Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends
Identifieur interne : 000991 ( PascalFrancis/Corpus ); précédent : 000990; suivant : 000992Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends
Auteurs : SHILONG PIAO ; Stephen Sitch ; Philippe Ciais ; Pierre Friedlingstein ; Philippe Peylin ; XUHUI WANG ; Anders Ahlstr M ; Alessandro Anav ; Josep G. Canadell ; NAN CONG ; Chris Huntingford ; Martin Jung ; Sam Levis ; Peter E. Levy ; JUNSHENG LI ; XIN LIN ; Mark R. Lomas ; MENG LU ; YIQI LUO ; YUECUN MA ; Ranga B. Myneni ; Ben Poulter ; ZHENZHONG SUN ; TAO WANG ; Nicolas Viovy ; Soenke Zaehle ; NING ZENGSource :
- Global change biology : (Print) [ 1354-1013 ] ; 2013.
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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.
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Format Inist (serveur)
| NO : | PASCAL 13-0223320 INIST |
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| ET : | Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends |
| AU : | SHILONG PIAO; SITCH (Stephen); CIAIS (Philippe); FRIEDLINGSTEIN (Pierre); PEYLIN (Philippe); XUHUI WANG; AHLSTR ÖM (Anders); ANAV (Alessandro); CANADELL (Josep G.); NAN CONG; HUNTINGFORD (Chris); JUNG (Martin); LEVIS (Sam); LEVY (Peter E.); JUNSHENG LI; XIN LIN; LOMAS (Mark R.); MENG LU; YIQI LUO; YUECUN MA; MYNENI (Ranga B.); POULTER (Ben); ZHENZHONG SUN; TAO WANG; VIOVY (Nicolas); ZAEHLE (Soenke); NING ZENG |
| AF : | Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University/Beijing 100871/Chine (1 aut., 6 aut., 10 aut., 20 aut., 23 aut.); Institute of Tibetan Plateau Research, Chinese Academy of Sciences/Beijing 100085/Chine (1 aut.); College of Engineering, Computing and Mathematics, University of Exeter/Exeter EX4 4QF/Royaume-Uni (2 aut., 4 aut., 8 aut.); Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ/Gif-sur-Yvette 91191/France (3 aut., 5 aut., 22 aut., 24 aut., 25 aut.); Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12/Lund 223 62/Suède (7 aut.); Global Carbon Project, Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research/Canberra/Australie (9 aut.); Centre for Ecology and Hydrology, Benson Lane/Wallingford, OX10 8BB/Royaume-Uni (11 aut.); Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64/Jena 07701/Allemagne (12 aut., 26 aut.); National Center for Atmospheric Research/Boulder, CO 80301/Etats-Unis (13 aut.); Centre for Ecology and Hydrology, Bush Estate, Penicuik/Midlothian EH26 0QB/Royaume-Uni (14 aut.); State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences/Beijing 100012/Chine (15 aut., 16 aut.); College of Water Sciences, Beijing Normal University/Beijing 100875/Chine (16 aut.); Department of Animal & Plant Sciences, University of Sheffield/Sheffield S10 2TN/Royaume-Uni (17 aut.); Institute of Biodiversity Science, Fudan University, 220 Handan Road/Shanghai 200433/Chine (18 aut.); Department of Microbiology and Plant Biology, University of Oklahoma/Norman, OK 73019/Etats-Unis (19 aut.); Department of Geography and Environment, Boston University, 675 Commonwealth Avenue/Boston, MA 02215/Etats-Unis (21 aut.); Department of Atmospheric and Oceanic Science, University of Maryland/College Park, MD 20740/Etats-Unis (27 aut.) |
| DT : | Publication en série; Niveau analytique |
| SO : | Global change biology : (Print); ISSN 1354-1013; Royaume-Uni; Da. 2013; Vol. 19; No. 7; Pp. 2117-2132; Bibl. 2 p. |
| LA : | Anglais |
| EA : | 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. |
| CC : | 002A14B01 |
| FD : | Evaluation; Cycle carbone; Modèle; Variation climat; Dioxyde de carbone; Fertilisation; Précipitation; Température |
| FG : | Facteur milieu |
| ED : | Evaluation; Carbon cycle; Models; Climate variation; Carbon dioxide; Fertilization; Precipitation; Temperature |
| EG : | Environmental factor |
| SD : | Evaluación; Ciclo carbono; Modelo; Variación clima; Carbono dióxido; Fertilización; Precipitación; Temperatura |
| LO : | INIST-27882.354000503050780120 |
| ID : | 13-0223320 |
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Canadell</name><affiliation><inist:fA14 i1="06"><s1>Global Carbon Project, Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research</s1><s2>Canberra</s2><s3>AUS</s3><sZ>9 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Nan Cong" sort="Nan Cong" uniqKey="Nan Cong" last="Nan Cong">NAN CONG</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Huntingford, Chris" sort="Huntingford, Chris" uniqKey="Huntingford C" first="Chris" last="Huntingford">Chris Huntingford</name><affiliation><inist:fA14 i1="07"><s1>Centre for Ecology and Hydrology, Benson Lane</s1><s2>Wallingford, OX10 8BB</s2><s3>GBR</s3><sZ>11 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Jung, Martin" sort="Jung, Martin" uniqKey="Jung M" first="Martin" last="Jung">Martin Jung</name><affiliation><inist:fA14 i1="08"><s1>Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64</s1><s2>Jena 07701</s2><s3>DEU</s3><sZ>12 aut.</sZ><sZ>26 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Levis, Sam" sort="Levis, Sam" uniqKey="Levis S" first="Sam" last="Levis">Sam Levis</name><affiliation><inist:fA14 i1="09"><s1>National Center for Atmospheric Research</s1><s2>Boulder, CO 80301</s2><s3>USA</s3><sZ>13 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Levy, Peter E" sort="Levy, Peter E" uniqKey="Levy P" first="Peter E." last="Levy">Peter E. Levy</name><affiliation><inist:fA14 i1="10"><s1>Centre for Ecology and Hydrology, Bush Estate, Penicuik</s1><s2>Midlothian EH26 0QB</s2><s3>GBR</s3><sZ>14 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Junsheng Li" sort="Junsheng Li" uniqKey="Junsheng Li" last="Junsheng Li">JUNSHENG LI</name><affiliation><inist:fA14 i1="11"><s1>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences</s1><s2>Beijing 100012</s2><s3>CHN</s3><sZ>15 aut.</sZ><sZ>16 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Xin Lin" sort="Xin Lin" uniqKey="Xin Lin" last="Xin Lin">XIN LIN</name><affiliation><inist:fA14 i1="11"><s1>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences</s1><s2>Beijing 100012</s2><s3>CHN</s3><sZ>15 aut.</sZ><sZ>16 aut.</sZ></inist:fA14></affiliation><affiliation><inist:fA14 i1="12"><s1>College of Water Sciences, Beijing Normal University</s1><s2>Beijing 100875</s2><s3>CHN</s3><sZ>16 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Lomas, Mark R" sort="Lomas, Mark R" uniqKey="Lomas M" first="Mark R." last="Lomas">Mark R. Lomas</name><affiliation><inist:fA14 i1="13"><s1>Department of Animal & Plant Sciences, University of Sheffield</s1><s2>Sheffield S10 2TN</s2><s3>GBR</s3><sZ>17 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Meng Lu" sort="Meng Lu" uniqKey="Meng Lu" last="Meng Lu">MENG LU</name><affiliation><inist:fA14 i1="14"><s1>Institute of Biodiversity Science, Fudan University, 220 Handan Road</s1><s2>Shanghai 200433</s2><s3>CHN</s3><sZ>18 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Yiqi Luo" sort="Yiqi Luo" uniqKey="Yiqi Luo" last="Yiqi Luo">YIQI LUO</name><affiliation><inist:fA14 i1="15"><s1>Department of Microbiology and Plant Biology, University of Oklahoma</s1><s2>Norman, OK 73019</s2><s3>USA</s3><sZ>19 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Yuecun Ma" sort="Yuecun Ma" uniqKey="Yuecun Ma" last="Yuecun Ma">YUECUN MA</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Myneni, Ranga B" sort="Myneni, Ranga B" uniqKey="Myneni R" first="Ranga B." last="Myneni">Ranga B. Myneni</name><affiliation><inist:fA14 i1="16"><s1>Department of Geography and Environment, Boston University, 675 Commonwealth Avenue</s1><s2>Boston, MA 02215</s2><s3>USA</s3><sZ>21 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Poulter, Ben" sort="Poulter, Ben" uniqKey="Poulter B" first="Ben" last="Poulter">Ben Poulter</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Zhenzhong Sun" sort="Zhenzhong Sun" uniqKey="Zhenzhong Sun" last="Zhenzhong Sun">ZHENZHONG SUN</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Tao Wang" sort="Tao Wang" uniqKey="Tao Wang" last="Tao Wang">TAO WANG</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Viovy, Nicolas" sort="Viovy, Nicolas" uniqKey="Viovy N" first="Nicolas" last="Viovy">Nicolas Viovy</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Zaehle, Soenke" sort="Zaehle, Soenke" uniqKey="Zaehle S" first="Soenke" last="Zaehle">Soenke Zaehle</name><affiliation><inist:fA14 i1="08"><s1>Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64</s1><s2>Jena 07701</s2><s3>DEU</s3><sZ>12 aut.</sZ><sZ>26 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Ning Zeng" sort="Ning Zeng" uniqKey="Ning Zeng" last="Ning Zeng">NING ZENG</name><affiliation><inist:fA14 i1="17"><s1>Department of Atmospheric and Oceanic Science, University of Maryland</s1><s2>College Park, MD 20740</s2><s3>USA</s3><sZ>27 aut.</sZ></inist:fA14></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">INIST</idno><idno type="inist">13-0223320</idno><date when="2013">2013</date><idno type="stanalyst">PASCAL 13-0223320 INIST</idno><idno type="RBID">Pascal:13-0223320</idno><idno type="wicri:Area/PascalFrancis/Corpus">000991</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a">Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO<sub>2 </sub>trends</title><author><name sortKey="Shilong Piao" sort="Shilong Piao" uniqKey="Shilong Piao" last="Shilong Piao">SHILONG PIAO</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation><affiliation><inist:fA14 i1="02"><s1>Institute of Tibetan Plateau Research, Chinese Academy of Sciences</s1><s2>Beijing 100085</s2><s3>CHN</s3><sZ>1 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Sitch, Stephen" sort="Sitch, Stephen" uniqKey="Sitch S" first="Stephen" last="Sitch">Stephen Sitch</name><affiliation><inist:fA14 i1="03"><s1>College of Engineering, Computing and Mathematics, University of Exeter</s1><s2>Exeter EX4 4QF</s2><s3>GBR</s3><sZ>2 aut.</sZ><sZ>4 aut.</sZ><sZ>8 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Ciais, Philippe" sort="Ciais, Philippe" uniqKey="Ciais P" first="Philippe" last="Ciais">Philippe Ciais</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Friedlingstein, Pierre" sort="Friedlingstein, Pierre" uniqKey="Friedlingstein P" first="Pierre" last="Friedlingstein">Pierre Friedlingstein</name><affiliation><inist:fA14 i1="03"><s1>College of Engineering, Computing and Mathematics, University of Exeter</s1><s2>Exeter EX4 4QF</s2><s3>GBR</s3><sZ>2 aut.</sZ><sZ>4 aut.</sZ><sZ>8 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Peylin, Philippe" sort="Peylin, Philippe" uniqKey="Peylin P" first="Philippe" last="Peylin">Philippe Peylin</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Xuhui Wang" sort="Xuhui Wang" uniqKey="Xuhui Wang" last="Xuhui Wang">XUHUI WANG</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Ahlstr M, Anders" sort="Ahlstr M, Anders" uniqKey="Ahlstr M A" first="Anders" last="Ahlstr M">Anders Ahlstr M</name><affiliation><inist:fA14 i1="05"><s1>Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 1<sub>2</sub></s1><s2>Lund 223 62</s2><s3>SWE</s3><sZ>7 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Anav, Alessandro" sort="Anav, Alessandro" uniqKey="Anav A" first="Alessandro" last="Anav">Alessandro Anav</name><affiliation><inist:fA14 i1="03"><s1>College of Engineering, Computing and Mathematics, University of Exeter</s1><s2>Exeter EX4 4QF</s2><s3>GBR</s3><sZ>2 aut.</sZ><sZ>4 aut.</sZ><sZ>8 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Canadell, Josep G" sort="Canadell, Josep G" uniqKey="Canadell J" first="Josep G." last="Canadell">Josep G. Canadell</name><affiliation><inist:fA14 i1="06"><s1>Global Carbon Project, Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research</s1><s2>Canberra</s2><s3>AUS</s3><sZ>9 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Nan Cong" sort="Nan Cong" uniqKey="Nan Cong" last="Nan Cong">NAN CONG</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Huntingford, Chris" sort="Huntingford, Chris" uniqKey="Huntingford C" first="Chris" last="Huntingford">Chris Huntingford</name><affiliation><inist:fA14 i1="07"><s1>Centre for Ecology and Hydrology, Benson Lane</s1><s2>Wallingford, OX10 8BB</s2><s3>GBR</s3><sZ>11 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Jung, Martin" sort="Jung, Martin" uniqKey="Jung M" first="Martin" last="Jung">Martin Jung</name><affiliation><inist:fA14 i1="08"><s1>Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64</s1><s2>Jena 07701</s2><s3>DEU</s3><sZ>12 aut.</sZ><sZ>26 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Levis, Sam" sort="Levis, Sam" uniqKey="Levis S" first="Sam" last="Levis">Sam Levis</name><affiliation><inist:fA14 i1="09"><s1>National Center for Atmospheric Research</s1><s2>Boulder, CO 80301</s2><s3>USA</s3><sZ>13 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Levy, Peter E" sort="Levy, Peter E" uniqKey="Levy P" first="Peter E." last="Levy">Peter E. Levy</name><affiliation><inist:fA14 i1="10"><s1>Centre for Ecology and Hydrology, Bush Estate, Penicuik</s1><s2>Midlothian EH26 0QB</s2><s3>GBR</s3><sZ>14 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Junsheng Li" sort="Junsheng Li" uniqKey="Junsheng Li" last="Junsheng Li">JUNSHENG LI</name><affiliation><inist:fA14 i1="11"><s1>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences</s1><s2>Beijing 100012</s2><s3>CHN</s3><sZ>15 aut.</sZ><sZ>16 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Xin Lin" sort="Xin Lin" uniqKey="Xin Lin" last="Xin Lin">XIN LIN</name><affiliation><inist:fA14 i1="11"><s1>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences</s1><s2>Beijing 100012</s2><s3>CHN</s3><sZ>15 aut.</sZ><sZ>16 aut.</sZ></inist:fA14></affiliation><affiliation><inist:fA14 i1="12"><s1>College of Water Sciences, Beijing Normal University</s1><s2>Beijing 100875</s2><s3>CHN</s3><sZ>16 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Lomas, Mark R" sort="Lomas, Mark R" uniqKey="Lomas M" first="Mark R." last="Lomas">Mark R. Lomas</name><affiliation><inist:fA14 i1="13"><s1>Department of Animal & Plant Sciences, University of Sheffield</s1><s2>Sheffield S10 2TN</s2><s3>GBR</s3><sZ>17 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Meng Lu" sort="Meng Lu" uniqKey="Meng Lu" last="Meng Lu">MENG LU</name><affiliation><inist:fA14 i1="14"><s1>Institute of Biodiversity Science, Fudan University, 220 Handan Road</s1><s2>Shanghai 200433</s2><s3>CHN</s3><sZ>18 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Yiqi Luo" sort="Yiqi Luo" uniqKey="Yiqi Luo" last="Yiqi Luo">YIQI LUO</name><affiliation><inist:fA14 i1="15"><s1>Department of Microbiology and Plant Biology, University of Oklahoma</s1><s2>Norman, OK 73019</s2><s3>USA</s3><sZ>19 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Yuecun Ma" sort="Yuecun Ma" uniqKey="Yuecun Ma" last="Yuecun Ma">YUECUN MA</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Myneni, Ranga B" sort="Myneni, Ranga B" uniqKey="Myneni R" first="Ranga B." last="Myneni">Ranga B. Myneni</name><affiliation><inist:fA14 i1="16"><s1>Department of Geography and Environment, Boston University, 675 Commonwealth Avenue</s1><s2>Boston, MA 02215</s2><s3>USA</s3><sZ>21 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Poulter, Ben" sort="Poulter, Ben" uniqKey="Poulter B" first="Ben" last="Poulter">Ben Poulter</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Zhenzhong Sun" sort="Zhenzhong Sun" uniqKey="Zhenzhong Sun" last="Zhenzhong Sun">ZHENZHONG SUN</name><affiliation><inist:fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Tao Wang" sort="Tao Wang" uniqKey="Tao Wang" last="Tao Wang">TAO WANG</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Viovy, Nicolas" sort="Viovy, Nicolas" uniqKey="Viovy N" first="Nicolas" last="Viovy">Nicolas Viovy</name><affiliation><inist:fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Zaehle, Soenke" sort="Zaehle, Soenke" uniqKey="Zaehle S" first="Soenke" last="Zaehle">Soenke Zaehle</name><affiliation><inist:fA14 i1="08"><s1>Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64</s1><s2>Jena 07701</s2><s3>DEU</s3><sZ>12 aut.</sZ><sZ>26 aut.</sZ></inist:fA14></affiliation></author><author><name sortKey="Ning Zeng" sort="Ning Zeng" uniqKey="Ning Zeng" last="Ning Zeng">NING ZENG</name><affiliation><inist:fA14 i1="17"><s1>Department of Atmospheric and Oceanic Science, University of Maryland</s1><s2>College Park, MD 20740</s2><s3>USA</s3><sZ>27 aut.</sZ></inist:fA14></affiliation></author></analytic><series><title level="j" type="main">Global change biology : (Print)</title><title level="j" type="abbreviated">Glob. chang. biol. : (Print)</title><idno type="ISSN">1354-1013</idno><imprint><date when="2013">2013</date></imprint></series></biblStruct></sourceDesc><seriesStmt><title level="j" type="main">Global change biology : (Print)</title><title level="j" type="abbreviated">Glob. chang. biol. : (Print)</title><idno type="ISSN">1354-1013</idno></seriesStmt></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Carbon cycle</term><term>Carbon dioxide</term><term>Climate variation</term><term>Evaluation</term><term>Fertilization</term><term>Models</term><term>Precipitation</term><term>Temperature</term></keywords><keywords scheme="Pascal" xml:lang="fr"><term>Evaluation</term><term>Cycle carbone</term><term>Modèle</term><term>Variation climat</term><term>Dioxyde de carbone</term><term>Fertilisation</term><term>Précipitation</term><term>Température</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 CO<sub>2</sub> trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO<sub>2</sub> sensitivity of NPP is compared to the results from four Free-Air CO<sub>2</sub> 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<sup>-1</sup>) than JU11 (118 ± 6 Pg C yr<sup>-1</sup>). In response to rising atmospheric CO<sub>2</sub> concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO<sub>2</sub> 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<sup>-1</sup> is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr<sup>-1</sup>). 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<sup>-1</sup> °C<sup>-1</sup>, within the uncertainty of what derived from RLS (-3.9 ± 1.1 Pg C yr<sup>-1</sup> °C<sup>-1</sup>). 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 CO<sub>2</sub> 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 CO<sub>2</sub> concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.</div></front></TEI><inist><standard h6="B"><pA><fA01 i1="01" i2="1"><s0>1354-1013</s0></fA01><fA03 i2="1"><s0>Glob. chang. biol. : (Print)</s0></fA03><fA05><s2>19</s2></fA05><fA06><s2>7</s2></fA06><fA08 i1="01" i2="1" l="ENG"><s1>Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO<sub>2 </sub>trends</s1></fA08><fA11 i1="01" i2="1"><s1>SHILONG PIAO</s1></fA11><fA11 i1="02" i2="1"><s1>SITCH (Stephen)</s1></fA11><fA11 i1="03" i2="1"><s1>CIAIS (Philippe)</s1></fA11><fA11 i1="04" i2="1"><s1>FRIEDLINGSTEIN (Pierre)</s1></fA11><fA11 i1="05" i2="1"><s1>PEYLIN (Philippe)</s1></fA11><fA11 i1="06" i2="1"><s1>XUHUI WANG</s1></fA11><fA11 i1="07" i2="1"><s1>AHLSTR ÖM (Anders)</s1></fA11><fA11 i1="08" i2="1"><s1>ANAV (Alessandro)</s1></fA11><fA11 i1="09" i2="1"><s1>CANADELL (Josep G.)</s1></fA11><fA11 i1="10" i2="1"><s1>NAN CONG</s1></fA11><fA11 i1="11" i2="1"><s1>HUNTINGFORD (Chris)</s1></fA11><fA11 i1="12" i2="1"><s1>JUNG (Martin)</s1></fA11><fA11 i1="13" i2="1"><s1>LEVIS (Sam)</s1></fA11><fA11 i1="14" i2="1"><s1>LEVY (Peter E.)</s1></fA11><fA11 i1="15" i2="1"><s1>JUNSHENG LI</s1></fA11><fA11 i1="16" i2="1"><s1>XIN LIN</s1></fA11><fA11 i1="17" i2="1"><s1>LOMAS (Mark R.)</s1></fA11><fA11 i1="18" i2="1"><s1>MENG LU</s1></fA11><fA11 i1="19" i2="1"><s1>YIQI LUO</s1></fA11><fA11 i1="20" i2="1"><s1>YUECUN MA</s1></fA11><fA11 i1="21" i2="1"><s1>MYNENI (Ranga B.)</s1></fA11><fA11 i1="22" i2="1"><s1>POULTER (Ben)</s1></fA11><fA11 i1="23" i2="1"><s1>ZHENZHONG SUN</s1></fA11><fA11 i1="24" i2="1"><s1>TAO WANG</s1></fA11><fA11 i1="25" i2="1"><s1>VIOVY (Nicolas)</s1></fA11><fA11 i1="26" i2="1"><s1>ZAEHLE (Soenke)</s1></fA11><fA11 i1="27" i2="1"><s1>NING ZENG</s1></fA11><fA14 i1="01"><s1>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University</s1><s2>Beijing 100871</s2><s3>CHN</s3><sZ>1 aut.</sZ><sZ>6 aut.</sZ><sZ>10 aut.</sZ><sZ>20 aut.</sZ><sZ>23 aut.</sZ></fA14><fA14 i1="02"><s1>Institute of Tibetan Plateau Research, Chinese Academy of Sciences</s1><s2>Beijing 100085</s2><s3>CHN</s3><sZ>1 aut.</sZ></fA14><fA14 i1="03"><s1>College of Engineering, Computing and Mathematics, University of Exeter</s1><s2>Exeter EX4 4QF</s2><s3>GBR</s3><sZ>2 aut.</sZ><sZ>4 aut.</sZ><sZ>8 aut.</sZ></fA14><fA14 i1="04"><s1>Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ</s1><s2>Gif-sur-Yvette 91191</s2><s3>FRA</s3><sZ>3 aut.</sZ><sZ>5 aut.</sZ><sZ>22 aut.</sZ><sZ>24 aut.</sZ><sZ>25 aut.</sZ></fA14><fA14 i1="05"><s1>Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 1<sub>2</sub></s1><s2>Lund 223 62</s2><s3>SWE</s3><sZ>7 aut.</sZ></fA14><fA14 i1="06"><s1>Global Carbon Project, Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research</s1><s2>Canberra</s2><s3>AUS</s3><sZ>9 aut.</sZ></fA14><fA14 i1="07"><s1>Centre for Ecology and Hydrology, Benson Lane</s1><s2>Wallingford, OX10 8BB</s2><s3>GBR</s3><sZ>11 aut.</sZ></fA14><fA14 i1="08"><s1>Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64</s1><s2>Jena 07701</s2><s3>DEU</s3><sZ>12 aut.</sZ><sZ>26 aut.</sZ></fA14><fA14 i1="09"><s1>National Center for Atmospheric Research</s1><s2>Boulder, CO 80301</s2><s3>USA</s3><sZ>13 aut.</sZ></fA14><fA14 i1="10"><s1>Centre for Ecology and Hydrology, Bush Estate, Penicuik</s1><s2>Midlothian EH26 0QB</s2><s3>GBR</s3><sZ>14 aut.</sZ></fA14><fA14 i1="11"><s1>State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences</s1><s2>Beijing 100012</s2><s3>CHN</s3><sZ>15 aut.</sZ><sZ>16 aut.</sZ></fA14><fA14 i1="12"><s1>College of Water Sciences, Beijing Normal University</s1><s2>Beijing 100875</s2><s3>CHN</s3><sZ>16 aut.</sZ></fA14><fA14 i1="13"><s1>Department of Animal & Plant Sciences, University of Sheffield</s1><s2>Sheffield S10 2TN</s2><s3>GBR</s3><sZ>17 aut.</sZ></fA14><fA14 i1="14"><s1>Institute of Biodiversity Science, Fudan University, 220 Handan Road</s1><s2>Shanghai 200433</s2><s3>CHN</s3><sZ>18 aut.</sZ></fA14><fA14 i1="15"><s1>Department of Microbiology and Plant Biology, University of Oklahoma</s1><s2>Norman, OK 73019</s2><s3>USA</s3><sZ>19 aut.</sZ></fA14><fA14 i1="16"><s1>Department of Geography and Environment, Boston University, 675 Commonwealth Avenue</s1><s2>Boston, MA 02215</s2><s3>USA</s3><sZ>21 aut.</sZ></fA14><fA14 i1="17"><s1>Department of Atmospheric and Oceanic Science, University of Maryland</s1><s2>College Park, MD 20740</s2><s3>USA</s3><sZ>27 aut.</sZ></fA14><fA20><s1>2117-2132</s1></fA20><fA21><s1>2013</s1></fA21><fA23 i1="01"><s0>ENG</s0></fA23><fA43 i1="01"><s1>INIST</s1><s2>27882</s2><s5>354000503050780120</s5></fA43><fA44><s0>0000</s0><s1>© 2013 INIST-CNRS. All rights reserved.</s1></fA44><fA45><s0>2 p.</s0></fA45><fA47 i1="01" i2="1"><s0>13-0223320</s0></fA47><fA60><s1>P</s1></fA60><fA61><s0>A</s0></fA61><fA64 i1="01" i2="1"><s0>Global change biology : (Print)</s0></fA64><fA66 i1="01"><s0>GBR</s0></fA66><fC01 i1="01" l="ENG"><s0>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 CO<sub>2</sub> trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO<sub>2</sub> sensitivity of NPP is compared to the results from four Free-Air CO<sub>2</sub> 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<sup>-1</sup>) than JU11 (118 ± 6 Pg C yr<sup>-1</sup>). In response to rising atmospheric CO<sub>2</sub> concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO<sub>2</sub> 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<sup>-1</sup> is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr<sup>-1</sup>). 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<sup>-1</sup> °C<sup>-1</sup>, within the uncertainty of what derived from RLS (-3.9 ± 1.1 Pg C yr<sup>-1</sup> °C<sup>-1</sup>). 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 CO<sub>2</sub> 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 CO<sub>2</sub> concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.</s0></fC01><fC02 i1="01" i2="X"><s0>002A14B01</s0></fC02><fC03 i1="01" i2="X" l="FRE"><s0>Evaluation</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="ENG"><s0>Evaluation</s0><s5>01</s5></fC03><fC03 i1="01" i2="X" l="SPA"><s0>Evaluación</s0><s5>01</s5></fC03><fC03 i1="02" i2="X" l="FRE"><s0>Cycle carbone</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="ENG"><s0>Carbon cycle</s0><s5>02</s5></fC03><fC03 i1="02" i2="X" l="SPA"><s0>Ciclo carbono</s0><s5>02</s5></fC03><fC03 i1="03" i2="X" l="FRE"><s0>Modèle</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="ENG"><s0>Models</s0><s5>03</s5></fC03><fC03 i1="03" i2="X" l="SPA"><s0>Modelo</s0><s5>03</s5></fC03><fC03 i1="04" i2="X" l="FRE"><s0>Variation climat</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="ENG"><s0>Climate variation</s0><s5>04</s5></fC03><fC03 i1="04" i2="X" l="SPA"><s0>Variación clima</s0><s5>04</s5></fC03><fC03 i1="05" i2="X" l="FRE"><s0>Dioxyde de carbone</s0><s2>NK</s2><s2>FX</s2><s5>05</s5></fC03><fC03 i1="05" i2="X" l="ENG"><s0>Carbon dioxide</s0><s2>NK</s2><s2>FX</s2><s5>05</s5></fC03><fC03 i1="05" i2="X" l="SPA"><s0>Carbono dióxido</s0><s2>NK</s2><s2>FX</s2><s5>05</s5></fC03><fC03 i1="06" i2="X" l="FRE"><s0>Fertilisation</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="ENG"><s0>Fertilization</s0><s5>06</s5></fC03><fC03 i1="06" i2="X" l="SPA"><s0>Fertilización</s0><s5>06</s5></fC03><fC03 i1="07" i2="X" l="FRE"><s0>Précipitation</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="ENG"><s0>Precipitation</s0><s5>07</s5></fC03><fC03 i1="07" i2="X" l="SPA"><s0>Precipitación</s0><s5>07</s5></fC03><fC03 i1="08" i2="X" l="FRE"><s0>Température</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="ENG"><s0>Temperature</s0><s5>08</s5></fC03><fC03 i1="08" i2="X" l="SPA"><s0>Temperatura</s0><s5>08</s5></fC03><fC07 i1="01" i2="X" l="FRE"><s0>Facteur milieu</s0><s5>26</s5></fC07><fC07 i1="01" i2="X" l="ENG"><s0>Environmental factor</s0><s5>26</s5></fC07><fC07 i1="01" i2="X" l="SPA"><s0>Factor medio</s0><s5>26</s5></fC07><fN21><s1>203</s1></fN21><fN44 i1="01"><s1>OTO</s1></fN44><fN82><s1>OTO</s1></fN82></pA></standard><server><NO>PASCAL 13-0223320 INIST</NO><ET>Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO<sub>2 </sub>trends</ET><AU>SHILONG PIAO; SITCH (Stephen); CIAIS (Philippe); FRIEDLINGSTEIN (Pierre); PEYLIN (Philippe); XUHUI WANG; AHLSTR ÖM (Anders); ANAV (Alessandro); CANADELL (Josep G.); NAN CONG; HUNTINGFORD (Chris); JUNG (Martin); LEVIS (Sam); LEVY (Peter E.); JUNSHENG LI; XIN LIN; LOMAS (Mark R.); MENG LU; YIQI LUO; YUECUN MA; MYNENI (Ranga B.); POULTER (Ben); ZHENZHONG SUN; TAO WANG; VIOVY (Nicolas); ZAEHLE (Soenke); NING ZENG</AU><AF>Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University/Beijing 100871/Chine (1 aut., 6 aut., 10 aut., 20 aut., 23 aut.); Institute of Tibetan Plateau Research, Chinese Academy of Sciences/Beijing 100085/Chine (1 aut.); College of Engineering, Computing and Mathematics, University of Exeter/Exeter EX4 4QF/Royaume-Uni (2 aut., 4 aut., 8 aut.); Laboratoire des Sciences du Climat et de l'Environnement, CEA CNRS UVSQ/Gif-sur-Yvette 91191/France (3 aut., 5 aut., 22 aut., 24 aut., 25 aut.); Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 1<sub>2</sub>/Lund 223 62/Suède (7 aut.); Global Carbon Project, Commonwealth Scientific and Industrial Research Organization, Marine and Atmospheric Research/Canberra/Australie (9 aut.); Centre for Ecology and Hydrology, Benson Lane/Wallingford, OX10 8BB/Royaume-Uni (11 aut.); Max Planck Institute for Biogeochemistry, P.O. Box 10 01 64/Jena 07701/Allemagne (12 aut., 26 aut.); National Center for Atmospheric Research/Boulder, CO 80301/Etats-Unis (13 aut.); Centre for Ecology and Hydrology, Bush Estate, Penicuik/Midlothian EH26 0QB/Royaume-Uni (14 aut.); State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences/Beijing 100012/Chine (15 aut., 16 aut.); College of Water Sciences, Beijing Normal University/Beijing 100875/Chine (16 aut.); Department of Animal & Plant Sciences, University of Sheffield/Sheffield S10 2TN/Royaume-Uni (17 aut.); Institute of Biodiversity Science, Fudan University, 220 Handan Road/Shanghai 200433/Chine (18 aut.); Department of Microbiology and Plant Biology, University of Oklahoma/Norman, OK 73019/Etats-Unis (19 aut.); Department of Geography and Environment, Boston University, 675 Commonwealth Avenue/Boston, MA 02215/Etats-Unis (21 aut.); Department of Atmospheric and Oceanic Science, University of Maryland/College Park, MD 20740/Etats-Unis (27 aut.)</AF><DT>Publication en série; Niveau analytique</DT><SO>Global change biology : (Print); ISSN 1354-1013; Royaume-Uni; Da. 2013; Vol. 19; No. 7; Pp. 2117-2132; Bibl. 2 p.</SO><LA>Anglais</LA><EA>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 CO<sub>2</sub> trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO<sub>2</sub> sensitivity of NPP is compared to the results from four Free-Air CO<sub>2</sub> 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<sup>-1</sup>) than JU11 (118 ± 6 Pg C yr<sup>-1</sup>). In response to rising atmospheric CO<sub>2</sub> concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO<sub>2</sub> 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<sup>-1</sup> is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr<sup>-1</sup>). 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<sup>-1</sup> °C<sup>-1</sup>, within the uncertainty of what derived from RLS (-3.9 ± 1.1 Pg C yr<sup>-1</sup> °C<sup>-1</sup>). 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 CO<sub>2</sub> 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 CO<sub>2</sub> concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.</EA><CC>002A14B01</CC><FD>Evaluation; Cycle carbone; Modèle; Variation climat; Dioxyde de carbone; Fertilisation; Précipitation; Température</FD><FG>Facteur milieu</FG><ED>Evaluation; Carbon cycle; Models; Climate variation; Carbon dioxide; Fertilization; Precipitation; Temperature</ED><EG>Environmental factor</EG><SD>Evaluación; Ciclo carbono; Modelo; Variación clima; Carbono dióxido; Fertilización; Precipitación; Temperatura</SD><LO>INIST-27882.354000503050780120</LO><ID>13-0223320</ID></server></inist></record>Pour manipuler ce document sous Unix (Dilib)
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