Distributed Computing World Climate Simulation

Burnt Offerings writes: "The BBC reports that scientists at climateprediction.com are nearing the completion and public release in late summer of a distributed computing project that simulates the world's climate from 1950-2050 AD. It seems that each user's simulation will have different initial conditions built into their runtime simulation and a single completed simulation from 1950-2050 AD takes on average eight-months (Doh!), assuming average household computing power. The results will be sent back to the project's team, where they will select the models that resulted in the 'real' climate patterns that have occured since 1950-2000. I presume they will then use these validated models to help extrapolate the world's climate from 2000-2050. Pretty cool (or should I say warm? or hot?)."

Comments

Weather != Climate

[cperciva]

Weather is chaotic, but climate is ... well, ok, climate might be chaotic, but we really don't know -- and if it is chaotic, it is still only chaotic on timescales of more than 50 years.

Predicting climate 50 years in the future is a computationally difficult task, but it isn't impossible the way that predicting weather would be.

Re:What do they mean by 'PC'

[A_Mythago]

On their FAQ (dated 5 Oct 2000!), they state they will support Linux initially and are looking for sponsorship to port the client to Windows. Considering the "What's New" page was last updated on 17 Aug 2001, the actual status of ports for different clients is unclear.

Re:Weather is a chaotic system

[sisukapalli1]

Extrapolation is usually not very reliable. In most of these chaotic systems, the fact that a model predicted accurately what has happenned in the last 50 years, does not mean very much because of the following factors:

(a) there may not be enough models that have been run, so we may pick something that "seems close"
(b) running a 50 year simulation (rather 100 year) in 8 months on small computers means that the model is not going to be very sophisticated
(c) there are random parameters, such as volcanic eruptions, man made emissions, deforestation/aforestation, etc., that won't get into the model properly

A prof of mine told this in the class: In the good old days, many mechanical engineers came up with formulae for heat transfer in pipes under various conditions. The formulae matched experimental data almost perfectly. They started extrapolating the results. Eventually, they found out that *ALL* those extrapolations violated the second law of thermodynamics -- and they went back to just interpolating.

S

INFORM yourself with the FACTS

[guanxi]

... or at least the best science has come up with so far, are downloadable from the Intergovernmental Panel on Climate Change (IPCC).

I'd start with the Summaries for Policy Makers, as a way of becoming very well infomrmed in just ~20pp.

AFAIK: It's a UN organization that is the center of research. Their reports are a consensus of almost all the leading scientists from every country on the globe, and their policy statements are approved line-by-line by governments. Even with all that, there are pretty strong statements.

Here's better background.

Re:Something isn't right.

[astroboy]

They're starting with different initial conditions and hoping that some subset results in 50 years of weather?

No. The term `starting conditions' appears in the BBC article, but if you go to the website it says:

The only systematic way to estimate future climate change is to run hundreds of thousands of state-of-the-art climate models with slightly different physics in order to represent uncertainties.

In large-scale simulations such as these, there are often bits of physics/chemistry/weather that have to be put in by hand because, usually, the relevant bits of science would be too expensive to calculate, or couldn't be seen on the resolution of the simulation. While it's usually pretty doable to come up with reasonable models for the unresolved effects, there are often parameters in the models that could take a range of values.

This ensemble of models allows for the callibration of the model parameters against 50 years of data; this gives some confidence in the predictive power of the models for the next 50 years.

This sort of parameter estimation based on calibration is very common for models of complex systems, and not just for computer models. Ideally, one wants to get to the point where such things aren't necessary and you can directly calculate all the science a priori of course, but these model calibrations are often useful steps along the way.