Climate models benefit from medical methods

January 18, 2013 by Tom Marshall
Climate models benefit from medical methods

Researchers have used mathematical techniques taken from the analysis of medical images to bring climate models into closer agreement.

The breakthrough could let scientists make better predictions of the future climate under different scenarios for .

At the moment, climate models often disagree even over very major features of the climate - for example, on the location and timing of monsoons. They find it particularly hard to represent and other precipitation accurately, both in their predictions for the future and in their simulations of historical climate.

So the search has been on for a way to bring the models' predictions closer to each other. But researchers at the University of Oxford realised that the software used to process medical scans was already doing something similar. Doctors often want to compare the scans of several patients' brains, for example, in search of common symptoms. To do this, they need to match up the various anatomical regions of the brain, which will be in different places in each patient.

'When you scan different brains, you need to compare them all to a common reference brain,' explains Adam Levy, an Oxford PhD student in . 'Here we need to get the outputs of climate models to match observations better, but the principles are similar.'

Levy and colleagues adapted specialised software, called FNIRT, used in to work out mathematical relationships between the same in different patients, allowing each image to be stretched and squashed until the areas of interest are in the same place. This is known as 'warping' the images.

Levy likens it to starting out with photos of two people's faces and trying to make them look alike by stretching, squashing and deforming them. 'You might move one person's nose up, or stretch the other's eyes to make them wider,' he says. 'But you don't want to do anything too weird - you can't fold one of the pictures over or cut holes in it, because then you're losing part of the image. So it's about trying to bring the two images as close together while also making sure the mapping is sensible and doesn't change things too far.'

It turns out that trying to compare the output of different climate models isn't so very different. Each model has its own idiosyncrasies and tends to make distinctive mistakes. By analysing where in space and time each set of outputs tends to predict a major climate feature and then using warping techniques to transform them so that they agree better with historical observations, the team found they improve the accuracy as well as the consistency of each model's predictions. That is, they can get climate models to agree better on how monsoons and other features will be affected by climate change, giving us more confidence in the models' predictions.

The group tested the concept on an extreme scenario for future climate change, in which unabated CO2 emissions have quadrupled atmospheric levels. They took 14 climate models and warped their historical simulations so that they agreed better with observations. They then applied these warps to their predictions in the hope that this would iron out disagreements between them. The result was a significant improvement in agreement between the models - around 15 per cent on average, though some areas benefited more than others. More than two thirds of the globe saw some increase in agreement.

'The long-term goal is to be able to make accurate predictions of average rainfall at a given time of year under a given CO2 emissions scenario,' Levy says. 'But for the moment we've used an idealised example with very dramatic growth in CO2 levels, to show that the techniques work.'

The team is now working on dedicated software to carry out warping on climate predictions, which are different from medical images in several important ways – for instance, they happen on the surface of a sphere. These peculiarities limit the effectiveness of simply adapting medical techniques to work on climate predictions; Levy thinks the new system, built from the ground up to work with , could bring bigger increases in accuracy.

The technique could also help with detecting the effects has already had on average rainfall rates, at the same time as demonstrating that these changes were due to human influence and not natural variability.

Explore further: Comparison with observations shows cloud simulations improving

More information: Levy, A. et al., (2012) Can correcting feature location in simulated mean climate improve agreement on projected changes?, Geophys. Res. Lett., doi:10.1029/2012GL053964, in press.

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1 / 5 (6) Jan 18, 2013
"We have learned a lot from experience about how to handle some of
the ways we fool ourselves. One example: Millikan measured the
charge on an electron by an experiment with falling oil drops, and
got an answer which we now know not to be quite right. It's a
little bit off, because he had the incorrect value for the
viscosity of air. It's interesting to look at the history of
measurements of the charge of the electron, after Millikan. If you
plot them as a function of time, you find that one is a little
bigger than Millikan's, and the next one's a little bit bigger than
that, and the next one's a little bit bigger than that, until
finally they settle down to a number which is higher."
1 / 5 (6) Jan 18, 2013
"Why didn't they discover that the new number was higher right away?
It's a thing that scientists are ashamed of--this history--because
it's apparent that people did things like this: When they got a
number that was too high above Millikan's, they thought something
must be wrong--and they would look for and find a reason why
something might be wrong. When they got a number closer to
Millikan's value they didn't look so hard. And so they eliminated
the numbers that were too far off, and did other things like that.
We've learned those tricks nowadays, and now we don't have that
kind of a disease."

- CARGO CULT SCIENCE by Richard Feynman
3.9 / 5 (7) Jan 18, 2013
Feynman is correct of course. Climate science like all modern science doesn't suffer from the effects that Claudius asserts.

"We've learned those tricks nowadays, and now we don't have that
kind of a disease."" - Claudius quoting Richard Feynman

Odd that Claudius would decide to prove his assertion by including a quote that does the exact opposite.

Perhaps he didn't read the entire thing.

Not reading is a common denialist failure.

5 / 5 (2) Jan 18, 2013
" the search has been on for a way to bring the models' predictions closer to each other"
That is good, but agreement with *observations* would be even better.
Let us know when they can quantitatively predict the timing and magnitude of the AMO and the ENSO, would you, please?
1.9 / 5 (9) Jan 18, 2013
None of the models predicted the last 17 years of flat global average temperature and they are thus falsified. Denial of natural climate change, most likely influenced more by simple turbulent chaos in the massive and slowly cycling oceans will become more and more shrill as two more decades of likely cooling drags on. It's not being willfully wrong that reveals their anti-scientific bias as much as their lowly name calling and support of videos that celebrate things like bombing school children to death as seen on the "1010 climate" clip on YouTube. The continuing bias of will be its downfall now that it's way too late to claim mere ignorance in the face of clear and brazen scientific fraud supported by the IPCC and daily debunked on skeptical blogs and in their growing body of peer reviewed output.

-=NikFromNYC=-, Ph.D. (Columbia/Harvard)

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