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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sanderson", 
        "givenName": "Benjamin M.", 
        "id": "sg:person.015440676217.87", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015440676217.87"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "International Center for Theoretical Physics, Trieste, Italy", 
          "id": "http://www.grid.ac/institutes/grid.419330.c", 
          "name": [
            "International Center for Theoretical Physics, Trieste, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Piani", 
        "givenName": "C.", 
        "id": "sg:person.016370414215.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016370414215.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Meteorological Office, Exeter, UK", 
          "id": "http://www.grid.ac/institutes/grid.17100.37", 
          "name": [
            "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
            "Meteorological Office, Exeter, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ingram", 
        "givenName": "W. J.", 
        "id": "sg:person.014131530715.33", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014131530715.33"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Tyndall Centre for Climate Change Research, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
            "Tyndall Centre for Climate Change Research, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stone", 
        "givenName": "D. A.", 
        "id": "sg:person.012044260032.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012044260032.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "AOPP, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Allen", 
        "givenName": "M. R.", 
        "id": "sg:person.0600474550.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600474550.17"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/pl00013733", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1049849175", 
          "https://doi.org/10.1007/pl00013733"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00216271", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022795012", 
          "https://doi.org/10.1007/bf00216271"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature02771", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1036499414", 
          "https://doi.org/10.1038/nature02771"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature03301", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1030001284", 
          "https://doi.org/10.1038/nature03301"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/386665b0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1040233966", 
          "https://doi.org/10.1038/386665b0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/35036559", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016303772", 
          "https://doi.org/10.1038/35036559"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-006-0111-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021690503", 
          "https://doi.org/10.1007/s00382-006-0111-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nature01092a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044980256", 
          "https://doi.org/10.1038/nature01092a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002170907", 
          "https://doi.org/10.1007/s003820050009"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2007-07-03", 
    "datePublishedReg": "2007-07-03", 
    "description": "A linear analysis is applied to a multi-thousand member \u201cperturbed physics\" GCM ensemble to identify the dominant physical processes responsible for variation in climate sensitivity across the ensemble. Model simulations are provided by the distributed computing project, climate prediction.net . A principal component analysis of model radiative response reveals two dominant independent feedback processes, each largely controlled by a single parameter change. The leading EOF was well correlated with the value of the entrainment coefficient\u2014a parameter in the model\u2019s atmospheric convection scheme. Reducing this parameter increases high vertical level moisture causing an enhanced clear sky greenhouse effect both in the control simulation and in the response to greenhouse gas forcing. This effect is compensated by an increase in reflected solar radiation from low level cloud upon warming. A set of \u2018secondary\u2019 cloud formation parameters partly modulate the degree of shortwave compensation from low cloud formation. The second EOF was correlated with the scaling of ice fall speed in clouds which affects the extent of cloud cover in the control simulation. The most prominent feature in the EOF was an increase in longwave cloud forcing. The two leading EOFs account for 70% of the ensemble variance in \u03bb\u2014the global feedback parameter. Linear predictors of feedback strength from model climatology are applied to observational datasets to estimate real world values of the overall climate feedback parameter. The predictors are found using correlations across the ensemble. Differences between predictions are largely due to the differences in observational estimates for top of atmosphere shortwave fluxes. Our validation does not rule out all the strong tropical convective feedbacks leading to a large climate sensitivity.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-007-0280-7", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "2-3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "30"
      }
    ], 
    "keywords": [
      "climate sensitivity", 
      "control simulation", 
      "clear-sky greenhouse effect", 
      "ice fall speed", 
      "greenhouse gas forcing", 
      "low cloud formation", 
      "climate feedback parameter", 
      "larger climate sensitivities", 
      "low-level clouds", 
      "dominant physical processes", 
      "reflected solar radiation", 
      "feedback parameters", 
      "gas forcing", 
      "perturbed physics", 
      "GCM ensemble", 
      "longwave cloud", 
      "model climatology", 
      "second EOF", 
      "GCM simulations", 
      "shortwave flux", 
      "radiative response", 
      "convection scheme", 
      "convective feedback", 
      "cloud formation", 
      "observational datasets", 
      "level clouds", 
      "observational estimates", 
      "cloud cover", 
      "model simulations", 
      "fall speed", 
      "greenhouse effect", 
      "ensemble variance", 
      "physical processes", 
      "EOF", 
      "feedback processes", 
      "solar radiation", 
      "feedback strength", 
      "entrainment coefficient", 
      "feedback patterns", 
      "cloud", 
      "ensemble", 
      "world values", 
      "principal component analysis", 
      "climatology", 
      "forcing", 
      "warming", 
      "prominent feature", 
      "cover", 
      "moisture", 
      "formation parameters", 
      "flux", 
      "component analysis", 
      "single parameter change", 
      "real-world value", 
      "top", 
      "simulations", 
      "estimates", 
      "variation", 
      "formation", 
      "process", 
      "dataset", 
      "values", 
      "patterns", 
      "thousands", 
      "changes", 
      "radiation", 
      "linear analysis", 
      "parameters", 
      "feedback", 
      "increase", 
      "extent", 
      "prediction", 
      "analysis", 
      "correlation", 
      "parameter changes", 
      "physics", 
      "features", 
      "sensitivity", 
      "speed", 
      "project", 
      "validation", 
      "variance", 
      "differences", 
      "response", 
      "scaling", 
      "coefficient", 
      "set", 
      "degree", 
      "effect", 
      "strength", 
      "scheme", 
      "members", 
      "linear predictor", 
      "predictors", 
      "compensation", 
      "computing projects"
    ], 
    "name": "Towards constraining climate sensitivity by linear analysis of feedback patterns in thousands of perturbed-physics GCM simulations", 
    "pagination": "175-190", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1043959932"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-007-0280-7"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-007-0280-7", 
      "https://app.dimensions.ai/details/publication/pub.1043959932"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:07", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_433.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-007-0280-7"
  }
]

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

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

229 TRIPLES      22 PREDICATES      130 URIs      113 LITERALS      6 BLANK NODES