Articles | Volume 15, issue 19
https://doi.org/10.5194/bg-15-5801-2018
https://doi.org/10.5194/bg-15-5801-2018
Research article
 | 
04 Oct 2018
Research article |  | 04 Oct 2018

Linking big models to big data: efficient ecosystem model calibration through Bayesian model emulation

Istem Fer, Ryan Kelly, Paul R. Moorcroft, Andrew D. Richardson, Elizabeth M. Cowdery, and Michael C. Dietze

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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (22 May 2018) by Sönke Zaehle
AR by Istem Fer on behalf of the Authors (26 Jun 2018)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (12 Jul 2018) by Sönke Zaehle
RR by Anonymous Referee #3 (28 Jul 2018)
ED: Publish subject to minor revisions (review by editor) (27 Aug 2018) by Sönke Zaehle
AR by Istem Fer on behalf of the Authors (05 Sep 2018)  Author's response   Manuscript 
ED: Publish subject to technical corrections (06 Sep 2018) by Sönke Zaehle
AR by Istem Fer on behalf of the Authors (13 Sep 2018)  Author's response   Manuscript 
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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.
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