Monday, September 15, 2014

The Irish Elk is not a good role model for a Higher Education sector

Professor Young said international rankings that show no Australian university in the top 100 for research citations demonstrate the need for dramatic higher education reform.”

No, international rankings in research citations demonstrate nothing.

High research citations are the result of working in important areas that lots of other people are working on. 

The most highly cited results in these important areas that lots of people are working will come from the scientists with the best toys.

We cannot afford the best toys.

I just did a bit of exploring on the internet, and in 2012 the Australian Commonwealth government spent about $850 million dollars on the Australian Research Council, which is the main funding source of curiosity-driven research in this country. In the same year, Samsung spent $42 billion dollars on research and development. There is no way you can expect Australian researchers who are interested in making conducting polymers for flat screen displays, for instance, to compete with that sort of funding. There is no point in the Australian Commonwealth funding work that is trying to compete with the work Samsung is doing, or with the funding priorities of the Max Planck Institute, or of the strategic goals of the California State University system. There are lots more foreigners than us and they have a lot more money. They can afford better toys.

We should spend our limited resources on research that is important to Australia, but less-so to the rest of the world. With things that are of value to the citizens of the Commonwealth of Australia, but that the big fish in the pond can’t be bothered with. There is no reason to suppose that the results of this work will be in topics that are flavour-of-the-decade overseas. If the funding mechanisms of public universities are doing their work properly, they won’t be.

An Australian university making the top 100 list would truly be a great tragedy.  It will mean that Australian problems are being ignored and universities working on problems relevant to our country are being starved of funding. If it ever happens, I will burn Professor Young in effigy and drown my sorrows in a whole lot of soju.

Wednesday, September 10, 2014

I know what you want to see: some half-arsed Climate Modelling!

The graph I showed in the last post wasn’t very good evidence for anthropogenic global warming. If I wanted to scare you, I would show you this graph instead.

This shows the correlation between carbon dioxide and temperature found in a brilliant set of data collected from ice cores at Vostok, Antarctica, where ‘0’ on the temperature axis is the average temperature for the last century or so. [Attribution to text files of raw data: J. R. Petit, J.M. Barnola, D. Raynaud, C. Lorius, Laboratoire de Glaciologie et de Geophysique de l'Environnement 38402 Saint Martin d'Heres Cedex, France; N. I. Barkov, Arctic and Antarctic Research Institute, Beringa Street 38, St. Petersburg 199226, Russia; J. Jouzel , G. Delaygue, Laboratoire des Sciences du Climat et de l'Environment (UMR CEA/CNRS 1572) 91191 Gif-sur-Yvette Cedex, France; V. M. Kotlyakov, Institute of Geography, Staromonetny, per 29, Moscow 109017, Russia]

I came to this data because I wanted to have a closer look at an assertion I have come across a number of times, that changes in carbon dioxide lag changes in temperature in ice core measurements. And yes, it does seem to, but it is a very unwise thing to base a full-blooded skepticism to global warming on. Because the lag is smaller than the uncertainty in the data. The age of the ice, and the age of the air trapped in the ice, is not the same: there is a difference of about 3000 years between the age of the trapped air and the age of the ice, which isn’t known with absolute accuracy, because it takes time for the snow and ice above a little bubble of air to be compact and impermeable enough to trap it there for good. The carbon dioxide content is obviously calculated from the air, while the temperature is calculated from the isotopic ratio of deuterium to hydrogen in the ice molecules. And the imprecision in aligning the exact times of these two sets of data is larger than the lag values that have been reported. It would be nice if this data gave a definitive answer as to how closely carbon dioxide and temperature changes track one another, but all we can say is that on a time scale of +/- 1000 years or so they move simultaneously.  I could just let you draw a line through the data extrapolating to the 400 ppm of carbon dioxide we have today, but I will do it myself.

This is a fit to the data assuming that all the change in temperature is due to radiative forcing by carbon dioxide, fixing T = 0 as 286 K and 284 ppm CO2, with the log of the concentration change giving a change in absorption which has to be compensated by increasing the temperature of a black body radiator, with one adjustable parameter (an invariant non-CO2 radiative forcing) adjusted to minimise the sum of the square of the differences between the fitted curve and the experimental data.

Scary, eh?

If this is the correct way to extrapolate the data, then we are about 6 degrees cooler than we should be, and are just in some sort of lag period - of some unknown length, but definitely less than a thousand years - waiting for this to happen.

I was on the brink of converting myself to global warming alarmism, but I thought I should have a look at the original papers first. Here are some great graphs from Petit et al., 'Climate and atmospheric history of the past 420,000 years from the Vostok ice core', Antarctica. Nature 399: 429-436.

Carbon dioxide is not the only thing that is correlated with temperature changes. Methane, another greenhouse gas, is correlated with temperature changes. (They did the maths in the paper, and r2 is 0.71 for CO2 and 0.73 for CH4). The temperature changes are also closely correlated with the predicted insolation – the amount of sunlight incident on the Earth, varying according to irregularities in its orbit. Dust and sodium (a proxy for aerosols, which we know are cooling) are negatively correlated with temperature changes (r2 is 0.70 for sodium). Ice volume (which is a proxy for water vapour, another powerful greenhouse gas) is positively correlated with temperature.

While insolation can only be a cause of warming, all of these other correlating things can be both a cause and an effect of increasing global temperature. We do not know, just by looking at this data, what is what. A sudden fall in dust and sodium, an increase in ice volume, and a sudden rise in CO2 and CH4 characterises the onset of all of the interglacial warm periods covered in this data. In the graph below I’ve fit the data again, but this time instead of adding an invariant radiative forcing by other things term have multiplied the carbon dioxide radiative forcing term by an adjustable constant to approximate the effect of all the other variables that are changing in synch with carbon dioxide. This constant turned out to be ‘41’ for the best fit, shown. So using this very crude fit, I can extrapolate the effects of *just* changing carbon dioxide concentration to 400 ppm, without any of those other things changing.(That's the line of green triangles hugging the axis from 300 to 410 ppm). This result seems absurdly Pollyanna-ish, even to me, and I'm sure I could make it looks scarier with a model for the experimental data with more adjustable parameters: but that's what the 'suck it and see' model gives me.
I've also put in the observed changes in modern times on this graph. It does make sense to attribute these to CO2 with a little help from the other greenhouse gases we've been putting into the atmosphere. And because we're extrapolating beyond the bounds of the historical data, we may be in a strange and uncharted perturbation of the global climate system. So maybe there is still a significant lag for us to catch up with. Maybe.

But there is one other thing that emerges from this ice core data that suggests very strongly that carbon dioxide concentrations are much more an effect than a cause of global warming. Have another look at this figure:

At the end of each interglacial period, the temperature drops before the carbon dioxide concentration does. This is not a minor effect lost in the uncertainty, like the possible lag in carbon dioxide concentration at the beginning of warming periods; it is a big lag of many thousands of years. Insolation and methane don't behave like that: they rise and fall in lockstep with temperature. What this tells me is that carbon dioxide has historically not been sufficient, by itself, to maintain a warming trend. So we can completely discount any panic-mongering positive feedbacks.

Thursday, September 4, 2014

Slicing Idea Space

The other day I became aware of a publication attacking the group Friends of Science in Medicine – of which friends I am one – published in the minor journal ‘Creative Approaches to Research’, taking Friends of Science in Medicine to task for unfairly attacking complementary medicine. As the publication originated in my own institution, I felt an obligation to respond. So I wrote a vituperative response.

However, the process of writing the response has crystallised something in my mind which I think is valuable. This something is the distinction between evidence-based and science-based policy. The main coherent argument of the published attack on Friends of Science in Medicine is that complementary medicine is evidence-based, so it is unfair to deny it a place with other evidence-based treatments at the public trough. And this argument is valid. Complementary medicine is evidence-based. There is a vast literature of studies giving positive evidence for complementary medicine. It is published in professional-looking journals. It is peer-reviewed, for what that is worth. The evidence is apparently there that all manner of weird treatments work. But if you look more carefully at this body of literature, it looks much less impressive. The design of the studies is flawed. Controls are missing. Data is cherry-picked to support a preconceived conclusion. Alternative hypotheses that could explain the results observed are not considered. The overwhelming bulk of the great mass of evidence is just not very good evidence. 

And the same is true for many other things that are not complementary medicine: any observation that can be selected from the overwhelming deluge of data that eternally gushes out at us is evidence. 

This picture is evidence for the Loch Ness monster. 


This graph is evidence for anthropogenic global warming.

What we need to do is test evidence. The process of testing evidence that has been proven to work is called "science". You might remember my amendment of a Richard Dawkins quote so that it made sense:

We see some evidence, and create a model that explains the evidence. A hypothesis that the evidence means, if we do action X, we will see result Y. We do action X, and see if we see result Y. We don’t do this just once. We think about what the implications of our model are, and what new things it predicts: if it predicts we will see result Y1 if we do never-before-attempted action X1, we try action X1, and see if our model has correctly predicted this new outcome. As described, this procedure seems flimsy, because obviously an infinite number of explanations are possible for anything we observed. So we apply one simple additional requirement: our model has to be consistent with all the other models that have been tested a lot, and not shown to fail yet. Our overall model of the universe has to be consistent. This process of testing the evidence is science, and policy that is based on models describing the evidence that have passed all of these tests is science-based policy.

Clearly, evidence-based policy is better than policy based on the things on the edges of the figure below. And clearly evidence-based will shade off into a penumbra of flimsier and flimsier evidence. If we don’t have science to base our decisions on, we should base them on whatever evidence is available. But, if we have science-based understanding of a phenomenon, we should preferably base our actions on the science, not simply the evidence per se. And if we are spending other people’s money, we should spend it in the most effective way we can. Which means a science-based way where we can, instead of an evidence-based way. Is that clear? 

To recapitulate:

Science-based policy is based on a model that explains the evidence;

It is based on a model that is testable;

It is based on a model that has been tested, and not found to fail;

It is based on a model that does not contradict all the models for different phenomenon that have been tested, and not found to fail.

When we are distributing resources, we should wherever we can distribute them in a science-based way. 

I reckon this distinction between science-based and evidence-based anything is a distinction that is underutilised and valuable, and we should make it, loudly, whenever it relevant.