Scientific Expertise

05. April 2009 · Write a comment · Categories: climate science, research methods · Tags: , , , , , ,

Whom do you believe: The Cato Institute, or the Hadley Centre? Both cannot be right. Yet both claim to be backed by real scientists.

First, to get this out of the way, the latest ad from Cato has been thoroughly debunked by RealClimate, including a critical look at whether the papers that Cato cites offer any support for Cato’s position (hint: they don’t), and a quick tour through related literature. So I won’t waste my time repeating their analysis.

The Cato folks attempted to answer back, but it’s largely by attacking red herrings. However, one point from this article jumped out at me:

“The fact that a scientist does not undertake original research on subject x does not have any bearing on whether that scientist can intelligently assess the scientific evidence forwarded in a debate on subject x”.

The thrust of this argument is an attempt to bury the idea of expertise, so that the opinions of the Cato institute’s miscellaneous collection of people with PhDs can somehow be equated with those of actual experts. Now, of course it is true that a (good) scientist in another field ought to be able to understand the basics of climate science, and know how to judge the quality of the research, the methods used, and the strength of the evidence, at least at some level. But unfortunately, real expertise requires a great deal of time and effort to acquire, no matter how smart you are.

If you want to publish in a field, you have to submit yourself to the peer-review process. The process is not perfect (incorrect results often do get published, and, on occasion, fabricated results too). But one thing it does do very well is to check whether authors are keeping up to date with the literature. That means that anyone who regularly publishes in good quality journals has to keep up to date with all the latest evidence. They cannot cherry pick.

Those who don’t publish in a particular field (either because they work in an unrelated field, or because they’re not active scientists at all) don’t have this obligation. Which means when they form opinions on a field other than their own, they are likely to be based on a very patchy reading of the field, and mixed up with a lot of personal preconceptions. They can cherry pick. Unfortunately, the more respected the scientist, the worse the problem. The most venerated (e.g. prize winners) enter a world in which so many people stroke their egos, they lose touch with the boundaries of their ignorance. I know this first hand, because some members of my own department have fallen into this trap: they allow their brilliance in one field to fool them into thinking they know a lot about other fields.

Hence, given two scientists who disagree with one another, it’s a useful rule of thumb to trust the one who is publishing regularly on the topic. More importantly, if there are thousands of scientists publishing regularly in a particular field and not one of them supports a particular statement about that field, you can be damn sure it’s wrong. Which is why the IPCC reviews of the literature are right, and Cato’s adverts are bullshit.

Disclaimer: I don’t publish in the climate science literature either (it’s not my field). I’ve spent enough time hanging out with climate scientists to have a good feel for the science, but I’ll also get it wrong occasionally. If in doubt, check with a real expert.

If you’re not yet scared…

01. April 2009 · 2 comments · Categories: advocacy, climate science, psychology · Tags: , , , ,

In our brainstorm session yesterday, someone (Faraz?) suggested I could kick off the ICSE session with a short video. The closest thing I can think of is this:

Wake Up, Freak Out – then Get a Grip

It’s not too long, it covers the recent science very well, and it is exactly the message I want to give – climate change is serious, urgent, demands massive systemic change, but is not something we should despair over. It also comes with a full transcript with detailed references into the primary scientific literature, which is well worth a browse.

Except that it scares the heck out of me every time I watch it. Could I really show this to an ICSE audience?

Modularizing Climate Models

31. March 2009 · 1 comment · Categories: climate modeling, climate science, research ideas · Tags: , ,

One of the things that came up in our weekly brainstorming session today was the question of whether climate models can be made more modular, to permit distributed development, and distributed execution. Carolyn has already blogged about some of these ideas. Here’s a little bit of history for this topic.

First, a very old (well, 1989) paper by Kalnay et al,  on Data Interchange Formats, in which they float the idea of “plug compatibility” for climate model components. For a long time, this idea seems to have been accepted as the long term goal for the architecture for climate models. But no-one appears to have come close. In 1996, David Randall wrote an interesting introspective on how university teams can (or can’t) participate in climate model building, in which he speculates that plug compatibility might not be achievable in practice because of the complexity of the physical processes being simulated, and the complex interactions between them. He also points out that all climate models (up to that point) had each been developed at a single site, and he talks a bit about why this appears to be necessarily so.

Fast forward to a paper by Dickinson et al in 2002, which summarizes the results of a series of workshops on how to develop a better software infrastructure for model sharing, and talks about some prototype software frameworks. Then, a paper by Larson et al in 2004, introducing a common component architecture for earth system models, and a bit about the Earth System Modeling Framework being developed at NCAR. And finally, Drake et al.’s Overview of the Community Climate System Model, which appears to use these frameworks very successfully.

Now, admittedly I haven’t looked closely at the CCSM. But I have looked closely at the Met Office’s Unified Model and the Canadian CCCma, and neither of them get anywhere close to the ideal of modularity. In both cases, the developers have to invest months of effort to ‘naturalize’ code contributed from other labs, in the manner described in Randall’s paper.

So, here’s the mystery. Has the CCSM really achieved the modularity that others are only dreaming of? And if so how? The key test would be how much effort it takes to ‘plug in’ a module developed elsewhere…

Why there’s no silver bullet

27. March 2009 · Write a comment · Categories: climate science, reducing emissions · Tags: , , , , , ,

There is no silver bullet for climate change, just as there’s no silver bullet for software engineering. To understand why this is, you need to understand the magnitude of the problem.

Firstly, there’s the question of what a “safe” temperature rise would be. There’s a broad consensus among climate scientists that about a rise of around 2°C (above pre-industrial levels) is a sensible upper limit. I’ve asked a number of climate scientists why this threshold, and the answer is that above this level, scary feedback effects start to kick in, and then we’re in serious trouble. If you look at the assessments from the IPCC, the lowest stabilization level they consider is 450 ppm (parts per million), but its clear from their figures that even at this level, we would overshoot the 2°C threshold. Since that report, some scientists have argued this is way too high, and 350ppm would be a better target. Worryingly, the last IPCC assessment was based on climate models that did not include feedback effects.

Then, there’s the question of how to get there. Stabilizing at 350-450ppm requires a reduction of greenhouse emissions of around 80% in industrialized nations by the year 2050. Monbiot argues that if you think in terms of a reduction per capita, you have to allow for population growth. So that really means a reduction more like 90% per person. And again, due to our uncertainty about feedback effects, the emissions targets may need to be even lower.

How do reduce emissions by 90% per person? The problem is that our emissions of greenhouse gases come from everything we do, and no one activity or industry dominates. I was looking for a good graphic for my ICSE talk, to illustrate this point, when I came across this chart of sources of emissions:

 

World Greenhouse Gas Emissions by Sector

World Greenhouse Gas Emissions by Sector

 

 

I think that’s enough on it’s own to show there is not likely to be a silver bullet. The only way to solve the problem is a systemic analysis of sources of emissions, and we have to take into account a huge number of different options. If you want more detail on the figures, Jon Rynn at Grist has started to put together some spreadsheets to add up all the sources of emissions, and some specific contributors.

BTW, the IPCC’s frequently asked questions is a great primer for anyone new to the physics of climate change.

Great women in climate science

23. March 2009 · 1 comment · Categories: climate modeling, climate science · Tags: , ,

In honour of Ada Lovelace day, I decided to write a post today about Prof Julia Slingo, the new chief scientist at the UK Met Office. News of Julia’s appointment came out in the summer last year during my visit to the Met Office, coincidentally on the same day that I met her, at a workshop on the HiGEM project (where, incidentally, I saw some very cool simulations of ocean temperatures). Julia’s role at the meeting was to represent the sponsor (NERC – the UK equivalent of Canada’s NSERC), but what impressed me about her talk was both her detailed knowledge of the project, and the way she nurtured it – she’ll make a great chief scientist.

Julia’s research has focussed on tropical variability, particularly improving our understanding of the monsoons, but she’s also played a key role in earth system modeling, and especially in the exploration of high resolution models. But best of all, she’s just published a very readable account of the challenges in developing the next generation of climate models. Highly recommended for a good introduction to the state of the art in climate modeling.

Some upcoming climate conferences

22. March 2009 · Write a comment · Categories: climate science, conferences · Tags: , ,

First a couple of local ones, in May:

Then, this one looks interesting: The World Climate Conference, in Geneva at the end of August. It looks like most of the program will be invited, but they will be accepting abstracts for a poster session. Given that the theme is to do with how climate information is generated and used, it sounds very appropriate.

Followed almost immediately by EnviroInfo2009, in Berlin, in September. I guess the field I want to name “Climate Informatics” would be a subfield of environmental informatics. Paper deadline is April 6.

Finally, there’s the biggy in Copenhagen in December, where, hopefully, the successor to the Kyoto agreement will be negotiated.

Is Climate Change Accelerating?

16. March 2009 · 1 comment · Categories: climate science · Tags: , ,

Over the last two years, evidence has accumulated that the IPCC reports released just two years ago underestimate the pace of climate change. Nature provides this summary. See also this article in Science Daily; and there are plenty more like it;

Emissions from fossil fuels growing faster than any of the scenarios included in the IPCC reports (news article ; original paper here). And recent studies indicate the effects are irreversible., at least for the next 1000 years.

Arctic Sea Ice, which is probably the most obvious “canary in the coal mine” is melting faster than the models predicted, and will likely never recover (Story from IPY here); 

Greenland and Antarctic ice sheets melting 100 years ahead of schedule (news report; original papers here and here). Meanwhile new studies show the effect on the coastlines will be worse than previously thought, especially in North America and around the Indian Ocean (press release here; original paper here).

Sea level rise following the worst case scenario given in the IPCC reports (news report; original paper here and here).

Oceans soaking up less CO2, and hence losing their role as a carbon sink. (news report; original paper here) 

And finally some emerging evidence of massive methane releases as the permafrost melts (news report; no peer-reviewed paper yet).

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.

Science or Ideology?

13. March 2009 · 1 comment · Categories: climate science, philosophy · Tags: , ,

I was recently asked (by a skeptic) whether I believed in global warming. It struck me that the very question is wrong-headed. Global warming isn’t a matter for belief. It’s not a religion. The real question is whether you understand the available evidence, and whether that evidence supports the theory. When we start talking about what we believe, we’re not doing science any more – we’re into ideology and pseudo-science.

Here’s the difference. Scientists proceed by analyzing all the available data, weighing it up, investigating its validity, and evaluating which theory best explains the evidence. It is a community endeavour, with checks and balances such as the peer review process. It is imperfect (because even scientists can make mistakes) but it is also self-correcting (although sometimes it takes a long time to discover mistakes).

Ideology starts with a belief, and then selects just that evidence that reinforces the belief. So if a blog post (or newspaper column) provides a few isolated data points to construct an entire argument about climate change, the chances are it’s ideology rather than science. Ideologists cherry-pick bits of evidence to reinforce an argument, rather than weighing up all the evidence. George Will’s recent column in the Washington Post is a classic example. When you look at all the data, his arguments just don’t stand up.

The deniers don’t do science. There is not one peer-reviewed publication in the field of climate science that sheds any doubt whatsoever on the theory of anthropogenic global warming. If the deniers were doing good science, they would be able to publish it. They don’t. They send it to the media. They are most definitely not scientists.

The key distinction between science and ideology is how you engage with the data.

Why people don’t grok climate change

12. March 2009 · 8 comments · Categories: climate science, philosophy, politics, psychology · Tags: , ,
  1. Because their salaries depend on them not understanding. Applies to anyone working for the big oil companies, and apparently to a handful of “scientists” funded by them .
  2. Because they cannot distinguish between pseudo-science and science. Seems to apply to some journalists, unfortunately.
  3. Because the dynamics of complex systems are inherently hard to understand. Shown to be a major factor by the experiments Sterman did on MIT students.
  4. Because all of the proposed solutions are incompatible with their ideology. Applies to most rightwing political parties, unfortunately.
  5. Because scientists are poor communicators. Or, more precisely, few scientists can explain their work well to non-scientists.
  6. Because they believe their god(s) would never let it happen. And there’s also a lunatic subgroup who welcome it as part of god’s plan (see rapture).
  7. Because most of the key ideas are counter-intuitive. After all, a couple of degrees warmer is too small to feel.
  8. Because the truth is just too scary. There seem to be plenty of people who accept that it’s happening, but don’t want to know any more because the whole thing is just too huge to think about.
  9. Because they’ve learned that anyone who claims the end of the world is coming must be a crackpot. Although these days, I suspect this one is just a rhetorical device used by people in groups (1) and (4), rather than a genuine reason.
  10. Because most of the people they talk to, and most of the stuff they read in the media also suffers from some of the above. Selective attention allows people to ignore anything that challenges their worldview.

But I fear the most insidious is because people think that changing their lightbulbs and sorting their recyclables counts as “doing your bit”. This idea allow you to stop thinking about it, and hence ignore just how serious a problem it really is.

Going to the EGU General Assembly

09. March 2009 · Write a comment · Categories: climate modeling, climate science, EGU 2009 · Tags: , , ,

Next month, I’ll be attending the European Geosciences Union’s General Assembly, in Austria. It will be my first trip to a major geosciences conference, and I’m looking forward to rubbing shoulders with thousands of geoscientists.

My colleague, Tim, will be presenting a poster in the Climate Prediction: Models, Diagnostics, and Uncertainty Analysis session on the Thursday, and I’ll be presenting a talk on the last day in the session on Earth System Modeling: Strategies and Software. My talk is entitled Are Earth System model software engineering practices fit for purpose? A case study.

While I’m there, I’ll also be taking in the Ensembles workshop that Tim is organising, and attending some parts of the Seamless Assessment session, to catch up with more colleagues from the Hadley Centre. Sometime soon I’ll write a blog post on what ensembles and seamless assessment are all about (for now, it will just have to sound mysterious…)

The rest of the time, I plan to talk to as many climate modellers as a I can from other centres, as part of my quest for comparison studies for the one we did at the Hadley Centre.

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