How can we help?

19. March 2009 · 3 comments · Categories: collaborative science, research ideas · Tags: , , , , , , ,

A group of us at the lab, led by Jon Pipitone, has been meeting every Tuesday lunchtime (well almost every Tuesday) for a few months, to brainstorm ideas for how software engineers can contribute to addressing the climate crisis. Jon has been blogging some of our sessions (here, here and here).

This week we attempted to create a matrix, where the rows are “challenge problems” related to the climate crisis, and the columns are the various research areas of software engineering (e.g. requirements analysis, formal methods, testing, etc…). One reason to do this is to figure out how to run a structured brainstorming session with a bigger set of SE researchers (e.g. at ICSE). Having sketched out the matrix, we then attempted to populate one row with ideas for research projects. I thought the exercise went remarkably well. One thing I took away from it was that it was pretty easy to think up research projects to populate many of the cells in the matrix (I had initially thought the matrix might be rather sparse by the time we were done).

We also decided that it would be helpful to characterize each of the rows a little more, so that SE researchers who are unfamiliar with some of the challenges would understand each challenge enough to stimulate some interesting discussions. So, here is an initial list of challenges (I added some links where I could). Note that I’ve grouped them according to who immediate audience is for any tools, techniques, practices…).

  1. Help the climate scientists to develop a better understanding of climate processes.
  2. Help the educators to to teach kids about climate science – how the science is done, and how we know what we know about climate change.
    • Support hands-on computational science (e.g. an online climate lab with building blocks to support construction of simple simulation models)
    • Global warming games
  3. Help the journalists & science writers to raise awareness of the issues around climate change for a broader audience.
    • Better public understanding of climate processes
    • Better public understanding of how climate science works
    • Visualizations of complex earth systems
    • connect data generators (eg scientists) with potential users (e.g. bloggers)
  4. Help the policymakers to design, implement and adjust a comprehensive set of policies for reducing greenhouse gas emissions.
  5. Help the political activists who put pressure on governments to change their policies, or to get better leaders elected when the current ones don’t act.
    • Social networking tools for activitists
    • Tools for persuasion (e.g. visualizations) and community building (e.g. Essence)
  6. Help individuals and communities to lower their carbon footprints.
  7. Help the engineers who are developing new technologies for renewable energy and energy efficiency systems.
    • green IT
    • Smart energy grids
    • waste reduction
    • renewable energy
    • town planning
    • green buildings/architecture
    • transportation systems (better public transit, electric cars, etc)
    • etc

Tools for collaborative sense-making

16. March 2009 · Write a comment · Categories: collaborative science, research ideas · Tags: ,

Okay, some of the tools produced by the Essence project are pretty cool:

http://globalsensemaking.wik.is/ESSENCE/Tools

I especially like Debategraph, but mainly for its aesthetic properties when you pull the graph around, and the ability to zoom in to nodes by clicking, rather than how it’s actually being used (mapping debates about whether climate change is happening? That’s so last century). Could we use a tool like this for playing with i* models?

understanding the science

16. March 2009 · Write a comment · Categories: climate modeling, climate science, collaborative science, philosophy · Tags: , , ,

I originally wrote this as a response to a post on RealClimate on hypothesis testing

I think one of the major challenges with public understanding of climate change is that most people have no idea of what climate scientists actually do. In the study I did last summer of the software development practices at the Hadley Centre, my original goal was to look just at the “software engineering” of climate simulation models -i.e. how the code is developed and tested. But the more time I spend with climate scientists, the more I’m fascinated by the kind of science they do, and the role of computational models within it.

The most striking observation I have is that climate scientists have a deep understanding of the fact that climate models are only approximations of earth system processes, and that most of their effort is devoted to improving our understanding of these processes (“All models are wrong, but some are useful” – George Box). They also intuitively understand the core ideas from general systems theory – that you can get good models of system-level processes even when many of the sub-systems are poorly understood, as long as you’re smart about choices of which approximations to use. The computational models have an interesting status in this endeavour: they seem to be used primarily for hypothesis testing, rather than for forecasting. A large part of the time, climate scientists are “tinkering” with the models, probing their weaknesses, measuring uncertainty, identifying which components contribute to errors, looking for ways to improve them, etc. But the public generally only sees the bit where the models are used to make long term IPCC-style predictions.

I never saw a scientist doing a single run of a model and comparing it against observations. The simplest use of models is to construct a “controlled experiment” by making a small change to the model (e.g. a potential improvement to how it implements some piece of the physics), comparing this against a control run (typically the previous run without the latest change), and comparing both runs against the observational data. In other words, there is a 3-way comparison: old model vs. new model vs. observational data, where it is explicitly acknowledged that there may be errors in any of the three. I also see more and more effort put into “ensembles” of various kinds: model intercomparison projects, perturbed physics ensembles, varied initial conditions, and so on. In this respect, the science seems to have changed (matured) a lot in the last few years, but that’s hard for me to verify.

It’s a pretty sophisticated science. I would suggest that the general public might be much better served by good explanations of how this science works, rather than with explanations of the physics and mathematics of climate systems.

Sharing research ideas by blogging

11. March 2009 · 1 comment · Categories: blogging, collaborative science · Tags: , ,

We had a discussion today with the grad students taking my class on empirical research methods, on the role of blogging by researchers. Some students thought that it was a bad idea to post their research ideas on their blogs, because other people might steal them. This is, of course, a perennial fear amongst grad students – that someone else will do the same research and publish it first. I argued strongly that it doesn’t happen, for two reasons:

  1. the idea is only a tiny part of the research – it’s what you do with the idea that really matters. Bill Buxton has a whole talk on this, the summary of which is:  The worst thing in the world is a precious idea; The worst person to have on your team is someone who thinks his idea is precious; Good ideas are cheap, they are not precious; The key is not to come up with ideas but to cultivate the adoption of ideas.
  2. even if someone else works on the same idea, they will approach it in different way, and both projects will be a contribution to knowledge (and therefore be worthy of publication).

After the class, Simon sent me a pointer to Michael Nielsen’s blog post on the importance of scientists sharing their ideas via blogs. It’s great reading.

Note: I’m particularly chuffed about the relevance of Neilsen’s post to climate science, as the Navier-Stokes equations he mentions in his example lie at the heart of climate simulation models.

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