Ontology type: schema:ScholarlyArticle Open Access: True
2017-08
AUTHORSEun-Soon Im, Yeon-Woo Choi, Joong-Bae Ahn
ABSTRACTThis study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2–Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981–2100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement. More... »
PAGES1241-1254
http://scigraph.springernature.com/pub.10.1007/s00704-016-1846-2
DOIhttp://dx.doi.org/10.1007/s00704-016-1846-2
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1035750258
JSON-LD is the canonical representation for SciGraph data.
TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT
[
{
"@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json",
"about": [
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0401",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Atmospheric Sciences",
"type": "DefinedTerm"
},
{
"id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/04",
"inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/",
"name": "Earth Sciences",
"type": "DefinedTerm"
}
],
"author": [
{
"affiliation": {
"alternateName": "Singapore-MIT Alliance for Research and Technology",
"id": "https://www.grid.ac/institutes/grid.429485.6",
"name": [
"Singapore-MIT Alliance for Research and Technology (SMART), Center for Environmental Sensing and Modeling (CENSAM), Singapore, Singapore"
],
"type": "Organization"
},
"familyName": "Im",
"givenName": "Eun-Soon",
"id": "sg:person.011323725677.30",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011323725677.30"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Pusan National University",
"id": "https://www.grid.ac/institutes/grid.262229.f",
"name": [
"Division of Earth Environmental System, Pusan National University, Jangjeon 2-dong, Geumjeong-gu, 609-735, Busan, South Korea"
],
"type": "Organization"
},
"familyName": "Choi",
"givenName": "Yeon-Woo",
"id": "sg:person.011430350257.45",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011430350257.45"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "Pusan National University",
"id": "https://www.grid.ac/institutes/grid.262229.f",
"name": [
"Division of Earth Environmental System, Pusan National University, Jangjeon 2-dong, Geumjeong-gu, 609-735, Busan, South Korea"
],
"type": "Organization"
},
"familyName": "Ahn",
"givenName": "Joong-Bae",
"id": "sg:person.013522113526.78",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013522113526.78"
],
"type": "Person"
}
],
"citation": [
{
"id": "https://doi.org/10.1016/j.ejrh.2015.06.005",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000615369"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00382-014-2130-8",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001340989",
"https://doi.org/10.1007/s00382-014-2130-8"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/2008jcli2082.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001418457"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00382-015-2713-z",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1001971608",
"https://doi.org/10.1007/s00382-015-2713-z"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-005-0214-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002192542",
"https://doi.org/10.1007/s00704-005-0214-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-005-0214-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1002192542",
"https://doi.org/10.1007/s00704-005-0214-4"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/joc.1362",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1005538553"
],
"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.10.1038/nature08823",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1006069800",
"https://doi.org/10.1038/nature08823"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5194/gmd-4-1051-2011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007451883"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/2010jcli3594.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007621023"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00382-013-1841-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1007995856",
"https://doi.org/10.1007/s00382-013-1841-6"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/2003gl017130",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011144462"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/jcli3990.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012353608"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-014-1117-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1012393531",
"https://doi.org/10.1007/s10584-014-1117-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/jcli-d-14-00449.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1013433899"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2015jd023853",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1014178492"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature06207",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1015014641",
"https://doi.org/10.1038/nature06207"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/wcc.147",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1018192357"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13143-012-0031-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019131839",
"https://doi.org/10.1007/s13143-012-0031-5"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/2006gl025779",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1019546351"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13143-012-0010-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020163201",
"https://doi.org/10.1007/s13143-012-0010-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/1525-7541(2003)004<1147:tvgpcp>2.0.co;2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1023719243"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/jcli-d-13-00599.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1024501365"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2014gl062018",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1025546638"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5194/hessd-11-6167-2014",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026292304"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/jcli-d-11-00239.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1026976580"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13143-015-0066-5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1027955874",
"https://doi.org/10.1007/s13143-015-0066-5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13143-013-0053-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028235353",
"https://doi.org/10.1007/s13143-013-0053-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature09763",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028595909",
"https://doi.org/10.1038/nature09763"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-006-9225-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028654584",
"https://doi.org/10.1007/s10584-006-9225-0"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-006-9225-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028654584",
"https://doi.org/10.1007/s10584-006-9225-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/2009jcli3361.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1030337444"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/2011jcli3979.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1031978768"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1029/2008gl034126",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1033419197"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/jgrd.50877",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1034843462"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2013gl059158",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1036870193"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3390/atmos6050677",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037566383"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10584-006-9228-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1037801261",
"https://doi.org/10.1007/s10584-006-9228-x"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1098/rsta.2007.2076",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038869427"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/grl.50420",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1043144488"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/2013jd020693",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1044361911"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s13143-013-0023-0",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1045389378",
"https://doi.org/10.1007/s13143-013-0023-0"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.5194/gmdd-4-997-2011",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046278552"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature01092",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046916950",
"https://doi.org/10.1038/nature01092"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature01092",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046916950",
"https://doi.org/10.1038/nature01092"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1038/nature01092",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1046916950",
"https://doi.org/10.1038/nature01092"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1002/joc.4039",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049260284"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00704-013-1034-6",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049746054",
"https://doi.org/10.1007/s00704-013-1034-6"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1256/qj.04.101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051046355"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1256/qj.04.101",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051046355"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/jcli-d-14-00504.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051518576"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1175/bams-d-11-00094.1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1051805105"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1080/16742834.2015.11447252",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1058410150"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.1127/0941-2948/2008/0300",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1062699752"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3354/cr01018",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071158815"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3354/cr01084",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071158884"
],
"type": "CreativeWork"
},
{
"id": "https://doi.org/10.3354/cr01292",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1071159135"
],
"type": "CreativeWork"
}
],
"datePublished": "2017-08",
"datePublishedReg": "2017-08-01",
"description": "This study assesses the hydroclimatic response to global warming over East Asia from multi-model ensemble regional projections. Four different regional climate models (RCMs), namely, WRF, HadGEM3-RA, RegCM4, and GRIMs, are used for dynamical downscaling of the Hadley Centre Global Environmental Model version 2\u2013Atmosphere and Ocean (HadGEM2-AO) global projections forced by the representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Annual mean precipitation, hydroclimatic intensity index (HY-INT), and wet and dry extreme indices are analyzed to identify the robust behavior of hydroclimatic change in response to enhanced emission scenarios using high-resolution (12.5 km) and long-term (1981\u20132100) daily precipitation. Ensemble projections exhibit increased hydroclimatic intensity across the entire domain and under both the RCP scenarios. However, a geographical pattern with predominantly intensified HY-INT does not fully emerge in the mean precipitation change because HY-INT is tied to the changes in the precipitation characteristics rather than to those in the precipitation amount. All projections show an enhancement of high intensity precipitation and a reduction of weak intensity precipitation, which lead to a possible shift in hydroclimatic regime prone to an increase of both wet and dry extremes. In general, projections forced by the RCP8.5 scenario tend to produce a much stronger response than do those by the RCP4.5 scenario. However, the temperature increase under the RCP4.5 scenario is sufficiently large to induce significant changes in hydroclimatic intensity, despite the relatively uncertain change in mean precipitation. Likewise, the forced responses of HY-INT and the two extreme indices are more robust than that of mean precipitation, in terms of the statistical significance and model agreement.",
"genre": "research_article",
"id": "sg:pub.10.1007/s00704-016-1846-2",
"inLanguage": [
"en"
],
"isAccessibleForFree": true,
"isPartOf": [
{
"id": "sg:journal.1086664",
"issn": [
"0177-798X",
"1434-4483"
],
"name": "Theoretical and Applied Climatology",
"type": "Periodical"
},
{
"issueNumber": "3-4",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "129"
}
],
"name": "Robust intensification of hydroclimatic intensity over East Asia from multi-model ensemble regional projections",
"pagination": "1241-1254",
"productId": [
{
"name": "readcube_id",
"type": "PropertyValue",
"value": [
"aa4d2d76546779ea1b93477075fe1dd8712f3d08e34da946983d4c8aee04be76"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00704-016-1846-2"
]
},
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1035750258"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00704-016-1846-2",
"https://app.dimensions.ai/details/publication/pub.1035750258"
],
"sdDataset": "articles",
"sdDatePublished": "2019-04-11T12:39",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000363_0000000363/records_70043_00000001.jsonl",
"type": "ScholarlyArticle",
"url": "https://link.springer.com/10.1007%2Fs00704-016-1846-2"
}
]
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.10.1007/s00704-016-1846-2'
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/s00704-016-1846-2'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00704-016-1846-2'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00704-016-1846-2'
This table displays all metadata directly associated to this object as RDF triples.
251 TRIPLES
21 PREDICATES
79 URIs
19 LITERALS
7 BLANK NODES