I had some interesting chats in the last few days with Christian Jakob, who’s visiting Hamburg at the same time as me. He’s just won a big grant to set up a new Australian Climate Research Centre, so we talked a lot about what models they’ll be using at the new centre, and the broader question of how to manage collaborations between academics and government research labs.

Christian has a paper coming out this month in BAMS on how to accelerate progress in climate model development. He points out that much of the progress now depends on the creation of new parameterizations for physical processes, but to do this more effectively requires better collaboration between the groups of people who run the coupled models and assess overall model skill, and the people who analyze observational data to improve our understanding (and simulation) of particular climate processes. The key point he makes in the paper is that process studies are often undertaken because they are interesting and or because data is available, but without much idea on whether improving a particular process will have any impact on overall model skill; conversely model skill is analyzed at modeling centers without much follow-through to identify which processes might be to blame for model weaknesses. Both activities lead to insights, but better coordination between them would help to push model development further and faster. Not that it’s easy of course: coupled models are now sufficiently complex that it’s notoriously hard to pin down the role of specific physical processes in overall model skill.

So we talked a lot about how the collaboration works. One problem seems to stem from the value of the models themselves. Climate models are like very large, very expensive scientific instruments. Only large labs (typically at government agencies) can now afford to develop and maintain fully fledged earth system models. And even then the full cost is never adequately accounted for in the labs’ funding arrangements. Funding agencies understand the costs of building and operating physical instruments, like large telescopes, or particle accelerators, as shared resources across a scientific community. But because software is invisible and abstract, they don’t think of it in the same way – there’s a tendency to think that it’s just part of the IT infrastructure, and can be developed by institutional IT support teams. But of course, the climate models need huge amounts of specialist expertise to develop and operate, and they really do need to be funded like other large scientific instruments.

The complexity of the models and the lack of adequate funding for model development means that the institutions that own the models are increasingly conservative in what they do with them. They work on small incremental changes to the models, and don’t undertake big revolutionary changes – they can’t afford to take the risk. There are some examples of labs taking such risks: for example in the early 1990’s ECMWF re-wrote their model from scratch, driven in part to make it more adaptable to new, highly parallel, hardware architectures. It took several years, and a big team of coders, bringing in the scientific experts as needed. At the end of it, they had a model that was much cleaner, and (presumably) more adaptable. But scientifically, it was no different from the model they had previously. Hence, lots of people felt this was not a good use of their time – they could have made better scientific progress during that time by continuing to evolve the old model. And that was years ago – the likelihood of labs making such radical changes these days is very low.

On the other hand, academics can try the big, revolutionary stuff – if it works, they get lots of good papers about how they’re pushing the frontiers, and if it doesn’t, they can write papers about why some promising new approach didn’t work as expected. But then getting their changes accepted into the models is hard. A key problem here is that there’s no real incentive for them to follow through. Academics are judged on papers, so once the paper is written they are done. But at that point, the contribution to the model is still a long way from being ready to incorporate for others to use. Christian estimates that it takes at least as long again to get a change ready to incorporate into a model as it does to develop it in the first place (and that’s consistent with what I’ve heard other modelers say). The academic has no incentive to continue to work on it to get it ready, and the institutions have no resources to take it and adopt it.

So again we’re back to the question of effective collaboration, beyond what any one lab or university group can do. And the need to start treating the models as expensive instruments, with much higher operation and maintenance costs than anyone has yet acknowledged. In particular, modeling centers need resources for a much bigger staff to support the efforts by the broader community to extend and improve the models.


  1. I think you’re over-stating your case. Sure, every measurement requires some sort of model, and lots of newer diagnostic techniques require some pretty complicated models, but do you really think this search will ever turn up any results?

  2. Josh: well, that’s just a game of semantics. The issue is a recognition that the models are (1) core community assets for climate science and (2) much more expensive to maintain than is generally acknowledged. But I’m not really saying anything new here – I’ve heard similar things often said in the commercial software sector – businesses rarely understand the real value of their software infrastructure, and they don’t even have good accounting tools to assess the cost/benefit proposition of the ongoing software maintenance costs of this infrastructure. The invisibleness of software is key – you pour millions of dollars into maintaining your software infrastructure, and there doesn’t appear to be any result for the money. If the software works well you barely notice it.
    You could equally well ask how likely it is that some big shot politician will ever be seen cutting the ribbon for a climate model (or any other software, come to think of it). Both your example and mine emphasize my point, and indicate, if anything, I’m under-stating it.

  3. Maybe we could build a new supercomputer every time a major new model is ready for the ribbon-cutting ceremonies (and to reify software for proper funding)!

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