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

Related authors

Extending the late 1963 to 1964 Mt Agung rescued searchlight aerosol profiles dataset at 32° N, from early 1963 to 1975

Juan Carlos Antuña-Marrero, Abel Calle, Juan Antonio Añel, Victoria Cachorro, Laura de la Torre, David Barriopedro, Ricardo García Herrera, and Javier Pacheco

Earth Syst. Sci. Data, 18, 4669–4676, https://doi.org/10.5194/essd-18-4669-2026,https://doi.org/10.5194/essd-18-4669-2026, 2026

Short summary

The Geoengineering Model Intercomparison Project (GeoMIP) contribution to CMIP7 – description of new experimental protocols and preliminary results

Daniele Visioni, Alan Robock, Alistair Duffey, Matthew Henry, Haruki Hirasawa, Walker R. Lee, Cindy Wang, Kelsey Roberts, Shingo Watanabe, Michelle S. Reboita, Masahiro Sugiyama, Ben Kravitz, Jim Haywood, Simone Tilmes, Frederic Bonou, Jack Chen, Timofei Sukodolov, Sandro Vattioni, Andrin Jörimann, Diego Villanueva, Ryan Vella, Paul Farron, Ewa M. Bednarz, Ulrike Niemeier, Colleen Golja, and Juan A. Anel

EGUsphere, https://doi.org/10.5194/egusphere-2026-2417,https://doi.org/10.5194/egusphere-2026-2417, 2026

Short summary

Nineteenth- and twentieth-century semi-quantitative surface ozone along subtropical European to tropical Africa Atlantic coasts

Juan A. Añel, Juan-Carlos Antuña-Marrero, Antonio Cid Samamed, Celia Pérez-Souto, Laura de la Torre, Maria Antonia Valente, Yuri Brugnara, Alfonso Saiz-Lopez, and Luis Gimeno

Earth Syst. Sci. Data, 17, 2437–2446, https://doi.org/10.5194/essd-17-2437-2025,https://doi.org/10.5194/essd-17-2437-2025, 2025

Short summary

Cited articles

Añel, J. A.: The Importance of Reviewing the Code, Communication of the ACM, 54, 40–41, https://doi.org/10.1145/1941487.1941502, 2011. a

Añel, J. A.: Comment on “Most computational hydrology is not reproducible, so is it really science?” by Hutton et al., Water Resour. Res., 53, 2572–2574, https://doi.org/10.1002/2016WR020190, 2017. a

Añel, J. A.: Reflections on the Scientific Method at the beginning of the twenty-first century, Contemp. Phys., 1, 60–62, https://doi.org/10.1080/00107514.2019.1579863, 2019. a

Añel, J. A., Montes, D. P., and Rodeiro Iglesias, J.: Cloud and Serverless Computing for Scientists, Springer, ISBN 978-3-03-041783-3, https://doi.org/10.1007/978-3-030-41784-0, 2020. a, b

Añel, J. A., García-Rodríguez, M., and Rodeiro, J.: Current status on the need for improved accessibility to climate models code, Geosci. Model Dev., 14, 923–934, https://doi.org/10.5194/gmd-14-923-2021, 2021. a, b, c, d, e

More articles (60)

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.