Articles | Volume 11, issue 3
Geosci. Model Dev., 11, 1181–1198, 2018
https://doi.org/10.5194/gmd-11-1181-2018
Geosci. Model Dev., 11, 1181–1198, 2018
https://doi.org/10.5194/gmd-11-1181-2018

Methods for assessment of models 29 Mar 2018

Methods for assessment of models | 29 Mar 2018

Error assessment of biogeochemical models by lower bound methods (NOMMA-1.0)

Volkmar Sauerland et al.

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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
AR by Volkmar Sauerland on behalf of the Authors (09 Oct 2017)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (16 Oct 2017) by Christoph Müller
RR by Anonymous Referee #1 (02 Nov 2017)
RR by Anonymous Referee #3 (17 Nov 2017)
ED: Reconsider after major revisions (21 Nov 2017) by Christoph Müller
AR by Svenja Lange on behalf of the Authors (31 Jan 2018)  Author's response
ED: Referee Nomination & Report Request started (31 Jan 2018) by Christoph Müller
RR by Anonymous Referee #1 (13 Feb 2018)
RR by Anonymous Referee #3 (16 Feb 2018)
ED: Publish subject to technical corrections (17 Feb 2018) by Christoph Müller
AR by Volkmar Sauerland on behalf of the Authors (20 Feb 2018)  Author's response    Manuscript
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Short summary
We present a concept to prove that a parametric model is well calibrated, i.e., that changes of its free parameters cannot lead to a much better model–data misfit anymore. The intention is motivated by the fact that calibrating global biogeochemical ocean models is important for assessment and inter-model comparison but computationally expensive.