Climate change - about the experiment

[clarisse]

Trying to predict climate change is hard. There are lots of factors involved – air temperature' date=' sea temperature and cloud cover all play a part – as do dozens of other variables. Therefore, there are a huge number of calculations involved.

 

One solution is for scientists to use the largest supercomputer they can find. But even the biggest supercomputers are only so good.

 

We think you can do better.

 

Using a technique known as distributed computing, we’re hoping to harness the power of thousands of PCs around the world. If 10,000 people sign up, we’ll be faster than the world’s biggest computer. And we’re hoping to be even better than that.[/quote']

 

http://www.bbc.co.uk/sn/hottopics/climatechange/aboutexperiment1.shtml

 

This looks pretty interesting and I suppose it would really be helpful to join.

 

I just don't know if this will not like, affect my computer. What do you think?

[JustStuit]

I thought climate and weather are chaotic and cannot be accurately predicted no matter what computer was used.

[clarisse]

I thought climate and weather are chaotic and cannot be accurately predicted no matter what computer was used.

 

Did you read the information in the website?

[JustStuit]

Yea, I couldn't find any information about the chaotic nature of the predictions.

[bascule]

I thought climate and weather are chaotic and cannot be accurately predicted no matter what computer was used.

 

Multi-decadal climate models are still useful as process studies even if their results are not accurate, which is certainly the case at the present time.

 

You may want to check out this blog entry of AASC president (and my former boss) Dr. Roger Pielke on this subject:

 

http://climatesci.atmos.colostate.edu/?p=12

[Cthulhu]

I thought climate and weather are chaotic and cannot be accurately predicted no matter what computer was used.

 

I believe short term regional weather is chaotic, but the average result of global weather over long periods of time is not. This means that while it is near impossible to predict today what the weather will likely be in new york on july 15th 2080, it is a lot easier to predict what the global climate will likely be in the year 2080.

[SkepticLance]

Cthulhu quote :

 

it is a lot easier to predict what the global climate will likely be in the year 2080

 

This statement is still unproven in the scientific sense. Only when there is proper empirical evidence to back up a climate model or models can we say that.

[bascule]

I believe short term regional weather is chaotic, but the average result of global weather over long periods of time is not. This means that while it is near impossible to predict today what the weather will likely be in new york on july 15th 2080, it is a lot easier to predict what the global climate will likely be in the year 2080.

 

No, you have it completely backwards. Short term, regional modelling has become highly accurate in recent years. Long term global climate models are inaccurate, because deficiencies in the model are compounded over a longer period of time, and furthermore nonlinearities in the climate system cannot be accounted for in global climate modelling given our present level of scientific understanding.

[Cthulhu]

Cthulhu quote :

 

it is a lot easier to predict what the global climate will likely be in the year 2080

 

This statement is still unproven in the scientific sense. Only when there is proper empirical evidence to back up a climate model or models can we say that.

 

It's a simple consequence of the difference between weather and climate. Analogous to the difference between the difficultly of predicting the result of a single coin toss and the result of 1000 coin tosses. The latter is a lot easier.

[Cthulhu]

No, you have it completely backwards. Short term, regional modelling has become highly accurate in recent years.

 

Yet we can predict next februarys global average temperature a lot better than we can predict the temperature in london in two weeks time. Climate predictions are not simply weather predictions far off in the future. It is an averaging of weather over some period of time. This averaging makes it less succeptible to the chaotic nature of weather.

 

Long term global climate models are inaccurate, because deficiencies in the model are compounded over a longer period of time, and furthermore nonlinearities in the climate system cannot be accounted for in global climate modelling given our present level of scientific understanding.

 

Sure, long term climate predictions will be less accurate than short term climate predictions. But long term climate predictions will not necessarily be less accurate than short term weather predictions.

[bascule]

Yet we can predict next februarys global average temperature a lot better than we can predict the temperature in london in two weeks time. Climate predictions are not simply weather predictions far off in the future. It is an averaging of weather over some period of time. This averaging makes it less succeptible to the chaotic nature of weather.

 

No, wrong, nonlinearities in the climate system which are not accounted for by the model have an increasing impact on the outcome as time goes on.

 

Sure, long term climate predictions will be less accurate than short term climate predictions. But long term climate predictions will not necessarily be less accurate than short term weather predictions.

 

Mesoscale atmospheric models are substantially more advanced and accurate than global climate models.

 

You obviously know nothing about the state of regional atmospheric modelling. Try working in the field for 5 years (with one of the top climate scientists in the country), then get back to me. Whatever you do, don't try to pass off misinformation as fact.

[Cthulhu]

No, wrong, nonlinearities in the climate system which are not accounted for by the model have an increasing impact on the outcome as time goes on.

 

Yes and weather models are compounded by the same problem, but more so because they are trying to predict much lower resolution events than climate models. Trying to predict the rainfall in london on october 28th this year is near impossible. However predicting the average rainfall across england in 2008 is a lot easier. I could just take the average rainfall of the last 5 years and be quite sure it will lie close. I cannot however average the rainfall in london over the last five october 28th's and expect to have a good prediction of rainfall for this october 28th. Regional weather is lower resolution and suffers from wilder variations than climate. (I note that USA Today's 5 day weather prediction on the back page cannot even predict cloud patterns 5 days in advance. That is their prediction for friday changes over monday, tuesday, wednesday and thursday)

 

Mesoscale atmospheric models are substantially more advanced and accurate than global climate models. You obviously know nothing about the state of regional atmospheric modelling. Try working in the field for 5 years (with one of the top climate scientists in the country), then get back to me. Whatever you do, don't try to pass off misinformation as fact.

 

I do know nothing about the state of regional atmospheric modelling. But I do know that predicting the global average temperature for 2008 will be a lot easier than predicting the temperature of london on September 18th this year. Perhaps you are talking about something else.

[MattC]

http://www.bbc.co.uk/sn/hottopics/climatechange/aboutexperiment1.shtml

 

This looks pretty interesting and I suppose it would really be helpful to join.

 

I just don't know if this will not like' date=' affect my computer. What do you think?[/quote']

 

It will definately affect your computer, but that's the point. If you are asking if this will "mess" your computer up, that's unlikely. It will, however, have a slight slowing effect on your computer, though presumably when you are actively using your computer the program will slow down or stop.

 

I have not used this software, but I have used similar software in the past. It is actually common for organizations to do things like this - I believe the first (or one of the first) examples or a distributed computing network like this is with the seta project, which analyzed incoming light (ie radio waves, among other things) to look for patters that might indicate life. Numerous personal computers, in people's homes are tapped into to analyze the data, making for a much more powerful computer than the organization could use. It seems to work, but remember, you can only help if you let your computer ber used by these people (presumably whe you are not using it). This means that your computer will use more power, generate more heat, and possibly even have a shorter lifespan because of this (assuming you leave it turned on for the express purpose of letting it run these calculations. Not that these effects will necessarily be severe or significant.