One is that it’s actually extremely easy to expose a gigantic error of the sort that would be required to create “global warming” from thin air.
Not so. Climate science is not cut and dry like physics. The confidence levels are necessarily smaller. There is a lot more room for a person to be irrational and get away with, doubly so for groups.
Most of the case for global warming is based on physics. Climate models are numerical solutions to a physics problem. If they were all sufficiently wrong, it would be simple to demonstrate. Or an energy-budget type analysis could convincingly show that it was one cause instead of another as long as it also identified where current energy-budget type analyses are wrong. I mean, obviously there’s a ton of evidence, and you don’t overturn a ton of evidence overnight. But if you were really, testably right, you could show where existing evidence went wrong.
ethics may have nothing to do with it. Just because a scientist genuinely believes in a theory doesn’t mean she arrived at that belief in a rational manner.
Last is that you don’t get refused a grant for being a “contrarian.” You get refused a grant for proposing bad science.
You get refused a grant for proposing something the grant issuer is not interested in.
And if the grant issuer is interested in furthering our knowledge of the climate? I think you’re far too quick to imply that any project that challenged conventional wisdom would be left unfunded because of bias, or groupthink, or whatever. Lots and lots of climate research could be construed as tests of global warming, where if global warming failed the test it would be “bad,” and it gets funded just fine. The grant-funded stochastic models I mentioned go against what other modelers are doing. If global warming is overturned, it will probably be by someone who just went out and did good science, with a project description a lot like that of everyone else who went out and did good science.
Most of the case for global warming is based on physics.
Understanding the low level physics does not mean your high level models are right. Simplifying assumptions must be made and key physical components may be overlooked entirely. That’s not to say that models are worthless, far from it. What it means is that you need additional evidence before placing very high confidence in them. Namely, your model must make successful predictions.
In the case of climate science we don’t have the luxury to wait and see if our models are successful. Even if we only had 75% confidence in our models, it would still be imperative to effect policy change. Nonetheless, the models are unverified, and I believe skepticism is warranted when someone claims very high confidence.
But back on the topic of physics, it’s true that there are conditions under which simplifying assumptions (let’s go with the single-object simplification of Arrhenius) are and are not safe even to get an order of magnitude estimate. Do small forcings get amplified or damped by more than 10x by feedbacks (like water vapor or cloud cover)? To what extent do other conditions (like… water vapor or cloud cover) affect the greenhouse effect from CO2?
The second one is fairly straightforward, since in the upper atmosphere (the part that radiates to space, at least in the wavelengths where the atmosphere absorbs) there’s not much competing with CO2 (since water precipitates out miles below), and so we can treat a change in CO2 as a change in energy flux and ignore some complicated stuff. The first bit is more complicated, maybe the clearest way is to look at non-CO2-caused variation in temperature: if there were too much damping or amplification we’d expect things to look different when the sun dimmed by a known amount (e.g. around 1700). We can tell from that that there’s a bit of amplification, but definitely less than 10x. So Arrhenius’ estimate should be fairly good.
I would certainly consider that positive evidence, but it’s not knock out evidence. It’s not even 90% confidence evidence, in my opinion. The data is just too noisy to draw high confidence conclusions after only a handful of years.
The IPCC must agree with you that that’s not 90% confidence evidence, because there’s a whole heck of a lot more evidence and they (well, several years ago) only gave human-caused global warming 95% certainty.
That’s just a good example of a prediction, compared to all the inference from physics and historical data.
Most of the case for global warming is based on physics. Climate models are numerical solutions to a physics problem. If they were all sufficiently wrong, it would be simple to demonstrate. Or an energy-budget type analysis could convincingly show that it was one cause instead of another as long as it also identified where current energy-budget type analyses are wrong. I mean, obviously there’s a ton of evidence, and you don’t overturn a ton of evidence overnight. But if you were really, testably right, you could show where existing evidence went wrong.
True, but it makes it a lot harder to explain things through mass deception if everyone’s adhering to scientific ethics. That’s a lot of the point of having them. Fun link: http://neuroskeptic.blogspot.com/2010/11/9-circles-of-scientific-hell .
And if the grant issuer is interested in furthering our knowledge of the climate? I think you’re far too quick to imply that any project that challenged conventional wisdom would be left unfunded because of bias, or groupthink, or whatever. Lots and lots of climate research could be construed as tests of global warming, where if global warming failed the test it would be “bad,” and it gets funded just fine. The grant-funded stochastic models I mentioned go against what other modelers are doing. If global warming is overturned, it will probably be by someone who just went out and did good science, with a project description a lot like that of everyone else who went out and did good science.
Understanding the low level physics does not mean your high level models are right. Simplifying assumptions must be made and key physical components may be overlooked entirely. That’s not to say that models are worthless, far from it. What it means is that you need additional evidence before placing very high confidence in them. Namely, your model must make successful predictions.
In the case of climate science we don’t have the luxury to wait and see if our models are successful. Even if we only had 75% confidence in our models, it would still be imperative to effect policy change. Nonetheless, the models are unverified, and I believe skepticism is warranted when someone claims very high confidence.
Successful predictions, you say?
But back on the topic of physics, it’s true that there are conditions under which simplifying assumptions (let’s go with the single-object simplification of Arrhenius) are and are not safe even to get an order of magnitude estimate. Do small forcings get amplified or damped by more than 10x by feedbacks (like water vapor or cloud cover)? To what extent do other conditions (like… water vapor or cloud cover) affect the greenhouse effect from CO2?
The second one is fairly straightforward, since in the upper atmosphere (the part that radiates to space, at least in the wavelengths where the atmosphere absorbs) there’s not much competing with CO2 (since water precipitates out miles below), and so we can treat a change in CO2 as a change in energy flux and ignore some complicated stuff. The first bit is more complicated, maybe the clearest way is to look at non-CO2-caused variation in temperature: if there were too much damping or amplification we’d expect things to look different when the sun dimmed by a known amount (e.g. around 1700). We can tell from that that there’s a bit of amplification, but definitely less than 10x. So Arrhenius’ estimate should be fairly good.
I would certainly consider that positive evidence, but it’s not knock out evidence. It’s not even 90% confidence evidence, in my opinion. The data is just too noisy to draw high confidence conclusions after only a handful of years.
The IPCC must agree with you that that’s not 90% confidence evidence, because there’s a whole heck of a lot more evidence and they (well, several years ago) only gave human-caused global warming 95% certainty.
That’s just a good example of a prediction, compared to all the inference from physics and historical data.