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State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales

Author

Listed:
  • Wang, Tao
  • Brender, Pierre
  • Ciais, Philippe
  • Piao, Shilong
  • Mahecha, Miguel D.
  • Chevallier, Frédéric
  • Reichstein, Markus
  • Ottlé, Catherine
  • Maignan, Fabienne
  • Arain, Altaf
  • Bohrer, Gil
  • Cescatti, Alessandro
  • Kiely, Gerard
  • Law, Beverly Elizabeth
  • Lutz, Merbold
  • Montagnani, Leonardo
  • Moors, Eddy
  • Osborne, Bruce
  • Panferov, Oleg
  • Papale, Dario
  • Vaccari, Francesco Primo

Abstract

Characterization of state-dependent model biases in land surface models can highlight model deficiencies, and provide new insights into model development. In this study, artificial neural networks (ANNs) are used to estimate the state-dependent biases of a land surface model (ORCHIDEE: ORganising Carbon and Hydrology in Dynamic EcosystEms). To characterize state-dependent biases in ORCHIDEE, we use multi-year flux measurements made at 125 eddy covariance sites that cover 7 different plant functional types (PFTs) and 5 climate groups. We determine whether the state-dependent model biases in five flux variables (H: sensible heat, LE: latent heat, NEE: net ecosystem exchange, GPP: gross primary productivity and Reco: ecosystem respiration) are transferable within and between three different timescales (diurnal, seasonal–annual and interannual), and between sites (categorized by PFTs and climate groups). For each flux variable at each site, the spectral decomposition method (singular system analysis) was used to reconstruct time series on the three different timescales.

Suggested Citation

  • Wang, Tao & Brender, Pierre & Ciais, Philippe & Piao, Shilong & Mahecha, Miguel D. & Chevallier, Frédéric & Reichstein, Markus & Ottlé, Catherine & Maignan, Fabienne & Arain, Altaf & Bohrer, Gil & Ces, 2012. "State-dependent errors in a land surface model across biomes inferred from eddy covariance observations on multiple timescales," Ecological Modelling, Elsevier, vol. 246(C), pages 11-25.
    • Handle: RePEc:eee:ecomod:v:246:y:2012:i:c:p:11-25
    DOI: 10.1016/j.ecolmodel.2012.07.017
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    References listed on IDEAS

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    1. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    2. L. Li & N. Vuichard & N. Viovy & Philippe Ciais & T. Wang & Eric Ceschia & W. Jans & M. Wattenbach & Pierre Béziat & T. Gruenwald & S. Lehuger & C. Bernhofer, 2011. "Importance of crop varieties and management practices: evaluation of a process-based model for simulating CO2 and H2O fluxes at five European maize (Zea mays L.) sites," Post-Print hal-00716508, HAL.
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    1. Wang, Qinying & He, Hong S. & Liu, Kai & Zong, Shengwei & Du, Haibo, 2023. "Comparing simulated tree biomass from daily, monthly, and seasonal climate input of terrestrial ecosystem model," Ecological Modelling, Elsevier, vol. 483(C).

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