Discover 2012

One of the main reasons I haven’t posted for a while is that all my spare “communication” time and energy (and more!) have gone into organising exhibits for a public engagement event this week: “Discover 2012”, part of National Science and Engineering Week.

These things always take much more work than you initially think…. Virtually all our exhibits are new this year. We’ve got three tables of the 22 at the event, and have gone a bit overboard in the number of exhibits to create redundancy: inevitably some break and go wrong on the day, or aren’t so successful with the public once you test them.

My friend and colleague Jenny Griggs has helped me with all the organising. About nine of us have designed the exhibits, with technical and practical support from our department (Geographical Sciences), and 37 of us – mostly post-doctoral researchers and PhD students – from Geography, Earth Sciences, Engineering and Maths to demonstrate the exhibits.

Anyway, I thought I’d show you what I’ve been up to and advertise it here. Needless to say, if you are in or near Bristol please do come along. I’m on shift tomorrow morning and also around at the end of Saturday.

Discover 2012

For National Science and Engineering Week

This Thursday to Saturday, 8th-10th March 2012, from 9am-6pm, The Galleries (Broadmead), Bristol, UK

Press release: http://www.bris.ac.uk/news/2012/8272.html

Ocean acidification

Carbon dioxide from fossil fuels is not only causing climate change but also making the oceans more acidic.

Race against your friends to make your water more acidic by blowing through a straw!

Try making sea shells bend and fizz with vinegar.

See how burning candles makes the surface of our “ocean” more acidic.

Climate quiz

Our well-loved interactive quiz about the earth, geography, weather and climate. Free sticker for taking part, “gold” sticker if you get to the end!

Ice is cool

Find out how ice comes in different kinds and different places in the world…even South America!

How much of our iceberg has melted? Has it changed the “sea level” in our tank?

Antarctic ice flow

Did you know ice can flow like a liquid? Our “slime” is like liquid ice. Be a scientist for a moment (with your own lab coat!) and race the liquid ice to the edge of Antarctica.

Antarctic explorers

What did Scott and his team wear in Antarctica? What do scientists wear there now?

See photographs and a children’s book about Scott’s expedition, and how different modern Antarctic expedition clothes are (on a mannequin!).

Greenland E-tracers

Scientists at Bristol throw detectors that look like Christmas baubles (“E-Tracers”) down holes in Greenland to measure water underneath the ice. Sometimes they get lost…

Throw your own E-tracer down a hole in our “glacier” – which one will come out first? Or will it ever come out…?

“Film trailer” and documentaries

Electronic tracers – a spoof film trailer and 3 film clips about E-Tracers.

Life on the Ice – a documentary about a group of scientists (including University of Bristol glaciologists!) spending three weeks in the world’s most northerly town.

 

Do come along!

 

A model of models

First, apologies for the delay after the overwhelmingly great start and my promises of new posts. I’ve been wanting to write for a week but had other urgent commitments (like teaching) I had to honour first. I hope to post once a week or fortnight, but it will be a bit variable depending on the day job and the interestingness of my activities and thoughts. I do have a lot of ideas lined up – I wouldn’t have started a blog if I didn’t – but at the moment it takes me time to set them down. I expect this to get faster.

Second, thanks for (mostly) sticking to the comments policy and making this a polite, friendly, interesting corner of the web.

Before I begin blogging about models, I ought to talk about what a model is. Aside from the occasional moment of confusion when describing one’s “modelling job” to friends and family, there are several things that might come to mind.

Model is a terribly over-burdened word. It can be an attractive clothes horse, a toy train, something reviewed by Top Gear, or a Platonic ideal. I will talk about three further meanings that relate to the sense of “something used to represent something else”: these are conceptual, statistical, and physical. They are distinct ideas, but in practice they overlap, which can add to the confusion.

A conceptual model is an idea, statement, or analogy that describes or explains something in the real world (or in someone’s imagination). It is ‘abstracted’, simpler than and separated from the thing it describes. In science, before you can do experiments and make predictions you must have an idea, a description, a concept of the thing you are studying. This conceptual model might include, for example, a tentative guess of the way one thing depends on another, which could then be explored with experiments.

A statistical model is a mathematical equation that describes the relationship between two or more things, ‘things’ being more commonly referred to as ‘variables’. A variable is a very broad term for something that varies (ahem), something interesting (or dull) that is studied and predicted by scientists or statisticians*: it could be the number of bees in a garden, the average rainfall in the UK, or the fraction of marine species caught in the North Atlantic that are sharks. A statistical model can often be represented in words as well as equations: for example, ‘inversely proportional’ means that as one variable increases a second variable decreases. The important thing about a statistical model is that it only describes and doesn’t explain.

A physical model is a set of mathematical equations that explains the relationship between two or more variables. It also refers to a computer program that contains these equations, and to help (or increase) the confusion, these computer models are often called simulators. By explain I mean that it is an expression of a theory, a physical law, a chemical reaction, biological process, or cause-and-effect: an expression not only of knowledge but understanding about the way things behave. The understanding might not be perfect – it might be a partial or simplified physical model – but the point is it attempts to describe the mechanisms, the internal cogs and wheels, rather than simply the outward behaviour.

Physical models are the main focus of this blog, but there are many interesting links between the three: physical models often incorporate statistical models to fill in the gaps where our understanding is poor; a statistical model may describe another model (conceptual or physical). There are myriad different types of physical model, and even more uses for them. In the next post, I will talk about a few physical models I use in my research.

A general note about my plans. I think it’s important to first set out some basic terms and concepts, particularly for those that not familiar with modelling, so please be patient if you are an expert. Before long I will also post more technical pieces, which will be labelled as such so as not to scare off non-experts. I’ll also start blogging about the day-to-day, such as interesting conference talks and random (mostly science-related) thoughts, rather than only pre-planned topics.

 

* The opposite of a variable is…a constant. These can be interesting too.

The Sceptical Compass

First, thank you. I have been overwhelmed by the response to this blog, and privileged to host the conversation of ninety five individuals on my first post. Here is a Wordle of the comments (not including my own):

Second, some thoughts on terminology. Over the last year I have started to talk with people who do not agree with the majority view on climate science. And there is no homogenous “sceptic” viewpoint. No binary grouping, Us and Them. I do use the terms “scientist” and “sceptic” for convenient shorthand (more on this later), but whenever I talk about public engagement I bring up the same points:

a) there is a continuous spectrum of viewpoints;

b) a large number of the unconvinced have numerate backgrounds (off the top of my head, physics, chemistry, computing, engineering, geology and finance seem to come up most frequently);

c) for various reasons, they have lost trust in the way we do, or the way we communicate, our science.

This week I’ve been thinking that the ‘spectrum’ description can be pushed further. If you’re familiar with the Political Compass, you’ll know that it extends the usual left-right political spectrum to a two dimensional graph of left-right and libertarian-authoritarian (if you don’t know it, I recommend you do the quiz). Here’s my proposed equivalent.

The horizontal axis is sceptism: the degree to which one critically evaluates evidence, does not accept arguments of authority, and updates ones viewpoint according to new information. This is the ‘Approach’ axis.

The vertical is the resulting ‘Conclusion’ axis: the degree to which one is convinced that humans are causing climate change and (if there is some degree of human cause) the scale and speed of that change. The sceptic/scientist shorthand I use corresponds to this axis. I have also started to use the less well-known upholder/dissenter and convinced/unconvinced.

The compass doesn’t include policy preferences, of course.

I’ve marked some examples. I don’t think it is a simple categorisation: like the Political Compass, people can move around through their lifetime, can be in different locations for different topics, and may be ‘smeared out’ vertically in the case of large uncertainty. I am not trying to label anyone here, and these are not rigidly defined regions. This is purely illustrative.

Convinced: horizontally, scientists and many non-scientists aspire to be sceptical; vertically, people in this region are convinced by the majority of these statements (for example, the majority of climate scientists).

Lukewarmer: horizontally, as previous; vertically, somewhat convinced (for example: concluding that humans cause some change but the rate is likely slow or very uncertain).

Unconvinced: horizontally, as previous; vertically, not convinced (for example, concluding there is warming but the human influence is small or negligible).

Believer: horizontally, uncritical and trusting of sources they consider authoritative; vertically, convinced of rapid, intense climate change and impacts caused by humans.

Unbeliever: horizontally, as previous; vertically, not convinced (for example, concluding there is no warming).

For the Bayesian nerds, I’ve just noticed the horizontal axis could be considered the width of one’s prior, and the vertical axis the mode of the resulting posterior.

I’ve chosen to put the dots at the vertical extremes for the uncritical side (Believer/Unbeliever) to reflect the fact that people who are not critically evaluating each statement, only trusting in another source or opinion, may be more likely to agree with the extreme ends and see the issues in black & white. I’ve chosen the Sceptical dots to be more moderate in the vertical (Convinced/Lukewarmer/Unconvinced) to reflect the fact that critical evaluations may lead to a more nuanced view with shades of grey. But I think of this as a continuous space.

There are no value judgements intended here. There are several reasons why there is not a one-to-one relationship between critical evaluation and conclusion: access to evidence; availability of time or technical expertise to evaluate it (reliance on judgement of others); general fallibility of humans. Scientists have differing opinions and interpretations of the same evidence, and we are not perfectly critical, so we can be at different levels on the vertical axis. For example:

– a scientist who models the physics of ice sheets might judge the statistically-based (‘semi-empirical’) methods that predict a rapid sea level rise as “not credible”: they would therefore be lower down the vertical scale;

– a scientist might search for an estimate of the current health impacts of climate change and, for lack of time or another reason, use a non-peer-reviewed estimate that reported severe impacts: they would therefore be higher up the vertical scale and further left horizontally.

I’d be interested to hear if people think this is a useful framework. If you don’t like it, please (kindly) suggest changes.

 

Third, the scope of this blog. I said to Peter Gleick that my aims were: to communicate my own research, because I am publicly funded, and because it gives the research greater exposure; to engage sceptics (see above!), and to practice writing for a general audience. This post is already too long, and the time too late, for me to list every topic I intend to cover but it will become apparent as I write posts. Some things I cannot do on this blog:

a) answer every question asked: this will depend on my knowledge and the extent to which I have time to answer (both can be improved by postponing to a later post);

b) address everyone’s problems with climate science: I am only one person, an early career researcher with a lot of things to wrap up by 31st July, and although I try to read outside my area I cannot promise to have the expertise or time to address every issue;

c) comment on policy choices.

I suppose this is just a restating of not pleasing all of the people.

 

Fourth, a comments policy.

So far I have let through every non-spam comment and automatically allowed previous posters to comment. I would like to trust people to be sensible with this and not have to start moderating out comments.

Therefore I ask you to comply with the following:

a) civility is essential;

b) accusations are not to be made;

c) the words denier, liar and fraud are not permitted (this list may increase): see (a) and (b);

d) generalisations are to be avoided;

e) if you have a particular bugbear or issue with earth system model uncertainty that is not related to the post topic please invite us once, perhaps twice, to discuss it in the very suitable Unthreaded section of Bishop Hill;

f) if you have a particular bugbear or issue with some other topic, or with policy, please discuss it elsewhere;

g) interpret comments in good faith: each is from a person, with limited free time, and frazzled nerves, and good intentions;

h) liberally sprinkle your comments with good-humour, honesty, and ‘smiley’ or ‘winky’ faces, to keep the tone convivial.

 

Thank you.

All Blog Names are Wrong

As soon as I thought of the name for this blog, I thought I might be on to a good thing. The George Box quote from which it is taken is one I repeat in my public talks and university lectures, to make the points that:

(a) climate* scientists do not believe their models can exactly reproduce the real world; and

(b) climate models are imperfect, but they can still be useful tools to understand the planet.

* I say ‘climate’ because it is more recognisable, but I mean ‘earth system’: the whole or any individual part of the planet. For example, I currently work with glaciologists modelling the ice sheets of Greenland and Antarctica.

Not everyone agreed with my assessment when I asked for opinions on Twitter. I was surprised that a senior academic tried to persuade me, fairly forcefully, not to use the name.

I’ve put most of the conversation here (emphasis mine). It highlights two schools of thinking on how best to communicate climate science and partly reflects, I think, the difference between the relatively calm conversations of the UK and the polarised, antagonistic debates more common in the USA. The scientists over there are attacked and are therefore (understandably) defensive. Over we are prodded, or huffed at, in the British way, and it is easier to respond candidly.

@flimsin: Probable title of my new blog: allmodelsarewrong.com. (George Box quote). Main point of my job is estimating how wrong. Whaddya think?

Hydrologist Peter Gleick (Pacific Institute) was not keen…

@PeterGleick: @flimsin Title is serious error.Buys into “everything is uncertain” meme.And argument that politicians don’t hear about uncertainties is BS.
@PeterGleick: @flimsin Another comment on your proposed blog title. Look at this essay, especially item 2 on “uncertainty” and “knowns versus unknowns.”

In this essay, Donald Brown writes that the climate ‘disinformation campaign‘ is

a social movement that…consistently uses scientific uncertainty arguments as the basis of its opposition

I started to defend my position…

@flimsin: @PeterGleick I just think we shouldn’t attempt to hide or spin the fact that models are not reality. My research is in quantifying uncerts.
@PeterGleick: @flimsin Of course. Do you really think the climate debate is about scientists claiming models are reality? And do you not see the
@PeterGleick@flimsin intentional efforts of many to overemphasize uncertainties while ignoring certainties?
@flimsin: @PeterGleick There’s more than one debate. I want to reflect the conversations inside sci community about best ways to quantify uncert.
@flimsin@PeterGleick More of a publically-accessible blog about my own research than a blog aimed at the public.
@flimsin: @PeterGleick Of course I see it. But I also see ppl in other research areas wanting to know more about how we deal with predictive uncerts.

He pressed the point, asking what kind of people supported me:

@PeterGleick: @flimsin great idea, but title is important, and using the first half of that famous quote would, I think, be big, big, mistake.
@PeterGleick: @flimsin @ret_ward other “climate scientists” think it good idea? Most positive comments I saw weren’t from climate scientists but skeptics.

I pointed out that several climate scientists had approved, including:

@AidanFarrow: @flimsin allmodelsarewrong.com > strongly approve
@icey_mark@flimsin it sounds a great space for conversations. You’ll have to have your armour on sometimes! Good luck and thanks for engaging
@ed_hawkins@flimsin Good name! I wouldn’t pick .com though. How about .org instead?
@richardabetts: @flimsin @d_m_hg @ret_ward @Realclim8gate Yep, I really like allmodelsarewrong.com (sub-heading “…but some are more useful than others”)
@clv101@flimsin Box quote is a great starting place for a blog. Not easy topic to cover well for a broad/public/sceptic audience though. Good luck!

though one was cautious:

@d_m_hg: @flimsin The 2nd part ‘some are useful’ finishes the idea-can it be incorporated somehow? Otherwise you might attract skeptic troublemakers.

(but I do want to attract them!) and Bob Ward, policy and communications director of the London School of Economic’s Grantham Research Institute, politely suggested an alternative:

@ret_ward@flimsin Some might confuse it with allmodelsareuseless! How about howskillfularemodels? 

But this tweet from Peter was the most unexpected:

@PeterGleick: @flimsin Last comment…. not all models are wrong.

Er…pardon? This is the crux of it. How can anyone make that claim? My best guess is that to make his point he is wilfully misinterpreting the word in the way he says others will, i.e. that wrong = useless.

@flimsin: @PeterGleick Sir, it appears we have a profound philosophical disagreement 🙂 Nothing can precisely simulate reality, only approximate.
@PeterGleick: @flimsin Does that make them “wrong?” “Wrong” to you means “uncertain.” “Wrong” to public means “you don’t know what you’re talking about.”
@flimsin: @PeterGleick Exactly – all the better to explain the difference. Better to improve scientific literacy than to patronise, I think.
@PeterGleick: @flimsin But who’s the audience? The public? Policymakers? Other scientists or science communicators? It matters, as does the title.
@flimsin@PeterGleick All those welcome. 1. Publicly funded -> communicate my research. 2. Research exposure 3. Engage sceptics. 4. Practice writing.

The excellent Richard Betts of the Met Office Hadley Centre put it rather well:

@richardabetts: @PeterGleick @flimsin Which model is right? Please can I have it?
@PeterGleick: @richardabetts flimsin Richard, which model is “wrong?” Wrong is the wrong term. It’s not what you mean, and it is misunderstood by public.
@flimsin: @PeterGleick @richardabetts All are wrong…better to try and educate that science has shades of grey than try to give appearance of B&W
@PeterGleick: @flimsin @richardabetts I repeat “wrong” is the wrong term. It WILL be misunderstood and misused. Read that essay: rockblogs.psu.edu/climate/

I found this a little heavy-handed. We are all entitled to our opinion, and I didn’t enjoy being shoehorned into someone else’s vision of science communication. I think this is a very dangerous approach, as Richard pointed out:

@richardabetts@flimsin @ret_ward Be wary of advice “This might be misused by the sceptics” Start of slippery slope from objective science into advocacy.
@richardabetts: @PeterGleick @flimsin Brown says “climate denial machine … has made claims that mainstream climate scientists are corrupt or liars” (cont)
@richardabetts: @PeterGleick @flimsin IMHO only way to combat this piece of disinformation is to prove otherwise by public discussion of science warts & all

As did physicist Jonathan Jones:

@nmrqip@richardabetts Yep. Lying “to avoid being misunderstood” never ends well @PeterGleick @flimsin

One of the problems we need to overcome is a lack of trust in climate scientists by some members of the public – or even other scientists – by showing that we do science no differently from anybody else. If we start to ‘spin’ the science, to gloss over the known unknowns, then we deserve these accusations.

Anyone that wants to talk about the ways we estimate confidence in predictions of the future (or studies of the past) is very welcome to come here and discuss it, at any level. Anyone that wants to misrepresent climate science by cherry-picking snippets of sentences will do that regardless, no matter what what the blog name or content.

Conclusion: if my blog causes this much debate before I’ve written anything, I think I’ve chosen the right name…

Hello world!

My first blog was about knitting. It had one post. I’m hoping to stick this one out for longer.

More soon…