Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2006-08-30

AUTHORS

P. Pall, M. R. Allen, D. A. Stone

ABSTRACT

Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius–Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO2 forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius–Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius–Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius–Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60°N and 60°S. This could explain why extreme precipitation changes may be more detectable then mean changes. More... »

PAGES

351-363

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-006-0180-2

DOI

http://dx.doi.org/10.1007/s00382-006-0180-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038887268


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": "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pall", 
        "givenName": "P.", 
        "id": "sg:person.01271041156.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01271041156.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Allen", 
        "givenName": "M. R.", 
        "id": "sg:person.0600474550.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600474550.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK", 
          "id": "http://www.grid.ac/institutes/grid.4991.5", 
          "name": [
            "Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Stone", 
        "givenName": "D. A.", 
        "id": "sg:person.012044260032.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012044260032.31"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1023/a:1005488920935", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1044362125", 
          "https://doi.org/10.1023/a:1005488920935"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050009", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002170907", 
          "https://doi.org/10.1007/s003820050009"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820050010", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1015242699", 
          "https://doi.org/10.1007/s003820050010"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/a:1005432803188", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024681169", 
          "https://doi.org/10.1023/a:1005432803188"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-003-0365-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1012281891", 
          "https://doi.org/10.1007/s00382-003-0365-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-006-0121-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047210404", 
          "https://doi.org/10.1007/s00382-006-0121-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00198617", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020911415", 
          "https://doi.org/10.1007/bf00198617"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-001-0218-4", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1014489329", 
          "https://doi.org/10.1007/s00382-001-0218-4"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00382-002-0296-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1085138454", 
          "https://doi.org/10.1007/s00382-002-0296-y"
        ], 
        "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.10.1007/s003820050189", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1023218126", 
          "https://doi.org/10.1007/s003820050189"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2006-08-30", 
    "datePublishedReg": "2006-08-30", 
    "description": "Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius\u2013Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO2 forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius\u2013Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius\u2013Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius\u2013Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60\u00b0N and 60\u00b0S. This could explain why extreme precipitation changes may be more detectable then mean changes.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-006-0180-2", 
    "isAccessibleForFree": false, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.12915419", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "4", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "28"
      }
    ], 
    "keywords": [
      "extreme precipitation", 
      "precipitation changes", 
      "Clausius\u2013Clapeyron", 
      "global mean precipitation change", 
      "extreme precipitation changes", 
      "climate model projections", 
      "global energy budget", 
      "Clausius\u2013Clapeyron relation", 
      "availability of moisture", 
      "climate models", 
      "heavy events", 
      "mean precipitation", 
      "precipitation distribution", 
      "daily precipitation", 
      "HadCM3 model", 
      "transient CO2", 
      "seasonal scale", 
      "model projections", 
      "global mean", 
      "regional scale", 
      "greenhouse gases", 
      "energy budget", 
      "precipitation", 
      "constant relative humidity", 
      "uppermost quantiles", 
      "zonal analysis", 
      "relative humidity", 
      "moisture", 
      "CO2", 
      "volume of air", 
      "events", 
      "lower percentiles", 
      "tropics", 
      "budget", 
      "scale", 
      "mid", 
      "changes", 
      "season", 
      "humidity", 
      "gases", 
      "previous studies", 
      "constraints", 
      "projections", 
      "quantiles", 
      "region", 
      "great agreement", 
      "percentile", 
      "agreement", 
      "model", 
      "distribution", 
      "scenarios", 
      "air", 
      "patterns", 
      "intensity", 
      "prediction", 
      "availability", 
      "conditions", 
      "analysis", 
      "best predictor", 
      "volume", 
      "ambient", 
      "increase", 
      "study", 
      "means", 
      "nature", 
      "phenomenon", 
      "relation", 
      "levels", 
      "predictors"
    ], 
    "name": "Testing the Clausius\u2013Clapeyron constraint on changes in extreme precipitation under CO2 warming", 
    "pagination": "351-363", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1038887268"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-006-0180-2"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-006-0180-2", 
      "https://app.dimensions.ai/details/publication/pub.1038887268"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-12-01T06:25", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20221201/entities/gbq_results/article/article_411.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-006-0180-2"
  }
]
 

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/s00382-006-0180-2'

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/s00382-006-0180-2'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-006-0180-2'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-006-0180-2'


 

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

186 TRIPLES      21 PREDICATES      104 URIs      85 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-006-0180-2 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 schema:author Neb0b21c8f50342a8b7fca6b8f3ab66e3
4 schema:citation sg:pub.10.1007/bf00198617
5 sg:pub.10.1007/s00382-001-0218-4
6 sg:pub.10.1007/s00382-002-0296-y
7 sg:pub.10.1007/s00382-003-0365-x
8 sg:pub.10.1007/s00382-006-0121-0
9 sg:pub.10.1007/s003820050009
10 sg:pub.10.1007/s003820050010
11 sg:pub.10.1007/s003820050189
12 sg:pub.10.1023/a:1005432803188
13 sg:pub.10.1023/a:1005488920935
14 sg:pub.10.1038/nature01092
15 schema:datePublished 2006-08-30
16 schema:datePublishedReg 2006-08-30
17 schema:description Increases in extreme precipitation greater than in the mean under increased greenhouse gases have been reported in many climate models both on global and regional scales. It has been proposed in a previous study that whereas global-mean precipitation change is primarily constrained by the global energy budget, the heaviest events can be expected when effectively all the moisture in a volume of air is precipitated out, suggesting the intensity of these events increases with availability of moisture, and significantly faster than the global mean. Thus under conditions of constant relative humidity one might expect the Clausius–Clapeyron relation to give a constraint on changes in the uppermost quantiles of precipitation distributions. This study examines if the phenomenon manifests on regional and seasonal scales also. Zonal analysis of daily precipitation in the HadCM3 model under a transient CO2 forcing scenario shows increased extreme precipitation in the tropics accompanied by increased drying at lower percentiles. At mid- to high-latitudes there is increased precipitation over all percentiles. The greatest agreement with Clausius–Clapeyron predicted change occurs at mid-latitudes. This pattern is consistent with other climate model projections, and suggests that regions in which the nature of the ambient flows change little give the greatest agreement with Clausius–Clapeyron prediction. This is borne out by repeating the analyses at gridbox level and over season. Furthermore, it is found that Clausius–Clapeyron predicted change in extreme precipitation is a better predictor than directly using the change in mean precipitation, particularly between 60°N and 60°S. This could explain why extreme precipitation changes may be more detectable then mean changes.
18 schema:genre article
19 schema:isAccessibleForFree false
20 schema:isPartOf N05353ee39710483390dd91148d4b713c
21 N5701e157515048f9a5cb4d12805d8608
22 sg:journal.1049631
23 schema:keywords CO2
24 Clausius–Clapeyron
25 Clausius–Clapeyron relation
26 HadCM3 model
27 agreement
28 air
29 ambient
30 analysis
31 availability
32 availability of moisture
33 best predictor
34 budget
35 changes
36 climate model projections
37 climate models
38 conditions
39 constant relative humidity
40 constraints
41 daily precipitation
42 distribution
43 energy budget
44 events
45 extreme precipitation
46 extreme precipitation changes
47 gases
48 global energy budget
49 global mean
50 global mean precipitation change
51 great agreement
52 greenhouse gases
53 heavy events
54 humidity
55 increase
56 intensity
57 levels
58 lower percentiles
59 mean precipitation
60 means
61 mid
62 model
63 model projections
64 moisture
65 nature
66 patterns
67 percentile
68 phenomenon
69 precipitation
70 precipitation changes
71 precipitation distribution
72 prediction
73 predictors
74 previous studies
75 projections
76 quantiles
77 region
78 regional scale
79 relation
80 relative humidity
81 scale
82 scenarios
83 season
84 seasonal scale
85 study
86 transient CO2
87 tropics
88 uppermost quantiles
89 volume
90 volume of air
91 zonal analysis
92 schema:name Testing the Clausius–Clapeyron constraint on changes in extreme precipitation under CO2 warming
93 schema:pagination 351-363
94 schema:productId N708064cd783743739b9a6963e280ae1c
95 Nf86876356f2a4c029921a61fd70c78e5
96 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038887268
97 https://doi.org/10.1007/s00382-006-0180-2
98 schema:sdDatePublished 2022-12-01T06:25
99 schema:sdLicense https://scigraph.springernature.com/explorer/license/
100 schema:sdPublisher Nad2cf5147c374015a5a8f801d92612df
101 schema:url https://doi.org/10.1007/s00382-006-0180-2
102 sgo:license sg:explorer/license/
103 sgo:sdDataset articles
104 rdf:type schema:ScholarlyArticle
105 N05353ee39710483390dd91148d4b713c schema:volumeNumber 28
106 rdf:type schema:PublicationVolume
107 N12b6cfd2fdf54b28a9506cebb55714dd rdf:first sg:person.012044260032.31
108 rdf:rest rdf:nil
109 N5701e157515048f9a5cb4d12805d8608 schema:issueNumber 4
110 rdf:type schema:PublicationIssue
111 N708064cd783743739b9a6963e280ae1c schema:name dimensions_id
112 schema:value pub.1038887268
113 rdf:type schema:PropertyValue
114 Nad2cf5147c374015a5a8f801d92612df schema:name Springer Nature - SN SciGraph project
115 rdf:type schema:Organization
116 Naee488014a0747f5bdb0e275dd0384be rdf:first sg:person.0600474550.17
117 rdf:rest N12b6cfd2fdf54b28a9506cebb55714dd
118 Neb0b21c8f50342a8b7fca6b8f3ab66e3 rdf:first sg:person.01271041156.16
119 rdf:rest Naee488014a0747f5bdb0e275dd0384be
120 Nf86876356f2a4c029921a61fd70c78e5 schema:name doi
121 schema:value 10.1007/s00382-006-0180-2
122 rdf:type schema:PropertyValue
123 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
124 schema:name Earth Sciences
125 rdf:type schema:DefinedTerm
126 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
127 schema:name Atmospheric Sciences
128 rdf:type schema:DefinedTerm
129 sg:grant.12915419 http://pending.schema.org/fundedItem sg:pub.10.1007/s00382-006-0180-2
130 rdf:type schema:MonetaryGrant
131 sg:journal.1049631 schema:issn 0930-7575
132 1432-0894
133 schema:name Climate Dynamics
134 schema:publisher Springer Nature
135 rdf:type schema:Periodical
136 sg:person.012044260032.31 schema:affiliation grid-institutes:grid.4991.5
137 schema:familyName Stone
138 schema:givenName D. A.
139 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012044260032.31
140 rdf:type schema:Person
141 sg:person.01271041156.16 schema:affiliation grid-institutes:grid.4991.5
142 schema:familyName Pall
143 schema:givenName P.
144 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01271041156.16
145 rdf:type schema:Person
146 sg:person.0600474550.17 schema:affiliation grid-institutes:grid.4991.5
147 schema:familyName Allen
148 schema:givenName M. R.
149 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0600474550.17
150 rdf:type schema:Person
151 sg:pub.10.1007/bf00198617 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020911415
152 https://doi.org/10.1007/bf00198617
153 rdf:type schema:CreativeWork
154 sg:pub.10.1007/s00382-001-0218-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014489329
155 https://doi.org/10.1007/s00382-001-0218-4
156 rdf:type schema:CreativeWork
157 sg:pub.10.1007/s00382-002-0296-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1085138454
158 https://doi.org/10.1007/s00382-002-0296-y
159 rdf:type schema:CreativeWork
160 sg:pub.10.1007/s00382-003-0365-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1012281891
161 https://doi.org/10.1007/s00382-003-0365-x
162 rdf:type schema:CreativeWork
163 sg:pub.10.1007/s00382-006-0121-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047210404
164 https://doi.org/10.1007/s00382-006-0121-0
165 rdf:type schema:CreativeWork
166 sg:pub.10.1007/s003820050009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002170907
167 https://doi.org/10.1007/s003820050009
168 rdf:type schema:CreativeWork
169 sg:pub.10.1007/s003820050010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015242699
170 https://doi.org/10.1007/s003820050010
171 rdf:type schema:CreativeWork
172 sg:pub.10.1007/s003820050189 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023218126
173 https://doi.org/10.1007/s003820050189
174 rdf:type schema:CreativeWork
175 sg:pub.10.1023/a:1005432803188 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024681169
176 https://doi.org/10.1023/a:1005432803188
177 rdf:type schema:CreativeWork
178 sg:pub.10.1023/a:1005488920935 schema:sameAs https://app.dimensions.ai/details/publication/pub.1044362125
179 https://doi.org/10.1023/a:1005488920935
180 rdf:type schema:CreativeWork
181 sg:pub.10.1038/nature01092 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046916950
182 https://doi.org/10.1038/nature01092
183 rdf:type schema:CreativeWork
184 grid-institutes:grid.4991.5 schema:alternateName Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK
185 schema:name Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, Clarendon Laboratory, Parks Road, OX1 3PU, Oxford, UK
186 rdf:type schema:Organization
 




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


...