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

de Mora, Lee; Yool, Andrew ORCID: https://orcid.org/0000-0002-9879-2776; Palmieri, Julien ORCID: https://orcid.org/0000-0002-0226-5243; Sellar, Alistair; Kuhlbrodt, Till; Popova, Ekaterina ORCID: https://orcid.org/0000-0002-2012-708X; Jones, Colin; Allen, J. Icarus. 2018 BGC-val: a model- and grid-independent Python toolkit to evaluate marine biogeochemical models. Geoscientific Model Development, 11 (10). 4215-4240. https://doi.org/10.5194/gmd-11-4215-2018

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Official URL: http://dx.doi.org/10.5194/gmd-11-4215-2018

Abstract/Summary

The biogeochemical evaluation toolkit, BGC-val, is a model- and grid-independent Python toolkit that has been built to evaluate marine biogeochemical models using a simple interface. Here, we present the ideas that motivated the development of the BGC-val software framework, introduce the code structure, and show some applications of the toolkit using model results from the Fifth Climate Model Intercomparison Project (CMIP5). A brief outline of how to access and install the repository is presented in Appendix , but the specific details on how to use the toolkit are kept in the code repository. The key ideas that directed the toolkit design were model and grid independence, front-loading analysis functions and regional masking, interruptibility, and ease of use. We present each of these goals, why they were important, and what we did to address them. We also present an outline of the code structure of the toolkit illustrated with example plots produced by the toolkit. After describing BGC-val, we use the toolkit to investigate the performance of the marine physical and biogeochemical quantities of the CMIP5 models and highlight some predictions about the future state of the marine ecosystem under a business-as-usual CO2 concentration scenario (RCP8.5).

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.5194/gmd-11-4215-2018
ISSN:	1991-9603
Date made live:	14 Nov 2018 11:25 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/521514

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.5194/gmd-11-4215-2018

ISSN:

1991-9603

Date made live:

14 Nov 2018 11:25 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/521514