Articles | Volume 14, issue 18
https://doi.org/10.5194/bg-14-4295-2017
https://doi.org/10.5194/bg-14-4295-2017
Research article
 | 
27 Sep 2017
Research article |  | 27 Sep 2017

Bayesian calibration of terrestrial ecosystem models: a study of advanced Markov chain Monte Carlo methods

Dan Lu, Daniel Ricciuto, Anthony Walker, Cosmin Safta, and William Munger

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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 2017) by Trevor Keenan
AR by Dan Lu on behalf of the Authors (30 Jun 2017)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (12 Jul 2017) by Trevor Keenan
RR by Jasper Vrugt (15 Aug 2017)
RR by Anonymous Referee #2 (25 Aug 2017)
ED: Publish subject to technical corrections (30 Aug 2017) by Trevor Keenan
AR by Dan Lu on behalf of the Authors (31 Aug 2017)  Author's response   Manuscript 
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Short summary
Calibration of terrestrial ecosystem models (TEMs) is important but challenging. This study applies an advanced sampling technique for parameter estimation of a TEM. The results improve the model fit and predictive performance.
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