Serendipity

I mentioned this in the comment thread on my earlier post on model IV&V, but I’m elevating it to a full post here because it’s an interesting point of discussion.

I had a very interesting lunch with David Randall at CSU yesterday, in which we talked about many of the challenges facing climate modelling groups as they deal with increasing complexity in the models. One topic that came up was the question of whether it’s time for the climate modeling labs to establish separate divisions for the science models (experimental tools for trying out new ideas) and production models (which would be used for assessments and policy support). This separation hasn’t happened in climate modelling, but may well be inevitable, if the the anticipated market for climate services ever materializes.

There are many benefits of such a separation. It would clarify the goals and roles within the modeling labs, and allow for a more explicit decision process that decides which ideas from the bleeding edge science models are mature enough for inclusion in the operational models. The latter would presumably only contain the more well-established science, would change less rapidly, and could be better engineered for robustness and usability. And better documented.

But there’s a huge downside: the separation would effectively mean two separate models need to be developed and maintained (thus potentially doubling the effort), and the separation would make it harder to get the latest science transferred into the production model. Which in turn would mean a risk that assessments such as the IPCC’s become even more dated than they are now: there’s already a several year delay because of the time it takes to complete model runs, share the data, analyze it, peer-review and publish results, and then compile the assessment reports. Divorcing science models from production models would make this delay worse.

But there’s an even bigger problem: the community is too small. There aren’t enough people who understand how to put together a climate model as it is; bifurcating the effort will make this shortfall even worse. David points out that part of the problem is that climate models are now so complex that nobody really understands the entire model; the other problem is that our grad schools aren’t producing many people who have both the aptitude and enthusiasm for climate modeling. There’s a risk that the best modellers will choose to stay in the science shops (because getting leading edge science into the models is much more motivating), leaving insufficient expertise to maintain quality in the production models.

So really, it comes down to some difficult questions about priorities: given the serious shortage of good modellers, do we push ahead with the current approach in which progress at the leading edge of the science is prioritized, or do we split the effort to create these production shops? It seems to me that what matters for the IPCC at the moment is a good assessment of the current science, not some separate climate forecasting service. If a commercial market develops for the latter (which is possible, once people really start to get serious about climate change), then someone will have to figure out how to channel the revenues into training a new generation of modellers.

When I mentioned this discussion on the earlier thread, Josh was surprised at the point that universities aren’t producing enough people with the aptitude and motivation:

“seems like this is a hot / growth area (maybe that impression is just due to the press coverage).”

Michael replied:

“Funding is weak and sporadic; the political visibility of these issues often causes revenge-taking at the top of the funding hierarchy. Recent news, for instance, seems to be of drastic cuts in Canadian funding for climate science. …

The limited budgets lead to attachment to awkward legacy codes, which drives away the most ambitious programmers. The nature of the problem stymies the most mathematically adept who are inclined to look for more purity. Software engineers take a back seat to physical scientists with little regard for software design as a profession. All in all, the work is drastically ill-rewarded in proportion to its importance, and it’s fair to say that while it attracts good people, it’s not hard to imagine a larger group of much higher productivity and greater computational science sophistication working on this problem.

And that is the nub of the problem. There’s plenty of scope for improving the quality of the models and the quality of the software in them. But if we can’t grow the pool of engaged talent, it won’t happen.