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "National Institute for Environmental Studies, Tsukuba, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "National Institute for Environmental Studies, Tsukuba, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Yasunaka", 
        "givenName": "Sayaka", 
        "id": "sg:person.010155440675.29", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010155440675.29"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.32197.3e", 
          "name": [
            "Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Imada", 
        "givenName": "Yukiko", 
        "id": "sg:person.014327166275.50", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014327166275.50"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s00382-008-0397-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1013019248", 
          "https://doi.org/10.1007/s00382-008-0397-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature08823", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1006069800", 
          "https://doi.org/10.1038/nature08823"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050284", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1001545167", 
          "https://doi.org/10.1007/s003820050284"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00204745", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021084561", 
          "https://doi.org/10.1007/bf00204745"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10872-009-0027-7", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000456005", 
          "https://doi.org/10.1007/s10872-009-0027-7"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820000075", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1009137936", 
          "https://doi.org/10.1007/s003820000075"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-012-1313-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1037199139", 
          "https://doi.org/10.1007/s00382-012-1313-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/320602a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049309236", 
          "https://doi.org/10.1038/320602a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00376-002-0059-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038961341", 
          "https://doi.org/10.1007/s00376-002-0059-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature06921", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1026158887", 
          "https://doi.org/10.1038/nature06921"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10872-006-0041-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1033693734", 
          "https://doi.org/10.1007/s10872-006-0041-y"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-04-06", 
    "datePublishedReg": "2012-04-06", 
    "description": "Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-012-1351-y", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6050804", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.6030017", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "5-6", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "40"
      }
    ], 
    "keywords": [
      "multi-model ensemble", 
      "Atlantic Multidecadal Oscillation", 
      "decadal climate predictability", 
      "Pacific Decadal Oscillation", 
      "hindcast experiments", 
      "predictive skill", 
      "climate predictability", 
      "temperature anomalies", 
      "surface air temperature anomalies", 
      "global mean temperature anomaly", 
      "subsurface temperature variability", 
      "air temperature anomalies", 
      "mean temperature anomalies", 
      "future emission scenarios", 
      "single-model ensembles", 
      "SAT variance", 
      "anthropogenic forcing", 
      "Decadal Oscillation", 
      "Multidecadal Oscillation", 
      "PDO index", 
      "oceanic temperature", 
      "decadal timescales", 
      "North Pacific", 
      "North Atlantic", 
      "temperature variability", 
      "emission scenarios", 
      "assimilation procedure", 
      "Intergovernmental Panel", 
      "dominant signal", 
      "climate change", 
      "predictable components", 
      "MIROC", 
      "variability", 
      "anomalies", 
      "ensemble", 
      "predictability", 
      "historical data", 
      "initial conditions", 
      "oscillations", 
      "forcing", 
      "Pacific", 
      "Atlantic", 
      "salinity", 
      "timescales", 
      "initialization", 
      "scenarios", 
      "temperature", 
      "changes", 
      "decades", 
      "data", 
      "experiments", 
      "conditions", 
      "components", 
      "variance", 
      "index", 
      "skills", 
      "basis", 
      "signals", 
      "results", 
      "version", 
      "overview", 
      "panel", 
      "improvement", 
      "procedure"
    ], 
    "name": "An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC", 
    "pagination": "1201-1222", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023684109"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-012-1351-y"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-012-1351-y", 
      "https://app.dimensions.ai/details/publication/pub.1023684109"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-10T10:05", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_557.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-012-1351-y"
  }
]
 

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/s00382-012-1351-y'

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/s00382-012-1351-y'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-012-1351-y'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-012-1351-y'


 

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

268 TRIPLES      22 PREDICATES      101 URIs      81 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-012-1351-y schema:about anzsrc-for:04
2 anzsrc-for:0401
3 anzsrc-for:0405
4 schema:author N6144611098d94e5382060956b64d4551
5 schema:citation sg:pub.10.1007/bf00204745
6 sg:pub.10.1007/s00376-002-0059-z
7 sg:pub.10.1007/s00382-008-0397-3
8 sg:pub.10.1007/s00382-012-1313-4
9 sg:pub.10.1007/s003820000075
10 sg:pub.10.1007/s003820050284
11 sg:pub.10.1007/s10872-006-0041-y
12 sg:pub.10.1007/s10872-009-0027-7
13 sg:pub.10.1038/320602a0
14 sg:pub.10.1038/nature06921
15 sg:pub.10.1038/nature08823
16 schema:datePublished 2012-04-06
17 schema:datePublishedReg 2012-04-06
18 schema:description Decadal climate predictability is examined in hindcast experiments by a multi-model ensemble using three versions of the coupled atmosphere-ocean model MIROC. In these hindcast experiments, initial conditions are obtained from an anomaly assimilation procedure using the observed oceanic temperature and salinity with prescribed natural and anthropogenic forcings on the basis of the historical data and future emission scenarios in the Intergovernmental Panel of Climate Change. Results of the multi-model ensemble in our hindcast experiments show that predictability of surface air temperature (SAT) anomalies on decadal timescales mostly originates from externally forced variability. Although the predictable component of internally generated variability has considerably smaller SAT variance than that of externally forced variability, ocean subsurface temperature variability has predictive skills over almost a decade, particularly in the North Pacific and the North Atlantic where dominant signals associated with Pacific decadal oscillation (PDO) and the Atlantic multidecadal oscillation (AMO) are observed. Initialization enhances the predictive skills of AMO and PDO indices and slightly improves those of global mean temperature anomalies. Improvement of these predictive skills in the multi-model ensemble is higher than that in a single-model ensemble.
19 schema:genre article
20 schema:inLanguage en
21 schema:isAccessibleForFree true
22 schema:isPartOf N0322c3ad952347fc8868cf3f9b113f12
23 N85f56b8f7320400e8164656855c3d135
24 sg:journal.1049631
25 schema:keywords Atlantic
26 Atlantic Multidecadal Oscillation
27 Decadal Oscillation
28 Intergovernmental Panel
29 MIROC
30 Multidecadal Oscillation
31 North Atlantic
32 North Pacific
33 PDO index
34 Pacific
35 Pacific Decadal Oscillation
36 SAT variance
37 air temperature anomalies
38 anomalies
39 anthropogenic forcing
40 assimilation procedure
41 basis
42 changes
43 climate change
44 climate predictability
45 components
46 conditions
47 data
48 decadal climate predictability
49 decadal timescales
50 decades
51 dominant signal
52 emission scenarios
53 ensemble
54 experiments
55 forcing
56 future emission scenarios
57 global mean temperature anomaly
58 hindcast experiments
59 historical data
60 improvement
61 index
62 initial conditions
63 initialization
64 mean temperature anomalies
65 multi-model ensemble
66 oceanic temperature
67 oscillations
68 overview
69 panel
70 predictability
71 predictable components
72 predictive skill
73 procedure
74 results
75 salinity
76 scenarios
77 signals
78 single-model ensembles
79 skills
80 subsurface temperature variability
81 surface air temperature anomalies
82 temperature
83 temperature anomalies
84 temperature variability
85 timescales
86 variability
87 variance
88 version
89 schema:name An overview of decadal climate predictability in a multi-model ensemble by climate model MIROC
90 schema:pagination 1201-1222
91 schema:productId N448c9431c6794e0b97e7b31977314c13
92 Nb2dab0d1bd3842719f76daa6177379a4
93 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023684109
94 https://doi.org/10.1007/s00382-012-1351-y
95 schema:sdDatePublished 2022-05-10T10:05
96 schema:sdLicense https://scigraph.springernature.com/explorer/license/
97 schema:sdPublisher Ne07da7ac4d164108bad8ce0add6ab732
98 schema:url https://doi.org/10.1007/s00382-012-1351-y
99 sgo:license sg:explorer/license/
100 sgo:sdDataset articles
101 rdf:type schema:ScholarlyArticle
102 N0322c3ad952347fc8868cf3f9b113f12 schema:issueNumber 5-6
103 rdf:type schema:PublicationIssue
104 N0f78ec903733488fb30fd2f0aa49bbdd rdf:first sg:person.011033746441.19
105 rdf:rest N18070f978ad541b9a61958ecd6793efc
106 N18070f978ad541b9a61958ecd6793efc rdf:first sg:person.011356656533.12
107 rdf:rest N2099a108fb0a4774bb8cea4981728c32
108 N2099a108fb0a4774bb8cea4981728c32 rdf:first sg:person.013374646715.17
109 rdf:rest N2be08b558bfd4550b8d75a2c12ce0522
110 N2be08b558bfd4550b8d75a2c12ce0522 rdf:first sg:person.010155440675.29
111 rdf:rest Ne9423ca71e8c4f2faeeacd83402947e3
112 N3d84ee6be204440a891f1fae5a2d14c5 rdf:first sg:person.0601416175.95
113 rdf:rest N8a718b99e6ba446cb42ddf1a10552ea0
114 N448c9431c6794e0b97e7b31977314c13 schema:name doi
115 schema:value 10.1007/s00382-012-1351-y
116 rdf:type schema:PropertyValue
117 N6144611098d94e5382060956b64d4551 rdf:first sg:person.016311203527.01
118 rdf:rest Nb219483335f64147af6d0d4c333584a1
119 N7ab0df6d7722452caa38651d0831dc01 rdf:first sg:person.01372606615.94
120 rdf:rest Ncb97245dcb8b452bb0d2ca6d5b051dc9
121 N85f56b8f7320400e8164656855c3d135 schema:volumeNumber 40
122 rdf:type schema:PublicationVolume
123 N8a718b99e6ba446cb42ddf1a10552ea0 rdf:first sg:person.013306720352.29
124 rdf:rest N935ef56a1f3347f48a8c5cca3e6456d0
125 N935ef56a1f3347f48a8c5cca3e6456d0 rdf:first sg:person.016316106377.80
126 rdf:rest N0f78ec903733488fb30fd2f0aa49bbdd
127 Nb219483335f64147af6d0d4c333584a1 rdf:first sg:person.0575555075.53
128 rdf:rest N7ab0df6d7722452caa38651d0831dc01
129 Nb2dab0d1bd3842719f76daa6177379a4 schema:name dimensions_id
130 schema:value pub.1023684109
131 rdf:type schema:PropertyValue
132 Ncb97245dcb8b452bb0d2ca6d5b051dc9 rdf:first sg:person.01324473415.61
133 rdf:rest Nf358fdcb5e28445ca190b8f9ad6010c2
134 Ne07da7ac4d164108bad8ce0add6ab732 schema:name Springer Nature - SN SciGraph project
135 rdf:type schema:Organization
136 Ne9423ca71e8c4f2faeeacd83402947e3 rdf:first sg:person.014327166275.50
137 rdf:rest rdf:nil
138 Nf358fdcb5e28445ca190b8f9ad6010c2 rdf:first sg:person.011051736466.17
139 rdf:rest N3d84ee6be204440a891f1fae5a2d14c5
140 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
141 schema:name Earth Sciences
142 rdf:type schema:DefinedTerm
143 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
144 schema:name Atmospheric Sciences
145 rdf:type schema:DefinedTerm
146 anzsrc-for:0405 schema:inDefinedTermSet anzsrc-for:
147 schema:name Oceanography
148 rdf:type schema:DefinedTerm
149 sg:grant.6030017 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-012-1351-y
150 rdf:type schema:MonetaryGrant
151 sg:grant.6050804 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-012-1351-y
152 rdf:type schema:MonetaryGrant
153 sg:journal.1049631 schema:issn 0930-7575
154 1432-0894
155 schema:name Climate Dynamics
156 schema:publisher Springer Nature
157 rdf:type schema:Periodical
158 sg:person.010155440675.29 schema:affiliation grid-institutes:grid.140139.e
159 schema:familyName Yasunaka
160 schema:givenName Sayaka
161 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010155440675.29
162 rdf:type schema:Person
163 sg:person.011033746441.19 schema:affiliation grid-institutes:grid.140139.e
164 schema:familyName Nozawa
165 schema:givenName Toru
166 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011033746441.19
167 rdf:type schema:Person
168 sg:person.011051736466.17 schema:affiliation grid-institutes:grid.410588.0
169 schema:familyName Sakamoto
170 schema:givenName Takashi T.
171 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011051736466.17
172 rdf:type schema:Person
173 sg:person.011356656533.12 schema:affiliation grid-institutes:grid.140139.e
174 schema:familyName Shiogama
175 schema:givenName Hideo
176 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12
177 rdf:type schema:Person
178 sg:person.01324473415.61 schema:affiliation grid-institutes:grid.410588.0
179 schema:familyName Mochizuki
180 schema:givenName Takashi
181 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01324473415.61
182 rdf:type schema:Person
183 sg:person.013306720352.29 schema:affiliation grid-institutes:grid.410588.0
184 schema:familyName Komuro
185 schema:givenName Yoshiki
186 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013306720352.29
187 rdf:type schema:Person
188 sg:person.013374646715.17 schema:affiliation grid-institutes:grid.26999.3d
189 schema:familyName Mori
190 schema:givenName Masato
191 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013374646715.17
192 rdf:type schema:Person
193 sg:person.01372606615.94 schema:affiliation grid-institutes:grid.410588.0
194 schema:familyName Ishii
195 schema:givenName Masayoshi
196 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01372606615.94
197 rdf:type schema:Person
198 sg:person.014327166275.50 schema:affiliation grid-institutes:grid.32197.3e
199 schema:familyName Imada
200 schema:givenName Yukiko
201 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014327166275.50
202 rdf:type schema:Person
203 sg:person.016311203527.01 schema:affiliation grid-institutes:grid.26999.3d
204 schema:familyName Chikamoto
205 schema:givenName Yoshimitsu
206 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016311203527.01
207 rdf:type schema:Person
208 sg:person.016316106377.80 schema:affiliation grid-institutes:grid.26999.3d
209 schema:familyName Watanabe
210 schema:givenName Masahiro
211 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016316106377.80
212 rdf:type schema:Person
213 sg:person.0575555075.53 schema:affiliation grid-institutes:grid.26999.3d
214 schema:familyName Kimoto
215 schema:givenName Masahide
216 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0575555075.53
217 rdf:type schema:Person
218 sg:person.0601416175.95 schema:affiliation grid-institutes:grid.410588.0
219 schema:familyName Tatebe
220 schema:givenName Hiroaki
221 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0601416175.95
222 rdf:type schema:Person
223 sg:pub.10.1007/bf00204745 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021084561
224 https://doi.org/10.1007/bf00204745
225 rdf:type schema:CreativeWork
226 sg:pub.10.1007/s00376-002-0059-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1038961341
227 https://doi.org/10.1007/s00376-002-0059-z
228 rdf:type schema:CreativeWork
229 sg:pub.10.1007/s00382-008-0397-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013019248
230 https://doi.org/10.1007/s00382-008-0397-3
231 rdf:type schema:CreativeWork
232 sg:pub.10.1007/s00382-012-1313-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037199139
233 https://doi.org/10.1007/s00382-012-1313-4
234 rdf:type schema:CreativeWork
235 sg:pub.10.1007/s003820000075 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009137936
236 https://doi.org/10.1007/s003820000075
237 rdf:type schema:CreativeWork
238 sg:pub.10.1007/s003820050284 schema:sameAs https://app.dimensions.ai/details/publication/pub.1001545167
239 https://doi.org/10.1007/s003820050284
240 rdf:type schema:CreativeWork
241 sg:pub.10.1007/s10872-006-0041-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1033693734
242 https://doi.org/10.1007/s10872-006-0041-y
243 rdf:type schema:CreativeWork
244 sg:pub.10.1007/s10872-009-0027-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000456005
245 https://doi.org/10.1007/s10872-009-0027-7
246 rdf:type schema:CreativeWork
247 sg:pub.10.1038/320602a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1049309236
248 https://doi.org/10.1038/320602a0
249 rdf:type schema:CreativeWork
250 sg:pub.10.1038/nature06921 schema:sameAs https://app.dimensions.ai/details/publication/pub.1026158887
251 https://doi.org/10.1038/nature06921
252 rdf:type schema:CreativeWork
253 sg:pub.10.1038/nature08823 schema:sameAs https://app.dimensions.ai/details/publication/pub.1006069800
254 https://doi.org/10.1038/nature08823
255 rdf:type schema:CreativeWork
256 grid-institutes:grid.140139.e schema:alternateName National Institute for Environmental Studies, Tsukuba, Japan
257 schema:name National Institute for Environmental Studies, Tsukuba, Japan
258 rdf:type schema:Organization
259 grid-institutes:grid.26999.3d schema:alternateName Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan
260 schema:name Atmosphere and Ocean Research Institute, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, 277-8568, Chiba, Japan
261 rdf:type schema:Organization
262 grid-institutes:grid.32197.3e schema:alternateName Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan
263 schema:name Department of Civil and Environmental Engineering, Tokyo Institute of Technology, Tokyo, Japan
264 rdf:type schema:Organization
265 grid-institutes:grid.410588.0 schema:alternateName Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
266 schema:name Japan Agency for Marine-Earth Science and Technology, Yokohama, Japan
267 Meteorological Research Institute, Tsukuba, Japan
268 rdf:type schema:Organization
 




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


...