Journal metrics

Journal metrics

  • IF value: 4.252 IF 4.252
  • IF 5-year value: 4.890 IF 5-year 4.890
  • CiteScore value: 4.49 CiteScore 4.49
  • SNIP value: 1.539 SNIP 1.539
  • SJR value: 2.404 SJR 2.404
  • IPP value: 4.28 IPP 4.28
  • h5-index value: 40 h5-index 40
  • Scimago H index value: 51 Scimago H index 51
Discussion papers | Copyright
https://doi.org/10.5194/gmd-2018-103
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Methods for assessment of models 14 May 2018

Methods for assessment of models | 14 May 2018

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Geoscientific Model Development (GMD).

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

Lee de Mora1, Andrew Yool2, Julien Palmieri2, Alistair Sellar3, Till Kuhlbrodt4, Ekaterina Popova2, Colin Jones5, and J. Icarus Allen1 Lee de Mora et al.
  • 1Plymouth Marine Laboratory, Prospect Place, The Hoe, Plymouth, PL1 3DH, UK
  • 2National Oceanography Centre, University of Southampton Waterfront Campus, European Way, Southampton SO14 3ZH, UK
  • 3Met Office Hadley Centre, Exeter, EX1 3PB, UK
  • 4NCAS, Department of Meteorology, University of Reading, Reading, RG6 6AH, UK
  • 5NCAS, School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK

Abstract. 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).

The key ideas that directed the toolkit design were model and grid independence, front loading analysis functions and regional masking, interuptabililty, 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 circulate and biogeochemical parts of the CMIP5 models, and highlight some predictions about the future state of the marine ecosystem under a business as usual CO2 concentration scenario (RCP 8.5).

Download & links
Lee de Mora et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
[Login for Authors/Topical Editors] [Subscribe to comment alert] Printer-friendly Version - Printer-friendly version Supplement - Supplement
Lee de Mora et al.
Model code and software

BGC-val: a model and grid independent python toolkit to evaluate marine biogeochemical models L. de Mora, A. Yool, J. Palmieri, A. Sellar, T. Kuhlbrodt, E. Popova, C. Jones, and J. I. Allen https://doi.org/10.5281/zenodo.1215935

Lee de Mora et al.
Viewed
Total article views: 350 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
254 89 7 350 21 8 7
  • HTML: 254
  • PDF: 89
  • XML: 7
  • Total: 350
  • Supplement: 21
  • BibTeX: 8
  • EndNote: 7
Views and downloads (calculated since 14 May 2018)
Cumulative views and downloads (calculated since 14 May 2018)
Viewed (geographical distribution)
Total article views: 350 (including HTML, PDF, and XML) Thereof 345 with geography defined and 5 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Cited
Saved
No saved metrics found.
Discussed
No discussed metrics found.
Latest update: 16 Aug 2018
Publications Copernicus
Download
Short summary
Climate change is expected to have a significant impact on the Earths 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.
Climate change is expected to have a significant impact on the Earths weather, ice caps, land...
Citation
Share