97% of scientists believe climate change is caused by humans, study finds

[May90]

Many of the climate parameters of interest are statistical, so this goes right back to the difference between statistical properties and the internal mechanics.

Yes, but such things as weather forecast for a week are not that much statistical. And yet we fail even to accurately predict whether it is going to rain tomorrow. I understand that one can make global prediction without having to predict every minor statistical detail, but if one cannot predict minor details, then wouldn't the global predictions have to be taken with a grain of salt? We don't need to know all the electron spins within each atom to be able to predict a temperature in the room 5 minutes after turning the heater on, but we can measure those spins within a few atoms, if we really want it; or, at the very least, we know how it can be done in principle, by using our existing theory.

We know, broadly, how atoms behave.

Every model we've developed for describing the path and distribution of electrons in an atom has failed. We have several that are close in some ways, but we've yet to establish a working model that explains all our observations of the behavior of electrons.

Yet, it seems to me, we know much more about electrons within atoms, than we do about local weather. Which is strange to me personally, given that weather is happening on macroscopic scale and, in theory, shouldn't require the precision anywhere near what we need for atomic level measurements, making it easier to do in principle.

[Garnier Fructis]

Yes, but such things as weather forecast for a week are not that much statistical. And yet we fail even to accurately predict whether it is going to rain tomorrow. I understand that one can make global prediction without having to predict every minor statistical detail, but if one cannot predict minor details, then wouldn't the global predictions have to be taken with a grain of salt? We don't need to know all the electron spins within each atom to be able to predict a temperature in the room 5 minutes after turning the heater on, but we can measure those spins within a few atoms, if we really want it; or, at the very least, we know how it can be done in principle, by using our existing theory.

I can't very well tell you how a test volume in a statistical system is going to evolve in time but that doesn't mean I can't tell you the temperature of the whole system.

[Ripster42]

Yes, but such things as weather forecast for a week are not that much statistical. And yet we fail even to accurately predict whether it is going to rain tomorrow. I understand that one can make global prediction without having to predict every minor statistical detail, but if one cannot predict minor details, then wouldn't the global predictions have to be taken with a grain of salt? We don't need to know all the electron spins within each atom to be able to predict a temperature in the room 5 minutes after turning the heater on, but we can measure those spins within a few atoms, if we really want it; or, at the very least, we know how it can be done in principle, by using our existing theory.


Yet, it seems to me, we know much more about electrons within atoms, than we do about local weather. Which is strange to me personally, given that weather is happening on macroscopic scale and, in theory, shouldn't require the precision anywhere near what we need for atomic level measurements, making it easier to do in principle.

It shouldn't be surprising. When you talk about something like weather as opposed to atomic behavior, there's way more "room" for things to have effects. To perfectly understand weather you need to perfectly understand atomic behavior, plus all the other phenomenon that happen at larger scales. That doesn't mean you can't describe behavior without perfect understanding all the way up and down the scale.

[Caolela]

Obfuscation90 hasn't yet gotten to the theoretical implications of dark matter and dark energy on global warming, in which case even describing peanut butter sandwiches becomes, "hoping to explain something on a global scale can be taken as anything more than a very crude approximation."

Sometimes, you just don't need to know certain things to reach sound conclusions.

[May90]

I can't very well tell you how a test volume in a statistical system is going to evolve in time but that doesn't mean I can't tell you the temperature of the whole system.

It shouldn't be surprising. When you talk about something like weather as opposed to atomic behavior, there's way more "room" for things to have effects. To perfectly understand weather you need to perfectly understand atomic behavior, plus all the other phenomenon that happen at larger scales. That doesn't mean you can't describe behavior without perfect understanding all the way up and down the scale.

But, my point is, in case of, say, gas within a volume, while we cannot predict fine details of the processes very well, we know the laws according to which those fine details evolve. Our model is whole, everything we observe statistically has an explanation on microscopic level. We are so confident in our models exactly because they come together very well, on both microscopic and macroscopic level. We do not view them in isolation from each other, but rather we try to explain them by similar means.

In case of climate and weather, I think the whole model should be able to explain both weather and climate processes, within the same framework. The global temperature evolution over time should have explanation that can also be applied to local weather processes and verified. In other words, it should have the property of us being able to make predictions both on microscopic and macroscopic scale with it - and then check if these predictions match the observations. Until we are able to do it, our model is incomplete and, as such, any predictions made with it, no matter how rigorously tested and verified, should not lead to far fetched conclusions.

[Endus]

But, my point is, in case of, say, gas within a volume, while we cannot predict fine details of the processes very well, we know the laws according to which those fine details evolve. Our model is whole, everything we observe statistically has an explanation on microscopic level. We are so confident in our models exactly because they come together very well, on both microscopic and macroscopic level. We do not view them in isolation from each other, but rather we try to explain them by similar means.

In case of climate and weather, I think the whole model should be able to explain both weather and climate processes, within the same framework. The global temperature evolution over time should have explanation that can also be applied to local weather processes and verified. In other words, it should have the property of us being able to make predictions both on microscopic and macroscopic scale with it - and then check if these predictions match the observations. Until we are able to do it, our model is incomplete and, as such, any predictions made with it, no matter how rigorously tested and verified, should not lead to far fetched conclusions.

The error you're making is that our climate models are that accurate. They do contain the understanding of underlying processes that you're disputing. We can explain weather events and how they develop.

You're acting as if these are two wildly different examples, when they're the same, and you're proving yourself wrong.

[May90]

The error you're making is that our climate models are that accurate. They do contain the understanding of underlying processes that you're disputing. We can explain weather events and how they develop.

You're acting as if these are two wildly different examples, when they're the same, and you're proving yourself wrong.

I disagree. I just have a hard time coming up with a decent analogy from physics, and I don't think gas within closed volume was an appropriate analogy here...

Let's consider tiger sharks within the ocean. We can roughly estimate the world tiger shark population. We know its distribution across the world. We know the seasonal migration patterns. Now, we absolutely cannot hope to know at any given moment where every single tiger shark is - however, we can track down individual sharks and predict very well in which area they will be, say, in a few days, if we had observed their behavior for a while and learned their habits.

How do we know seasonal migration patterns of those sharks? We know them, because we know the behavior of individuals and can generalize it statistically to the entire population. If we couldn't predict the behavior of individuals, then how could we hope to predict the behavior of the entire population? Population consists of individuals, and if we don't know the laws of behavior of a given individual, then, even if we manage to somehow understand the general population patterns, we will never know if, say, next year it will change due to certain change of conditions on the planet, because we don't know what conditions influence those patterns. In turn, if we do know those laws, then we should be able to apply them to a given individual and make a prediction of its behavior for the nearest future.

If we think we can predict the global temperature change for the next 5 years, but can't even predict whether it is going to rain tomorrow, then it means that we still don't understand very well how local weather functions. And if that is true, then we don't know how the weather across the globe comes together to form the climate. As such, we don't know what processes can influence this climate. We might think that we can predict the temperature change for the next few years very well, and we might even get decent predictions, matching the observations - then one year the pattern will be suddenly broken, our prediction will turn out to be horribly wrong, and we won't know why, because we only know the general pattern, we don't know all the processes that might influence it.

---

All I'm saying is, we should be very careful with interpretation of our model and predictions it makes, even if so far they have been in good accordance with observations.

[Endus]

I disagree. I just have a hard time coming up with a decent analogy from physics, and I don't think gas within closed volume was an appropriate analogy here...

Let's consider tiger sharks within the ocean. We can roughly estimate the world tiger shark population. We know its distribution across the world. We know the seasonal migration patterns. Now, we absolutely cannot hope to know at any given moment where every single tiger shark is - however, we can track down individual sharks and predict very well in which area they will be, say, in a few days, if we had observed their behavior for a while and learned their habits.

How do we know seasonal migration patterns of those sharks? We know them, because we know the behavior of individuals and can generalize it statistically to the entire population. If we couldn't predict the behavior of individuals, then how could we hope to predict the behavior of the entire population? Population consists of individuals, and if we don't know the laws of behavior of a given individual, then, even if we manage to somehow understand the general population patterns, we will never know if, say, next year it will change due to certain change of conditions on the planet, because we don't know what conditions influence those patterns. In turn, if we do know those laws, then we should be able to apply them to a given individual and make a prediction of its behavior for the nearest future.

And this analogy applies directly to weather and climate. For some reason, you don't see it. You're defeating your own conclusion, here.

If we think we can predict the global temperature change for the next 5 years, but can't even predict whether it is going to rain tomorrow, then it means that we still don't understand very well how local weather functions. And if that is true, then we don't know how the weather across the globe comes together to form the climate. As such, we don't know what processes can influence this climate. We might think that we can predict the temperature change for the next few years very well, and we might even get decent predictions, matching the observations - then one year the pattern will be suddenly broken, our prediction will turn out to be horribly wrong, and we won't know why, because we only know the general pattern, we don't know all the processes that might influence it.

Yeah, what you're talking about here just isn't true. Meteorology is way more accurate than you apparently think.

All I'm saying is, we should be very careful with interpretation of our model and predictions it makes, even if so far they have been in good accordance with observations.

We are careful.

And models don't just work with future predictions. The baseline for models is generated by seeing how well they could predict past trends, given the data on the actual events that we have to feed into them. Not only do our current climate models do so, they have also, for decades, accurately predicted the warming trends we have observed.

So after 30+ years of accurate predictions, your argument is boiling down to "well, but you MIGHT be wrong, someday!" Well, get back to us when that fictional "someday" comes around. I'm not going to work with non-data you made up as if that's comparable to real-world data we actually have about actual events.

[May90]

And this analogy applies directly to weather and climate. For some reason, you don't see it. You're defeating your own conclusion, here.

Yeah, what you're talking about here just isn't true. Meteorology is way more accurate than you apparently think.

We are careful.

And models don't just work with future predictions. The baseline for models is generated by seeing how well they could predict past trends, given the data on the actual events that we have to feed into them. Not only do our current climate models do so, they have also, for decades, accurately predicted the warming trends we have observed.

So after 30+ years of accurate predictions, your argument is boiling down to "well, but you MIGHT be wrong, someday!" Well, get back to us when that fictional "someday" comes around. I'm not going to work with non-data you made up as if that's comparable to real-world data we actually have about actual events.

You have a point here. If so, I have to ask: why can't we reliably predict even whether it is going to rain tonight - by reliably I mean, say, 95% level of confidence? Are these processes heavily statistical in nature? Do they require more information than we can collect by regular means? Is it a combination of both, plus maybe something else?

[PragmaticGamer]

So this means 3% got it right? Climate change has been happening for a very, very long time. And with or without mankind, it would continue to do so.

Generally what we're seeing happening in decades, happened on a geologic time frame (e.g., the mini Ice Age took a few centuries).

Or involved mega disasters (like a massive volcano explosion).

Now, maybe I'm just oblivious, but I'm pretty sure that I'm only 40-ish years old, which means what I've seen in my lifetime hasn't been over the span of a thousand years.

And I'm pretty sure that there's not been any volcanic explosions of that magnitude (yet).

[Ripster42]

You have a point here. If so, I have to ask: why can't we reliably predict even whether it is going to rain tonight - by reliably I mean, say, 95% level of confidence? Are these processes heavily statistical in nature? Do they require more information than we can collect by regular means? Is it a combination of both, plus maybe something else?

Who knows how accurate this is, but it seems like they do predict with 95% confidence levels.

[May90]

Who knows how accurate this is, but it seems like they do predict with 95% confidence levels.

From the article, it seems so. From my experience though, it is nowhere near that level... Perhaps it is just a confirmation bias? That is we tend to consciously think about it only when the forecast fails and ignore the majority of times when it doesn't?

[Blizzhoof]

Weird that it just happens to be the same exact percentage that the fake study from years ago came up with. Actual data from satellites over the past 19 years disagrees with the hypothesis of man made global warming as do the over 30k American scientists who have signed this petition: http://www.petitionproject.org/

I'm not saying this study is fake...but it's fake.

[Ripster42]

Weird that it just happens to be the same exact percentage that the fake study from years ago came up with. Actual data from satellites over the past 19 years disagrees with the hypothesis of man made global warming as do the over 30k American scientists who have signed this petition: http://www.petitionproject.org/

I'm not saying this study is fake...but it's fake.

I can tell you haven't read the thread.

[corebit]

What does this study really accomplish or wants to accomplish? To say "the majority has spoken" so it's settled matter and any dissenting or critical opinions are to be shut down? I thought science is all about continuing to find things to prove us wrong (to make a case stronger), and not about counting the number of people who agree with you.

[Garnier Fructis]

Weird that it just happens to be the same exact percentage that the fake study from years ago came up with. Actual data from satellites over the past 19 years disagrees with the hypothesis of man made global warming as do the over 30k American scientists who have signed this petition: http://www.petitionproject.org/

I'm not saying this study is fake...but it's fake.

Which 'fake study?' If you're talking about Cook, that wasn't debunked. And actual data from satellites shows no warming over the last 19 years if take the wrong data set and then proceed to not calibrate that data set. I.e., trying to pass off the wrong data set as surface temperature and then failing to properly analyze that data before straight up plotting it.

We've also got a list of thousands of scientists against Evolution. Guess what? They're wrong.

- - - Updated - - -

What does this study really accomplish or wants to accomplish? To say "the majority has spoken" so it's settled matter and any dissenting or critical opinions are to be shut down? I thought science is all about continuing to find things to prove us wrong (to make a case stronger), and not about counting the number of people who agree with you.

It's main use, and both sides of have long since realized this, is that most people really are just lazy and decide what they think based on what they think scientists believe. You're not going to reach these people with arguments or facts, so you have to show them that scientists are essentially of one mind on this. Skeptics know this, and one of their great successes over the years is successfully convincing the American public that scientists are like half and half on this. It's wildly wrong, but wildly effective at killing legislation or any effort to mitigate. Because who votes for measures to solve a problem they don't think exists?

That's why you have people like Legates and Monckton doing wrong statistics and arriving at ridiculous results to 'prove' there's no consensus. They don't care that they're full of shit, because they know that the average Joe isn't going to scrutinize it closely. They'll just read the headline and think 'oh, I guess there is no consensus.'

It's sad, but necessary to inform people what scientists actually believe with studies like this.

[Endus]

You have a point here. If so, I have to ask: why can't we reliably predict even whether it is going to rain tonight - by reliably I mean, say, 95% level of confidence? Are these processes heavily statistical in nature? Do they require more information than we can collect by regular means? Is it a combination of both, plus maybe something else?

You apparently don't understand how meteorology works. When they say "30% chance of showers", they don't mean "70% chance we're wrong". They mean there are going to be scattered showers in the region, and there's only about a 30% chance you'll see them where you happen to be. Or that there's a particular storm path that might swing one of several ways, and the data isn't clear as to which, so it may take another route. There's still a storm, and still rain, even if it's not affecting you, as an individual.

[Hooked]

The scientists should put their money where their mouth is, they need to give us something that we can hold them to. If they are wrong they lose their jobs, period. Otherwise its just an echo chamber of people agreeing with each other to feel like part of the club.

[Aehl]

We've also got a list of thousands of scientists against Evolution. Guess what? They're wrong.

I find it very interesting that in your worldview, that thousands of scientists can reach a "consensus" on evolution and be wrong...but thousands of scientists can reach a "consensus" on global warming and they are right.

Seems to me that there is a dichotomy in that statement.

You have "faith" in one but no "faith" in the other?

Odd.

- - - Updated - - -

There's still a storm, and still rain, even if it's not affecting you, as an individual.

Or they are 100% wrong and it doesnt rain at all. Which has happened more than once.

[Garnier Fructis]

I find it very interesting that in your worldview, that thousands of scientists can reach a "consensus" on evolution and be wrong...but thousands of scientists can reach a "consensus" on global warming and they are right.

Seems to me that there is a dichotomy in that statement.

The first problem here is that the list of scientists against evolution are massively dwarfed by those in favor of it, so the consensus is in that case also against them. So you're comparing a minority to a majority to try and act like I've got some double standard when I don't.

The second problem is that neither subject's truth is determined by consensus. The fact that we have consensus for both is just icing on the cake for that part.

The third problem is that you're missing the point of my reply, which is compiling lists of dissenters is dumb.