Articles | Volume 11, issue 10
https://doi.org/10.5194/gmd-11-4215-2018
https://doi.org/10.5194/gmd-11-4215-2018
Methods for assessment of models
 | 
17 Oct 2018
Methods for assessment of models |  | 17 Oct 2018

BGC-val: a model- and grid-independent Python toolkit to evaluate marine biogeochemical models

Lee de Mora, Andrew Yool, Julien Palmieri, Alistair Sellar, Till Kuhlbrodt, Ekaterina Popova, Colin Jones, and J. Icarus Allen

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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
AR by Lee de Mora on behalf of the Authors (25 Jul 2018)  Author's response   Manuscript 
ED: Publish subject to minor revisions (review by editor) (31 Jul 2018) by Julia Hargreaves
AR by Lee de Mora on behalf of the Authors (21 Aug 2018)  Manuscript 
ED: Publish subject to minor revisions (review by editor) (04 Sep 2018) by Julia Hargreaves
AR by Lee de Mora on behalf of the Authors (07 Sep 2018)  Author's response 
ED: Publish as is (24 Sep 2018) by Julia Hargreaves
AR by Lee de Mora on behalf of the Authors (24 Sep 2018)
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Short summary
Climate change is expected to have a significant impact on the Earth's weather, ice caps, land surface, and ocean. Computer models of the Earth system are the only tools available to make predictions about how the climate may change in the future. However, in order to trust the model predictions, we must first demonstrate that the models have a realistic description of the past. The BGC-val toolkit was built to rapidly and simply evaluate the behaviour of models of the Earth's oceans.