Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations
Identifieur interne : 006442 ( Main/Exploration ); précédent : 006441; suivant : 006443Evaluation of global observations‐based evapotranspiration datasets and IPCC AR4 simulations
Auteurs : B. Mueller [Suisse] ; S. I. Seneviratne [Suisse] ; C. Jimenez [France] ; T. Corti [Suisse] ; M. Hirschi [Suisse] ; G. Balsamo [Royaume-Uni] ; P. Ciais [France] ; P. Dirmeyer [États-Unis] ; J. B. Fisher [États-Unis] ; Z. Guo [États-Unis] ; M. Jung [Allemagne] ; F. Maignan [France] ; M. F. Mccabe [Australie] ; R. Reichle [États-Unis] ; M. Reichstein [Allemagne] ; M. Rodell [États-Unis] ; J. Sheffield [États-Unis] ; A. J. Teuling [Suisse, Pays-Bas] ; K. Wang [États-Unis] ; E. F. Wood [États-Unis] ; Y. Zhang [Australie]Source :
- Geophysical Research Letters [ 0094-8276 ] ; 2011-03.
Descripteurs français
- Pascal (Inist)
- Amérique du Sud, Australie, Bilan eau, Changement climatique, Changement échelle, Chine, Donnée observation, Erreur systématique, Etats Unis Ouest, Evapotranspiration, Incertitude, Inde, Modèle circulation générale, Modèle climat, Monde, Méthode covariance turbulence, Observation par satellite, Projet, Réanalyse, Satellite, Simulation numérique, Surface sol, Télédétection spatiale, Variance.
- Wicri :
- geographic : Australie, Inde.
- topic : Changement climatique, Satellite, Simulation.
English descriptors
- KwdEn :
- Amazon basin, Australia, Auxiliary material, Bias, California institute, Central european, Central european basins, China, Climate models, Climate science, Cluster analysis, Cluster tree, College park, Csiro land, Dataset, Dataset categories, Datasets, Diagnostic datasets, Eddy covariance method, Empirical upscaling, Environmental engineering, European centre, Evapotranspiration, General circulation models, Global, Global evapotranspiration datasets, Global land, Global land evapotranspiration datasets, Global patterns, Ground surface, Gswp, Gswp lsms, Gswp simulations, Hierarchical cluster analysis, India, Individual datasets, Interquartile ranges, Ipcc, Large uncertainties, Lsms, Meteorol, Mueller, Nasa goddard space flight center, Observation data, Other categories, Planck institute, Precipitation, Princeton university, Propulsion laboratory, Reanalyses, Reanalysis, Reanalysis datasets, Reference datasets, Remote sens, River basins, Satellite observation, Simulation, Simulations display, Soil moisture, South America, Space remote sensing, Standard deviation, Standard deviations, Terrestrial water storage, Time period, Upscaling, Variance, Weather forecasts, Western U.S., Wilcoxon test, climate change, digital simulation, evapotranspiration, global, projects, satellites, uncertainties, water balance.
- Teeft :
- Amazon basin, Auxiliary material, California institute, Central european, Central european basins, Climate science, Cluster analysis, Cluster tree, College park, Csiro land, Dataset, Dataset categories, Datasets, Diagnostic datasets, Empirical upscaling, Environmental engineering, European centre, Evapotranspiration, Global, Global evapotranspiration datasets, Global land, Global land evapotranspiration datasets, Global patterns, Gswp, Gswp lsms, Gswp simulations, Hierarchical cluster analysis, Individual datasets, Interquartile ranges, Ipcc, Large uncertainties, Lsms, Meteorol, Mueller, Nasa goddard space flight center, Other categories, Planck institute, Precipitation, Princeton university, Propulsion laboratory, Reanalyses, Reanalysis, Reanalysis datasets, Reference datasets, Remote sens, River basins, Simulation, Simulations display, Soil moisture, Standard deviation, Standard deviations, Terrestrial water storage, Time period, Weather forecasts, Wilcoxon test.
Abstract
Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite‐based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy‐covariance flux measurements, and atmospheric water balance datasets. The LandFlux‐EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations‐based products. Though differently constrained with observations, the analyzed reference datasets display similar large‐scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter‐product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.
Url:
DOI: 10.1029/2010GL046230
Affiliations:
- Allemagne, Australie, France, Pays-Bas, Royaume-Uni, Suisse, États-Unis
- Gueldre (province), Maryland, New Jersey, Île-de-France
- College Park (Maryland), Paris, Princeton (New Jersey), Wageningue
- Université de Princeton, Université de Wageningue, Université du Maryland
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Le document en format XML
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Fisher</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>NASA Jet Propulsion Laboratory, California Institute of Technology, California, Pasadena</wicri:regionArea><wicri:noRegion>Pasadena</wicri:noRegion></affiliation></author><author><name sortKey="Guo, Z" sort="Guo, Z" uniqKey="Guo Z" first="Z." last="Guo">Z. Guo</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Center for Ocean‐Land‐Atmosphere Studies, Maryland, Calverton</wicri:regionArea><wicri:noRegion>Calverton</wicri:noRegion></affiliation></author><author><name sortKey="Jung, M" sort="Jung, M" uniqKey="Jung M" first="M." last="Jung">M. 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Mccabe</name><affiliation wicri:level="1"><country xml:lang="fr">Australie</country><wicri:regionArea>School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales</wicri:regionArea><wicri:noRegion>New South Wales</wicri:noRegion></affiliation></author><author><name sortKey="Reichle, R" sort="Reichle, R" uniqKey="Reichle R" first="R." last="Reichle">R. Reichle</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>NASA Goddard Space Flight Center, Maryland, Greenbelt</wicri:regionArea><wicri:noRegion>Greenbelt</wicri:noRegion></affiliation></author><author><name sortKey="Reichstein, M" sort="Reichstein, M" uniqKey="Reichstein M" first="M." last="Reichstein">M. Reichstein</name><affiliation wicri:level="1"><country xml:lang="fr">Allemagne</country><wicri:regionArea>Max Planck Institute for Biogeochemistry, Jena</wicri:regionArea><wicri:noRegion>Jena</wicri:noRegion><wicri:noRegion>Jena</wicri:noRegion></affiliation></author><author><name sortKey="Rodell, M" sort="Rodell, M" uniqKey="Rodell M" first="M." last="Rodell">M. Rodell</name><affiliation wicri:level="1"><country xml:lang="fr">États-Unis</country><wicri:regionArea>NASA Goddard Space Flight Center, Maryland, Greenbelt</wicri:regionArea><wicri:noRegion>Greenbelt</wicri:noRegion></affiliation></author><author><name sortKey="Sheffield, J" sort="Sheffield, J" uniqKey="Sheffield J" first="J." last="Sheffield">J. Sheffield</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Civil and Environmental Engineering, Princeton University, New Jersey, Princeton</wicri:regionArea><orgName type="university">Université de Princeton</orgName><placeName><settlement type="city">Princeton (New Jersey)</settlement><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Teuling, A J" sort="Teuling, A J" uniqKey="Teuling A" first="A. J." last="Teuling">A. J. Teuling</name><affiliation wicri:level="1"><country xml:lang="fr">Suisse</country><wicri:regionArea>Institute for Atmospheric and Climate Science, ETH Zurich, Zurich</wicri:regionArea><wicri:noRegion>Zurich</wicri:noRegion></affiliation><affiliation wicri:level="4"><country xml:lang="fr">Pays-Bas</country><wicri:regionArea>Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen</wicri:regionArea><orgName type="university">Université de Wageningue</orgName><placeName><settlement type="city">Wageningue</settlement><region nuts="2">Gueldre (province)</region></placeName></affiliation></author><author><name sortKey="Wang, K" sort="Wang, K" uniqKey="Wang K" first="K." last="Wang">K. Wang</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Geography, University of Maryland, Maryland, College Park</wicri:regionArea><orgName type="university">Université du Maryland</orgName><placeName><settlement type="city">College Park (Maryland)</settlement><region type="state">Maryland</region></placeName></affiliation></author><author><name sortKey="Wood, E F" sort="Wood, E F" uniqKey="Wood E" first="E. F." last="Wood">E. F. Wood</name><affiliation wicri:level="4"><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Civil and Environmental Engineering, Princeton University, New Jersey, Princeton</wicri:regionArea><orgName type="university">Université de Princeton</orgName><placeName><settlement type="city">Princeton (New Jersey)</settlement><region type="state">New Jersey</region></placeName></affiliation></author><author><name sortKey="Zhang, Y" sort="Zhang, Y" uniqKey="Zhang Y" first="Y." last="Zhang">Y. Zhang</name><affiliation wicri:level="1"><country xml:lang="fr">Australie</country><wicri:regionArea>CSIRO Land and Water, Canberra, ACT</wicri:regionArea><wicri:noRegion>ACT</wicri:noRegion></affiliation></author></analytic><monogr></monogr><series><title level="j" type="main">Geophysical Research Letters</title><title level="j" type="alt">GEOPHYSICAL RESEARCH LETTERS</title><idno type="ISSN">0094-8276</idno><idno type="eISSN">1944-8007</idno><imprint><biblScope unit="vol">38</biblScope><biblScope unit="issue">6</biblScope><biblScope unit="page-count">7</biblScope><date type="published" when="2011-03">2011-03</date></imprint><idno type="ISSN">0094-8276</idno></series></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0094-8276</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Amazon basin</term><term>Australia</term><term>Auxiliary material</term><term>Bias</term><term>California institute</term><term>Central european</term><term>Central european basins</term><term>China</term><term>Climate models</term><term>Climate science</term><term>Cluster analysis</term><term>Cluster tree</term><term>College park</term><term>Csiro land</term><term>Dataset</term><term>Dataset categories</term><term>Datasets</term><term>Diagnostic datasets</term><term>Eddy covariance method</term><term>Empirical upscaling</term><term>Environmental engineering</term><term>European centre</term><term>Evapotranspiration</term><term>General circulation models</term><term>Global</term><term>Global evapotranspiration datasets</term><term>Global land</term><term>Global land evapotranspiration datasets</term><term>Global patterns</term><term>Ground surface</term><term>Gswp</term><term>Gswp lsms</term><term>Gswp simulations</term><term>Hierarchical cluster analysis</term><term>India</term><term>Individual datasets</term><term>Interquartile ranges</term><term>Ipcc</term><term>Large uncertainties</term><term>Lsms</term><term>Meteorol</term><term>Mueller</term><term>Nasa goddard space flight center</term><term>Observation data</term><term>Other categories</term><term>Planck institute</term><term>Precipitation</term><term>Princeton university</term><term>Propulsion laboratory</term><term>Reanalyses</term><term>Reanalysis</term><term>Reanalysis datasets</term><term>Reference datasets</term><term>Remote sens</term><term>River basins</term><term>Satellite observation</term><term>Simulation</term><term>Simulations display</term><term>Soil moisture</term><term>South America</term><term>Space remote sensing</term><term>Standard deviation</term><term>Standard deviations</term><term>Terrestrial water storage</term><term>Time period</term><term>Upscaling</term><term>Variance</term><term>Weather forecasts</term><term>Western U.S.</term><term>Wilcoxon test</term><term>climate change</term><term>digital simulation</term><term>evapotranspiration</term><term>global</term><term>projects</term><term>satellites</term><term>uncertainties</term><term>water balance</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Amérique du Sud</term><term>Australie</term><term>Bilan eau</term><term>Changement climatique</term><term>Changement échelle</term><term>Chine</term><term>Donnée observation</term><term>Erreur systématique</term><term>Etats Unis Ouest</term><term>Evapotranspiration</term><term>Incertitude</term><term>Inde</term><term>Modèle circulation générale</term><term>Modèle climat</term><term>Monde</term><term>Méthode covariance turbulence</term><term>Observation par satellite</term><term>Projet</term><term>Réanalyse</term><term>Satellite</term><term>Simulation numérique</term><term>Surface sol</term><term>Télédétection spatiale</term><term>Variance</term></keywords><keywords scheme="Teeft" xml:lang="en"><term>Amazon basin</term><term>Auxiliary material</term><term>California institute</term><term>Central european</term><term>Central european basins</term><term>Climate science</term><term>Cluster analysis</term><term>Cluster tree</term><term>College park</term><term>Csiro land</term><term>Dataset</term><term>Dataset categories</term><term>Datasets</term><term>Diagnostic datasets</term><term>Empirical upscaling</term><term>Environmental engineering</term><term>European centre</term><term>Evapotranspiration</term><term>Global</term><term>Global evapotranspiration datasets</term><term>Global land</term><term>Global land evapotranspiration datasets</term><term>Global patterns</term><term>Gswp</term><term>Gswp lsms</term><term>Gswp simulations</term><term>Hierarchical cluster analysis</term><term>Individual datasets</term><term>Interquartile ranges</term><term>Ipcc</term><term>Large uncertainties</term><term>Lsms</term><term>Meteorol</term><term>Mueller</term><term>Nasa goddard space flight center</term><term>Other categories</term><term>Planck institute</term><term>Precipitation</term><term>Princeton university</term><term>Propulsion laboratory</term><term>Reanalyses</term><term>Reanalysis</term><term>Reanalysis datasets</term><term>Reference datasets</term><term>Remote sens</term><term>River basins</term><term>Simulation</term><term>Simulations display</term><term>Soil moisture</term><term>Standard deviation</term><term>Standard deviations</term><term>Terrestrial water storage</term><term>Time period</term><term>Weather forecasts</term><term>Wilcoxon test</term></keywords><keywords scheme="Wicri" type="geographic" xml:lang="fr"><term>Australie</term><term>Inde</term></keywords><keywords scheme="Wicri" type="topic" xml:lang="fr"><term>Changement climatique</term><term>Satellite</term><term>Simulation</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract">Quantification of global land evapotranspiration (ET) has long been associated with large uncertainties due to the lack of reference observations. Several recently developed products now provide the capacity to estimate ET at global scales. These products, partly based on observational data, include satellite‐based products, land surface model (LSM) simulations, atmospheric reanalysis output, estimates based on empirical upscaling of eddy‐covariance flux measurements, and atmospheric water balance datasets. The LandFlux‐EVAL project aims to evaluate and compare these newly developed datasets. Additionally, an evaluation of IPCC AR4 global climate model (GCM) simulations is presented, providing an assessment of their capacity to reproduce flux behavior relative to the observations‐based products. Though differently constrained with observations, the analyzed reference datasets display similar large‐scale ET patterns. ET from the IPCC AR4 simulations was significantly smaller than that from the other products for India (up to 1 mm/d) and parts of eastern South America, and larger in the western USA, Australia and China. The inter‐product variance is lower across the IPCC AR4 simulations than across the reference datasets in several regions, which indicates that uncertainties may be underestimated in the IPCC AR4 models due to shared biases of these simulations.</div></front></TEI><affiliations><list><country><li>Allemagne</li><li>Australie</li><li>France</li><li>Pays-Bas</li><li>Royaume-Uni</li><li>Suisse</li><li>États-Unis</li></country><region><li>Gueldre (province)</li><li>Maryland</li><li>New Jersey</li><li>Île-de-France</li></region><settlement><li>College Park (Maryland)</li><li>Paris</li><li>Princeton (New Jersey)</li><li>Wageningue</li></settlement><orgName><li>Université de Princeton</li><li>Université de Wageningue</li><li>Université du Maryland</li></orgName></list><tree><country name="Suisse"><noRegion><name sortKey="Mueller, B" sort="Mueller, B" uniqKey="Mueller B" first="B." last="Mueller">B. Mueller</name></noRegion><name sortKey="Corti, T" sort="Corti, T" uniqKey="Corti T" first="T." last="Corti">T. Corti</name><name sortKey="Corti, T" sort="Corti, T" uniqKey="Corti T" first="T." last="Corti">T. Corti</name><name sortKey="Hirschi, M" sort="Hirschi, M" uniqKey="Hirschi M" first="M." last="Hirschi">M. Hirschi</name><name sortKey="Hirschi, M" sort="Hirschi, M" uniqKey="Hirschi M" first="M." last="Hirschi">M. Hirschi</name><name sortKey="Mueller, B" sort="Mueller, B" uniqKey="Mueller B" first="B." last="Mueller">B. Mueller</name><name sortKey="Mueller, B" sort="Mueller, B" uniqKey="Mueller B" first="B." last="Mueller">B. Mueller</name><name sortKey="Seneviratne, S I" sort="Seneviratne, S I" uniqKey="Seneviratne S" first="S. I." last="Seneviratne">S. I. Seneviratne</name><name sortKey="Teuling, A J" sort="Teuling, A J" uniqKey="Teuling A" first="A. J." last="Teuling">A. J. Teuling</name></country><country name="France"><region name="Île-de-France"><name sortKey="Jimenez, C" sort="Jimenez, C" uniqKey="Jimenez C" first="C." last="Jimenez">C. Jimenez</name></region><name sortKey="Ciais, P" sort="Ciais, P" uniqKey="Ciais P" first="P." last="Ciais">P. Ciais</name><name sortKey="Maignan, F" sort="Maignan, F" uniqKey="Maignan F" first="F." last="Maignan">F. Maignan</name></country><country name="Royaume-Uni"><noRegion><name sortKey="Balsamo, G" sort="Balsamo, G" uniqKey="Balsamo G" first="G." last="Balsamo">G. Balsamo</name></noRegion></country><country name="États-Unis"><noRegion><name sortKey="Dirmeyer, P" sort="Dirmeyer, P" uniqKey="Dirmeyer P" first="P." last="Dirmeyer">P. Dirmeyer</name></noRegion><name sortKey="Fisher, J B" sort="Fisher, J B" uniqKey="Fisher J" first="J. B." last="Fisher">J. B. Fisher</name><name sortKey="Guo, Z" sort="Guo, Z" uniqKey="Guo Z" first="Z." last="Guo">Z. Guo</name><name sortKey="Reichle, R" sort="Reichle, R" uniqKey="Reichle R" first="R." last="Reichle">R. Reichle</name><name sortKey="Rodell, M" sort="Rodell, M" uniqKey="Rodell M" first="M." last="Rodell">M. Rodell</name><name sortKey="Sheffield, J" sort="Sheffield, J" uniqKey="Sheffield J" first="J." last="Sheffield">J. Sheffield</name><name sortKey="Wang, K" sort="Wang, K" uniqKey="Wang K" first="K." last="Wang">K. Wang</name><name sortKey="Wood, E F" sort="Wood, E F" uniqKey="Wood E" first="E. F." last="Wood">E. F. Wood</name></country><country name="Allemagne"><noRegion><name sortKey="Jung, M" sort="Jung, M" uniqKey="Jung M" first="M." last="Jung">M. Jung</name></noRegion><name sortKey="Reichstein, M" sort="Reichstein, M" uniqKey="Reichstein M" first="M." last="Reichstein">M. Reichstein</name></country><country name="Australie"><noRegion><name sortKey="Mccabe, M F" sort="Mccabe, M F" uniqKey="Mccabe M" first="M. F." last="Mccabe">M. F. Mccabe</name></noRegion><name sortKey="Zhang, Y" sort="Zhang, Y" uniqKey="Zhang Y" first="Y." last="Zhang">Y. Zhang</name></country><country name="Pays-Bas"><region name="Gueldre (province)"><name sortKey="Teuling, A J" sort="Teuling, A J" uniqKey="Teuling A" first="A. J." last="Teuling">A. J. Teuling</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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