A Sensitive Subject

Grab yourself a cup of tea. This is a long one.

Yesterday was historic for me. It was the first time I presented a result about ‘climate sensitivity’ (more on this later). This is how it felt to get that result two weeks ago:

In June 2006, we were just a bunch of bright-eyed, bushy-tailed researchers, eager to make a difference in the brave new world of “using palaeodata to reduce uncertainties in climate prediction”. Little did we know the road would be so treacherous, so windy, and so, so long….

Many years later, when we were all old and grey, we finally reached the first of our goals: a preliminary result. On Thursday 12th April 2012, Dr Jonathan C. Rougier produced a plot named, simply, ‘sensitivity.pdf’…

I was presenting these results in a session at the big (11  000 participants) annual European Geosciences Union conference in Vienna. The first speaker was James Hansen, who is rather big in climate circles, and the second David Stainforth, who is first author of the first big climateprediction.net result (they dish out climate models for people to run in the background on their computers). I was third, slightly rattled from only finishing my slides just before the session and running through them only once.

If any of you were at the session, I’d prefer you not to talk about our final result, at least for now…it is so preliminary, and I’d prefer this to be a ‘black box’ discussion of our work without prejudice or assumptions from our preliminary numbers.

A little history…

Our project (my first climate job since leaving particle physics) had the rather lovely name of PalaeoQUMP * and the aim of reducing uncertainty about climate sensitivity. By ‘reducing uncertainty’ I mean making the error bars smaller, pinning down the range in which we think the number lies. Climate sensitivity is the global warming you would get if you doubled the concentrations of carbon dioxide in the atmosphere. The earth is slow at reacting to change, so you have to wait until the temperature has stopped changing. Svante Arrhenius (Swedish scientist, 1859-1927) had a go at this in 1896. He did “tedious calculations” by hand and came up with 5.5degC. He added that this was probably too high, and in 1901 revised it to 4degC.

The idea was to reproduce the method of the original lovely-named project QUMP, the internal name given to the Met Office Hadley Centre research into Quantifying Uncertainty in Model Predictions. They compared a large group of climate model simulations with observations of recent climate, to see which were the most realistic and therefore which were more likely to be reliable for predicting the future. QUMP was the foundation for the UK Climate Projections, which provide “information designed to help those needing to plan how they will adapt to a changing climate”. We planned to repeat their work, but looking much further back in time – using what knowledge we have of the climate 6000 years ago (the ‘Mid-Holocene’) and 21 000 years ago (the height of the last ice age, or ‘Last Glacial Maximum’), instead of the recent past.

Fairly early into this project I wrote - with Michel Crucifix and Sandy Harrison – a review paper about people’s efforts to estimate climate sensitivity, which I’ve just put on arxiv.org because I support open science.

PalaeoQUMP ended in 2010 without us publishing any scientific results, for a variety of reasons: ambitious aims, loss of collaborators from the project, and my own personal reasons. Two of the original members – Jonty Rougier (statistician) and Mat Collins (climate modeller, formerly at the Met Office Hadley Centre) – and I continued to work with our climate simulations when we found time. We got distracted along the way from the original goal of climate sensitivity by interesting questions about how best to learn about past climates, but pootled along happily.

But late last year a group of scientists led by Andreas Schmittner published a result that was very similar to our original plan: comparing a large number of climate model simulations to information about the Last Glacial Maximum to try and reduce the uncertainty in climate sensitivity. Their result certainly had a small uncertainty, and it was also much lower than most people had found previously: a 90% probability of being in the range 1.4 to 2.8 degC. This sent a mini-ripple around people interested in climate sensitivity, palaeoclimate and future predictions. The authors were quite critical of their own work, making the possible weak points clear. One of the main weaknesses was that their method needed a very large number of simulations, so they had to use a climate model with a very simple representation of the atmosphere (because it is faster to run). They invited others to repeat their method and test it.

So we took up the gauntlet…

We have a group, an ensemble, of 17 versions of a climate model. The model is called HadCM3, which is a fairly old (but therefore quite fast and well-understood) version of the Hadley Centre climate model. It has a much better representation of the atmosphere than the one used by Andreas Schmittner. In this case “better” is not too controversial: we have atmospheric circulation, they don’t.

We created the different model versions by changing the values of the ‘input parameters’. These are control dials that change the way the model behaves. Unfortunately we don’t know the correct setting for these dials, for lots of reasons: we don’t have the right observations to test them with, or a setting that gives good simulations of temperature might be a bad setting for rainfall. So these are uncertain parameters and we use lots of different settings to create a group of model versions which are all plausible. This group is known as a perturbed parameter ensemble.

We use the ensemble to simulate the Last Glacial Maximum (LGM), the preindustrial period (as a reference), and a climate the same as the preindustrial but with double the CO2 concentrations (to calculate climate sensitivity). We can then compare the LGM simulations to reconstructions of the LGM climate. These reconstructions are based on fossilised plants and animals: by looking at the kinds of species that were fossilised (e.g. something that likes cold climates) and where they lived (e.g. further south than they live today), it is possible to get a surprisingly consistent picture of climates of the past. Reconstructing past climates is difficult, and it’s even harder to estimate the uncertainty, the error bars. I won’t discuss these difficulties in this particular post, and generalised attacks on you know who will not be tolerated in the comments! We used reconstructions of air temperature based on pollen ** and reconstructions of sea surface temperatures based on numerous bugs and things. Andreas Schmittner and his group used the same.

We’re using a shiny new statistical method from Jonty Rougier and two collaborators, which has not yet been published (still in review) but is available online if you want to deluge yourself with charmingly written but quite tricky statistics. It’s a general and simple (compared with previous approaches) way to deal with the very title of this blog: the wrongness of models. The description below is full of ‘saying that’, ‘judge’, ‘reckon’ and so on. Statistics, and science, are full of ‘judgements’: yes, subjectivity. We have to simplify, approximate, and guess-to-the-best-of-our-abilities-and-knowledge. A lot of the statements below are not “This Is The Truth” but more “This Is What We Have Decided To Do To Get An Answer And In Future Work These Decisions May Change”. Please bear this in mind!

Think of an ensemble of climate simulations of temperature. These might be from one model with lots of different values for the control parameters, or they might be completely different models from different research institutes. Most of them look vaguely similar to each other. One is a bit of an oddity. Two look nearly identical. Here is a slightly abstract picture of this:

The crosses in the picture are mostly the same sort of distance from the centre spot, but in different places. One is quite a lot further out. Two are practically on top of each other.

How should we combine all these together to estimate the real temperature? A simple average of everything? Do we give the odd-one-out a smaller contribution? Do we give the near-identical ones smaller contributions too? What if a different model is an oddity for rainfall? Even if we come up with different contributions, different weightings, for each model, the real problem is often relating these back to the original “design” of the ensemble. If our model only has one uncertain parameter, it’s easy. We can steadily increase that control dial for each of the different simulations. Then we compare all the simulations to the real world, find the “best” setting for that parameter, and use this for predicting future climate. This is easy because we know the relationship between each version of the model: each one has a slightly higher setting of the parameter. But if we have a lot of uncertain parameters, it is much harder to find the best settings for all of them at once. It is even worse if we have an ensemble of models from different research institutes, which each have a lot of different uncertain parameters and it is impossible to work out a relationship between all the models.

These problems have given statisticians headaches for several years. We like statisticians, so we want to give them a nice cup of tea and an easier life.

Jonty and Michael and Leanna’s method tries to do make life easier, and begins by asking the question the other way round. Can we throw out some of the models so that the ones that are left are all similar to each other? Then we can stop worrying about how to give them different contributions: we can stop using the individual crosses and just use the average of the rest (the centre spot).

We also don’t need to know the relationship between different models. Instead of using observations of the real world to pick out the “best” model, we will take the average of all of them and let the observations “drag” this average towards reality (I will explain this part later).

How do you decide which models to throw out? This is basically a judgement call. One way is to look at the difference between a model and the average of the others. If any are very far away from the average, chuck them. Another is to squint and look at the simulations and see if any look very different from the others. Yes, really! The point is that it is easier to do this, to justify the decisions, and to use the average, than to decide what contribution to give each model.

The next part to their cunning is reckoning that all the models are equally good – or equally bad, depending on the emptiness or fullness of your glass – at simulating reality. In other words, the models are closer to the ensemble average than reality is. We can add a red star for “reality” outside the cluster of models:

(Notice I’ve now thrown away the outlier model and one of the two near-identical ones.) This is saying that models are probably more like each other than they are like the real world. I think most visitors to this blog would agree…

There is one more decision. The difficulty is not just in combining models but also interpreting the spread in results. Does the ensemble cover the whole range of uncertainty? We think it probably doesn’t, no matter how many models you have or how excellent and cunning your choices in varying the uncertain input parameters. We will say that it does have the same kind of “shape”: maybe the ensemble spread is bigger for Arctic temperatures than for tropical temperatures, so we’ll take that as useful information for the model uncertainty. But we think it should be scaled up, should be multiplied by a number. How much should we scale it up? More on this later…

All of this was just to turn the ensemble into a prediction of LGM temperatures (from the LGM ensemble) and climate sensitivity (from the doubled CO2 ensemble), with uncertainties for each. We will now compare and then combine the LGM temperatures with the reconstructions.

Here is the part where we inflate – actually the technical term, like a balloon – the ensemble spread to give us model uncertainty. How far? The short answer is: until the prediction agrees with the reconstruction. The long answer is a slightly bizarre analogy that comes to mind. Imagine you and a friend are standing about 10 feet apart. You want to hold hands, but you can’t reach. This is what happens if your uncertainties are too small. The prediction and the reconstruction just can’t hold hands; they can’t be friends. Now imagine that you so much want to hold their hand that your arms start growing….growing…growing… until you can reach their hand, perhaps even far enough for a cuddle. You are the model ensemble, and we have just inflated your arms / uncertainty. Your friend is the reconstruction. Your friend’s arms don’t change, because we (choose to) believe the estimates of uncertainty that the reconstruction people give us. But luckily we can inflate your arms, so that now you “agree” with each other. [ For those who want more detail, the hand-holding is a histogram of standardised predictive errors that looks sensible: centred at zero and has most of the mass between [-3,3]. ]

Now we combine the reconstructions with the ensemble “prediction” of the LGM. This gives the best-of-both-worlds. The reconstructions give us information from the real world (albeit more indirect than we would like). The model gives us the link between LGM temperatures and climate sensitivity. The model ensemble and reconstructions are combined in a “fair” way, by taking into account the uncertainties on each side. If the model ensemble has a small uncertainty and the reconstructions have a large uncertainty, then the combined result is closer to the model prediction, and vice versa. This is a weighted average of two things, which is easier than a weighted average of many things (the approach I described earlier). [ Those who want more details: this is essentially a Kalman Filter, but in this context it is known as Bayes Linear updating. ].

To recap:

Reconstructions - we use plant- and bug-based reconstructions of LGM temperatures.

Model prediction – after throwing out models that aren’t very similar, we take the average of the others as our “prediction” of Last Glacial Maximum (LGM) temperatures and climate sensitivity.

Model uncertainty – we multiply the spread of the ensemble by a scaling factor so that the LGM prediction agrees with the reconstructions.

LGM “prediction” – we combine the model prediction with the reconstructions. The combination is closer to whichever has the smallest uncertainty, model or reconstruction.

Now for climate sensitivity. The climate sensitivity gets “dragged” by the reconstructions in the same way as the LGM temperatures. (For this we have to assume that the model uncertainty is the same in the past as the future: this is not at all guaranteed, but inconveniently we don’t have any observations of the future to check). If the LGM “prediction” is generally colder than the LGM reconstructions, it gets dragged to a less-cold LGM and the climate sensitivity gets dragged to a less-warm temperature. And that’s…*jazz hands*….a joint Bayes Linear update of a HadCM3 perturbed parameter ensemble by two LGM proxy-based reconstructions under judgements of ensemble exchangeability and co-exchangeability of reality.

I’m afraid the result itself is going to be a cliffhanger. As I said at the top, I want to talk about the method without being distracted by our preliminary result. But if you’ve got this far…thank you for persevering through my exploratory explanations of some state-of-the-art statistics in climate prediction.

Just as I post this, I am begininning my travels home from Vienna so apologies for comments getting stuck in moderation while I am offline.

Update: I’ve fixed the link to the Rougier et al. manuscript.

Caveat 1. Please note that my descriptions may be a bit over-simplified or, to use the technical term, “hand-wavy”. Our method is slightly different from the statistics manuscript I linked to above, but near enough to be worth reading if you want the technical details. If anyone is keen to see my incomprehensible and stuffed-to-bursting slides, I’ve put them on my Academia.edu page. I’ve hidden the final result of climate sensitivity (and the discussion of it)…

Caveat 2. This work is VERY PRELIMINARY, so don’t tell anyone, ok? Also please be kind – I stayed up too late last night writing this, purely because I am all excited about it.

* Not listed on the PalaeoQUMP website is Ben Booth (who has commented here about his aerosol paper), an honorary member who helped me a lot with the climate modelling.

** N.B. if you want to use the pollen data, contact Pat “Bart” Bartlein for a new version because the old files have a few points with “screwed up missing data codes”, as he put it. These are obvious because the uncertainties are something like 600 degrees.***

*** No jokes about palaeoclimate reconstruction uncertainties please.

How to be Engaging

I’ve started writing my promised post on models used in climate science, but thought I’d get this more topical post out first.

I went to an interesting conference session yesterday on communicating climate science, convened by Asher Minns (Tyndall Centre), Joe Smith (Open University), and Lorraine Whitmarsh (Cardiff University). A few people presented their research into different practices, and the speakers and convenors discussed audience questions afterwards. Paul Stapleton has also blogged about the session here.

A good stand-out point was presented by Mathieu Jahnich: research has found that the public prefer hopeful campaigns (in communicating climate science), not shocking images or negative, hopeless campaigns. I think most of us instinctively know this.

Hebba Haddad, a PhD student from the University of Exeter, spoke on topics close to my heart: the effect of communicating uncertainties in climate science, and the effect of the ‘voice’ in which it is presented. The first relates to the amount of information given about the uncertainty in a prediction: for example, saying “60-80% probability” rather than “70% probability”. The second relates to the phrasing: for example, using the warmer, more friendly and open phrasing of “We…” on an institute website, rather than the cooler, more distant “The centre…”.

She pointed out that scientists, of course, often attempt to transfer as much information as possible (the deficit model - a view that if only enough information were given, people would make rational decisions…), highlight the uncertainties, and use technical language. Science communicators, on the other hand, are more likely to understand their audience, understate uncertainties, convey simpler messages, and use a warmer, friendlier style.

Hebba carried out a study on 152 psychology students. The standout results for me were that:

  1. greater communication of uncertainty reduced belief in climate science;
  2. if little uncertainty is communicated, then the tone makes little difference to the level of engagement;
  3. if a lot of uncertainty is communicated, then a warm tone leads to much greater engagement than a distant tone.

This makes sense: if there is a lot of uncertainty, people use heuristics (short-cuts) to determine their trust in information. These particular students responded well to a personal, friendly tone. And in a later session, someone made the distinction between “relational trust”, which is based on similarity of intentions or values, and ”calculative trust”, or “confidence”, based on past behaviour. They said that in everyday situations people tend to make decisions based on calculative trust, but in unfamiliar situations they use relational trust: another heuristic in times of uncertainty.

But this is interesting, because I think a large part of the audience who visit this blog (thank you) contradict these findings. Your trust in the science increases the more I talk about uncertainty! And I think you place greater importance in “calculative” rather than “relational” trust. In other words, you use the past behaviour of the scientist as a measure of trust, not similarity in values. I’ve found that whenever I talk about limitations of modelling, or challenge statements about climate science and impacts that I believe are not robust, my “trust points” go up because it demonstrates transparency and honesty. (See previous post for squandering of some of those points…). Using a warm, polite tone helps a lot, which supports Hebba’s findings. But I would wager that the degree of similarity to my audience is much less important than my ability to demonstrate trustworthiness.

Lorraine commented that Hebba’s finding of the importance of a warm tone is a challenge for scientists, who are used to talking (particularly writing) in a passive tone: “It was found that…” rather than “We found…”. To combat this, and increase public trust, Joe urged climate scientists to be “energetic digital scholars”, “open” and “public.” He thought we should not try to present climate science as “fact” but as “ambitious, unfolding, and uncertain”.

A US scientist in the audience asked for advice on how to engage online in such a polarised debate, and another audience member asked if giving simple messages (without all uncertainties) might compromise public trust in scientists. Joe kindly invited me to comment on these social media and uncertainty aspects. I speedily dumped the contents of my brain onto the room about how this blog and related efforts, giving a transparent, warts-and-all view of science as an unfolding process, had been very successful in increasing trust. In fact I had so much to say that I was asked to stop, would you believe (er, perhaps you would…).

For those of you that don’t trust the IPCC too much, I merely note that Jean-Pascal van Ypersele tapped me on the shoulder after I spoke about the importance of communicating uncertainties transparently, and asked me to email him the blog link…

Some tweeting about the session led to some lovely supportive messages from across the spectrum of opinions (thank you) and also some criticisms by people you might expect to be supportive. I’ve Storified these below.

And finally, Leo Hickman welcomes our ‘Rapunzel’ approach to communication. I was one of the invited palaeoclimate scientists at that meeting (actually, I probably invited myself), and can confirm it was very civil and productive.

 

Storify of the post-session Twitter conversation:

 

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.

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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…