The importance of describing simple methods in climate sensitivity literature

“Show your working!” – is the universal phrase drilled into science and maths students to show a clear demonstration of the steps and thought processes used to reach a solution (and to be awarded full marks on the exam). 

Beyond the classroom, “show your working” becomes the methods section on every scientific paper, and is critical for the transparency and replicability of the study. However, what happens if parts of the method are considered assumed knowledge, or cut in the interests of a word count? 

An inability to fully replicate the results of a study became the unexpected glitch at the start of my PhD. Eager to familiarise myself with global climate model datasets, I set out to replicate the results of a widely cited paper which calculates the equilibrium climate sensitivity (ECS) across 27 climate models. The ECS is the theoretical global mean temperature response to a doubling of atmospheric CO₂ relative to preindustrial levels. A commonly used method to calculate the ECS is to apply an ordinary least squares regression to global annual mean temperature and radiative flux anomalies. 

Despite the simplicity of a linear regression between two variables, we obtained ECS estimates for some climate models that differed from those reported in the original study, even though we followed the described methodology. However, the methodology provided only limited detail on how the raw climate model output – available at regional and monthly scales – was processed to obtain global annual mean anomalies. Differences in these intermediate processing steps can, in turn, lead to differences in ECS estimates.

Limited reporting of data-processing steps is common in the ECS literature. Whether these steps are considered assumed knowledge or deemed too simple to warrant explicit description, we demonstrate that, for some models, they can materially affect the resulting ECS estimate. While the primary aim of our study is to recommend a standardised data-processing pathway for ECS calculations, a secondary aim is to highlight the lack of transparency in key methodological details across the literature. A central takeaway is the importance of clearly documenting all processing steps – effectively, to “show your working” – and to emphasise the critical role of a detailed methods section.