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Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends

Identifieur interne : 000378 ( Istex/Checkpoint ); précédent : 000377; suivant : 000379

Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends

Auteurs : Shilong Piao [République populaire de Chine] ; Stephen Sitch [Royaume-Uni] ; Philippe Ciais [France] ; Pierre Friedlingstein [Royaume-Uni] ; Philippe Peylin [France] ; Xuhui Wang [République populaire de Chine] ; Anders Ahlström [Suède] ; Alessandro Anav [Royaume-Uni] ; Josep G. Canadell [Australie] ; Nan Cong [République populaire de Chine] ; Chris Huntingford [Royaume-Uni] ; Martin Jung [Allemagne] ; Sam Levis [États-Unis] ; Peter E. Levy [Royaume-Uni] ; Junsheng Li [République populaire de Chine] ; Xin Lin [République populaire de Chine] ; Mark R. Lomas [Royaume-Uni] ; Meng Lu [République populaire de Chine] ; Yiqi Luo [États-Unis] ; Yuecun Ma [République populaire de Chine] ; Ranga B. Myneni [États-Unis] ; Ben Poulter [France] ; Zhenzhong Sun [République populaire de Chine] ; Tao Wang [France] ; Nicolas Viovy [France] ; Soenke Zaehle [Allemagne] ; Ning Zeng [États-Unis]

Source :

RBID : ISTEX:219D5E7881D0E9F2FA1B32146EF1B51A9FCB2166

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English descriptors

Abstract

The purpose of this study was to evaluate 10 process‐based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity (GPP) is compared with flux‐tower‐based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11). The net primary productivity (NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free‐Air CO2 Enrichment (FACE) experiments. The simulated global net biome productivity (NBP) is compared with the residual land sink (RLS) of the global carbon budget from Friedlingstein et al. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr−1) than JU11 (118 ± 6 Pg C yr−1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5–20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr−1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr−1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980–2009. Both model‐to‐model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is −3.0 ± 1.5 Pg C yr−1 °C−1, within the uncertainty of what derived from RLS (−3.9 ± 1.1 Pg C yr−1 °C−1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation‐based GPP and NBP can be fortuitous. Carbon–nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.

Url:
DOI: 10.1111/gcb.12187


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ISTEX:219D5E7881D0E9F2FA1B32146EF1B51A9FCB2166

Le document en format XML

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<name sortKey="Lu, Meng" sort="Lu, Meng" uniqKey="Lu M" first="Meng" last="Lu">Meng Lu</name>
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<series>
<title level="j" type="main">Global Change Biology</title>
<title level="j" type="alt">GLOBAL CHANGE BIOLOGY</title>
<idno type="ISSN">1354-1013</idno>
<idno type="eISSN">1365-2486</idno>
<imprint>
<biblScope unit="vol">19</biblScope>
<biblScope unit="issue">7</biblScope>
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<biblScope unit="page-count">16</biblScope>
<date type="published" when="2013-07">2013-07</date>
</imprint>
<idno type="ISSN">1354-1013</idno>
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<idno type="ISSN">1354-1013</idno>
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<keywords scheme="KwdEn" xml:lang="en">
<term>Abbreviation</term>
<term>Annual precipitation</term>
<term>Annual temperature</term>
<term>Anomaly</term>
<term>Atmospheric</term>
<term>Bgpp</term>
<term>Biogeochemical</term>
<term>Biome</term>
<term>Biome production</term>
<term>Biome productivity</term>
<term>Blackwell publishing</term>
<term>Bnbp</term>
<term>Bonan levis</term>
<term>Boreal</term>
<term>Boreal regions</term>
<term>Carbon cycle</term>
<term>Carbon cycle models</term>
<term>Ciais</term>
<term>Cint</term>
<term>Climate change</term>
<term>Climate system</term>
<term>Climate variability</term>
<term>Clm4c</term>
<term>Clm4cn</term>
<term>Community land model</term>
<term>Contributive</term>
<term>Contributive effect</term>
<term>Contributive response</term>
<term>Covariance</term>
<term>Detrended</term>
<term>Different models</term>
<term>Different regions</term>
<term>Different sites</term>
<term>Dint</term>
<term>Ecology</term>
<term>Ecosystem</term>
<term>Ecosystem respiration</term>
<term>Eddy</term>
<term>Eddy covariance</term>
<term>Error bars</term>
<term>Experiment site</term>
<term>Experimental site</term>
<term>Face experiments</term>
<term>Feedback</term>
<term>Fertilization</term>
<term>Fertilization effect</term>
<term>Frequency distribution</term>
<term>Friedlingstein</term>
<term>Geophysical</term>
<term>Geophysical research</term>
<term>Geophysical research letters</term>
<term>Global</term>
<term>Global biogeochemical cycles</term>
<term>Global carbon cycle</term>
<term>Global change biology</term>
<term>Global scale</term>
<term>Global warming</term>
<term>Grassland</term>
<term>Grid</term>
<term>Grid points</term>
<term>Gridded</term>
<term>Growth rate</term>
<term>Highest dint</term>
<term>Huntingford</term>
<term>Hyland</term>
<term>Interannual</term>
<term>Interannual correlation</term>
<term>Interannual variability</term>
<term>Interannual variation</term>
<term>Interannual variations</term>
<term>Jung</term>
<term>Land surface model</term>
<term>Large differences</term>
<term>Model abbreviations</term>
<term>Model output</term>
<term>Model simulations</term>
<term>Model tree ensemble approach</term>
<term>Models show</term>
<term>Myneni</term>
<term>National academy</term>
<term>Nature geoscience</term>
<term>Nontropical regions</term>
<term>Northern hemisphere</term>
<term>Physical meteorology</term>
<term>Piao</term>
<term>Positive correlation</term>
<term>Positive feedback</term>
<term>Poulter</term>
<term>Precipitation</term>
<term>Primary production</term>
<term>Process models</term>
<term>Rbnpp</term>
<term>Rcint</term>
<term>Relative response</term>
<term>Same site</term>
<term>Satellite fapar</term>
<term>Similar climate</term>
<term>Simulation</term>
<term>Sitch</term>
<term>Soil moisture</term>
<term>Spatial distribution</term>
<term>Standard deviation</term>
<term>Standard error</term>
<term>Tellus series</term>
<term>Temperate</term>
<term>Temperate regions</term>
<term>Temperature change</term>
<term>Temperature sensitivity</term>
<term>Temperature variations</term>
<term>Terrestrial</term>
<term>Terrestrial carbon cycle</term>
<term>Time series</term>
<term>Triffid</term>
<term>Tropical forests</term>
<term>Tropical regions</term>
<term>Uxes</term>
<term>Variability</term>
<term>Variables detrended</term>
<term>Vega</term>
<term>Vegetation</term>
<term>Vegetation growth</term>
<term>Vegetation productivity</term>
<term>Viovy</term>
<term>Wang</term>
<term>Warming</term>
<term>Warming experiment</term>
<term>Warming experiment sites</term>
<term>Warming experiments</term>
<term>Zaehle</term>
<term>Zaehle dalmonech</term>
</keywords>
<keywords scheme="Teeft" xml:lang="en">
<term>Abbreviation</term>
<term>Annual precipitation</term>
<term>Annual temperature</term>
<term>Anomaly</term>
<term>Atmospheric</term>
<term>Bgpp</term>
<term>Biogeochemical</term>
<term>Biome</term>
<term>Biome production</term>
<term>Biome productivity</term>
<term>Blackwell publishing</term>
<term>Bnbp</term>
<term>Bonan levis</term>
<term>Boreal</term>
<term>Boreal regions</term>
<term>Carbon cycle</term>
<term>Carbon cycle models</term>
<term>Ciais</term>
<term>Cint</term>
<term>Climate change</term>
<term>Climate system</term>
<term>Climate variability</term>
<term>Clm4c</term>
<term>Clm4cn</term>
<term>Community land model</term>
<term>Contributive</term>
<term>Contributive effect</term>
<term>Contributive response</term>
<term>Covariance</term>
<term>Detrended</term>
<term>Different models</term>
<term>Different regions</term>
<term>Different sites</term>
<term>Dint</term>
<term>Ecology</term>
<term>Ecosystem</term>
<term>Ecosystem respiration</term>
<term>Eddy</term>
<term>Eddy covariance</term>
<term>Error bars</term>
<term>Experiment site</term>
<term>Experimental site</term>
<term>Face experiments</term>
<term>Feedback</term>
<term>Fertilization</term>
<term>Fertilization effect</term>
<term>Frequency distribution</term>
<term>Friedlingstein</term>
<term>Geophysical</term>
<term>Geophysical research</term>
<term>Geophysical research letters</term>
<term>Global</term>
<term>Global biogeochemical cycles</term>
<term>Global carbon cycle</term>
<term>Global change biology</term>
<term>Global scale</term>
<term>Global warming</term>
<term>Grassland</term>
<term>Grid</term>
<term>Grid points</term>
<term>Gridded</term>
<term>Growth rate</term>
<term>Highest dint</term>
<term>Huntingford</term>
<term>Hyland</term>
<term>Interannual</term>
<term>Interannual correlation</term>
<term>Interannual variability</term>
<term>Interannual variation</term>
<term>Interannual variations</term>
<term>Jung</term>
<term>Land surface model</term>
<term>Large differences</term>
<term>Model abbreviations</term>
<term>Model output</term>
<term>Model simulations</term>
<term>Model tree ensemble approach</term>
<term>Models show</term>
<term>Myneni</term>
<term>National academy</term>
<term>Nature geoscience</term>
<term>Nontropical regions</term>
<term>Northern hemisphere</term>
<term>Physical meteorology</term>
<term>Piao</term>
<term>Positive correlation</term>
<term>Positive feedback</term>
<term>Poulter</term>
<term>Precipitation</term>
<term>Primary production</term>
<term>Process models</term>
<term>Rbnpp</term>
<term>Rcint</term>
<term>Relative response</term>
<term>Same site</term>
<term>Satellite fapar</term>
<term>Similar climate</term>
<term>Simulation</term>
<term>Sitch</term>
<term>Soil moisture</term>
<term>Spatial distribution</term>
<term>Standard deviation</term>
<term>Standard error</term>
<term>Tellus series</term>
<term>Temperate</term>
<term>Temperate regions</term>
<term>Temperature change</term>
<term>Temperature sensitivity</term>
<term>Temperature variations</term>
<term>Terrestrial</term>
<term>Terrestrial carbon cycle</term>
<term>Time series</term>
<term>Triffid</term>
<term>Tropical forests</term>
<term>Tropical regions</term>
<term>Uxes</term>
<term>Variability</term>
<term>Variables detrended</term>
<term>Vega</term>
<term>Vegetation</term>
<term>Vegetation growth</term>
<term>Vegetation productivity</term>
<term>Viovy</term>
<term>Wang</term>
<term>Warming</term>
<term>Warming experiment</term>
<term>Warming experiment sites</term>
<term>Warming experiments</term>
<term>Zaehle</term>
<term>Zaehle dalmonech</term>
</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr">
<term>Changement climatique</term>
<term>écologie</term>
<term>écosystème</term>
<term>Réchauffement climatique</term>
<term>Surface en herbe</term>
<term>Simulation</term>
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</teiHeader>
<front>
<div type="abstract">The purpose of this study was to evaluate 10 process‐based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity (GPP) is compared with flux‐tower‐based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11). The net primary productivity (NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free‐Air CO2 Enrichment (FACE) experiments. The simulated global net biome productivity (NBP) is compared with the residual land sink (RLS) of the global carbon budget from Friedlingstein et al. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr−1) than JU11 (118 ± 6 Pg C yr−1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5–20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr−1 is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr−1). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980–2009. Both model‐to‐model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is −3.0 ± 1.5 Pg C yr−1 °C−1, within the uncertainty of what derived from RLS (−3.9 ± 1.1 Pg C yr−1 °C−1). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation‐based GPP and NBP can be fortuitous. Carbon–nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>Allemagne</li>
<li>Australie</li>
<li>France</li>
<li>Royaume-Uni</li>
<li>République populaire de Chine</li>
<li>Suède</li>
<li>États-Unis</li>
</country>
<region>
<li>Maryland</li>
<li>Massachusetts</li>
<li>Île-de-France</li>
</region>
<settlement>
<li>Boston</li>
<li>College Park (Maryland)</li>
<li>Gif‐sur‐Yvette</li>
<li>Pékin</li>
</settlement>
<orgName>
<li>Université de Pékin</li>
<li>Université du Maryland</li>
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<name sortKey="Piao, Shilong" sort="Piao, Shilong" uniqKey="Piao S" first="Shilong" last="Piao">Shilong Piao</name>
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<name sortKey="Cong, Nan" sort="Cong, Nan" uniqKey="Cong N" first="Nan" last="Cong">Nan Cong</name>
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<name sortKey="Lin, Xin" sort="Lin, Xin" uniqKey="Lin X" first="Xin" last="Lin">Xin Lin</name>
<name sortKey="Lin, Xin" sort="Lin, Xin" uniqKey="Lin X" first="Xin" last="Lin">Xin Lin</name>
<name sortKey="Lu, Meng" sort="Lu, Meng" uniqKey="Lu M" first="Meng" last="Lu">Meng Lu</name>
<name sortKey="Ma, Yuecun" sort="Ma, Yuecun" uniqKey="Ma Y" first="Yuecun" last="Ma">Yuecun Ma</name>
<name sortKey="Piao, Shilong" sort="Piao, Shilong" uniqKey="Piao S" first="Shilong" last="Piao">Shilong Piao</name>
<name sortKey="Piao, Shilong" sort="Piao, Shilong" uniqKey="Piao S" first="Shilong" last="Piao">Shilong Piao</name>
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</noRegion>
<name sortKey="Anav, Alessandro" sort="Anav, Alessandro" uniqKey="Anav A" first="Alessandro" last="Anav">Alessandro Anav</name>
<name sortKey="Friedlingstein, Pierre" sort="Friedlingstein, Pierre" uniqKey="Friedlingstein P" first="Pierre" last="Friedlingstein">Pierre Friedlingstein</name>
<name sortKey="Huntingford, Chris" sort="Huntingford, Chris" uniqKey="Huntingford C" first="Chris" last="Huntingford">Chris Huntingford</name>
<name sortKey="Levy, Peter E" sort="Levy, Peter E" uniqKey="Levy P" first="Peter E." last="Levy">Peter E. Levy</name>
<name sortKey="Lomas, Mark R" sort="Lomas, Mark R" uniqKey="Lomas M" first="Mark R" last="Lomas">Mark R. Lomas</name>
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<noRegion>
<name sortKey="Ciais, Philippe" sort="Ciais, Philippe" uniqKey="Ciais P" first="Philippe" last="Ciais">Philippe Ciais</name>
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<name sortKey="Viovy, Nicolas" sort="Viovy, Nicolas" uniqKey="Viovy N" first="Nicolas" last="Viovy">Nicolas Viovy</name>
<name sortKey="Wang, Tao" sort="Wang, Tao" uniqKey="Wang T" first="Tao" last="Wang">Tao Wang</name>
</country>
<country name="Suède">
<noRegion>
<name sortKey="Ahlstrom, Anders" sort="Ahlstrom, Anders" uniqKey="Ahlstrom A" first="Anders" last="Ahlström">Anders Ahlström</name>
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<noRegion>
<name sortKey="Canadell, Josep G" sort="Canadell, Josep G" uniqKey="Canadell J" first="Josep G." last="Canadell">Josep G. Canadell</name>
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<noRegion>
<name sortKey="Jung, Martin" sort="Jung, Martin" uniqKey="Jung M" first="Martin" last="Jung">Martin Jung</name>
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<name sortKey="Zaehle, Soenke" sort="Zaehle, Soenke" uniqKey="Zaehle S" first="Soenke" last="Zaehle">Soenke Zaehle</name>
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<name sortKey="Levis, Sam" sort="Levis, Sam" uniqKey="Levis S" first="Sam" last="Levis">Sam Levis</name>
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<name sortKey="Zeng, Ning" sort="Zeng, Ning" uniqKey="Zeng N" first="Ning" last="Zeng">Ning Zeng</name>
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