The problem with the activation maps is precisely that one is NOT looking at an image, so there's no way to fine tune the algorithms. Therefore, fMRI is NOT well understood in the way that CT or MRI are.

Consider that in imaging, you have the luxury of comparing the output of a brain scan to the known physical structure of the brain. Is there a hippocampus? No? Well then it didn't work, go back and fiddle until you can show me a hippocampus.

In fMRI, apart from low level sensory corticies (where visual field mapping techniques can reproduce broad level retinotopic maps), researchers are operating in a vacuum in which there is no hard and fast error signal to fine tune the methods.

Science has to proceed very cautiously in such a situation. This is particularly true when one has hundreds of thousands of voxels to sift through because it's easy to find any pattern in noise, if you have enough noise.

So I would argue that fMRI offers a very different set of challenges compared to MRI and CT scans, and therefore it's very important to keep a sharp, critical eye on the statistics used, as these authors are doing.

To illustrate this point further, here is a link to a poster in which someone put a dead salmon into a magnet and found that (in the absence of proper statistical controls) its decomposing brain was apparently reacting to the emotional content of pictures:

http://prefrontal.org/files/posters/Bennett-Salmon-2009.pdf

These are really complicated systems, and one of the reasons we model them is that they're too complicated for any one person to understand every single aspect; models are a sane way to integrate the results of studies requiring disparate expertise (or at least different people).

I am a modeller, and I simulate the brain a system which is also mind bogglegingly complicated.

The secret ingredient that makes modelling work as an enterprise is the ability to make predictions, and then test them through experimental manipulations. The reasoning is that if your model captures a gem of truth, then it should be able to accurately predict data that you as the experimenter haven't seen yet.

Or at least, that's the theory. The ugly truth is that even in brain science that's a standard which is rarely lived up to.

And the situation is bound to be worse with modelling in climate science. One cannot even perform experiments because we don't have multiple earths to play around with.

So, while modelling is a way for scientists to explore theories and communicate, as you indicate, I fear that the climate modelling process is fundamentally bankrupt because it's impossible to run experiments for the purpose of testing models.

They do make pretty pictures though. A video of a virtual earth turning from blue to orange is extremely compelling!

He also claimed that the winters would become _shorter_, and I assume the summers then would also be warmer.

You assume it? If the poles are made colder by warming in the winter, why do you get to assume it works differently in the summer?

I feel like I'm being sold a conspiracy theory.

So your counter argument that an observed weather phenomenon on the opposite side of the planet casts into doubt the mountain of data that the north pole is losing its sea ice (to the detriment of Polar Bears) is scientific?

Did the GGGP of this post not just say that global warming causes COLDER poles?

How can I possibly debate this issue with you or anyone else when the climate change camp gets to count both warmer and colder temperatures at the poles as favorable for their position?

It's an impossible position you've put your opponents in; none of the evidence counts against you.

But all the "global warming doesn't exist people" are going to jump on this like every bit of news about cold weather to claim it contradicts the idea that there's global warming, which it doesn't.

Global warming advocates are quick to leap on any image of an ice shelf falling into the water as if it supports the idea that there's global warming, which it doesn't.

You must take into account water/air circulation in the whole system at the very least too. Or choose to take the the butterfly or shit happens explanations.

What is clear to me is that our understanding of atmospheric dynamics is so awful (and rightfully so, it's complicated), that an explanation can be cobbled together using pesudo atmospheric lingo to explain any set of data as a result of man made influence.

The truth of the matter is that we don't really know what's going on. But that doesn't stop many people from boldly claiming that "X causes Y" with undeserved confidence.

What's also unscientific about this process is the way that the GW movement latches onto emotionally appealing icons to make their case (e.g. Polar bears, Katrina)

So from global warming we can actually expect colder winters at the poles.

Truly this is a theory that cannot be disproven.

When we thought the poles were melting, the infamous pictures of a wet polar bear on a little ice shelf were everywhere and we were told that this was the direct result of warming.

So now it seems the global warming theory can have its ice and melt it too.

Actually,it can take researchers YEARS to debug a very complex proof of something like the Riemann hypothesis. To begin to understand the proof, even a Riemann scholar might need to first learn entire fields of mathematics.

I've heard it said of de Branges at Purdue that there are few people capable or willing to confirm his work because of the difficulty involved.