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    Author(s): Kevin Schaefer; Christopher R. Schwalm; Chris Williams; M. Altaf Arain; Alan Barr; Jing M. Chen; Kenneth J. Davis; Dimitre Dimitrov; Timothy W. Hilton; David Y. Hollinger; Elyn Humphreys; Benjamin Poulter; Brett M. Raczka; Andrew D. Richardson; Alok Sahoo; Peter Thornton; Rodrigo Vargas; Hans Verbeeck; Ryan Anderson; Ian Baker; T. Andrew Black; Paul Bolstad; Jiquan Chen; Peter S. Curtis; Ankur R. Desai; Michael Dietze; Danilo Dragoni; Christopher Gough; Robert F. Grant; Lianhong Gu; Atul Jain; Chris Kucharik; Beverly Law; Shuguang Liu; Erandathie Lokipitiya; Hank A. Margolis; Roser Matamala; J. Harry McCaughey; Russ Monson; J. William Munger; Walter Oechel; Changhui Peng; David T. Price; Dan Ricciuto; William J. Riley; Nigel Roulet; Hanqin Tian; Christina Tonitto; Margaret Torn; Ensheng Weng; Xiaolu Zhou
    Date: 2012
    Source: Journal of Geophysical Research. 117(G3): 15 pp.
    Publication Series: Scientific Journal (JRNL)
    Station: Northern Research Station
    PDF: Download Publication  (1.0 MB)

    Description

    Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such asεmax (LUE), Vcmax (unstressed Rubisco catalytic capacity) or Jmax (the maximum electron transport rate).

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    Citation

    Schaefer, Kevin; Schwalm, Christopher R.; Williams, Chris; Arain, M. Altaf; Barr, Alan; Chen, Jing M.; Davis, Kenneth J.; Dimitrov, Dimitre; Hilton, Timothy W.; Hollinger, David Y.; Humphreys, Elyn; Poulter, Benjamin; Raczka, Brett M.; Richardson, Andrew D.; Sahoo, Alok; Thornton, Peter; Vargas, Rodrigo; Verbeeck, Hans; Anderson, Ryan; Baker, Ian; Black, T. Andrew; Bolstad, Paul; Chen, Jiquan; Curtis, Peter S.; Desai, Ankur R.; Dietze, Michael; Dragoni, Danilo; Gough, Christopher; Grant, Robert F.; Gu, Lianhong; Jain, Atul; Kucharik, Chris; Law, Beverly; Liu, Shuguang; Lokipitiya, Erandathie; Margolis, Hank A.; Matamala, Roser; McCaughey, J. Harry; Monson, Russ; Munger, J. William; Oechel, Walter; Peng, Changhui; Price, David T.; Ricciuto, Dan; Riley, William J.; Roulet, Nigel; Tian, Hanqin; Tonitto, Christina; Torn, Margaret; Weng, Ensheng; Zhou, Xiaolu. 2012. A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis. Journal of Geophysical Research, Vol. 117(G3).

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    Keywords

    gross primary productivity, model performance, modeling

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