Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-15-5801-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
04 Oct 2018
Research article |
| 04 Oct 2018
| 04 Oct 2018
Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation
Istem Fer
CORRESPONDING AUTHOR
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Ryan Kelly
RK Analytics, Durham, NC 27712, USA
Paul R. Moorcroft
Department Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA
Andrew D. Richardson
School of Informatics, Computing and Cyber Systems, Northern Arizona University Flagstaff, AZ 86011, USA
Center for Ecosystem Science and Society, Northern Arizona University, Flagstaff, AZ 86011, USA
Elizabeth M. Cowdery
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Michael C. Dietze
Department of Earth and Environment, Boston University, Boston, MA 02215, USA
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Discussed (preprint)
Latest update: 14 Dec 2024
Short summary
The computer models we use to understand and forecast the ecosystem changes have multiple components that determine their outcomes. Due to our limited observation capacities, these components bear uncertainties that in return affect our predictions. While there are techniques for reducing these uncertainties, they are not applicable to every model due to computational and statistical barriers. This research presents a method that lowers those barriers and allows us to improve model predictions.
The computer models we use to understand and forecast the ecosystem changes have multiple...
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