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

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

Ecological Modelling, 2012, vol. 246, issue C, 11-25

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.

Keywords: Eddy covariance; Land surface model; State-dependent model bias; Neural networks; Singular system analysis; Timescale (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:246:y:2012:i:c:p:11-25

DOI: 10.1016/j.ecolmodel.2012.07.017

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