Code accessibility and code quality across phases of the models of the Coupled Model Intercomparison Project

Michael García-Rodríguez, Javier Rodeiro-Iglesias, and Juan A. Añel

Model code and software

Source code and analysis results for CFSv2 CMIP5 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932623

Source code and analysis results for GEOS-5 CMIP5 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932661

Source code and analysis results for GFDL CMIP5 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932683

Source code and analysis results for GISS CMIP5 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932705

Source code and analysis results for IPSL CMIP5 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932753

Source code and analysis results for CESM1 CMIP6 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16933339

Source code and analysis results for GISS CMIP6 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16933370

Source code and analysis results for HiRAM CMIP6 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16932804

Source code and analysis results for IPSL CMIP6 model Michael García-Rodríguez et al. https://doi.org/10.5281/zenodo.16933458

FortranAnalyser Michael García-Rodríguez https://doi.org/10.5281/zenodo.5942943

Editorial statement

The Climate Modelling Intercomparison Project coordinates climate projections made by the world's flagship climate models which feed into reports such as the IPCC. It should remain a concern that even in the 6th version of the project, relatively few of the models have made their code accessible to other scientists, let alone the general public. Furthermore, the consideration of what may or may not constitute good code is of interest to all those involved in geoscientifc model development. This paper may come at an interesting point in time. Since their inception in the 1960s, prognostic climate models have always used fortran and similar languages to create algorithms which directly reproduce our basic geoscientific understanding of how the climate evolves, through differential equations. The authors propose that things may be evolving in a new direction, with the potential incorporation of machine learning and other forms of AI into future generations of the models. This will surely present new challenges to the concept and assessment of scientific reproducibility and code development.

Short summary

We studied how accessible and reliable the computer code behind major climate models has been over time. By reviewing different phases of the Coupled Model Intercomparison Project, we found improvements in transparency and coding practices, but also gaps that limit reproducibility. Our work suggests practical steps to make future climate research more open, traceable, and trustworthy for scientists and society.