How to Get Enough Computing Power for Climate Modeling

The Bishop Hill blog posted notes from Professor Tim Palmer, who recently gave a lecture on the computational challenges of weather and/or climate modeling. He asks:

How much resolution is needed to capture climate change details? For example, convective instabilities (~km scale) aren't included in climate models; should they be? Does higher resolution reduce uncertainty? There’s no good theory for estimating how well climate simulations converge with increasing resolution. Even worse, the equations themselves change with finer resolution as new features have to be included...

He answers his own question with an obvious truth: "We need bigger computers." But that raises a new question: where do we get them?

The answer, I suggest, is right in front of our noses--quite literally. We already have enough computing power on our desks or in our laptops. Climate modeling is probably the perfect application for a worldwide network of personal computers.

It's not like can't be done--it already has been! Oxford University networked 3.5 personal million computers back in 2002 to find a cure for anthrax. Dr. Graham Richards' Screensaver Lifesaver" project was a huge success, and it seems like it could be replicated.

I envision "screensaver" software that runs on an all-volunteer network of PCs in their idle time. Assign every station a point on the global grid and give it access to "live" meteorological measurements from as many observation stations as possible. Then, using a set of competing climate models (more on that later!), have each station generate the data each climate model would predict for the area around its unique grid point. As more people volunteer their computers for the project, make the grid increasingly fine.

The primary point of this network would be test competing climate models. To that end, any person would be invited to turn their theory about the weather into an algorithm that could run on this system. The network could test out any number of theoretical models, so I would make the "model building" component an essentially "open source" system, with just enough editorial control to keep hackers from implanting malware in the system.

It would seem appropriate to require every climate modeler to disclose his or her algorithm (but not the source code). An "open source" system of this sort should make the results of every model accessible to all researchers at all times. That would allow the maximum number of researchers to learn from other people's successes--and failures.

The Intelligence of the Blogosphere

Glenn Reynolds of Instapundit linked to this Harvard blogger who included Instapundit in his research on the interconnectedness of blogs, as shown in this chart:

The new science of artificial "neural networks" suggests that one can model the blogosphere (or any human community) as a neural network. Each blogger has a set of regular inputs (like the dendrites on a neuron) and a single output (like the axon). "Learning" occurs as synapses form and/or break between dendrites and neurons (and/or strengthen and weaken), with the "intelligence" of the system emerging as a result of interconnected nodes.

There's no real question whether the blogosphere (and other networks of communicators, such as the traditional media) is a neural network--the more interesting question is "how intelligent is it?"

That turns out to be a remarkably easy question to explore. Politics divides the blogosphere neatly into left and right sides, and both sides of the blogosphere are constantly making predictions. Those predictions get tested every election cycle. That means researchers should be able to:

1. map out the general structure of the neural network on both sides,


2. collect pre-election predictions,


3. compare those to public opinion polls (as a "baseline"), and


4. compare the "spread" between predictions and polls to the actual results.

A neural network built around the axis of the DailyKos/DemocraticUnderground/FireDogLake blogs might prove to be more "intelligent" than the competing net that includes Drudge/Instapundit/RedState, or it might not. As a matter of pure science, I'd love to know which is better.

And, as a political junkie, I'd love to add all the "intelligence" I can find to my own side!