Well, people complain that about the lack of experimentation in climate science, models are a way of running experiments and also learning more about what's going on. Often time the models are driven by observations. For example, in my thesis research I observed a diurnal variation of atmospheric water vapor, but observations alone can't tell what is CAUSING the diurnal variation. It could be evapotranspiration, it could be a sea breeze effect, it might be something entirely different that I haven't thought of. I would like to run numerical weather prediction models driven by meteorological data that covers the time period I have observations for. If I do that, then I gain access to all the meteorological output from the model, and that gives me much more detailed data than is available from just the observations. If the model output agrees with my observations, then there is a reasonable expectation that whatever causes the diurnal variation in the model is the same thing that is causing the variation that I see in the observations.
It is a very powerful concept that models give output that agrees with the observational input data AND is consistent with the laws of physics. This is one reason that modeling is so universally used in earth science--we learn things that we simply don't have access to in any other way.
For long-term climate models I think people get too wrapped up in the numbers. We've seen the field of numerical weather prediction mature rapidly over the past few decades, so that 5 day forecasts today might have the same accuracy as 2 day forecasts 30 years ago. There is an advantage in numerical prediction, though, because every day you're confronted with a new set of observations that must agree with the forecast, which allows for rapid improvement. Climate models are very similar to numerical weather prediction models (in some case the same models), but instead of finding out whether a forecast is a bust in a day or two, you get feedback over the course of decades for climate models.
That doesn't mean that the models are wrong, though--to a large extent they are based on proven NWP models, and of course they're based on the laws of physics, which we know quite well. What it does mean is that you shouldn't expect the numbers to be perfect. They are just always just models, not the real thing.
I think it's always important to remember what the great numerical analyst Richard Hamming said:
"The purpose of computing is insight, not numbers" [Hamming's Motto]
EDIT: Dr. Jekyll, ha ha, that's funny. Well, the thing is I always try to give thoughtful answers to what I think are legitimate and interesting questions.
I think the numerical models should also used for insight--it does not seem to me that we are in any sort of situation where we can look at climate model output and say "In 2072 the global mean temperature will be 2.3 degrees C warmer than it is today." Nevertheless it does make a difference what the model output tells us, even if we don't know the exact value.
That was the situation in the past with numerical weather prediction models, which were used for forecaster "guidance." These days, though, the models are rapidly becoming better forecasters than the humans, so they've gone beyond guidance.
The fallacy of the decade 2000-2010 being the warmest ever is just another false statement made by the highly imaginative AGW believers like Big Gryph. Instrumental measurements of 133 years says nothing about the Earth's record temperatures whether you believe that the Planet is 4.5 billion years old or not.
Showing Global Warming through climate models is just another 'Hollywood stunt" performed by highly imaginative climate scientists. It's great that science has an imagination, but why are they imposing their will on others based on that "Hollywood Presentation"?
pegminer - "Insight?" Model outputs don't give insight. They are projections based on falsifiable assumptions simply due to the premise of Anthropogenic Global Warming. When climate science can be more absolute on the degree of warming caused by 1ppm of atmospheric CO2 increase directly related to human CO2 emissions, then climate models can be used in a more trustworthy manner to show an anthropogenic forcing. Until then, you are just another producer/director in this fictitious Hollywood Production.
You say that the data is a climate model output. That is a slight exaggeration. Certainly models were used to help reanalyze (AKA torture) the existing data. So, by looking at the real data they can't see any warming but if you use some data from a non-accurate model in the analysis then they can see some. I have heard more convincing arguments.
As to the point about "warmest decade on record" that is just semantics. We know of many warmer periods. We just did not have any thermometers at the time to record.
http://www.greenworldtrust.org.uk/Scienc...
The IPCC regularly show model outputs. Presumably they think they mean something?
To me they mean that the best climate scientists in the world do not know enough to make a working model of the climate.
Climate models are a useful tool for understanding climate, but useless at prediction.
What really upsets me, is that so many studies in different fields use climate model predictions as a basis for their conclusions.
Global warming is over. Ended in 2012. all non solids separate into nothingness in the upper atmosphere by nature. Mike
What we know is that when the models are compared to reality the climate models are a laughable failure.
I think it should be clear by now why the models fail. They are all based on the false idea that CO2 drives temperature. They far overrate the impact CO2 has on temperature and they almost totally disregard solar variations and other natural cycles.
The climate models all say that as temperature increases so does the level of CO2. [correct so far - except they believe CO2 levels rise due to human emissions rather than natural emissions] But then they extrapolate far too much impact from the increased CO2 levels --- way too much sensitivity is built into the models. So they fail.
Until the false paradigm of high sensitivity is put to bed, they will continue to fail.
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Well AGW is measured via land and ocean surface temps, not deep ocean temps Models have improved some but are still not infallible We have enough real time data to show that 2000-2010 was the warmest decade on record. This isn't by accident
Models have their uses, but it is usually denialists who bring them up.
Allows for the Socialists to engineer control of the populace
I've asked a few questions about climate models, mostly regarding the global surface temperature projections since those are the prominent ones when discussing things like the 2C limit or the speed of future damage due to surface air temperature warming.
A lot times I get back answers like "I really don't understand your fascination with models. Models frequently fall short of reliable." or "Models are not the proof of sensitivity" or "You continuously focus on the insignificant aspects of the AGWT", etc.
So it seems that the attitude towards climate model performance vs recent temperatures has been one that sort of sloughs it off. Models aren't the be all, end all, stop focusing on models....
This brings us to the surface temperature slowdown. It has been claimed clearly that warming hasn't stopped because the deep ocean heat content has been increasing instead of surface air temperatures for the past decade and more. If you ask somebody how they know that, they'll point to Balmaseda and Trenberth's recent study on ocean heat content to 2000m. skepticalscience.com has all the details: http://www.skepticalscience.com/new-research-confirms-global-warming-has-accelerated.html
And yet, that study was done using climate models. The data is a climate model output. Can you understand some people's fascination with certain models yet not others? How does one choose which to believe?
