I was going to put this in general questions, but thought it could get opinionated fast.
Since Trump’s victory, Scott Adams has moved away from (not really, but really) admiring Trump and towards climate science. In this blog post, he goes on to make a point about how climate believers and skeptics aren’t really in disagreement, but are actually arguing two completely different things.
Now, I think I might agree with the underlying principle of this article. That is, a lot of arguments fail to go anywhere because neither side understands the other.
However, I must say I don’t quite understand the specific example he’s chosen. Apparently, there’s a distinction between “science” and “what science says”. I don’t know, can someone explain to me exactly what he’s trying to say here? I know I should have written a much longer post explaining my thoughts and then asking for input, but really I’m just asking for someone to reword this post so that an idiot like myself can comprehend it. I’m genuinely lost here.
You get a nice refreshing drink out of reading tea leaves. If you’re terribly interested in pulling meaning from something where there is none, that’d be my pick.
The blog entry is better than word salad, but I’m not seeing any argument that a history of too many recreational drugs wouldn’t explain.
I think his point is that it’s possible to believe in the scientific method and still have criticisms of climate science (as opposed to “there’s no global warming because God told me so”, e.g.). So Chelsea Clinton’s comment was glib and not helpful.
Of course, I think he’s almost certainly overestimating how much critics who are “just asking questions” really believe in the scientific method.
Scott Adams is an angry, bitter aging Dunning-Kruger. The answer to almost any question about Scott Adams is “because Scott Adams if a fucking nitwit asshole.”
Maybe he is talking about the difference between science and politics. Science doesn’t deal in truth; it deals in probabilities. Politics doesn’t like probability - they need surety.
It’s the difference between reading a scientific paper, and reading the press release about the paper. The paper will say that such-and-such a model predicts a probability of up to 3 feet of rise in the oceans or suchlike. The press release will say “Scientists say oceans will flood the world!!!”
He may also be talking about the tendency to respond to questions like “how sure are you of your predictions, what is their track record, and how much will it cost if we do nothing vs. something” with cries of “denialism!!”
Scott Adams lost me when he framed it as an argument between a “believer” and a “skeptic”. Those words, in this context, do not mean what he thinks they mean.
Believers hang on to bad arguments despite evidence. Skeptics point out the flaws in these arguments using evidence.
I’m reminded of the Australian Vaccination Network that was ordered to change its name because it was found to be deceptive, and then decided to call itself the Autralian Vaccine-Skeptics Network - an abuse of the term “skeptic”.*
Climate change models are imperfect and subject to change over time, but they do in fact represent applications of the scientific method by those trained in climate science. That’s as opposed to a gaggle of pseudo-experts largely from unrelated fields who don’t just want to debate what actions are reasonable to take based on projections, but deny that anything of concern is happening.
*I find it bizarre that news organizations such as the Wall St. Journal profess to be “skeptical” about climate change and decry scientific consensus (“science has been wrong before!”), but on the other hand seem to be firmly pro-vaccination and have no problem with the overwhelming consensus on that subject.
Adams is such an insufferable ass. He thinks he’s really, really smart and insightful. And the problem isn’t that he’s a 0/10, but he’s a 5 or 6/10 with the ego and arrogance of an 11/10.
The post itself is pretty half-baked. The whole climate change debate is so twisted, with such lameness on both sides, that’s it’s impossible to summarize as simply as he tries to do. He does state well, however, one of my issues with the whole climate change thing:
The prediction models are not credible because prediction models with that much complexity are rarely correct.
Yeah, my response to the predictions, which range from “this is bad” to “we’re all gonna die!!!” is: well, we’ll see. I think climate change is happening, and I think humans have contributed to that change to a large extent. What I don’t see from the pro side is a solid cost-benefit analysis with countermeasures that really seem as though they’re going to work. The fact that the right remains unconvinced and unimpressed then gets used as a political bludgeon against them, and nothing really gets done.
The con side continuously gets misrepresented by the Left as “deniers”: many are; many, however, question the rate of change, its impact, our ability to do anything about it, and the benefit of various countermeasures.
He is making a distinction between science and climate modeling. Science is the scientific method and the information derived from that. Climate modeling is what climate scientists are using to predict the progression of Global warming. He says because Climate modeling is not based on the scientific method but rather the scientist best prediction of what will occur it is possible to doubt the models without doubting science.
Wow, did Scott Adams learn a whole different concept of the scientific method than the rest of us, then?
Derive a predictive model from various assumptions about how the physical world works and perhaps add current real-word data.
Posit as a hypothesis that what the model predicts at some point in the future will correspond to the actual climate data at that time.
Test that hypothesis in the future by comparing the modeled and actual climate data.
That seems one of the clearer demonstrations of the scientific method I’ve seen, actually.
Sure, there may be good reason to check the models even before it’s time to actually test the hypotheses, because we happen to live in, and are very concerned about the condition of, the lab. We may also want to check multiple models making multiple assumptions for planning purposes. But the model-builders still are using the scientific method.
Building a model and seeing how well it works is the scientific method. If it doesn’t work, discard it and build another model. So far, the climate predictions (which in some sense date back to the 19th century) seem to work. Add CO2 and the temperature rises. Then the ice caps start to melt. It has happened more or less similar to the models. 2016 was the warmest year of recorded history, beating the previous record-holder 2015, which beat the previous record-holder 2014. The Northwest passage is now open. Both the Greenland and the Antarctic glaciers are melting fast. What more do you want?
Scott Adams got a bit of fame, a bit of attention, a bit of adulation and some ass. It all went to his head and he’s as big of a narcissistic fuckwad as his hero Trump.
There’s this big climate summit confernece held once in a while. They liked to issue a prediction for the first summer the Arctic Ocean would be ice free. After each meeting they set the year sooner and sooner. I think they decided to give up this prediction.
Yeah, models could be wrong. Things could be actually getting worse than the present models are predicting.
Slamming the models in one direction only is a horrible, stupid, infantile thing to do.
You make decisions based on the best models you got. What else are you going to base decisions on? The opinion of a cartoonist?
(This same lame argument was made in the old Ozone Hole debates. The Scientists could be wrong! It turns out they were. The reduction in Ozone was happening faster than their models predicted. Deniers never get this.)
Same thing happened in the 2016 election models. Many people complained some models were over-estimating Trump’s chance of victory. In fact, they were under-estimating it.
People are terrible at understanding model error and risk estimates. Most people have wrong instincts and don’t know they’re wrong. The scientists and statisticians commonly make mistakes, even though they know their instincts often lead them astray.
*If *that was what climate models did, then you might have a point. However, that is not how climate models work.
Lets say you want to model the path a rocket will take. You need to know some things, like how much power the rocket puts out, the drag coeffecient, the altitude the rocket will be fired from, etc. You then take those well known numbers and do some crunching. You then fire the rocket and compare that to the answer you found by crunching the numbers.
The actual test will likely be off from the model because there are things you cannot know, and since you cannot know them you cannot put them in the model. Wind strength and direction over the flight path as an example. With a rocket the unknowns will, most likely, be small enough that they can be ignored.
Climate models are highly complex. There are a lot of unknowns. There are a lot of things that the modelers cannot model very well yet, like clouds and dust. Additionally, the models are chaotic and very slight changes in the input values can lead to very dramatic changes in the output (I am hunting up a paper on this. Basically a model was run twice with the initial state changed by about 1/100th of a degree. The difference between the model runs was very large. I will come back and post it when I find it)
The modelers, to keep the models from spinning out of control, have been tuning the models. Tuning the models is as much of an art as it is a science. For example:
Once again, the piece in italics is the important piece. Basically, does tuning the models make the projections useless?
Then there are the assumptions that are stuck into the models. For example, what is the Transient Climate Response to a doubling of CO2? This is average temperature change over 20 years with a doubling of CO2. The IPCC says that the TCR is between 1.5 to 4.5 degree C. This range, besides having a pretty damned large spread, isn’t well known. Others think the TCR is more like 1.1. The IPCC relies on models (I will get to the multi-model mean in a moment) that have wildly different values for TCR.
Another assumption, and this is a huge one, is what feedbacks effect the climate. These feedbacks are not well understood. The IPCC thinks the feedbacks are high and that is how the high temperature estimates come about. However, these are not well known or understood either. Plus, some feedbacks are positive and some are negative and some are fast and some are slow. And to make it even more interesting, some feedbacks go both ways. Cloud, for example, have a different affect depending on the type of cloud and the altitude. Some clouds warm, some cool.
The IPCC and climate modelers have been using a mutli-model or ensemble mean quite a lot. The idea is that if you take a lot of the models and average them, the normal climate randomness will average out:
From Gavin Schmidt at NASA:
The assumption baked into the multi-model mean is that the forced and variable components across the models are modeled accurately, which isn’t clear.
However, these issues aren’t publicized and what is occurring is that the climate modelers are adding things to the models to get the models to match what has happened. This isn’t a process of build a model from first principles, run it and then check the run against reality. It is, build a model and watch it spin out of control. Guess some of the numbers and stick them in so it doesn’t spin out of control. Tweak the numbers in the model until they resemble the past. Then run the model and average it against other models and call that the projection for the future.
Adams point is that Rex Tillerson is talking about modeling, a tool used by climate researchers. Climate modeling isn’t very mature and there are a ton of unknowns. Clinton bringing up the scientific method is a total non sequitur.
Note, this isn’t a slam on climate modelers. Climate models are hard.