Yes, somewhat. Models are a useful tool to see what happens when the science is known. They work well to check if you understand your theory well: You model it and experiment with it at the same time. If you model matches the real world then you are good to go.
But if you try to make a model based only on your observations, without also being able to experiment, then that's not science, that's tautology.
Your model will do whatever you want, and you have no way of knowing if it has anything to do with the real world because you can not run multiple experiments. In some cases if you wait long enough you may randomly get enough variation in the real world to be confident in your model (for example stellar evolution).
But using a model to predict something you have never seen in the real world? That's not science, that's speculation.
I think you're confused about how models work in a scientific sense. They can be used for prediction, and if they couldn't they wouldn't have any value at all. Of course they sometimes don't line up with the real world; why do you think there are a metric shit-ton of scientists in Antarctica?
In terms of the article, the theory of climate is pretty well understood at this stage, and the models are sound at the macro scale. The hard parts are now in factoring in relatively small changes to make the numbers line up even better, and working through the implications of their predictions... which is what the article seems to be doing.
> They can be used for prediction, and if they couldn't they wouldn't have any value at all.
Correct - they can't be used for prediction, and therefor they have no value at all (for prediction - they are useful for validation).
> Of course they sometimes don't line up with the real world
Then you improve them, but you don't try to predict from them. By improving them you increase your knowledge, but you don't predict from them because the only thing that comes out of a model is what you put into it. You learn nothing about a real world from a model. You learn about your understanding of the real world from the model. (They better you know the real world the better you model - the model teach you about your own knowledge. It has zero value in teaching you about the world since nothing can come out of it that you didn't put into it.)
> why do you think there are a metric shit-ton of scientists in Antarctica?
To do research about the real world? Is this supposed to be a big revelation? Not sure waht you were trying to imply here.
> The hard parts are now in factoring in relatively small changes to make the numbers line up even better
Well obviously. The details are always the hard part - the details is also where all the interesting stuff happens. You can make the best model in the world, and then miss the one tiny detail that only happens outside the domain of your model, and all the predictions are worthless.
If what you are saying is true, then every scientific and mathematical formula would be worthless, since after all, you just get out of it what you put into it. e=mc^2? f=ma? v=ir? Yep, no bearing on the real world or any predictive power whatsoever.
You are ktizo both have the same misconception. Perhaps this explains your belief in the value of models.
Those formulas are not models! They are exact mathematical representations of a phenomena!
A model by it's nature can not include everything, they include everything possible of course, but the world is too complex for them to include everything, so they must estimate. If you have a feedback loop with the real world you can tune your model to make it useful, but you can never get out of it anything you did not put in, since it's impossible to include everything.
If a model did include everything then of course it would work perfectly. But it's not possible to do that in the real world.
Those formulas are models. They describe the world and can predict it's behaviour, but can also break down under certain conditions. For example, what happens when I put 240V across a regular 20Ohm resistor? According to V=IR you get 12A running through it - but I doubt you'll get that for very long.
By your argument, V=IR must then be a "model" and is useless, with no predictive power, etc, etc. In practice, the only difference between those formulae and a more complex model is one of degree.
No, those formulas are exact, you are just using them incorrectly.
You think V=IR describes a resistor, it does not. It describes electricity. So it's not surprising that when applied to a resistor it doesn't work. You are trying to model a resistor, but you are not including everything in your model, so it fails.
Which is exactly what I'm warning about.
> By your argument, V=IR must then be a "model" and is useless, with no predictive power
V=IR is not a model. Using it to describe a physical resistor is a model.
Suppose I never touched a real resistor in my life, and the only information I had was an understanding based on some other law that V=IR. Now I want to use that information to predict what a resistor will do. I will fail - among other things it doesn't take into account inductance or capacitance.
Which is why it is very very important to know what your equation describes. You thought that V=IR completely describes a physical resistor, when it does not.
Now you know why it's incredibly dangerous to try to learn anything from a model. If even something as simple as Ohm's law can be confusing, imagine how many error a model with hundreds of equations has.
>You thought that V=IR completely describes a physical resistor, when it does not.
V=IR is a mathematical model of a resistor, useful under most circumstances except pathological ones. Can the model be improved? In most cases yes, but then it loses some value due to complexity.
This is why there are scientists in Antarctica: they're testing the model, gathering new data to see how well the models hold up. But for some reason you're acting as if those poor scientists haven't realised this yet.
If you want to continue to argue the point feel free, but you're disagreeing with every scientist and engineer on the planet. Try looking at http://en.wikipedia.org/wiki/Mathematical_model - what do you see? Ooh - equations.
"essentially, all models are wrong, but some are useful" -- George E. P. Box
It's the basis for a model of a resistor, but it's an exact equation of resistance. There is a difference.
> they're testing the model, gathering new data to see how well the models hold up.
No, they are not testing the model, they are trying to improve the model by recording what it did in the past.
To test the model you make a prediction of what it will do in the future, then wait and see if it happens the way your model shows. It can't be done for climate though since it would take decades. They do try to test it by giving it old data and seeing if it predicts current conditions - which is great, but does not help in predicting the future since the input will be different this time.
> you're acting as if those poor scientists haven't realised this yet.
They most definitely do realize how poor the results are (just see how many weasel words they put in the paper linked to this topic), they just have no better options.
Do you really not understand the difference between a model and a fundamental equation? A model does not have perfect input, and a model may not include all things that can affect the output.
An physical law (an equation usually), is exact. It might not apply to real physical objects (like the ideal gas law), but that doesn't make the equation a model - the equation is correct for where it's used, it just doesn't cover everything.
Excellent quote BTW, but don't take from that that everything is a model. The real world is not a model - if I test something and it does something in the real world, I have real information. In a model I only have the information I put in it, and it's simply impossible for me to put in everything.
They made a model - but they missed something. Good thing they were able to test it, and see what they missed. But if you don't have the ability to test? (Like climate.) Then your model is useless.
A model is useful only if you can test it. The model will help you know what to test, the model will help you see if you understand the topic. But a model you can not test is useless.
(Just to repeat myself: I'm not arguing about models, I'm arguing about models that can't be tested.)
The glacier model is wrong, but wrong in the wrong direction (Actually I think that's combined results from multiple models, but anyway...). How do they know? Because there are climatologists out on the ice, or in satellites taking measurements-1. Testing the model - you know, all that stuff that is supposedly impossible to do.
1- there are even high resolution GPS modules in Greenland which can estimate ice loss by how far up the crust lifts (as well as detecting plate tectonics).
Science often models things it has never seen in the real world. Sometimes, when it becomes possible to make a measurement, the model is found to be accurate. Like in the classic xkcd "Science. It Works, Bitches." cartoon about the microwave background radiation. - http://xkcd.com/54/
Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states.
In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at. Engineering is usually where you use models when the science is known. [edit] And economics is where you use models when the science isn't known, and then you worship them and hope that money falls out.
That's not true. It's a fallacy that the first step in the scientific method is formulating a hypothesis. It's completely unnecessary. The first step is "let's see what happens". You do not need any speculation or hypothesis first. That comes later - after you have collected your data then you try to understand and predict.
> Sometimes, when it becomes possible to make a measurement, the model is found to be accurate.
And for more often the model is wrong. But you have no idea if it's wrong or right if you can not test the real world.
> microwave background radiation
That's not a model, and the fact the you think it is makes we wonder. That's an exact mathematical representation of the phenomena. A model is imprecise, it includes as many parameters as possible, but by necessity can not include everything since the world is too complex.
> Models do not just do whatever you want. Many unexpected behaviours turn up in models and some of them can be almost impossible to know the future behaviour of in advance of running them, even when you know all the input states.
That's called Chaos. And the interesting thing about Chaos is that tiny changes in the input (for example what decimal precision you use) cause large changes in the output. If your model is chaotic then it's utterly useless for any conclusions whatsoever because it's completely impossible for you to enter the input with the same level of precision as the real world. Chaos is fun to look at, but pretty useless for prediction.
There's a second thing possible called emergent behavior. But that too can not be modeled without understanding the real world first. What you do is keep changing the model till it matches the real world, then pull out the seemingly simple rules that cause complex behavior. But the model will fail as soon as you go outside the domain it was built in. Just because something acts the same way every time in the limited circumstances you tried does not mean it will keep doing so forever. That's a common extrapolation fallacy.
So again, useless for prediction since prediction by definition puts you in a circumstance you have not yet seen.
> In science, models are often what you use when the science isn't known, as you can use them as a guide to pick up on interesting things to go and look at.
Operative word: To go and look at. Not to draw conclusions from. Engineering uses models to validate the assumptions, Science uses models to verify understanding. In no field are (should) models be used to draw conclusions.
It is true, you cannot do science without speculation. And it doesn't matter if you speculate before or after gathering a particular set of data, speculation is still required.
I didn't say that you shouldn't test your model, was just pointing out that you can model something based on an incomplete understanding and it can still be considered part of the scientific process before you have gone and checked your results. It can even be science even when there is currently no known way of checking the results, as long as you can point to a reasonable path that might lead you there.
Chaotic models are used in prediction. Regularly. Computational fluid dynamics wouldn't exist as a discipline without this.
And I never claimed that you should draw your final conclusions from a model, just that they can be a fantastically useful tool for exploring the unknown.
They can also be a trap if you trust the model more than the data, but that isn't the fault of modelling.
"A model...[is] really much more of a metaphor, an attempt to find an analogy between something you want to understand and something to really do understand, either heuristically or by a theory."
El Niño is the main thing that springs to mind when thinking about predictive powers of (short term) climate modelling. They are getting reasonably good at that.
Also, your coin flip is not a predictive model any more than claiming that using the same numbers every week on the lottery is a predictive model. For one thing, it makes no attempt to model anything, and for another, it has no attempt to be predictive, it just attempts to be right nearly half the time (edge), through understanding of the likely odds, which is a different thing altogether.
I mean, I have a model for predicting the result of coin flips which is often predictive. It's called "heads".