Ontology type: schema:ScholarlyArticle Open Access: True
2020-09-23
AUTHORSYukiko Imada, Hiroaki Kawase, Masahiro Watanabe, Miki Arai, Hideo Shiogama, Izuru Takayabu
ABSTRACTRisk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning. More... »
PAGES37
http://scigraph.springernature.com/pub.10.1038/s41612-020-00141-y
DOIhttp://dx.doi.org/10.1038/s41612-020-00141-y
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1131097425
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": "Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan",
"id": "http://www.grid.ac/institutes/grid.237586.d",
"name": [
"Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, 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"
},
{
"affiliation": {
"alternateName": "Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan",
"id": "http://www.grid.ac/institutes/grid.237586.d",
"name": [
"Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan"
],
"type": "Organization"
},
"familyName": "Kawase",
"givenName": "Hiroaki",
"id": "sg:person.016017526721.89",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016017526721.89"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Atmosphere and Ocean Research Institute, the University of Tokyo, 5-1-5 Kashiwanoha, 277-8568, Kashiwa, 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, 277-8568, Kashiwa, 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": "Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, 236-0001, Yokohama, Kanagawa, Japan",
"id": "http://www.grid.ac/institutes/grid.410588.0",
"name": [
"Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, 236-0001, Yokohama, Kanagawa, Japan"
],
"type": "Organization"
},
"familyName": "Arai",
"givenName": "Miki",
"id": "sg:person.07757733353.49",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07757733353.49"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba, Ibaraki, Japan",
"id": "http://www.grid.ac/institutes/grid.140139.e",
"name": [
"National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba, Ibaraki, 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": "Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan",
"id": "http://www.grid.ac/institutes/grid.237586.d",
"name": [
"Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan"
],
"type": "Organization"
},
"familyName": "Takayabu",
"givenName": "Izuru",
"id": "sg:person.07650420461.01",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07650420461.01"
],
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1038/s41586-018-0673-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1109832848",
"https://doi.org/10.1038/s41586-018-0673-2"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nclimate3287",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1085411043",
"https://doi.org/10.1038/nclimate3287"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature09762",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043530920",
"https://doi.org/10.1038/nature09762"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nclimate2927",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011320779",
"https://doi.org/10.1038/nclimate2927"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nclimate2971",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043159698",
"https://doi.org/10.1038/nclimate2971"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/d41586-018-05839-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1105906567",
"https://doi.org/10.1038/d41586-018-05839-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s40641-016-0033-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1017429140",
"https://doi.org/10.1007/s40641-016-0033-y"
],
"type": "CreativeWork"
}
],
"datePublished": "2020-09-23",
"datePublishedReg": "2020-09-23",
"description": "Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning.",
"genre": "article",
"id": "sg:pub.10.1038/s41612-020-00141-y",
"inLanguage": "en",
"isAccessibleForFree": true,
"isFundedItemOf": [
{
"id": "sg:grant.7530579",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1290454",
"issn": [
"2397-3722"
],
"name": "npj Climate and Atmospheric Science",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "3"
}
],
"keywords": [
"regional climate model",
"general circulation model",
"synoptic fields",
"rainfall events",
"non-hydrostatic regional climate model",
"large-scale circulation fields",
"regional extreme rainfall",
"large ensemble simulations",
"regional rainfall events",
"heavy rainfall events",
"heavy rain events",
"heavy rainfall occurrence",
"static pressure patterns",
"pressure patterns",
"particular weather events",
"extreme temperature events",
"scale of thousands",
"anthropogenic warming",
"rainfall system",
"climate models",
"circulation model",
"extreme rainfall",
"circulation fields",
"rainfall occurrence",
"rainfall statistics",
"ensemble simulations",
"event attribution",
"rain events",
"heavy rainfall",
"precipitous terrain",
"moisture increase",
"temperature events",
"warmer climate",
"weather events",
"heat waves",
"global warming",
"climate change adaptation",
"western Japan",
"attribution science",
"dynamical components",
"background conditioning",
"rainfall",
"warming",
"favorable conditions",
"change adaptation",
"event occurrence",
"events",
"dynamic component",
"climate",
"occurrence",
"uncertainty",
"terrain",
"boundary conditions",
"regional geography",
"patterns",
"waves",
"simulations",
"Japan",
"scale",
"field",
"conditions",
"model",
"attribution",
"components",
"thousands",
"alterations",
"increase",
"geography",
"statistics",
"degree",
"system",
"products",
"science",
"EA",
"adaptation",
"perspective",
"likelihood",
"effective approach",
"cases",
"approach",
"regard",
"risk",
"Herein",
"findings",
"conditioning"
],
"name": "Advanced risk-based event attribution for heavy regional rainfall events",
"pagination": "37",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1131097425"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1038/s41612-020-00141-y"
]
}
],
"sameAs": [
"https://doi.org/10.1038/s41612-020-00141-y",
"https://app.dimensions.ai/details/publication/pub.1131097425"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:37",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_848.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1038/s41612-020-00141-y"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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/s41612-020-00141-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.10.1038/s41612-020-00141-y'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41612-020-00141-y'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41612-020-00141-y'
This table displays all metadata directly associated to this object as RDF triples.
216 TRIPLES
22 PREDICATES
117 URIs
102 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1038/s41612-020-00141-y | schema:about | anzsrc-for:04 |
2 | ″ | ″ | anzsrc-for:0401 |
3 | ″ | schema:author | N1c381cdf30b549e89503cb5a9eb447e1 |
4 | ″ | schema:citation | sg:pub.10.1007/s40641-016-0033-y |
5 | ″ | ″ | sg:pub.10.1038/d41586-018-05839-x |
6 | ″ | ″ | sg:pub.10.1038/nature09762 |
7 | ″ | ″ | sg:pub.10.1038/nclimate2927 |
8 | ″ | ″ | sg:pub.10.1038/nclimate2971 |
9 | ″ | ″ | sg:pub.10.1038/nclimate3287 |
10 | ″ | ″ | sg:pub.10.1038/s41586-018-0673-2 |
11 | ″ | schema:datePublished | 2020-09-23 |
12 | ″ | schema:datePublishedReg | 2020-09-23 |
13 | ″ | schema:description | Risk-based event attribution (EA) science involves probabilistically estimating alterations of the likelihoods of particular weather events, such as heat waves and heavy rainfall, owing to global warming, and has been considered as an effective approach with regard to climate change adaptation. However, risk-based EA for heavy rain events remains challenging because, unlike extreme temperature events, which often have a scale of thousands of kilometres, heavy rainfall occurrences depend on mesoscale rainfall systems and regional geographies that cannot be resolved using general circulation models (GCMs) that are currently employed for risk-based EA. Herein, we use GCM large-ensemble simulations and high-resolution downscaled products with a 20-km non-hydrostatic regional climate model (RCM), whose boundary conditions are obtained from all available GCM ensemble simulations, to show that anthropogenic warming increased the risk of two record-breaking regional heavy rainfall events in 2017 and 2018 over western Japan. The events are examined from the perspective of rainfall statistics simulated by the RCM and from the perspective of background large-scale circulation fields simulated by the GCM. In the 2017 case, precipitous terrain and a static pressure pattern in the synoptic field helped reduce uncertainty in the dynamical components, whereas in the 2018 case, a static pressure pattern in the synoptic field provided favourable conditions for event occurrence through a moisture increase under warmer climate. These findings show that successful risk-based EA for regional extreme rainfall relies on the degree to which uncertainty induced by the dynamic components is reduced by background conditioning. |
14 | ″ | schema:genre | article |
15 | ″ | schema:inLanguage | en |
16 | ″ | schema:isAccessibleForFree | true |
17 | ″ | schema:isPartOf | Ne3bc46f47def49fcb2045a1d62ff894f |
18 | ″ | ″ | Nea11376204a944878d4df2e00efdb756 |
19 | ″ | ″ | sg:journal.1290454 |
20 | ″ | schema:keywords | EA |
21 | ″ | ″ | Herein |
22 | ″ | ″ | Japan |
23 | ″ | ″ | adaptation |
24 | ″ | ″ | alterations |
25 | ″ | ″ | anthropogenic warming |
26 | ″ | ″ | approach |
27 | ″ | ″ | attribution |
28 | ″ | ″ | attribution science |
29 | ″ | ″ | background conditioning |
30 | ″ | ″ | boundary conditions |
31 | ″ | ″ | cases |
32 | ″ | ″ | change adaptation |
33 | ″ | ″ | circulation fields |
34 | ″ | ″ | circulation model |
35 | ″ | ″ | climate |
36 | ″ | ″ | climate change adaptation |
37 | ″ | ″ | climate models |
38 | ″ | ″ | components |
39 | ″ | ″ | conditioning |
40 | ″ | ″ | conditions |
41 | ″ | ″ | degree |
42 | ″ | ″ | dynamic component |
43 | ″ | ″ | dynamical components |
44 | ″ | ″ | effective approach |
45 | ″ | ″ | ensemble simulations |
46 | ″ | ″ | event attribution |
47 | ″ | ″ | event occurrence |
48 | ″ | ″ | events |
49 | ″ | ″ | extreme rainfall |
50 | ″ | ″ | extreme temperature events |
51 | ″ | ″ | favorable conditions |
52 | ″ | ″ | field |
53 | ″ | ″ | findings |
54 | ″ | ″ | general circulation model |
55 | ″ | ″ | geography |
56 | ″ | ″ | global warming |
57 | ″ | ″ | heat waves |
58 | ″ | ″ | heavy rain events |
59 | ″ | ″ | heavy rainfall |
60 | ″ | ″ | heavy rainfall events |
61 | ″ | ″ | heavy rainfall occurrence |
62 | ″ | ″ | increase |
63 | ″ | ″ | large ensemble simulations |
64 | ″ | ″ | large-scale circulation fields |
65 | ″ | ″ | likelihood |
66 | ″ | ″ | model |
67 | ″ | ″ | moisture increase |
68 | ″ | ″ | non-hydrostatic regional climate model |
69 | ″ | ″ | occurrence |
70 | ″ | ″ | particular weather events |
71 | ″ | ″ | patterns |
72 | ″ | ″ | perspective |
73 | ″ | ″ | precipitous terrain |
74 | ″ | ″ | pressure patterns |
75 | ″ | ″ | products |
76 | ″ | ″ | rain events |
77 | ″ | ″ | rainfall |
78 | ″ | ″ | rainfall events |
79 | ″ | ″ | rainfall occurrence |
80 | ″ | ″ | rainfall statistics |
81 | ″ | ″ | rainfall system |
82 | ″ | ″ | regard |
83 | ″ | ″ | regional climate model |
84 | ″ | ″ | regional extreme rainfall |
85 | ″ | ″ | regional geography |
86 | ″ | ″ | regional rainfall events |
87 | ″ | ″ | risk |
88 | ″ | ″ | scale |
89 | ″ | ″ | scale of thousands |
90 | ″ | ″ | science |
91 | ″ | ″ | simulations |
92 | ″ | ″ | static pressure patterns |
93 | ″ | ″ | statistics |
94 | ″ | ″ | synoptic fields |
95 | ″ | ″ | system |
96 | ″ | ″ | temperature events |
97 | ″ | ″ | terrain |
98 | ″ | ″ | thousands |
99 | ″ | ″ | uncertainty |
100 | ″ | ″ | warmer climate |
101 | ″ | ″ | warming |
102 | ″ | ″ | waves |
103 | ″ | ″ | weather events |
104 | ″ | ″ | western Japan |
105 | ″ | schema:name | Advanced risk-based event attribution for heavy regional rainfall events |
106 | ″ | schema:pagination | 37 |
107 | ″ | schema:productId | N2ef43547c7fd416481fb7a4d1419c532 |
108 | ″ | ″ | N6e953355889e4c099cb6f7a1289588ee |
109 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1131097425 |
110 | ″ | ″ | https://doi.org/10.1038/s41612-020-00141-y |
111 | ″ | schema:sdDatePublished | 2022-05-20T07:37 |
112 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
113 | ″ | schema:sdPublisher | N5957bc306c6744dd9835696e7322ee40 |
114 | ″ | schema:url | https://doi.org/10.1038/s41612-020-00141-y |
115 | ″ | sgo:license | sg:explorer/license/ |
116 | ″ | sgo:sdDataset | articles |
117 | ″ | rdf:type | schema:ScholarlyArticle |
118 | N1c381cdf30b549e89503cb5a9eb447e1 | rdf:first | sg:person.014327166275.50 |
119 | ″ | rdf:rest | Nc02edeaa50e842c8ab89016c0ab06003 |
120 | N2ef43547c7fd416481fb7a4d1419c532 | schema:name | dimensions_id |
121 | ″ | schema:value | pub.1131097425 |
122 | ″ | rdf:type | schema:PropertyValue |
123 | N425673e7ae5f492393a2fd0709935d1f | rdf:first | sg:person.07757733353.49 |
124 | ″ | rdf:rest | Nda7f85085c6947c98ba894556b92f8d6 |
125 | N5957bc306c6744dd9835696e7322ee40 | schema:name | Springer Nature - SN SciGraph project |
126 | ″ | rdf:type | schema:Organization |
127 | N6e953355889e4c099cb6f7a1289588ee | schema:name | doi |
128 | ″ | schema:value | 10.1038/s41612-020-00141-y |
129 | ″ | rdf:type | schema:PropertyValue |
130 | N8a9167d07b274b3f8898f00bc5282aad | rdf:first | sg:person.016316106377.80 |
131 | ″ | rdf:rest | N425673e7ae5f492393a2fd0709935d1f |
132 | Nbad54aa099e3441fb7910dbeaaefd1e3 | rdf:first | sg:person.07650420461.01 |
133 | ″ | rdf:rest | rdf:nil |
134 | Nc02edeaa50e842c8ab89016c0ab06003 | rdf:first | sg:person.016017526721.89 |
135 | ″ | rdf:rest | N8a9167d07b274b3f8898f00bc5282aad |
136 | Nda7f85085c6947c98ba894556b92f8d6 | rdf:first | sg:person.011356656533.12 |
137 | ″ | rdf:rest | Nbad54aa099e3441fb7910dbeaaefd1e3 |
138 | Ne3bc46f47def49fcb2045a1d62ff894f | schema:issueNumber | 1 |
139 | ″ | rdf:type | schema:PublicationIssue |
140 | Nea11376204a944878d4df2e00efdb756 | schema:volumeNumber | 3 |
141 | ″ | rdf:type | schema:PublicationVolume |
142 | anzsrc-for:04 | schema:inDefinedTermSet | anzsrc-for: |
143 | ″ | schema:name | Earth Sciences |
144 | ″ | rdf:type | schema:DefinedTerm |
145 | anzsrc-for:0401 | schema:inDefinedTermSet | anzsrc-for: |
146 | ″ | schema:name | Atmospheric Sciences |
147 | ″ | rdf:type | schema:DefinedTerm |
148 | sg:grant.7530579 | http://pending.schema.org/fundedItem | sg:pub.10.1038/s41612-020-00141-y |
149 | ″ | rdf:type | schema:MonetaryGrant |
150 | sg:journal.1290454 | schema:issn | 2397-3722 |
151 | ″ | schema:name | npj Climate and Atmospheric Science |
152 | ″ | schema:publisher | Springer Nature |
153 | ″ | rdf:type | schema:Periodical |
154 | sg:person.011356656533.12 | schema:affiliation | grid-institutes:grid.140139.e |
155 | ″ | schema:familyName | Shiogama |
156 | ″ | schema:givenName | Hideo |
157 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011356656533.12 |
158 | ″ | rdf:type | schema:Person |
159 | sg:person.014327166275.50 | schema:affiliation | grid-institutes:grid.237586.d |
160 | ″ | schema:familyName | Imada |
161 | ″ | schema:givenName | Yukiko |
162 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.014327166275.50 |
163 | ″ | rdf:type | schema:Person |
164 | sg:person.016017526721.89 | schema:affiliation | grid-institutes:grid.237586.d |
165 | ″ | schema:familyName | Kawase |
166 | ″ | schema:givenName | Hiroaki |
167 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016017526721.89 |
168 | ″ | rdf:type | schema:Person |
169 | sg:person.016316106377.80 | schema:affiliation | grid-institutes:grid.26999.3d |
170 | ″ | schema:familyName | Watanabe |
171 | ″ | schema:givenName | Masahiro |
172 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016316106377.80 |
173 | ″ | rdf:type | schema:Person |
174 | sg:person.07650420461.01 | schema:affiliation | grid-institutes:grid.237586.d |
175 | ″ | schema:familyName | Takayabu |
176 | ″ | schema:givenName | Izuru |
177 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07650420461.01 |
178 | ″ | rdf:type | schema:Person |
179 | sg:person.07757733353.49 | schema:affiliation | grid-institutes:grid.410588.0 |
180 | ″ | schema:familyName | Arai |
181 | ″ | schema:givenName | Miki |
182 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07757733353.49 |
183 | ″ | rdf:type | schema:Person |
184 | sg:pub.10.1007/s40641-016-0033-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1017429140 |
185 | ″ | ″ | https://doi.org/10.1007/s40641-016-0033-y |
186 | ″ | rdf:type | schema:CreativeWork |
187 | sg:pub.10.1038/d41586-018-05839-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1105906567 |
188 | ″ | ″ | https://doi.org/10.1038/d41586-018-05839-x |
189 | ″ | rdf:type | schema:CreativeWork |
190 | sg:pub.10.1038/nature09762 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1043530920 |
191 | ″ | ″ | https://doi.org/10.1038/nature09762 |
192 | ″ | rdf:type | schema:CreativeWork |
193 | sg:pub.10.1038/nclimate2927 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1011320779 |
194 | ″ | ″ | https://doi.org/10.1038/nclimate2927 |
195 | ″ | rdf:type | schema:CreativeWork |
196 | sg:pub.10.1038/nclimate2971 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1043159698 |
197 | ″ | ″ | https://doi.org/10.1038/nclimate2971 |
198 | ″ | rdf:type | schema:CreativeWork |
199 | sg:pub.10.1038/nclimate3287 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1085411043 |
200 | ″ | ″ | https://doi.org/10.1038/nclimate3287 |
201 | ″ | rdf:type | schema:CreativeWork |
202 | sg:pub.10.1038/s41586-018-0673-2 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1109832848 |
203 | ″ | ″ | https://doi.org/10.1038/s41586-018-0673-2 |
204 | ″ | rdf:type | schema:CreativeWork |
205 | grid-institutes:grid.140139.e | schema:alternateName | National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba, Ibaraki, Japan |
206 | ″ | schema:name | National Institute for Environmental Studies, 16-2 Onogawa, 305-8506, Tsukuba, Ibaraki, Japan |
207 | ″ | rdf:type | schema:Organization |
208 | grid-institutes:grid.237586.d | schema:alternateName | Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan |
209 | ″ | schema:name | Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, 305-0052, Tsukuba, Ibaraki, Japan |
210 | ″ | rdf:type | schema:Organization |
211 | grid-institutes:grid.26999.3d | schema:alternateName | Atmosphere and Ocean Research Institute, the University of Tokyo, 5-1-5 Kashiwanoha, 277-8568, Kashiwa, Chiba, Japan |
212 | ″ | schema:name | Atmosphere and Ocean Research Institute, the University of Tokyo, 5-1-5 Kashiwanoha, 277-8568, Kashiwa, Chiba, Japan |
213 | ″ | rdf:type | schema:Organization |
214 | grid-institutes:grid.410588.0 | schema:alternateName | Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, 236-0001, Yokohama, Kanagawa, Japan |
215 | ″ | schema:name | Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, 236-0001, Yokohama, Kanagawa, Japan |
216 | ″ | rdf:type | schema:Organization |