Changes in daily precipitation under enhanced greenhouse conditions View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1997-09

AUTHORS

K. J. Hennessy, J. M. Gregory, J. F. B. Mitchell

ABSTRACT

. An increase in global average precipitation of about 10% is simulated by two global climate models with mixed layer oceans in response to an equilibrium doubling of carbon dioxide. The UKHI model was developed in the United Kingdom at the Hadley Centre for Climate Prediction and Research and the CSIRO9 model was developed in Australia by the CSIRO Division of Atmospheric Research. Regional changes in daily precipitation simulated by these models have been compared. Both models simulate fewer wet days in middle latitudes, and more wet days in high latitudes. At middle and low latitudes, there is a shift in the precipitation type toward more intense convective events, and fewer moderate non-convective events. At high latitudes, the precipitation type remains non-convective and all events simply get heavier, resulting in fewer light events and more moderate and heavy events. The probability of heavy daily precipitation increases by more than 50% in many locations. Extreme events with a probability of 1% or less were considered in terms of return periods (the average period between events of the same magnitude). For a given return period of at least 1 y, precipitation intensity in Europe, USA, Australia and India increases by 10 to 25%. For a given precipitation intensity, the average return period becomes shorter by a factor of 2 to 5. Given that larger changes in frequency occur for heavier simulated events, changes may be even greater for more-extreme events not resolved by models. More... »

PAGES

667-680

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s003820050189

DOI

http://dx.doi.org/10.1007/s003820050189

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023218126


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "CSIRO Division of Atmospheric Research, Private Bag No. 1, Aspendale, Victoria, 3195, Australia, AU", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "CSIRO Division of Atmospheric Research, Private Bag No. 1, Aspendale, Victoria, 3195, Australia, AU"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hennessy", 
        "givenName": "K. J.", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB", 
          "id": "http://www.grid.ac/institutes/grid.17100.37", 
          "name": [
            "Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gregory", 
        "givenName": "J. M.", 
        "id": "sg:person.0776106250.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776106250.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB", 
          "id": "http://www.grid.ac/institutes/grid.17100.37", 
          "name": [
            "Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mitchell", 
        "givenName": "J. F. B.", 
        "id": "sg:person.012141527247.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012141527247.99"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "1997-09", 
    "datePublishedReg": "1997-09-01", 
    "description": "Abstract.\u2002An increase in global average precipitation of about 10% is simulated by two global climate models with mixed layer oceans in response to an equilibrium doubling of carbon dioxide. The UKHI model was developed in the United Kingdom at the Hadley Centre for Climate Prediction and Research and the CSIRO9 model was developed in Australia by the CSIRO Division of Atmospheric Research. Regional changes in daily precipitation simulated by these models have been compared. Both models simulate fewer wet days in middle latitudes, and more wet days in high latitudes. At middle and low latitudes, there is a shift in the precipitation type toward more intense convective events, and fewer moderate non-convective events. At high latitudes, the precipitation type remains non-convective and all events simply get heavier, resulting in fewer light events and more moderate and heavy events. The probability of heavy daily precipitation increases by more than 50% in many locations. Extreme events with a probability of 1% or less were considered in terms of return periods (the average period between events of the same magnitude). For a given return period of at least 1\u2005y, precipitation intensity in Europe, USA, Australia and India increases by 10 to 25%. For a given precipitation intensity, the average return period becomes shorter by a factor of 2 to 5. Given that larger changes in frequency occur for heavier simulated events, changes may be even greater for more-extreme events not resolved by models.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s003820050189", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "9", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "13"
      }
    ], 
    "keywords": [
      "return period", 
      "daily precipitation", 
      "wet days", 
      "precipitation intensity", 
      "precipitation type", 
      "extreme events", 
      "high latitudes", 
      "mixed layer ocean", 
      "global average precipitation", 
      "global climate models", 
      "intense convective events", 
      "average return period", 
      "layer ocean", 
      "Hadley Centre", 
      "climate models", 
      "equilibrium doubling", 
      "climate predictions", 
      "heavy events", 
      "precipitation increases", 
      "convective events", 
      "average precipitation", 
      "Atmospheric Research", 
      "low latitudes", 
      "middle latitudes", 
      "light events", 
      "regional changes", 
      "latitudes", 
      "precipitation", 
      "CSIRO Division", 
      "large changes", 
      "carbon dioxide", 
      "events", 
      "Australia", 
      "Ocean", 
      "period", 
      "changes", 
      "USA", 
      "India", 
      "model", 
      "intensity", 
      "greenhouse conditions", 
      "location", 
      "dioxide", 
      "doubling", 
      "Europe", 
      "shift", 
      "prediction", 
      "increase", 
      "conditions", 
      "types", 
      "center", 
      "days", 
      "probability", 
      "response", 
      "frequency", 
      "United Kingdom", 
      "research", 
      "terms", 
      "factors", 
      "division", 
      "Kingdom", 
      "UKHI model", 
      "CSIRO9 model", 
      "moderate non-convective events", 
      "non-convective events", 
      "heavy daily precipitation increases", 
      "daily precipitation increases"
    ], 
    "name": "Changes in daily precipitation under enhanced greenhouse conditions", 
    "pagination": "667-680", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1023218126"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s003820050189"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s003820050189", 
      "https://app.dimensions.ai/details/publication/pub.1023218126"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-12-01T19:10", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211201/entities/gbq_results/article/article_290.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s003820050189"
  }
]
 

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/s003820050189'

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/s003820050189'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s003820050189'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s003820050189'


 

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

141 TRIPLES      21 PREDICATES      93 URIs      85 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s003820050189 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author N9f883836606849b5bc13e2400fa93aba
4 schema:datePublished 1997-09
5 schema:datePublishedReg 1997-09-01
6 schema:description Abstract. An increase in global average precipitation of about 10% is simulated by two global climate models with mixed layer oceans in response to an equilibrium doubling of carbon dioxide. The UKHI model was developed in the United Kingdom at the Hadley Centre for Climate Prediction and Research and the CSIRO9 model was developed in Australia by the CSIRO Division of Atmospheric Research. Regional changes in daily precipitation simulated by these models have been compared. Both models simulate fewer wet days in middle latitudes, and more wet days in high latitudes. At middle and low latitudes, there is a shift in the precipitation type toward more intense convective events, and fewer moderate non-convective events. At high latitudes, the precipitation type remains non-convective and all events simply get heavier, resulting in fewer light events and more moderate and heavy events. The probability of heavy daily precipitation increases by more than 50% in many locations. Extreme events with a probability of 1% or less were considered in terms of return periods (the average period between events of the same magnitude). For a given return period of at least 1 y, precipitation intensity in Europe, USA, Australia and India increases by 10 to 25%. For a given precipitation intensity, the average return period becomes shorter by a factor of 2 to 5. Given that larger changes in frequency occur for heavier simulated events, changes may be even greater for more-extreme events not resolved by models.
7 schema:genre article
8 schema:inLanguage en
9 schema:isAccessibleForFree false
10 schema:isPartOf N8872de24108b46f4bb6e1996056ee67f
11 N8c2efc692dd34c2e8d1daff9a0623411
12 sg:journal.1049631
13 schema:keywords Atmospheric Research
14 Australia
15 CSIRO Division
16 CSIRO9 model
17 Europe
18 Hadley Centre
19 India
20 Kingdom
21 Ocean
22 UKHI model
23 USA
24 United Kingdom
25 average precipitation
26 average return period
27 carbon dioxide
28 center
29 changes
30 climate models
31 climate predictions
32 conditions
33 convective events
34 daily precipitation
35 daily precipitation increases
36 days
37 dioxide
38 division
39 doubling
40 equilibrium doubling
41 events
42 extreme events
43 factors
44 frequency
45 global average precipitation
46 global climate models
47 greenhouse conditions
48 heavy daily precipitation increases
49 heavy events
50 high latitudes
51 increase
52 intense convective events
53 intensity
54 large changes
55 latitudes
56 layer ocean
57 light events
58 location
59 low latitudes
60 middle latitudes
61 mixed layer ocean
62 model
63 moderate non-convective events
64 non-convective events
65 period
66 precipitation
67 precipitation increases
68 precipitation intensity
69 precipitation type
70 prediction
71 probability
72 regional changes
73 research
74 response
75 return period
76 shift
77 terms
78 types
79 wet days
80 schema:name Changes in daily precipitation under enhanced greenhouse conditions
81 schema:pagination 667-680
82 schema:productId N359f7e32d24e47df8553a8a57570c42c
83 Nea7dad8a039746e68dd4191d17c17376
84 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023218126
85 https://doi.org/10.1007/s003820050189
86 schema:sdDatePublished 2021-12-01T19:10
87 schema:sdLicense https://scigraph.springernature.com/explorer/license/
88 schema:sdPublisher N7025d6cdf276430e9f50681d76f0cc86
89 schema:url https://doi.org/10.1007/s003820050189
90 sgo:license sg:explorer/license/
91 sgo:sdDataset articles
92 rdf:type schema:ScholarlyArticle
93 N359f7e32d24e47df8553a8a57570c42c schema:name dimensions_id
94 schema:value pub.1023218126
95 rdf:type schema:PropertyValue
96 N7025d6cdf276430e9f50681d76f0cc86 schema:name Springer Nature - SN SciGraph project
97 rdf:type schema:Organization
98 N7a515a5909c04d95abd43199e7aaee50 schema:affiliation grid-institutes:None
99 schema:familyName Hennessy
100 schema:givenName K. J.
101 rdf:type schema:Person
102 N8872de24108b46f4bb6e1996056ee67f schema:issueNumber 9
103 rdf:type schema:PublicationIssue
104 N8c2efc692dd34c2e8d1daff9a0623411 schema:volumeNumber 13
105 rdf:type schema:PublicationVolume
106 N9f883836606849b5bc13e2400fa93aba rdf:first N7a515a5909c04d95abd43199e7aaee50
107 rdf:rest Ncbd51f26954141418a235e551b9a4b52
108 Na1a0923e5dab4f96b24fd77ca1deb75f rdf:first sg:person.012141527247.99
109 rdf:rest rdf:nil
110 Ncbd51f26954141418a235e551b9a4b52 rdf:first sg:person.0776106250.41
111 rdf:rest Na1a0923e5dab4f96b24fd77ca1deb75f
112 Nea7dad8a039746e68dd4191d17c17376 schema:name doi
113 schema:value 10.1007/s003820050189
114 rdf:type schema:PropertyValue
115 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
116 schema:name Earth Sciences
117 rdf:type schema:DefinedTerm
118 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
119 schema:name Atmospheric Sciences
120 rdf:type schema:DefinedTerm
121 sg:journal.1049631 schema:issn 0930-7575
122 1432-0894
123 schema:name Climate Dynamics
124 schema:publisher Springer Nature
125 rdf:type schema:Periodical
126 sg:person.012141527247.99 schema:affiliation grid-institutes:grid.17100.37
127 schema:familyName Mitchell
128 schema:givenName J. F. B.
129 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012141527247.99
130 rdf:type schema:Person
131 sg:person.0776106250.41 schema:affiliation grid-institutes:grid.17100.37
132 schema:familyName Gregory
133 schema:givenName J. M.
134 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0776106250.41
135 rdf:type schema:Person
136 grid-institutes:None schema:alternateName CSIRO Division of Atmospheric Research, Private Bag No. 1, Aspendale, Victoria, 3195, Australia, AU
137 schema:name CSIRO Division of Atmospheric Research, Private Bag No. 1, Aspendale, Victoria, 3195, Australia, AU
138 rdf:type schema:Organization
139 grid-institutes:grid.17100.37 schema:alternateName Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB
140 schema:name Hadley Centre for Climate Prediction and Research, United Kingdom Meteorological Office, London Road, Bracknell, Berkshire, RG12 2SY, UK, GB
141 rdf:type schema:Organization
 




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


...