You constantly run and re-try computer models until you find one that you can publish.
After 1000 different models you build one that seems to say there is some catastrophic effect caused by by humans with 99% confidence.
The model then gets published, adds to the growing body of climate models that were made the exact same way your one was.
Tell me - do you not see what is utterly incorrect with the above process?
The above isn't how it works. But if it was, it might not even be wrong. Let's start with that. First off, why were they creating 1000 or even 1000's of models? You're on HN so I assume you program. In a big program how many times do you have to rewrite things before you get them working perfectly? Then how are these models accuracy determined? Well, with predictive power. The current weather models have high predictive power to 3 days (the European model has about 5). After that it is clear that error greatly increases. But that's weather, not climate, and I want to differentiate the two here. Climate modeling really is different from weather modeling. We have a lot of data on it, so it is more performing analysis to figure out the cause for changes. So let's ask ourselves how can we check the prediction power of our climate model? Well if our analysis is correct we can go about it two ways, or really check with both. We can use historic data (if you've ever looked into stock analysis, this is a common practice there). So we feed in our key variables and ask what temperature we get back. Does it match what we measured? And how well? We can also use it on new data, because as each day goes by we get more data.
I'll stop there, as I don't want to write an entire paper. But a key part to science is its predictive power. If we don't have predictive power it isn't science, plane and simple.
p.s. If you just plot the raw data (not modified for methods in which that data was gathered) you see a trend much worse than what current climate models predict.
p.s.s Current models have held up in predictions over the past few decades. But there is always room for improvement.
Here's a paper (~200 citations) in JGR-A, by a colleague, to illustrate how it really works: http://onlinelibrary.wiley.com/doi/10.1029/2008JD010015/full
Briefly, it's well-known that model representations of cloud ice are a significant source of uncertainty in climate modeling. This is because the ice scatters light upward out of the atmosphere, and can also trap emerging long-wave radiation from below.
Cloud ice is hard to represent for many reasons - to mention just two, the particular form of the ice crystals (spherical or planar, termed "microphysical properties") matters, as does the vertical profile of the ice/water mixture in the cloud.
How to improve their representation? One way is to measure cloud properties over years-to-decades time scales (which is now being done - no accident) and use this data to introduce the right cloud-process components into climate models. The paper surveys the measurements, proposes specific model improvements, and model-vs-data checks.
As I said, I happen to work with the lead author of the paper above (although I'm not a climate scientist at all). He is, in fact, not twiddling knobs until catastrophic outcomes emerge. Sure, that's a logical possibility, i.e., someone could do that, but it does not correspond to reality by any stretch. Honestly, the notion is ridiculous, the gap between that notion and reality so large as to be hard to convey.
Probably, at macro scale, the difference is that your straw man is purely open-loop (no checks on the knob-twiddling) whereas the one in the paper is brutally closed-loop (we will identify and make independent satellite measurements to quantify your model's accuracy).
The paper describes working with others to design new measurements to get information about the key processes driving climate, so that the models can be improved and we can see what large-scale human forcing is doing to the climate system. Humanity is not a bit player any more, we're a driver, and we have to own up to that role.
What I find interesting is that these people believe in this grand conspiracy. And personally I think that is where you start looking when trying to understand the root cause. There are definitely a lot of factors involved though. (Not in any order)
For one, I think people believe scientists are rich. Which not only couldn't be further from the truth, but that we specifically don't go into it for the money, but the love. I think this comes from the idea of "if you're smart you'll be rich", that is so pervasive. Even just the scientist community is small, and I don't know a single person that knows a person that would purposefully falsify data. Maybe there are some there, but not within a few degrees of freedom.
tldr: People think scientists are interested in money. They are, so why aren't scientists?
The second part I think is the lack of trust for authority. I think this has to do with the fact that people treat all "science" as equal (and I think we call things science that shouldn't be). An example would be that our certainty in the detection of a gravitational wave is much different than our certainty that having a glass of wine a night is good for you (not going to get into p-values here). The error analysis is completely different. You can't even compare them. One has significantly more variables that contribute to error. Hell, most people think math and engineering is science.
tldr: People don't understand basic science or error.
The third point is that people do lie. And there definitely is p hacking going on in some of the softer sciences. Combine that with media that use the word scientist as a rubber stamp for credibility, and these people definitely have evidence for "corrupt" (mistakes and naivety aren't malice) researchers. Plus, we definitely are developing an atmosphere of where we are distrustful of authority, especially in politics. So I think when you add all these factors in, it doesn't actually become unreasonable for them to be suspicious.
tldr: There is actually reason for them to be suspicious.
But how to combat it is an interesting part. From experience I can tell you that going through the data and analysis with these type of people won't get you anywhere. Their eyes glaze over. So you actually have to find a piece that they can understand and agree with. As an example, this guy I drink with has been hard line anti-global warming and hit all three points I mentioned above. Then one day he started to talk to me about how he read about the deforestation for farm animals is changing temperatures. Well from that piece I had my in, and could actually break his barrier and slowly get him to understand. He likes the environment, most people do. But from there I could talk about lack of oxygen production because of deforestation, albedo, carbonation of the oceans and its effect on coral (tie back to oxygen) and other aquatic life, methane production, and other effects. I had his ear because I was talking about what he cared about and eased into it. These topics are easier to understand than CO2 equivalent levels. Because they are right when they say that H2O is a worse greenhouse gas (). Saying things like "100% of scientists agree" or "look at the data" don't mean anything. They've been trained against that. You HAVE to cross the isle. You HAVE to find their interests.
tldr: Gain their respect. Talk about easy to understand things that THEY care about the most and ease into the more technical parts.
It is human to fight. That when we discuss we tend to more argue. That it is my web of beliefs vs another person's. We really like to be right. So you have to stop arguing and just talk. People LOVE to talk about what they are interested in. DO THAT.