Comment by roc | Hacker News Reader

Comment by roc | Hacker News Reader

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> "It seems like bulk of their work is based on computer models"

Then it seems that you are confused. Climate science is about gathering data, putting forth a testable hypothesis and seeing how future data fits the prediction. Yes, they use computer models. In the same way that you or I might gather data from a wind tunnel test and make a computer model of wind flow from that data to extrapolate a theory and create new testable hypotheses of a more-aerodynamic shape.

But so long as new data is used to test those hypotheses, the use of a computer model is irrelevant. Science is being done. Testable hypotheses are being created and new data is being compared to those predictions. As long as we aren't running around half-cocked, believing the predictions of hypotheses that have not yet been tested, there's no concern.

And the core hypotheses under the umbrella of Climate science have been tested. Some disproved by data, leading to changes in thinking, further research and new testable theories. Some supported by data. None of the 'alternate' hypotheses can say the same. (No, it's not the solar cycle. No, it's not distorted temperature readings from data stations in urban areas.)

The very reason that there is a general consensus around the so called 'tent-pole' theories in Climate Science is precisely because they have been tested and have been shown to make accurate predictions and continue to do so. Some of them, for decades now.

Somehow I find that thought uncomfortable that the complexity of factors impacting something as complex as climate is getting reduced to a wind tunnel test.

One is a highly controlled scenario where all(most all) the externalities can be controlled. The other some extremely complex modeling, which will, at least in my view, always depend on some consensus among the participants which factors are more important than others.

I am far from a climate change skeptic, but the best example for the fundamental failure of grasping complex scenario with some factor-based prediction systems has been demonstrated with the complete failure of economics in predicting something as "simple" as the financial crisis. But then again, I am coming from a social science perspective assuming that as soon as humans are involved data and data analysis will always be subjective.

Predicting the financial crisis is probably orders of magnitude more complex that climate change. Climate science is based on well known (although complex) systems of equations and laws that have been studied independently from each other for hundreds of year (heat diffusion, fluid flow, etc...). The tricky part is combining all the pieces is a system the size of the planet where a lot of quantities are still relatively unknown.

Compare that with the financial crisis where there are no hard laws and the behavior of the system depends directly and inextrinsibly from how a large group of people scattered around the world will reach to certain hypothetical scenario in an unknown point in history. Will investor A be rational? Panic? How about B? What happens if B sees A panicking? Or vice versa?

I'm not an economist, by can give you an example on epidemiology I worked on recently. Starting with one of the simplest epidemic models (SIR), as soon as you add a simple behavioral assumption (people can become afraid of the disease and behave more carefully) the behavior of the systems changes completely.

Predicting the financial crisis is probably orders of magnitude more complex that climate change.

Orders of magnitude simpler, I'd think. "If real wages and household incomes keep dropping while household indebtedness keeps rising, you will eventually have a financial crisis."

Marx never needed any math to work this out, just the good sense to realize that money never appears out of thin air.

There's as much content to that prediction as there is to the prediction "if you release carbon dioxide into the environment at an accelerated rate, the average temperature of the Earth will increase".

What people are meaning by "prediction" here is something much more specific than what you mean and you know it.

Don't mistake a bubble with a financial crisis.

You are not "far from a climate change skeptic". You are a climate change skeptic. At best, your post is the vague claim that complex systems cannot be modeled or tested empirically, and is empirically wrong in its claim that no-one predicted the financial crisis.

Call me a skeptic too in that case. Chaotic systems are inherently unpredictable to us, though they are ofcourse deterministic.

The climate is chaotic (http://www.mendeley.com/research/nonlinearities-feedbacks-an...).

Chaotic systems have a horizon of predictability (http://www.britannica.com/EBchecked/media/3227/Sensitivity-o...), so climate models are still useful to an extent. They will tell us, with some degree of accuracy, roughly what will happen in the near future. The long term remains a mystery and there's little that can be done about that.

It goes much too far to say that the climate is chaotic and therefore totally unpredictable in the long run. After all, if you buy that whole hog, you're also led to say that if you moved the Earth to Mercury's orbit it would be impossible to predict that the Earth would get hotter, in the long term. This results from the flaw of confusing weather (which is indeed chaotic) with statistics about weather (i.e. climate).

A butterfly flapping its wings can cause a hurricane. But it can also prevent a hurricane. All those perturbations don't change the fact that, within an order of magnitude, we can predict how many hurricanes will occur in a given year. Alternatively, think about the classical three body problem: even though it's the preeminent example of a chaotic system, you can still make broad predictions about it. Center of mass, average speed of the bodies, average collision time. This isn't some obscure controversial field, either: in physics, these emergent statistical properties form the basis of statistical mechanics and therefore thermodynamics as a whole.

Chaos doesn't mean you have to throw your hands up and give up on making any long term predictions at all about a system.

>Chaos doesn't mean you have to throw your hands up and give up on making any long term predictions at all about a system.

But we can't tell what the weather will be in 45 days in New York.

One of the attributes of chaotic systems is their self-similarity. The behavior on one level is similar to the behavior on another - in this case weather is similar to climate. We can make predictions about the weather in the next few days, just as we can with climate, but in the next few months or years (or centuries or millenia for climate) is another story. The tiny pertrubations in the system which we do not model (the butterflies) end up throwing our predictions off in the long run.

Yes, and that's why nobody is claiming to be able to predict the weather in the long run. Nevertheless, some things still can be predicted, even in chaotic systems. What is your point?

My point is that pollution should be reduced, climate models should still be developed and that at the same time we shouldn't take their predictions as dogma and continue to question them.

I agree with all of what you say above and am skeptical of the models as they exist now, as another comment of mine elsewhere suggests.

I'm more interested in the theoretical question here, though. I'm unconvinced that self-similarity in chaotic systems means that since weather is a chaotic system, climate is itself a chaotic system that's unpredictable in the long term. One way of thinking about chaos is that at some point in the future, a system's position in phase space will become wholly uncorrelated with the system's initial position.

So for weather, tomorrow's weather is correlated with today's weather, but weather two weeks from now (or whenever) is wholly uncorrelated. But with climate you do end up with persistent correlations, year on year, decade on decade, millenia on millenia. Temperate regions show (anti-)correlations of deviation from annual average temperature between 180 day periods, and daily solar energy impingement is correlated with positive deviation from global mean temperature.

I've always made a mental analogy between that and a gas (the n-body problem): even though the positions of gas molecules rapidly become uncorrelated from their starting position, certain statistical properties--e.g., the pressure the gas exerts on the walls of its container--are highly correlated from one moment to another. Not just initially but into the indefinite future.

I'm open to arguments that that analogy is flawed and could see some assumptions that I'm not 100% confident in, but I don't see any obvious flaws. What's going wrong here, in your view?

The point is, while the models are inaccurate, the data (and there is a lot of data) indicate that warming is happening. This is shown not merely in temperature measurements; but in ice coverage changes, both in the Arctic Ocean and at mountaintops; and in biological change: increased growing seasons, earlier spring flowering, earlier bird migrations (and in some cases increased overwintering bird populations), increased ranges of species, including pests; not to mention permafrost melting and increased droughts and other severe weather anomalies in many areas.

That argumentation is a bit silly. Chaotic systems are not necessarily unpredictable. For example take the pendulum that swings among the magnets (one of those classic examples). So the exact path of the pendulum might be unpredictable, but it is predictable that it will reach equilibrium eventually. What I mean is: some aspects can be predictable while others are not predictable. Therefore your argument is wrong.

Based on the second claim of stfu’s post, “as soon as humans are involved data and data analysis will always be subjective”, I think he is really trying to highlight the unpredictability of a model that must generalize human choices using statistics. Since even conservative scientists will agree that climate change has some anthropogenic dimension, we should understand the limitations of modeling it.

Being skeptical of the model, however, does not prohibit you from accepting what the model is suggesting. Historical data and scientific opinion can be enough to convince us that climate change is occurring. But no model can predict how our reaction to that knowledge will affect climate change in the future.

Are you seriously arguing the analogy?

As long as they are producing testable hypotheses and gathering new data to (in)validate their predictions, they are doing science. And if their theories make accurate predictions, it literally does not matter if those theories came from computer modelling, mathematical proofs, inspiration or divination.

If we could make accurate predictions about the function of the universe by casting chicken bones, that would be science. Even though we wouldn't have the first clue what mechanisms might make it so.

There's no shortage of such situations in science, where theories produce useful results despite no understanding of the underlying, clearly complex systems that drive them. e.g. The Placebo Effect, Gravity, etc.

You seem to be arguing that every time science approaches a complex situation, which it doesn't understand and can't explain from first principles, that we cannot apply the Scientific Method. And that is patently false. That is exactly how we have come as far as we have. By creating falsifiable hypotheses, testing their predictions and refining those hypotheses.

Your hand-waving doubts and fears about complexity and methods is fundamentally anti-science. You are not only a 'climate skeptic', you are apparently skeptical of man's ability to use science to investigate and understand the universe at all.

> One is a highly controlled scenario where all(most all) the externalities can be controlled. The other some extremely complex modeling, which will, at least in my view, always depend on some consensus among the participants which factors are more important than others.

You do know that precisely modeling just a few seconds of turbulent flow would take more computer time than all the climate simulations in history, right? They're both approximations of intractably complex situations.

The mathematics of economies falls in the field of evolutionary dynamics. This is a relatively young field, having its origins in the 1960-1970 time frame, and much of the fundamental principles are still being worked out. For one thing, evolutionary dynamics necessarily involves quite a bit of graph theory, much of which is difficult or impossible to describe with continuous functions.

The mathematics of climate, on the other hand, falls in the field of statistical mechanics. While still a rather complicated field, statistical dynamics is much older, starting with Boltzmann in the late 19th century, and considerably more of the principles have been worked out. Statistical dynamics also deals more with continuous functions which, while not exactly easy to work with, can still be manipulated more easily than graphs.

It will never be possible to credibly predict financial crises: if you learn that in a month your stocks are going to drop 50% you will try to sell them now. But, since other people have heard the same prediction, they will do the same, and the drop would happen today, rather than in a month. Such a prediction can only be possible if it's made by someone people do not believe. Which is, accidentally, completely in agreement with very classic economic theory.

That being said, you are right in that climate models are woefully inadequate.

Predicting the precise moment of the "crisis", the pop, is semi-impossible due to markets being anti-inductive, yes.

Spotting the structural, macroeconomic factors that will create a financial crisis is actually pretty easy.

Complete failure of neoclassical economics, perhaps. There were plenty of people who predicted the GFC with varying degrees of accuracy; Steve Keen being but one.

The problem is that these people (and others) don't know enough to realize their economic predictions are worthless. There is truth to the old saw, "Economists are good at predicting recessions. They've predicted eight of the last three. . . "

In many cases the prognosticators are fully aware that their predictions are worthless but persist in the facade to build up a reputation. It takes only a few lucky coincidences to get set up for life. People love to hear bold predictions.

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