Most citizen computing projects can do no wrong. That does not apply to climate science. ClimatePrediction.net raises a few hackles, often in situations such as a recent meeting about investment in supercomputer, and when someone said, “I hear you can do a lot of this on PS3s nowadays?” there was a degree of hostility.

It’s just a way of addressing certain problems. People think of models as being done on a petaFLOP Cray XT-6, but most climate scientists don’t have access to these. They can have access to citizen scientists.

Climate modelling depends on:

• Complexity, e.g. number of processes, number of aspects of the sytem
• Resolution of your model, e.g. 100km scale, 10km scale or 1km scale
• Duration of the run
• Ensemble size, (groups of models) and this is where citizen cyberscience comes in

Often need to run models may times, and this is where ensembles come in.

Uncertainty in models varies. Uncertainty was felt to be underreported, so added subjective assessment of uncertainty, some of numbers are a little bit rounded, as they were decided on through discussion.

Suspected the model ranges were too small was because all the models matched the 20th century numbers ‘suspiciously well’. Need unrealistic models as well as realistic ones. You have to go outside the range that is fits perfectly in order to be sure you know what that range of forecasts are consistent with current observations.

Serious money to do a run, so they are looking for good models, not ‘bad’ ones.

By doing tests of different models (using citizen science), see that there -40 error bar was too pessimistic in terms of uncertainty, and the +60 was about right.

Learnt that the lower bound too low, upper bound about right, but this was through experimentation, not discussion. Therefore is testable.

What next? Using volunteer computing to see how extreme weather and climate are related, as global warming can cause both extreme hot and cold weather events, e.g. heatwave in Russian, Pakistani floods. Were they one event or two? Where they related to global warming?

Looking at the flooding in UK in 2003, simulating seasons where damaging weather events occur, both with and without the signature of climate change, to see if it had an effect. Looking for influence of external driver – human influence.

These are rare events, so have to model them many times to see if risk of extreme event has increased.

Projects in development, embedding regional models in simulations.

Have only used participants to provide compute power, so haven’t engaged participants brains. Big challenge faced is that only a few hundred people take part.