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Inversion of terrestrial ecosystem model parameter values against eddy covariance measurements by Monte Carlo sampling
Author(s) -
Knorr Wolfgang,
Kattge Jens
Publication year - 2005
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/j.1365-2486.2005.00977.x
Subject(s) - eddy covariance , environmental science , covariance , monte carlo method , inversion (geology) , carbon cycle , atmospheric sciences , sampling (signal processing) , metropolis–hastings algorithm , terrestrial ecosystem , probability density function , gaussian , climate change , ecosystem , climatology , meteorology , mathematics , markov chain monte carlo , statistics , physics , geology , ecology , biology , paleontology , structural basin , detector , optics , quantum mechanics
Effective measures to counter the rising levels of carbon dioxide in the Earth's atmosphere require that we better understand the functioning of the global carbon cycle. Uncertainties about, in particular, the terrestrial carbon cycle's response to climate change remain high. We use a well‐known stochastic inversion technique originally developed in nuclear physics, the Metropolis algorithm, to determine the full probability density functions (PDFs) of parameters of a terrestrial ecosystem model. By thus assimilating half‐hourly eddy covariance measurements of CO 2 and water fluxes, we can substantially reduce the uncertainty of approximately five model parameters, depending on prior uncertainties. Further analysis of the posterior PDF shows that almost all parameters are nearly Gaussian distributed, and reveals some distinct groups of parameters that are constrained together. We show that after assimilating only 7 days of measurements, uncertainties for net carbon uptake over 2 years for the forest site can be substantially reduced, with the median estimate in excellent agreement with measurements.