Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 10, issue 9
Articles | Volume 10, issue 9
https://doi.org/10.5194/gmd-10-3519-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-10-3519-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 10, issue 9
Development and technical paper
 | 
25 Sep 2017
Development and technical paper |  | 25 Sep 2017

Reverse engineering model structures for soil and ecosystem respiration: the potential of gene expression programming

Iulia Ilie, Peter Dittrich, Nuno Carvalhais, Martin Jung, Andreas Heinemeyer, Mirco Migliavacca, James I. L. Morison, Sebastian Sippel, Jens-Arne Subke, Matthew Wilkinson, and Miguel D. Mahecha

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Latest update: 10 Dec 2024
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Short summary
Accurate representation of land-atmosphere carbon fluxes is essential for future climate projections, although some of the responses of CO2 fluxes to climate often remain uncertain. The increase in available data allows for new approaches in their modelling. We automatically developed models for ecosystem and soil carbon respiration using a machine learning approach. When compared with established respiration models, we found that they are better in prediction as well as offering new insights.
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