For the Computing in Atmospheric Sciences workhop next month, I’ll be giving a talk entitled “On the relationship between earth system models and the labs that build them”. Here’s the abstract:
In this talk I will discuss a number of observations from a comparative study of four major climate modeling centres:
- the UK Met Office Hadley Centre (UKMO), in Exeter, UK
- the National Centre for Atmospheric Research (NCAR) in Boulder Colorado,
- the Max-Planck Institute for Meteorology (MPI-M) in Hamburg, Germany
- the Institute Pierre Simon Laplace (IPSL) in Paris, France).
The study focussed on the organizational structures and working practices at each centre with respect to earth system model development, and how these affect the history and current qualities of their models. While the centres share a number of similarities, including a growing role for software specialists and greater use of open source tools for managing code and the testing process, there are marked differences in how the different centres are funded, in their organizational structure and in how they allocate resources. These differences are reflected in the program code in a number of ways, including the nature of the coupling between model components, the portability of the code, and (potentially) the quality of the program code.
While all these modelling centres continually seek to refine their software development practices and the software quality of their models, they all struggle to manage the growth (in terms of size and complexity) in the models. Our study suggests that improvements to the software engineering practices at the centres have to take account of differing organizational constraints at each centre. Hence, there is unlikely to be a single set of best practices that work anywhere. Indeed, improvement in modelling practices usually come from local, grass-roots initiatives, in which new tools and techniques are adapted to suit the context at a particular centre. We suggest therefore that there is need for a stronger shared culture of describing current model development practices and sharing lessons learnt, to facilitate local adoption and adaptation.