Category: boxquote

Tuning to the climate signal

This is a mirror of a PLOS blogpost.


This is part 3 of a series of introductory posts about the principles of climate modelling. Others in the series: 1 | 2

My sincere apologies for the delays in posting and moderating. Moving house took much more time and energy than I expected. Normal service resumes.

I’d also like to mark the very recent passing of George Box, who was the eminent and important statistician to whom I owe the name of my blog, which forms a core part of my scientific values. The ripples of his work and philosophy travelled very far. My condolences and very best wishes to his family.

The question I asked at the end of the last post was:

“Can we ever have a perfect reality simulator?”

I showed a model simulation with pixels (called “grid boxes” or “grid cells”) a few kilometres across: in other words, big. Pixel size, also known as resolution, is limited by available computing power. If we had infinite computing power how well could we do? Imagine we could build a climate model representing the entire “earth system” – atmosphere, oceans, ice sheets and glaciers, vegetation and so on – with pixels a metre across, or a centimetre. Pixels the size of an atom. If we could do all those calculations, crunch all those numbers, could we have a perfect simulator of reality?

I’m so certain of the answer to this question, I named my blog after it.

A major difficulty with trying to simulate the earth system is that we can’t take it to pieces to see how it works. Climate modellers are usually physicists by training, and our instinct when trying to understand a thing is to isolate sections of it, or to simplify and abstract it. But we have limited success if we try to look at isolated parts of the planet, because everything interacts with everything else, and difficulties with simplifications, because important things happen at every scale in time and space. We need to know a bit about everything. This is one of my favourite things about the job, and one of the most difficult.

For a perfect simulation of reality, we would need perfect understanding of every physical, chemical and biological process – every interaction and feedback, every cause and effect. We are indeed improving climate models as time goes on. In the 1960s, the first weather and climate models simulated atmospheric circulation, but other important parts of the earth system such as the oceans, clouds, and carbon cycle were either included in very simple ways (for example, staying fixed rather than changing through time) or left out completely. Through the decades we have developed the models, “adding processes”, aiming to make them better simulators of reality.

But there will always be processes we think are important but don’t understand well, and processes that happen on scales smaller than the pixel size, or faster than the model “timestep” (how often calculations are done, like the frame rate of a film). We include these, wherever possible, in simplified form. This is known as parameterisation.

Parameterisation is a key part of climate modelling uncertainty, and the reason for much of the disagreement between predictions. It is the lesser of two evils when it comes to simulating important processes: the other being to ignore them. Parameterisations are designed using observations, theoretical knowledge, and studies using very high resolution models.

For example, clouds are much smaller than the pixels of most climate models. Here is the land map from a lower resolution climate model than the one in the last post, called HadCM3.

Land-sea map for UK Met Office Unified Model HadCM3

If each model pixel could only show “cloud” or “not cloud”,  then a simulation of cloud cover would be very unrealistic: a low resolution, blocky map where each block of cloud is tens or even hundreds of kilometres across. We would rather each model pixel was covered in a percentage of cloud, rather than 0% or 100%. The simplest way to do this is to relate percentage cloud to percentage relative humidity: at 100% relative humidity, the pixel is 100% covered in cloud; as relative humidity decreases, so does cloud cover.

Parameterisations are not Laws of Nature. In a sense they are Laws of Models, designed by us wherever we do not know, or cannot use, laws of nature. Instead of “physical constants” that we measure in the real world, like the speed of light, they have “parameters” that we control. In the cloud example, there is a control dial for the lowest relative humidity at which cloud can form. This critical threshold doesn’t exist in real life, because the world is not made of giant boxes. Some parameters are equivalent to things that exist, but for the most part they are “unphysical constants”.

The developers of a model play god, or at least play a car radio, by twiddling these control dials until they pick up the climate signal: in other words, they test different values of the parameters to find the best possible simulation of the real world. For climate models, the test is usually to reproduce the changes of the last hundred and fifty years or so, but sometimes to reproduce older climates such as the last ice age. For models of Greenland and Antarctica, we only have detailed observations from the last twenty years.

As our understanding improves and our computing power increases, we replace the parameterisations with physical processes. But we will never have perfect understanding of everything, nor infinite computing power to calculate it all. Parameterisation is a necessary evil. We can never have a perfect reality simulator, and all models are… imperfect.

In case you do lie awake worrying that the entire universe is a simulation: it’s fine, we can probably check that.



We have nothing to fear

[This is a mirror of a post published at PLOS. Formatting may be better over there.]


I’m scared.

I must be, because I’ve been avoiding writing this post for some time, when previously I’ve been so excited to blog I’ve written until the early hours of the morning.

I’m a climate scientist in the UK. I’m quite early in my career: I’ve worked in climate science for six and a half years since finishing my PhD in physics. I’m not a lecturer or a professor, I’m a researcher with time-limited funding. And in the past year or so I’ve spent a lot of time talking about climate science on Twittermy blog and in the comments sections of a climate sceptic blog.

So far I’ve been called a moron, a grant-grubber, disingenuous, and Clintonesque (they weren’t a fan: they meant hair-splitting), and I’ve had my honesty and scientific objectivity questioned. I’ve been told I’m making a serious error, a “big, big mistake”, that my words will be misunderstood and misused, and that I have been irritating in imposing my views on others. You might think these insults and criticisms were all from climate sceptics disparaging my work, but those in the second sentence are from a professor in climate change impacts and a climate activist. While dipping my toes in the waters of online climate science discussion, I seem to have been bitten by fish with, er, many different views.

I’m very grateful to PLOS for inviting me to blog about climate science, but it exposes me to a much bigger audience. Will I be attacked by big climate sceptic bloggers? Will I be deluged by insults in the comments, or unpleasant emails, from those who want me to tell a different story about climate change? More worryingly for my career, will I be seen by other climate scientists as an uppity young (ahem, youngish) thing, disrespectful or plain wrong about other people’s research? (Most worrying: will anyone return here to read my posts?)

I’m being a little melodramatic. But in the past year I’ve thought a lot about Fear. Like many, I sometimes find myself with imposter syndrome, the fear of being found out as incompetent, which is “commonly associated with academics”. But I’ve also been heartened by recent blog posts encouraging us to face fears of creating, and of being criticised, such as this by Gia Milinovich (a bit sweary):

“You have to face your fears and insecurity and doubt. […] That’s scary. That’s terrifying. But doing it will make you feel alive.”

Fear is a common reaction to climate change itself. A couple of days ago I had a message from an old friend that asked “How long until we’re all doomed then?” It was tongue-in-cheek, but there are many that are genuinely fearful. Some parts of the media emphasise worst case scenarios and catastrophic implications, whether from a desire to sell papers or out of genuine concern about the impacts of climate change. Some others emphasise the best case scenarios, reassuring us that everything will be fine, whether from a desire to sell papers or out of genuine concern and frustration about the difficulties of tackling climate change.

Never mind fear: it can all be overwhelming, confusing, repetitive. You might want to turn the page, to change the channel. Sometimes I’m the same.

I started blogging to try and find a new way of talking about climate science. The title of my blog is taken from a quote by a statistician:

“essentially, all models are wrong, but some are useful” – George E. P. Box (b 1919)

By “model” I mean any computer software that aims to simulate the Earth’s climate, or parts of the planet (such as forests and crops, or the Antarctic ice sheet), which we use to try to understand and predict climate changes and their impacts in the past and future. These models can never be perfect; we must always keep this in mind. On the other hand, these imperfections do not mean they are useless. The important thing is to understand their strengths and limitations.

I want to focus on the process, the way we make climate predictions, which can seem mysterious to many (including me, until about a month before starting my first job). I don’t want to try and convince you that all the predictions are doom and gloom, or conversely that everything is fine. Instead I want to tackle some of the tricky scientific questions head-on. How can we even try to predict the future of our planet? How confident are we about these predictions, and why? What could we do differently?

When people hear what I do, one of the first questions they ask is often this:

“How can we predict climate change in a hundred years, when we can’t even predict the weather in two weeks?”

To answer this question we need to define the difference between climate and weather. Here’s a good analogy I heard recently, from J. Marshall Shepherd

“Weather is like your mood. Climate is like your personality.”

And another from John Kennedy:

“Practically speaking: weather’s how you choose an outfit, climate’s how you choose your wardrobe.”

Climate, then, is long-term weather. More precisely, climate is the probability of different types of weather.

Why is it so different to predict those two things? I’m going to toss a coin four times in a row. Before I start, I want you to predict what the four coin tosses are going to be: something like “heads, tails, heads, tails”. If you get it right, you win the coin*. Ready?

[ four virtual coin tosses…]

50p coin on cafe table

[ …result is tails, tails, tails, heads ]

Did you get it right? I’m a nice person, so I’m going to give you another chance. I’m going to ask: how many heads in the next four?

four more virtual coin tosses… ]




…results is two heads out of four ]

The first of these is like predicting weather, and the second like climate. Weather is a sequence of day-by-day events, like the sequence of heads and tails. (In fact, predicting a short sequence of weather is a little easier than predicting coin tosses, because the weather tomorrow is often similar to today). Climate is the probability of different types of weather, like the probability of getting heads.

If everything stays the same, then the further you go into the future, the harder it is to predict an exact sequence and the easier it is to predict a probability. As I’ll talk about in later posts, everything is not staying the same… But hopefully this shows that trying to predict climate is not an entirely crazy idea in the way that the original question suggests.

My blog posts here at PLOS will be about common questions and misunderstandings in climate science, topical climate science news, and my own research. They won’t be about policy or what actions we should take. I will maintain my old blog all posts at PLOS will also be mirrored there, and some additional posts that are particularly technical or personal might only be posted there.

At my old blog we’ve had interesting discussions between people from across the spectrum of views, and I hope to continue that here. To aid this I have a firm commenting policy:

  • be civil; do not accuse; do not describe anyone as a denier (alternatives: sceptic, dissenter, contrarian), liar, fraud, or alarmist; do not generalise or make assumptions about others;
  • interpret comments in good faith; give others the benefit of the doubt; liberally sprinkle your comments with good humour, honesty, and, if you like them, cheerful emoticons, to keep the tone friendly and respectful;
  • stay on-topic.

I’m extremely happy to support PLOS in their commitments to make science accessible to all and to strengthen the scientific process by publishing repeat studies and negative results. I’m also very grateful to everyone that has supported and encouraged me over the past year: climate scientists and sceptics, bloggers and Tweeters. Thank you all.

And thank for you reading. My next post will be about another big question in climate science:

How can we do scientific experiments on our planet?

See you next time.

* You don’t, but if you were a volunteer at one of my talks you would.

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: (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 > 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 (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:

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…