Articles | Volume 19, issue 10
https://doi.org/10.5194/gmd-19-4385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-4385-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Methods for assessment of models
| 
21 May 2026
Methods for assessment of models |
| 21 May 2026
| 21 May 2026
Meta-modelling of carbon fluxes from crop and grassland multi-model outputs
Roland Hollós
Agricultural Institute, Centre for Agricultural Research HUN-REN, Martonvásár, 2462, Hungary
Department of Meteorology, ELTE Eötvös Loránd University, Budapest, 1117, Hungary
Global Change Research Institute, Czech Academy of Sciences, Brno, 603 00, Czech Republic
Nándor Zrinyi
Department of Meteorology, ELTE Eötvös Loránd University, Budapest, 1117, Hungary
Doctoral School of Earth Sciences, ELTE Eötvös Loránd University, Budapest, 1117, Hungary
Zoltán Barcza
Department of Meteorology, ELTE Eötvös Loránd University, Budapest, 1117, Hungary
Global Change Research Institute, Czech Academy of Sciences, Brno, 603 00, Czech Republic
Gianni Bellocchi
VetAgro Sup, Unité Mixte de Recherche sur l'Ecosystème Prairial (UREP), UCA, INRAE, Clermont-Ferrand, 63000, France
Renáta Sándor
Agricultural Institute, Centre for Agricultural Research HUN-REN, Martonvásár, 2462, Hungary
János Ruff
Institute of Mathematics and Informatics, University of Pécs, Pécs, 7624, Hungary
Agricultural Institute, Centre for Agricultural Research HUN-REN, Martonvásár, 2462, Hungary
Faculty of Agricultural and Food Sciences and Environmental Management, University of Debrecen, Debrecen, 4032, Hungary
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Short summary
This work builds upon and extends previous multi-model ensemble studies by introducing five meta-modelling approaches to predict ecosystem-scale C fluxes. Our results show that meta-models consistently outperform both the multi-model median and the best individual process-based models, improving explained variance and substantially reducing bias, even for challenging fluxes such as total ecosystem respiration and net ecosystem exchange.
This work builds upon and extends previous multi-model ensemble studies by introducing five...
