Applying a dual optimization method to quantify carbon fluxes: recent progress in carbon flux inversion |
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Authors: | Heng Zheng Yong Li Jingming Chen Ting Wang Qing Huang Yao Sheng |
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Affiliation: | 1. School of Mathematical Sciences, Beijing Normal University, Beijing, 100875, China 2. Department of Geography and Program in Planning, University of Toronto, Toronto, ON, M5S 3G3, Canada 3. International Institute of Earth System Science, Nanjing University, Nanjing, 210093, China 4. Department of Mathematics and Statistics, University of Otago, Dunedin, 9054, New Zealand
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Abstract: | The widely performed Bayesian synthesis inversion method (BSIM) utilizes prior carbon flux and atmospheric carbon dioxide observations to optimize the unknown flux. The prior flux is usually computed from ecological models with large biases. The BSIM is useful in solving the problem of insufficient data, but it will increase the inaccuracies in the estimates caused by the biased prior flux. In this study, we propose a dual optimization method (DOM) to introduce a set of scaling factors as new state variables to correct for the prior flux according to information on plant functional types. The DOM estimates the scaling factors and carbon flux simultaneously by minimizing the cost function. The statistical properties of the DOM, which compare favorably with the BSIM, are provided in this article. We tested the DOM through simulation experiments which represent a true ecosystem. The results, according to the root mean squared error, show that the DOM has a higher accuracy than the BSIM in flux estimates. |
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Keywords: | Carbon flux . Dual optimization method Bayesian synthesis inversion method . Scaling factor Cost function |
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