Predicting future uncertainty constraints on global warming projections View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-01-11

AUTHORS

H. Shiogama, D. Stone, S. Emori, K. Takahashi, S. Mori, A. Maeda, Y. Ishizaki, M. R. Allen

ABSTRACT

Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change. More... »

PAGES

18903

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep18903

DOI

http://dx.doi.org/10.1038/srep18903

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1027872095

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/26750491


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/2008/0406", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Physical Geography and Environmental Geoscience", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shiogama", 
        "givenName": "H.", 
        "id": "sg:person.011356656533.12", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA", 
          "id": "http://www.grid.ac/institutes/grid.184769.5", 
          "name": [
            "Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stone", 
        "givenName": "D.", 
        "id": "sg:person.012044260032.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012044260032.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Emori", 
        "givenName": "S.", 
        "id": "sg:person.016137466477.58", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137466477.58"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takahashi", 
        "givenName": "K.", 
        "id": "sg:person.01011266747.73", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01011266747.73"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba 278-8510, Japan", 
          "id": "http://www.grid.ac/institutes/grid.143643.7", 
          "name": [
            "Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba 278-8510, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mori", 
        "givenName": "S.", 
        "id": "sg:person.011602002215.72", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011602002215.72"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan", 
          "id": "http://www.grid.ac/institutes/grid.26999.3d", 
          "name": [
            "Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Maeda", 
        "givenName": "A.", 
        "id": "sg:person.010136470451.39", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010136470451.39"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan", 
          "id": "http://www.grid.ac/institutes/grid.140139.e", 
          "name": [
            "Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Ishizaki", 
        "givenName": "Y.", 
        "id": "sg:person.015751527531.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015751527531.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Physics, University of Oxford, OX1 3QY, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "School of Geography and the Environment, University of Oxford, OX1 3QY, Oxford, UK", 
            "Department of Physics, University of Oxford, OX1 3QY, 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.10.1038/ngeo2371", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021098855", 
          "https://doi.org/10.1038/ngeo2371"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/416723a", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1022821020", 
          "https://doi.org/10.1038/416723a"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-003-0313-9", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047305108", 
          "https://doi.org/10.1007/s00382-003-0313-9"
        ], 
        "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.10.1038/nature12829", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016927016", 
          "https://doi.org/10.1038/nature12829"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-009-9765-1", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045216985", 
          "https://doi.org/10.1007/s10584-009-9765-1"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-010-9800-2", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1000824091", 
          "https://doi.org/10.1007/s10584-010-9800-2"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/ncomms1252", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1021882550", 
          "https://doi.org/10.1038/ncomms1252"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-014-1223-z", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051870262", 
          "https://doi.org/10.1007/s10584-014-1223-z"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate1783", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1048169539", 
          "https://doi.org/10.1038/nclimate1783"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate1758", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015832949", 
          "https://doi.org/10.1038/nclimate1758"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050291", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1007895196", 
          "https://doi.org/10.1007/s003820050291"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/nclimate2077", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1045188824", 
          "https://doi.org/10.1038/nclimate2077"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10584-008-9406-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050947087", 
          "https://doi.org/10.1007/s10584-008-9406-0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2016-01-11", 
    "datePublishedReg": "2016-01-11", 
    "description": "Projections of global mean temperature changes (\u0394T) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by \"current knowledge\" of the \u0394Ts uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of \u0394T can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the \u0394Ts uncertainty in the 2040 s by 2029, and more than 60% of the \u0394Ts uncertainty in the 2090\u2009s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2\u2009\u00b0C (3\u2009\u00b0C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.", 
    "genre": "article", 
    "id": "sg:pub.10.1038/srep18903", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.6128687", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2768897", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5849596", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.5848515", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1045337", 
        "issn": [
          "2045-2322"
        ], 
        "name": "Scientific Reports", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "6"
      }
    ], 
    "keywords": [
      "Representative Concentration Pathways", 
      "Model Intercomparison Project Phase 5 ensemble", 
      "global mean temperature change", 
      "high forcing scenario", 
      "global warming projections", 
      "global climate models", 
      "surface air temperature", 
      "mean temperature change", 
      "current observation network", 
      "anthropogenic climate change", 
      "climate models", 
      "forcing scenario", 
      "warming projections", 
      "concentration pathways", 
      "observation network", 
      "true timing", 
      "climate change", 
      "air temperature", 
      "future reductions", 
      "pseudo observations", 
      "temperature changes", 
      "intrinsic uncertainty", 
      "uncertainty", 
      "future observations", 
      "uncertainty constraints", 
      "projections", 
      "ensemble", 
      "timing", 
      "changes", 
      "\u0394T", 
      "scenarios", 
      "temperature", 
      "years", 
      "future progress", 
      "constraints", 
      "simulations", 
      "model", 
      "error", 
      "understanding", 
      "future", 
      "current knowledge", 
      "world", 
      "reduction", 
      "advances", 
      "results", 
      "network", 
      "mechanism", 
      "knowledge", 
      "progress", 
      "decision-making strategies", 
      "pathway", 
      "way", 
      "strategies", 
      "advantages", 
      "policy discourse", 
      "observations", 
      "discourse", 
      "climate policy discourse", 
      "\u0394Ts uncertainty", 
      "likely future reductions", 
      "Intercomparison Project Phase 5 ensemble", 
      "Project Phase 5 ensemble", 
      "Phase 5 ensemble", 
      "uncertainties of \u0394T", 
      "threshold 20 (30) years", 
      "sequential decision-making strategies", 
      "future uncertainty constraints"
    ], 
    "name": "Predicting future uncertainty constraints on global warming projections", 
    "pagination": "18903", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1027872095"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1038/srep18903"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "26750491"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1038/srep18903", 
      "https://app.dimensions.ai/details/publication/pub.1027872095"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:38", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_687.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1038/srep18903"
  }
]
 

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.10.1038/srep18903'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/srep18903'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/srep18903'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/srep18903'


 

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

256 TRIPLES      22 PREDICATES      107 URIs      85 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1038/srep18903 schema:about anzsrc-for:04
2 anzsrc-for:0406
3 schema:author N760175f443884d189d1b7152011b27a1
4 schema:citation sg:pub.10.1007/s00382-003-0313-9
5 sg:pub.10.1007/s003820050291
6 sg:pub.10.1007/s10584-008-9406-0
7 sg:pub.10.1007/s10584-009-9765-1
8 sg:pub.10.1007/s10584-010-9800-2
9 sg:pub.10.1007/s10584-014-1223-z
10 sg:pub.10.1038/35036559
11 sg:pub.10.1038/416723a
12 sg:pub.10.1038/nature12829
13 sg:pub.10.1038/nclimate1758
14 sg:pub.10.1038/nclimate1783
15 sg:pub.10.1038/nclimate2077
16 sg:pub.10.1038/ncomms1252
17 sg:pub.10.1038/ngeo2371
18 schema:datePublished 2016-01-11
19 schema:datePublishedReg 2016-01-11
20 schema:description Projections of global mean temperature changes (ΔT) in the future are associated with intrinsic uncertainties. Much climate policy discourse has been guided by "current knowledge" of the ΔTs uncertainty, ignoring the likely future reductions of the uncertainty, because a mechanism for predicting these reductions is lacking. By using simulations of Global Climate Models from the Coupled Model Intercomparison Project Phase 5 ensemble as pseudo past and future observations, we estimate how fast and in what way the uncertainties of ΔT can decline when the current observation network of surface air temperature is maintained. At least in the world of pseudo observations under the Representative Concentration Pathways (RCPs), we can drastically reduce more than 50% of the ΔTs uncertainty in the 2040 s by 2029, and more than 60% of the ΔTs uncertainty in the 2090 s by 2049. Under the highest forcing scenario of RCPs, we can predict the true timing of passing the 2 °C (3 °C) warming threshold 20 (30) years in advance with errors less than 10 years. These results demonstrate potential for sequential decision-making strategies to take advantage of future progress in understanding of anthropogenic climate change.
21 schema:genre article
22 schema:inLanguage en
23 schema:isAccessibleForFree true
24 schema:isPartOf N67a6ce754bb9414e84d7cfcdb831182b
25 Nfb17811c15c64fb0ad220e47ab8231fc
26 sg:journal.1045337
27 schema:keywords Intercomparison Project Phase 5 ensemble
28 Model Intercomparison Project Phase 5 ensemble
29 Phase 5 ensemble
30 Project Phase 5 ensemble
31 Representative Concentration Pathways
32 advances
33 advantages
34 air temperature
35 anthropogenic climate change
36 changes
37 climate change
38 climate models
39 climate policy discourse
40 concentration pathways
41 constraints
42 current knowledge
43 current observation network
44 decision-making strategies
45 discourse
46 ensemble
47 error
48 forcing scenario
49 future
50 future observations
51 future progress
52 future reductions
53 future uncertainty constraints
54 global climate models
55 global mean temperature change
56 global warming projections
57 high forcing scenario
58 intrinsic uncertainty
59 knowledge
60 likely future reductions
61 mean temperature change
62 mechanism
63 model
64 network
65 observation network
66 observations
67 pathway
68 policy discourse
69 progress
70 projections
71 pseudo observations
72 reduction
73 results
74 scenarios
75 sequential decision-making strategies
76 simulations
77 strategies
78 surface air temperature
79 temperature
80 temperature changes
81 threshold 20 (30) years
82 timing
83 true timing
84 uncertainties of ΔT
85 uncertainty
86 uncertainty constraints
87 understanding
88 warming projections
89 way
90 world
91 years
92 ΔT
93 ΔTs uncertainty
94 schema:name Predicting future uncertainty constraints on global warming projections
95 schema:pagination 18903
96 schema:productId N6dc254ce09b342a1bf12e974015704f2
97 Nd79b0cae86d6411780d0e63c2939fa66
98 Nee7abce561ca4a00b32869f3edda6e61
99 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027872095
100 https://doi.org/10.1038/srep18903
101 schema:sdDatePublished 2022-01-01T18:38
102 schema:sdLicense https://scigraph.springernature.com/explorer/license/
103 schema:sdPublisher N3197b45b8c1f4ba0809cbc66e1c2ea97
104 schema:url https://doi.org/10.1038/srep18903
105 sgo:license sg:explorer/license/
106 sgo:sdDataset articles
107 rdf:type schema:ScholarlyArticle
108 N113b83a8313548b2ba3eeb3848443da7 rdf:first sg:person.011602002215.72
109 rdf:rest Nf8bb7a819d5148028d9b64c84447c3ac
110 N3197b45b8c1f4ba0809cbc66e1c2ea97 schema:name Springer Nature - SN SciGraph project
111 rdf:type schema:Organization
112 N5e59a42f573c489787d3b6d10f7c3e88 rdf:first sg:person.0600474550.17
113 rdf:rest rdf:nil
114 N67a6ce754bb9414e84d7cfcdb831182b schema:issueNumber 1
115 rdf:type schema:PublicationIssue
116 N6dc254ce09b342a1bf12e974015704f2 schema:name doi
117 schema:value 10.1038/srep18903
118 rdf:type schema:PropertyValue
119 N75168e173abb45c194700343103ba591 rdf:first sg:person.016137466477.58
120 rdf:rest Nd4813f5cf0d4435f9f7ef57740e38cbc
121 N760175f443884d189d1b7152011b27a1 rdf:first sg:person.011356656533.12
122 rdf:rest Nd0a80bcaee1a4e0e8719951318428825
123 N851ce439ee6f4477b58c9af8949b3621 rdf:first sg:person.015751527531.19
124 rdf:rest N5e59a42f573c489787d3b6d10f7c3e88
125 Nd0a80bcaee1a4e0e8719951318428825 rdf:first sg:person.012044260032.31
126 rdf:rest N75168e173abb45c194700343103ba591
127 Nd4813f5cf0d4435f9f7ef57740e38cbc rdf:first sg:person.01011266747.73
128 rdf:rest N113b83a8313548b2ba3eeb3848443da7
129 Nd79b0cae86d6411780d0e63c2939fa66 schema:name dimensions_id
130 schema:value pub.1027872095
131 rdf:type schema:PropertyValue
132 Nee7abce561ca4a00b32869f3edda6e61 schema:name pubmed_id
133 schema:value 26750491
134 rdf:type schema:PropertyValue
135 Nf8bb7a819d5148028d9b64c84447c3ac rdf:first sg:person.010136470451.39
136 rdf:rest N851ce439ee6f4477b58c9af8949b3621
137 Nfb17811c15c64fb0ad220e47ab8231fc schema:volumeNumber 6
138 rdf:type schema:PublicationVolume
139 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
140 schema:name Earth Sciences
141 rdf:type schema:DefinedTerm
142 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
143 schema:name Physical Geography and Environmental Geoscience
144 rdf:type schema:DefinedTerm
145 sg:grant.2768897 http://pending.schema.org/fundedItem sg:pub.10.1038/srep18903
146 rdf:type schema:MonetaryGrant
147 sg:grant.5848515 http://pending.schema.org/fundedItem sg:pub.10.1038/srep18903
148 rdf:type schema:MonetaryGrant
149 sg:grant.5849596 http://pending.schema.org/fundedItem sg:pub.10.1038/srep18903
150 rdf:type schema:MonetaryGrant
151 sg:grant.6128687 http://pending.schema.org/fundedItem sg:pub.10.1038/srep18903
152 rdf:type schema:MonetaryGrant
153 sg:journal.1045337 schema:issn 2045-2322
154 schema:name Scientific Reports
155 schema:publisher Springer Nature
156 rdf:type schema:Periodical
157 sg:person.01011266747.73 schema:affiliation grid-institutes:grid.140139.e
158 schema:familyName Takahashi
159 schema:givenName K.
160 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01011266747.73
161 rdf:type schema:Person
162 sg:person.010136470451.39 schema:affiliation grid-institutes:grid.26999.3d
163 schema:familyName Maeda
164 schema:givenName A.
165 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010136470451.39
166 rdf:type schema:Person
167 sg:person.011356656533.12 schema:affiliation grid-institutes:grid.140139.e
168 schema:familyName Shiogama
169 schema:givenName H.
170 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12
171 rdf:type schema:Person
172 sg:person.011602002215.72 schema:affiliation grid-institutes:grid.143643.7
173 schema:familyName Mori
174 schema:givenName S.
175 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011602002215.72
176 rdf:type schema:Person
177 sg:person.012044260032.31 schema:affiliation grid-institutes:grid.184769.5
178 schema:familyName Stone
179 schema:givenName D.
180 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012044260032.31
181 rdf:type schema:Person
182 sg:person.015751527531.19 schema:affiliation grid-institutes:grid.140139.e
183 schema:familyName Ishizaki
184 schema:givenName Y.
185 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015751527531.19
186 rdf:type schema:Person
187 sg:person.016137466477.58 schema:affiliation grid-institutes:grid.140139.e
188 schema:familyName Emori
189 schema:givenName S.
190 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016137466477.58
191 rdf:type schema:Person
192 sg:person.0600474550.17 schema:affiliation grid-institutes:grid.4991.5
193 schema:familyName Allen
194 schema:givenName M. R.
195 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600474550.17
196 rdf:type schema:Person
197 sg:pub.10.1007/s00382-003-0313-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047305108
198 https://doi.org/10.1007/s00382-003-0313-9
199 rdf:type schema:CreativeWork
200 sg:pub.10.1007/s003820050291 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007895196
201 https://doi.org/10.1007/s003820050291
202 rdf:type schema:CreativeWork
203 sg:pub.10.1007/s10584-008-9406-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050947087
204 https://doi.org/10.1007/s10584-008-9406-0
205 rdf:type schema:CreativeWork
206 sg:pub.10.1007/s10584-009-9765-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045216985
207 https://doi.org/10.1007/s10584-009-9765-1
208 rdf:type schema:CreativeWork
209 sg:pub.10.1007/s10584-010-9800-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000824091
210 https://doi.org/10.1007/s10584-010-9800-2
211 rdf:type schema:CreativeWork
212 sg:pub.10.1007/s10584-014-1223-z schema:sameAs https://app.dimensions.ai/details/publication/pub.1051870262
213 https://doi.org/10.1007/s10584-014-1223-z
214 rdf:type schema:CreativeWork
215 sg:pub.10.1038/35036559 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016303772
216 https://doi.org/10.1038/35036559
217 rdf:type schema:CreativeWork
218 sg:pub.10.1038/416723a schema:sameAs https://app.dimensions.ai/details/publication/pub.1022821020
219 https://doi.org/10.1038/416723a
220 rdf:type schema:CreativeWork
221 sg:pub.10.1038/nature12829 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016927016
222 https://doi.org/10.1038/nature12829
223 rdf:type schema:CreativeWork
224 sg:pub.10.1038/nclimate1758 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015832949
225 https://doi.org/10.1038/nclimate1758
226 rdf:type schema:CreativeWork
227 sg:pub.10.1038/nclimate1783 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048169539
228 https://doi.org/10.1038/nclimate1783
229 rdf:type schema:CreativeWork
230 sg:pub.10.1038/nclimate2077 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045188824
231 https://doi.org/10.1038/nclimate2077
232 rdf:type schema:CreativeWork
233 sg:pub.10.1038/ncomms1252 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021882550
234 https://doi.org/10.1038/ncomms1252
235 rdf:type schema:CreativeWork
236 sg:pub.10.1038/ngeo2371 schema:sameAs https://app.dimensions.ai/details/publication/pub.1021098855
237 https://doi.org/10.1038/ngeo2371
238 rdf:type schema:CreativeWork
239 grid-institutes:grid.140139.e schema:alternateName Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
240 Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
241 schema:name Center for Global Environmental Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
242 Center for Social and Environmental Systems Research, National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki 305-8506, Japan
243 rdf:type schema:Organization
244 grid-institutes:grid.143643.7 schema:alternateName Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba 278-8510, Japan
245 schema:name Department of Industrial Administration, Faculty of Science and Technology, Tokyo University of Science, Chiba 278-8510, Japan
246 rdf:type schema:Organization
247 grid-institutes:grid.184769.5 schema:alternateName Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
248 schema:name Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
249 rdf:type schema:Organization
250 grid-institutes:grid.26999.3d schema:alternateName Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan
251 schema:name Graduate School of Arts and Sciences, The University of Tokyo, 3-8-1 Komaba, Meguro-ku, Tokyo, 153-8902, Japan
252 rdf:type schema:Organization
253 grid-institutes:grid.4991.5 schema:alternateName Department of Physics, University of Oxford, OX1 3QY, Oxford, UK
254 schema:name Department of Physics, University of Oxford, OX1 3QY, Oxford, UK
255 School of Geography and the Environment, University of Oxford, OX1 3QY, Oxford, UK
256 rdf:type schema:Organization
 




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


...