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

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Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. / Piao, Shilong; Sitch, Stephen; Ciais, Philippe; Friedlingstein, Pierre; Peylin, Philippe; Wang, Xuhui; Ahlström, Anders; Anav, Alessandro; Canadell, Josep G.; Cong, Nan; Huntingford, Chris; Jung, Martin; Levis, Sam; Levy, Peter E.; Li, Junsheng; Lin, Xin; Lomas, Mark R.; Lu, Meng; Luo, Yiqi; Ma, Yuecun; Myneni, Ranga B.; Poulter, Ben; Sun, Zhenzhong; Wang, Tao; Viovy, Nicolas; Zaehle, Soenke; Zeng, Ning.

In: Global Change Biology, Vol. 19, No. 7, 2013, p. 2117-2132.

Research output: Contribution to journalArticle

Harvard

Piao, S, Sitch, S, Ciais, P, Friedlingstein, P, Peylin, P, Wang, X, Ahlström, A, Anav, A, Canadell, JG, Cong, N, Huntingford, C, Jung, M, Levis, S, Levy, PE, Li, J, Lin, X, Lomas, MR, Lu, M, Luo, Y, Ma, Y, Myneni, RB, Poulter, B, Sun, Z, Wang, T, Viovy, N, Zaehle, S & Zeng, N 2013, 'Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends', Global Change Biology, vol. 19, no. 7, pp. 2117-2132. https://doi.org/10.1111/gcb.12187

APA

Piao, S., Sitch, S., Ciais, P., Friedlingstein, P., Peylin, P., Wang, X., ... Zeng, N. (2013). Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology, 19(7), 2117-2132. https://doi.org/10.1111/gcb.12187

CBE

Piao S, Sitch S, Ciais P, Friedlingstein P, Peylin P, Wang X, Ahlström A, Anav A, Canadell JG, Cong N, Huntingford C, Jung M, Levis S, Levy PE, Li J, Lin X, Lomas MR, Lu M, Luo Y, Ma Y, Myneni RB, Poulter B, Sun Z, Wang T, Viovy N, Zaehle S, Zeng N. 2013. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Global Change Biology. 19(7):2117-2132. https://doi.org/10.1111/gcb.12187

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Vancouver

Author

Piao, Shilong ; Sitch, Stephen ; Ciais, Philippe ; Friedlingstein, Pierre ; Peylin, Philippe ; Wang, Xuhui ; Ahlström, Anders ; Anav, Alessandro ; Canadell, Josep G. ; Cong, Nan ; Huntingford, Chris ; Jung, Martin ; Levis, Sam ; Levy, Peter E. ; Li, Junsheng ; Lin, Xin ; Lomas, Mark R. ; Lu, Meng ; Luo, Yiqi ; Ma, Yuecun ; Myneni, Ranga B. ; Poulter, Ben ; Sun, Zhenzhong ; Wang, Tao ; Viovy, Nicolas ; Zaehle, Soenke ; Zeng, Ning. / Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. In: Global Change Biology. 2013 ; Vol. 19, No. 7. pp. 2117-2132.

RIS

TY - JOUR

T1 - Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends

AU - Piao, Shilong

AU - Sitch, Stephen

AU - Ciais, Philippe

AU - Friedlingstein, Pierre

AU - Peylin, Philippe

AU - Wang, Xuhui

AU - Ahlström, Anders

AU - Anav, Alessandro

AU - Canadell, Josep G.

AU - Cong, Nan

AU - Huntingford, Chris

AU - Jung, Martin

AU - Levis, Sam

AU - Levy, Peter E.

AU - Li, Junsheng

AU - Lin, Xin

AU - Lomas, Mark R.

AU - Lu, Meng

AU - Luo, Yiqi

AU - Ma, Yuecun

AU - Myneni, Ranga B.

AU - Poulter, Ben

AU - Sun, Zhenzhong

AU - Wang, Tao

AU - Viovy, Nicolas

AU - Zaehle, Soenke

AU - Zeng, Ning

PY - 2013

Y1 - 2013

N2 - 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 etal. [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 etal. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 +/- 15Pg Cyr-1) than JU11 (118 +/- 6Pg Cyr-1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 +/- 0.8Pg Cyr-1 is remarkably close to the mean value of RLS (2.1 +/- 1.2 Pg Cyr-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 +/- 1.5Pg Cyr-1 degrees C-1, within the uncertainty of what derived from RLS (-3.9 +/- 1.1Pg Cyr-1 degrees 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.

AB - 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 etal. [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 etal. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 +/- 15Pg Cyr-1) than JU11 (118 +/- 6Pg Cyr-1). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 +/- 0.8Pg Cyr-1 is remarkably close to the mean value of RLS (2.1 +/- 1.2 Pg Cyr-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 +/- 1.5Pg Cyr-1 degrees C-1, within the uncertainty of what derived from RLS (-3.9 +/- 1.1Pg Cyr-1 degrees 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.

KW - carbon cycle

KW - CO2 fertilization

KW - model evaluation

KW - precipitation

KW - sensitivity

KW - temperature sensitivity

U2 - 10.1111/gcb.12187

DO - 10.1111/gcb.12187

M3 - Article

VL - 19

SP - 2117

EP - 2132

JO - Global Change Biology

JF - Global Change Biology

SN - 1354-1013

IS - 7

ER -