Internet project forecasts global warming

[midnight armadillo]

Internet project forecasts global warming
Michael Hopkin
Biggest-ever climate simulation warns temperatures may rise by 11C.

"The greenhouse effect could be far more severe than experts had previously predicted, according to results from the world's biggest climate-modelling study. In the worst-case scenario, doubling carbon-dioxide levels compared with pre-industrial times increases global temperatures by an average of more than 11C.

But as well as a predicting a bigger maximum rise, the project has also increased the range of possible temperature changes.

The results are the first from climateprediction.net, a project that harnesses the world's desktop computers to predict climate change. More than 90,000 people have downloaded software that uses the spare capacity of their computers to run global climate simulations."

Read the rest at:
http://www.nature.com/news/2005/050124/full/050124-10.html
and there is a corresponding Letter to Nature as well, available in the
magazine or online if you have access (library, university, etc). 11C means total melt of the polar caps...

Don't buy beachfront property.

[Viking12]

1. Abstract

The range of possibilities for future climate evolution1-3 needs to be taken into account when planning climate change mitigation and adaptation strategies. This requires ensembles of multi-decadal simulations to assess both chaotic climate variability and model response uncertainty4-9. Statistical estimates of model response uncertainty, based on observations of recent climate change10-13, admit climate sensitivities—defined as the equilibrium response of global mean temperature to doubling levels of atmospheric carbon dioxide—substantially greater than 5 K. But such strong responses are not used in ranges for future climate change14 because they have not been seen in general circulation models. Here we present results from the 'climateprediction.net' experiment, the first multi-thousand-member grand ensemble of simulations using a general circulation model and thereby explicitly resolving regional details15-21. We find model versions as realistic as other state-of-the-art climate models but with climate sensitivities ranging from less than 2 K to more than 11 K. Models with such extreme sensitivities are critical for the study of the full range of possible responses of the climate system to rising greenhouse gas levels, and for assessing the risks associated with specific targets for stabilizing these levels.

[TO Kid]

2. We all know how accurate those simulations are

Especially those "simulations" from the 1970s that "proved" that we're heading for an ice age. Mind you, with the -35 windchill today those projections seem kinda reasonable.

[midnight armadillo]

3. This is an often-disproved claim

The statement that the global cooling prediction of 30 years ago was not correct thereby invalidates modern climate science is an old canard. To lay this to rest, refer to the Wikipedia(http://en.wikipedia.org/wiki/Global_cooling) :

1970s Awareness

Concern peaked in the early 1970s, partly because of the cooling trend then apparent (a cooling period began in 1945, and two decades of a cooling trend <3> (http://www.env.leeds.ac.uk/envi2150/lecture7/lecture7.html) suggested a trough had been reached after several decades of warming), and partly because much less was then known about world climate and causes of ice ages. Although there was a cooling trend then, it should be realised that climate scientists were perfectly well aware that predictions based on this trend was not possible - because the trend was poorly studied and not understood (for example:<4> (http://www.wmc.care4free.net/sci/iceage/mason.1976.html)). However in the popular press the possibility of cooling was reported generally without the caveats present in the scientific reports.

The term "global cooling" did not become attached to concerns about an impending glacial period until after the term "global warming" was popularized. In the 1970s the compilation of records to produce hemispheric, or global, temperature records had just begun.

A history of the discovery of global warming states that: While neither scientists nor the public could be sure in the 1970s whether the world was warming or cooling, people were increasingly inclined to believe that global climate was on the move, and in no small way. <5> (http://www.aip.org/history/climate/20ctrend.htm#L_0338).

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Besides, in the 70's the best computers had about as much computing power as your average 21st century pocket calculator, so to compare computational models between now and then is a little ridiculous on that basis alone.

[Viking12]

4. The global cooling myth

Every now and again, the myth that "we shouldn't believe global warming predictions now, because in the 1970's they were predicting an ice age and/or cooling" surfaces. Recently, George Will mentioned it in his column (see Will-full ignorance) and the egregious Crichton manages to say "in the 1970's all the climate scientists believed an ice age was coming" (see Michael Crichton’s State of Confusion ). You can find it in various other places too . But its not an argument used by respectable and knowledgeable skeptics, because it crumbles under analysis. That doesn't stop it repeatedly cropping up in newsgroups though.


I should clarify that I'm talking about predictions in the scientific press. There were some regrettable things published in the popular press (e.g. Newsweek; though National Geographic did better). But we're only responsible for the scienti press. If you want to look at an analysis of various papers that mention the subject, then try http://www.wmconnolley.org.uk/sci/iceage/.

Where does the myth come from? Naturally enough, there is a kernel of truth behind it all. Firstly, there was a trend of cooling from the 40's to the 70's (although that needs to be qualified, as hemispheric or global temperature datasets were only just beginning to be assembled then). But people were well aware that extrapolating such a short trend was a mistake (Mason, 1976) . Secondly, it was becoming clear that ice ages followed a regular pattern and that interglacials (such as we are now in) were much shorter that the full glacial periods in between. Somehow this seems to have morphed (perhaps more in the popular mind than elsewhere) into the idea that the next ice age was predicatable and imminent. Thirdly, there were concerns about the relative magnitudes of aerosol forcing (cooling) and CO2 forcing (warming), although this latter strand seems to have been short lived.

The state of the science at the time (say, the mid 1970's), based on reading the papers is, in summary: "...we do not have a good quantitative understanding of our climate machine and what determines its course. Without the fundamental understanding, it does not seem possible to predict climate..." (which is taken directly from NAS, 1975). In a bit more detail, people were aware of various forcing mechanisms - the ice age cycle; CO2 warming; aerosol cooling - but didn't know which would be dominant in the near future. By the end of the 1970's, though, it had become clear that CO2 warming would probably be dominant; that conclusion has subsequently strengthened.

http://www.realclimate.org/index.php?p=94#more-94

[Viking12]

5. Is Climate Modelling Science?

At first glance this seems like a strange question. Isn't science precisely the quantification of observations into a theory or model and then using that to make predictions? Yes. And are those predictions in different cases then tested against observations again and again to either validate those models or generate ideas for potential improvements? Yes, again. So the fact that climate modelling was recently singled out as being somehow non-scientific seems absurd.

-snip-

The 20th Century though still provides the test that appears to be most convincing. That is to say, the models are run over the whole period, with our best guesses for what the forcings were, and the results compared to the observed record. If by leaving out the anthropogenic effects you fail to match the observed record, while if you include them, you do, you have a quick-and-dirty way to do 'detection and attribution'. (There is a much bigger literature that discusses more subtle and powerful ways to do D&A, so this isn't the whole story by any means). The most quoted example of this is from the Stott et al. (2000) paper shown in the figure. Similar results can be found in simple models (Crowley, 2000) and in more up to date models (Meehl et al, 2004).

It's important to note that if the first attempt to validate the model fails (e.g. the signal is too weak (or too strong), or the spatial pattern is unrealistic), this leads to a re-examination of the physics of the model. This may then lead to additional changes, for example, the incorporation of ozone feedbacks to solar changes, or the calculation of vegetation feedbacks to orbital forcing - which in each case improved the match to the observations. Sometimes though it is the observations that turn out to be wrong. For instance, for the Last Glacial Maximum, model-data mis-matches highlighted by Rind and Peteet (1985) for the tropical sea surface temperatures, have subsequently been more or less resolved in favour of the models.

So, in summary, the model results are compared to data, and if there is a mismatch, both the data and the models are re-examined. Sometimes the models can be improved, sometimes the data was mis-interpreted. Every time this happens and we get improved matches between them, we have a little more confidence in their projections for the future, and we go out and look for better tests. That is in fact pretty close to the textbook definition of science.


http://www.realclimate.org/index.php?p=100#more-100

[meow mix]

6. ya, way off base. consider all our FUCKING GLACIERS melting

away..

but some people just dont care about that huh? fine then!
starve to death along with everyone else.

[Dead_Parrot]

7. This one is a bit different...

...with around 30,000 machines to play with, a lot of the "forcast" runs use historical data - running variations on the model to see which versions come up with the right answers (my machine is working out the weather for 1914 at the moment!). The accurate models are then used to calibrate the future forcast runs.