Impacts of greenhouse gases and aerosol direct and indirect effects on clouds and radiation in atmospheric GCM simulations of the ... View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2004-11-09

AUTHORS

Johannes Quaas, Olivier Boucher, Jean-Louis Dufresne, Hervé Le Treut

ABSTRACT

Among anthropogenic perturbations of the Earth’s atmosphere, greenhouse gases and aerosols are considered to have a major impact on the energy budget through their impact on radiative fluxes. We use three ensembles of simulations with the LMDZ general circulation model to investigate the radiative impacts of five species of greenhouse gases (CO2, CH4, N2O, CFC-11 and CFC-12) and sulfate aerosols for the period 1930–1989. Since our focus is on the atmospheric changes in clouds and radiation from greenhouse gases and aerosols, we prescribed sea-surface temperatures in these simulations. Besides the direct impact on radiation through the greenhouse effect and scattering of sunlight by aerosols, strong radiative impacts of both perturbations through changes in cloudiness are analysed. The increase in greenhouse gas concentration leads to a reduction of clouds at all atmospheric levels, thus decreasing the total greenhouse effect in the longwave spectrum and increasing absorption of solar radiation by reduction of cloud albedo. Increasing anthropogenic aerosol burden results in a decrease in high-level cloud cover through a cooling of the atmosphere, and an increase in the low-level cloud cover through the second aerosol indirect effect. The trend in low-level cloud lifetime due to aerosols is quantified to 0.5 min day−1 decade−1 for the simulation period. The different changes in high (decrease) and low-level (increase) cloudiness due to the response of cloud processes to aerosols impact shortwave radiation in a contrariwise manner, and the net effect is slightly positive. The total aerosol effect including the aerosol direct and first indirect effects remains strongly negative. More... »

PAGES

779-789

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00382-004-0475-0

DOI

http://dx.doi.org/10.1007/s00382-004-0475-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1034321830


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"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0405", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Oceanography", 
        "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": "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.463916.f", 
          "name": [
            "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Quaas", 
        "givenName": "Johannes", 
        "id": "sg:person.0723513371.80", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723513371.80"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire d\u2019Optique Atmosph\u00e9rique, Unversit\u00e9 de Lille/C.N.R.S., Villeneuve d\u2019Ascq, France", 
          "id": "http://www.grid.ac/institutes/grid.497265.b", 
          "name": [
            "Laboratoire d\u2019Optique Atmosph\u00e9rique, Unversit\u00e9 de Lille/C.N.R.S., Villeneuve d\u2019Ascq, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boucher", 
        "givenName": "Olivier", 
        "id": "sg:person.01301253676.11", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301253676.11"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.463916.f", 
          "name": [
            "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Dufresne", 
        "givenName": "Jean-Louis", 
        "id": "sg:person.01075255253.14", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01075255253.14"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France", 
          "id": "http://www.grid.ac/institutes/grid.463916.f", 
          "name": [
            "Laboratoire de M\u00e9t\u00e9orologie Dynamique, IPSL/C.N.R.S., Paris, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Le Treut", 
        "givenName": "Herv\u00e9", 
        "id": "sg:person.01077116450.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077116450.69"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/339365a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1038295809", 
          "https://doi.org/10.1038/339365a0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s003820100185", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043744883", 
          "https://doi.org/10.1007/s003820100185"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf00251808", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1042853097", 
          "https://doi.org/10.1007/bf00251808"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1038/340437a0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1043969196", 
          "https://doi.org/10.1038/340437a0"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2004-11-09", 
    "datePublishedReg": "2004-11-09", 
    "description": "Among anthropogenic perturbations of the Earth\u2019s atmosphere, greenhouse gases and aerosols are considered to have a major impact on the energy budget through their impact on radiative fluxes. We use three ensembles of simulations with the LMDZ general circulation model to investigate the radiative impacts of five species of greenhouse gases (CO2, CH4, N2O, CFC-11 and CFC-12) and sulfate aerosols for the period 1930\u20131989. Since our focus is on the atmospheric changes in clouds and radiation from greenhouse gases and aerosols, we prescribed sea-surface temperatures in these simulations. Besides the direct impact on radiation through the greenhouse effect and scattering of sunlight by aerosols, strong radiative impacts of both perturbations through changes in cloudiness are analysed. The increase in greenhouse gas concentration leads to a reduction of clouds at all atmospheric levels, thus decreasing the total greenhouse effect in the longwave spectrum and increasing absorption of solar radiation by reduction of cloud albedo. Increasing anthropogenic aerosol burden results in a decrease in high-level cloud cover through a cooling of the atmosphere, and an increase in the low-level cloud cover through the second aerosol indirect effect. The trend in low-level cloud lifetime due to aerosols is quantified to 0.5\u00a0min\u00a0day\u22121\u00a0decade\u22121 for the simulation period. The different changes in high (decrease) and low-level (increase) cloudiness due to the response of cloud processes to aerosols impact shortwave radiation in a contrariwise manner, and the net effect is slightly positive. The total aerosol effect including the aerosol direct and first indirect effects remains strongly negative.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s00382-004-0475-0", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": [
      {
        "id": "sg:journal.1049631", 
        "issn": [
          "0930-7575", 
          "1432-0894"
        ], 
        "name": "Climate Dynamics", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7-8", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "23"
      }
    ], 
    "keywords": [
      "greenhouse gases", 
      "radiative impact", 
      "cloud cover", 
      "high-level cloud cover", 
      "LMDZ general circulation model", 
      "low-level cloud cover", 
      "greenhouse effect", 
      "second aerosol indirect effect", 
      "atmospheric GCM simulations", 
      "total aerosol effect", 
      "sea surface temperature", 
      "strong radiative impact", 
      "general circulation model", 
      "aerosol indirect effect", 
      "first indirect effect", 
      "greenhouse gas concentrations", 
      "low-level cloudiness", 
      "ensemble of simulations", 
      "reduction of clouds", 
      "scattering of sunlight", 
      "total greenhouse effect", 
      "GCM simulations", 
      "circulation model", 
      "aerosol effect", 
      "cloud albedo", 
      "cloud processes", 
      "shortwave radiation", 
      "radiative fluxes", 
      "longwave spectrum", 
      "atmospheric changes", 
      "cloud lifetime", 
      "anthropogenic perturbations", 
      "Earth's atmosphere", 
      "atmospheric levels", 
      "simulation period", 
      "aerosols", 
      "energy budget", 
      "gas concentration", 
      "solar radiation", 
      "cloudiness", 
      "atmosphere", 
      "cloud", 
      "cover", 
      "gases", 
      "net effect", 
      "indirect effects", 
      "albedo", 
      "major impact", 
      "budget", 
      "flux", 
      "cooling", 
      "direct impact", 
      "radiation", 
      "period", 
      "impact", 
      "perturbations", 
      "changes", 
      "different changes", 
      "ensemble", 
      "simulations", 
      "trends", 
      "temperature", 
      "sunlight", 
      "concentration", 
      "increase", 
      "model", 
      "process", 
      "decrease", 
      "burden results", 
      "reduction", 
      "effect", 
      "results", 
      "species", 
      "spectra", 
      "absorption", 
      "response", 
      "levels", 
      "scattering", 
      "lifetime", 
      "focus", 
      "min", 
      "manner"
    ], 
    "name": "Impacts of greenhouse gases and aerosol direct and indirect effects on clouds and radiation in atmospheric GCM simulations of the 1930\u20131989 period", 
    "pagination": "779-789", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1034321830"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s00382-004-0475-0"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s00382-004-0475-0", 
      "https://app.dimensions.ai/details/publication/pub.1034321830"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-05-10T09:50", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/article/article_382.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s00382-004-0475-0"
  }
]
 

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/s00382-004-0475-0'

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/s00382-004-0475-0'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00382-004-0475-0'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00382-004-0475-0'


 

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

188 TRIPLES      22 PREDICATES      113 URIs      99 LITERALS      6 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s00382-004-0475-0 schema:about anzsrc-for:04
2 anzsrc-for:0401
3 anzsrc-for:0405
4 anzsrc-for:0406
5 schema:author Neff4b64ec9c94d10bceb8217fe9a6fc2
6 schema:citation sg:pub.10.1007/bf00251808
7 sg:pub.10.1007/s003820100185
8 sg:pub.10.1038/339365a0
9 sg:pub.10.1038/340437a0
10 schema:datePublished 2004-11-09
11 schema:datePublishedReg 2004-11-09
12 schema:description Among anthropogenic perturbations of the Earth’s atmosphere, greenhouse gases and aerosols are considered to have a major impact on the energy budget through their impact on radiative fluxes. We use three ensembles of simulations with the LMDZ general circulation model to investigate the radiative impacts of five species of greenhouse gases (CO2, CH4, N2O, CFC-11 and CFC-12) and sulfate aerosols for the period 1930–1989. Since our focus is on the atmospheric changes in clouds and radiation from greenhouse gases and aerosols, we prescribed sea-surface temperatures in these simulations. Besides the direct impact on radiation through the greenhouse effect and scattering of sunlight by aerosols, strong radiative impacts of both perturbations through changes in cloudiness are analysed. The increase in greenhouse gas concentration leads to a reduction of clouds at all atmospheric levels, thus decreasing the total greenhouse effect in the longwave spectrum and increasing absorption of solar radiation by reduction of cloud albedo. Increasing anthropogenic aerosol burden results in a decrease in high-level cloud cover through a cooling of the atmosphere, and an increase in the low-level cloud cover through the second aerosol indirect effect. The trend in low-level cloud lifetime due to aerosols is quantified to 0.5 min day−1 decade−1 for the simulation period. The different changes in high (decrease) and low-level (increase) cloudiness due to the response of cloud processes to aerosols impact shortwave radiation in a contrariwise manner, and the net effect is slightly positive. The total aerosol effect including the aerosol direct and first indirect effects remains strongly negative.
13 schema:genre article
14 schema:inLanguage en
15 schema:isAccessibleForFree true
16 schema:isPartOf N78b906ad959d484cb574c6062dbe2a0d
17 Nc5a2807a52784c5faf9e3f10e33998d4
18 sg:journal.1049631
19 schema:keywords Earth's atmosphere
20 GCM simulations
21 LMDZ general circulation model
22 absorption
23 aerosol effect
24 aerosol indirect effect
25 aerosols
26 albedo
27 anthropogenic perturbations
28 atmosphere
29 atmospheric GCM simulations
30 atmospheric changes
31 atmospheric levels
32 budget
33 burden results
34 changes
35 circulation model
36 cloud
37 cloud albedo
38 cloud cover
39 cloud lifetime
40 cloud processes
41 cloudiness
42 concentration
43 cooling
44 cover
45 decrease
46 different changes
47 direct impact
48 effect
49 energy budget
50 ensemble
51 ensemble of simulations
52 first indirect effect
53 flux
54 focus
55 gas concentration
56 gases
57 general circulation model
58 greenhouse effect
59 greenhouse gas concentrations
60 greenhouse gases
61 high-level cloud cover
62 impact
63 increase
64 indirect effects
65 levels
66 lifetime
67 longwave spectrum
68 low-level cloud cover
69 low-level cloudiness
70 major impact
71 manner
72 min
73 model
74 net effect
75 period
76 perturbations
77 process
78 radiation
79 radiative fluxes
80 radiative impact
81 reduction
82 reduction of clouds
83 response
84 results
85 scattering
86 scattering of sunlight
87 sea surface temperature
88 second aerosol indirect effect
89 shortwave radiation
90 simulation period
91 simulations
92 solar radiation
93 species
94 spectra
95 strong radiative impact
96 sunlight
97 temperature
98 total aerosol effect
99 total greenhouse effect
100 trends
101 schema:name Impacts of greenhouse gases and aerosol direct and indirect effects on clouds and radiation in atmospheric GCM simulations of the 1930–1989 period
102 schema:pagination 779-789
103 schema:productId N0a75107ec080414b9739d59da9b4d3ab
104 N76859f4334924542b4613413e8003a8a
105 schema:sameAs https://app.dimensions.ai/details/publication/pub.1034321830
106 https://doi.org/10.1007/s00382-004-0475-0
107 schema:sdDatePublished 2022-05-10T09:50
108 schema:sdLicense https://scigraph.springernature.com/explorer/license/
109 schema:sdPublisher N8aabfa52ad8147b8b96b319ee7068cb7
110 schema:url https://doi.org/10.1007/s00382-004-0475-0
111 sgo:license sg:explorer/license/
112 sgo:sdDataset articles
113 rdf:type schema:ScholarlyArticle
114 N0a75107ec080414b9739d59da9b4d3ab schema:name dimensions_id
115 schema:value pub.1034321830
116 rdf:type schema:PropertyValue
117 N2b0cb2f3de7c4b179c8584b400f5b03a rdf:first sg:person.01077116450.69
118 rdf:rest rdf:nil
119 N3fef187dbe19403ca4e84d55e7fd087d rdf:first sg:person.01075255253.14
120 rdf:rest N2b0cb2f3de7c4b179c8584b400f5b03a
121 N76859f4334924542b4613413e8003a8a schema:name doi
122 schema:value 10.1007/s00382-004-0475-0
123 rdf:type schema:PropertyValue
124 N78b906ad959d484cb574c6062dbe2a0d schema:volumeNumber 23
125 rdf:type schema:PublicationVolume
126 N8aabfa52ad8147b8b96b319ee7068cb7 schema:name Springer Nature - SN SciGraph project
127 rdf:type schema:Organization
128 Nc5a2807a52784c5faf9e3f10e33998d4 schema:issueNumber 7-8
129 rdf:type schema:PublicationIssue
130 Neff4b64ec9c94d10bceb8217fe9a6fc2 rdf:first sg:person.0723513371.80
131 rdf:rest Nf54584ecb2944e529232d66c2a61c265
132 Nf54584ecb2944e529232d66c2a61c265 rdf:first sg:person.01301253676.11
133 rdf:rest N3fef187dbe19403ca4e84d55e7fd087d
134 anzsrc-for:04 schema:inDefinedTermSet anzsrc-for:
135 schema:name Earth Sciences
136 rdf:type schema:DefinedTerm
137 anzsrc-for:0401 schema:inDefinedTermSet anzsrc-for:
138 schema:name Atmospheric Sciences
139 rdf:type schema:DefinedTerm
140 anzsrc-for:0405 schema:inDefinedTermSet anzsrc-for:
141 schema:name Oceanography
142 rdf:type schema:DefinedTerm
143 anzsrc-for:0406 schema:inDefinedTermSet anzsrc-for:
144 schema:name Physical Geography and Environmental Geoscience
145 rdf:type schema:DefinedTerm
146 sg:journal.1049631 schema:issn 0930-7575
147 1432-0894
148 schema:name Climate Dynamics
149 schema:publisher Springer Nature
150 rdf:type schema:Periodical
151 sg:person.01075255253.14 schema:affiliation grid-institutes:grid.463916.f
152 schema:familyName Dufresne
153 schema:givenName Jean-Louis
154 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01075255253.14
155 rdf:type schema:Person
156 sg:person.01077116450.69 schema:affiliation grid-institutes:grid.463916.f
157 schema:familyName Le Treut
158 schema:givenName Hervé
159 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01077116450.69
160 rdf:type schema:Person
161 sg:person.01301253676.11 schema:affiliation grid-institutes:grid.497265.b
162 schema:familyName Boucher
163 schema:givenName Olivier
164 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01301253676.11
165 rdf:type schema:Person
166 sg:person.0723513371.80 schema:affiliation grid-institutes:grid.463916.f
167 schema:familyName Quaas
168 schema:givenName Johannes
169 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0723513371.80
170 rdf:type schema:Person
171 sg:pub.10.1007/bf00251808 schema:sameAs https://app.dimensions.ai/details/publication/pub.1042853097
172 https://doi.org/10.1007/bf00251808
173 rdf:type schema:CreativeWork
174 sg:pub.10.1007/s003820100185 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043744883
175 https://doi.org/10.1007/s003820100185
176 rdf:type schema:CreativeWork
177 sg:pub.10.1038/339365a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1038295809
178 https://doi.org/10.1038/339365a0
179 rdf:type schema:CreativeWork
180 sg:pub.10.1038/340437a0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043969196
181 https://doi.org/10.1038/340437a0
182 rdf:type schema:CreativeWork
183 grid-institutes:grid.463916.f schema:alternateName Laboratoire de Météorologie Dynamique, IPSL/C.N.R.S., Paris, France
184 schema:name Laboratoire de Météorologie Dynamique, IPSL/C.N.R.S., Paris, France
185 rdf:type schema:Organization
186 grid-institutes:grid.497265.b schema:alternateName Laboratoire d’Optique Atmosphérique, Unversité de Lille/C.N.R.S., Villeneuve d’Ascq, France
187 schema:name Laboratoire d’Optique Atmosphérique, Unversité de Lille/C.N.R.S., Villeneuve d’Ascq, France
188 rdf:type schema:Organization
 




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


...