Actually, no. The burden of proof is still on them. ‘Skill’ has nothing to do with it.
Tuned models like this have a very poor track record for predictive accuracy. Trying to predict future behavior of complex adaptive systems also has a very poor track record. The models economists have been using to predict future GDP and employment are just as sophisticated as climate models, and they have the advantage that the economy changes a lot faster than climate so they have the ability to test their models and refine them much more quickly. They’ve also been at it much longer. And guess what? Past a few months these models have shown that they are no better than throwing a dart at a dart board.
Likewise the financial collapse happened despite billions of dollars being spent on very complex financial models - none of which predicted the collapse, or have much of a track record for predictive accuracy at all. And the goals of the financial people are much more modest than climate modeling - all they’re looking for is a tiny percentage improvement over chance. And that’s exceedingly difficult.
There are lots of fields that are attempting to use these kinds of models. Climate science is the only one I know of where these models are considered to be reasonably predictive. In every other field, the models are used very carefully, and generally for setting up experiments to help understand what they are trying to model - not to predict their future behavior. And certainly not to bet the farm on those predictions.
And for me, the biggest thing about the ‘climate gate’ emails was not the discussions of the scientists, but a view into the atrocious state of the management and coding of the climate models, the software change control (nonexistent), and other missing professional practices. I would not base trillion-dollar decisions on software that is maintained by some guy scratching his head over which version of multiple cut-and-pasted code modules he should be using, or trying to ‘correct’ the database of numerous found errors by just making guesses based on his own non-climate-scientist knowledge. But that appears to have been the case. And this is not surprising - having been involved in scientific programming, I know how awful the processes and practices around it can be.
Perhaps the state of affairs is better now. Are all the models completely open source? Is the data open source? Can anyone download the source code to these models, run the data themselves from public databases, and replicate the results? If not, they must be. This is a public issue with trillions of dollars at stake. There’s no room here for professional secrets, professional jealously, attempts to commercialize the data, or anything of the sort. All this work has to be done with full open access. Is that the case now? It wasn’t at the time of Climategate. The scientists were still treating the code and the data as their own property.
In most scientific fields, if you want to publish a peer-reviewed paper you must make all data, the source for the models used, and anything else required to replicate the results available to all reviewers. Is that the case in climate science now?
Or an unforeseen negative feedback… Or many of them… Or a secondary effect causing a tertiary change that no one could predict.
An obvious possibility comes from cloud formation. And the IPCC admits that the role of clouds as a feedback mechanism is still poorly understood. But even in the past couple of decades there have been unexpected climate responses. Complex systems are hard to understand and predict because they are evolved, and not designed. That means the mechanisms may not be straightforward, and the feedbacks totally inexplicable from our point of view.
For example, what happens if you release a handful of wolves into an ecosystem? You might predict that the deer population would go down, but would you predict that the course of rivers would change? But that can happen. We don’t know how our ecosystem and planet will respond to a warmer world. Or more precisely, we don’t know how this one will respond. The world that responded to a temperature shift 50,000 years ago is very different than this one, and almost certainly in ways we don’t understand.
Another aspect of complex adaptive systems is that they are not amenable to scientific reductionism. You can’t break it down into little pieces and understand it that way, like you can with a car engine or a space shuttle. The nature of the system is baked into the billions of relationships between the networked nodes. So there may be a limit to how much we can understand about climate through the types of scientific reductionism we’re engaged in now - just as we can’t understand consciousness by breaking the brain down into small pieces and studying how they work. We can learn how the parts fit together, but that doesn’t mean we can predict how it will behave. Consciousness is an emergent property of the network, not something that can be discovered by looking at individual neurons, or even how they relate to each other.
Actually denying that skill is important puts you closer to the level of many that do deny all the rest.
You need a cite for that, I suspect that once again the same discredited sources I warned you about are still getting at you.
Incidentally, there are many more variables that affect the economical models and the data on many of those variables is lacking, what I have seen is that climate scientists have an “easier” time as the “human agents” they have to deal with they are more constant or less prone to change their rules.
It would be reasonable if this was said 50 years ago, but is less so now. The bet that is going now among the powers that be is to bet that nothing will happen, I do not think we are that lucky as the uncertainty goes both ways.
And there it is again, no investigation found anything wrong with the science I have to say that once again you are relying on the blind sources of information.
That you do not this should be reason enough to wonder if the ones giving you the information should be dumped.
Here is for example the source code and data from Michael Mann:
http://www.meteo.psu.edu/holocene/public_html/Mann/tools/tools.php
BTW RealClimate made a site (starting in 2009) to point at many other places where the data and sources are available, the point here is that after several years of that we should had had most of the skeptical scientists come up with refutations to them, that has not happened. or their efforts only lead to them to fall flat on their faces.
I’d have a lot more respect for conservatives if they argued these points, rather than rejecting AGW out-of-hand. The fact that they stick to the latter is what causes me to suspect that they’re less interested in long term issues and more interested in short-term economics.
These are not the talking points on Fox News. Instead, Fox News makes climate scientists look like a bunch of trained apes espousing a fake scientific theory in order to pursue a liberal agenda of … well, I’m never sure about this last part, that’s where it starts getting really murky.
I say this as someone who is an economic conservative and who has voted for more Republicans than Democrats in his lifetime. I’d appreciate it if Republicans got back to the business of being economic conservatives.
Admittedly, a true conservative likes to stick with what always worked, and is suspicious of newfangled theories. But a true conservative also very carefully analyses risk. Any Republican who attacked this issue from a risk analysis viewpoint would be ostracized by the rest of the party.
I’m just tuning into this thread, but can anyone recommend a good book summarizing the climate controversy, explaining the intuitive appeal of climate change denial arguments, and addressing those arguments?
In the case of the appeal (that is mostly an artefact made by industry and politicians in the USA) check the chapters on *The Public and Climate Change *
As for addressing the arguments, a great resource is Skeptical Science, that does for climate change what Talkorigins.org does for evolution, it points at scientific cites and papers that debunk many of those arguments.
We’d be in trouble if this was true, but it isn’t. Modern science proceeds by evidence-based methodology, and if one is going to challenge an established body of evidence then one had better have a correspondingly persuasive body of evidence of one’s own, proportional to the body of knowledge that one is trying to contradict. If you can do that on a big enough issue, you’re in line for the Nobel Prize. If you can’t, then you’re just a babbling fraud. The reason that denialists are criticized by the scientific community is that – far from having such overwhelming evidence – what they always turn up with is bad data and flawed methodologies, and occasionally obvious indications of being politically or financially motivated.
You seem to be under the impression that climate science is some kind of seething maelstrom of religious-like “orthodoxy”. Guess what? It’s just science. Evidence trumps everything, and the guy making actually valid discoveries, regardless of what “political” side it happens to help, is going to be a scientific hero. The problem isn’t in the science. The seething maelstrom of religion is among the non-scientific masses (and their politicians) who like their oil and coal and the economic benefits it brings, as for instance the mouth-breathing troglodytes I referenced here who claim climate science to be a conspiratorial hoax. :rolleyes:
Okay, so how likely is this? We’re talking about an effect which would have major impacts on climate both past and present. One that we apparently missed completely. How much are we willing to bet that this unforeseen effect exists?
This argument reminds me a lot of the scare tactics surrounding GMOs. Every time something new is found, it’s trumpeted as “Look! GMOs are harmful!” And then, when that doesn’t pan out, the same people shrug and say, “So? We still haven’t shown that they aren’t harmful.” Because you can never prove that! And of course, it doesn’t matter that every investigation so far which has looked for such things has come up with nothing, and that backwards-facings models which don’t account for such factors do pretty damn well.
I see this as sort of the equivalent of saying “Yeah, but maaaaybe homeopathy works via some means we don’t know”. It’s denialism in the clothing of skepticism, and in the face of the problems we know AGW will cause, I don’t think that’s acceptable. If you have any concrete reason, by all means, investigate it and put forth the evidence. Lord knows Lindzen et. al. tried with the cloud hypothesis, but that didn’t pan out.
“Ninety-seven percent of climate scientists agree that climate-warming trends over the past century are very likely due to human activities,1and most of the leading scientific organizations worldwide have issued public statements endorsing this position. The following is a partial list of these organizations, along with links to their published statements and a selection of related resources.”
Statement on climate change from 18 scientific associations
“Observations throughout the world make it clear that climate change is occurring, and rigorous scientific research demonstrates that the greenhouse gases emitted by human activities are the primary driver.” (2009)2
Not really, winters are not the only season happening in the planet and not all regions have the same. Do you have any scientist supporting what you claim regarding the unforeseen feedbacks? Because so far there are countless “final nails” from contrarians that claim that one particular item shows that global warming is not happening or that humans are not responsible.
If contrarians would think about it just a little they would had realized that if one item was the “final nail in the coffin” then no more “final nails” would be needed ad nauseam. And after all that huffing and puffing, scientific organizations continue to report that whoever the contrarians are listening to they are not convincing anyone where it counts.
Long story short, the theory of the enhanced greenhouse effect predicted the most warming in winter, in the NH hemisphere, over land. That is one key prediction from the theory, based on physics and the characteristics of warming from an enhanced greenhouse effect.
When it was happening (there was a large increase in the very areas that were predicted to warm rapidly) it was considered proof that the warming was from the enhanced greenhouse effect. Now that there is twenty years of a cooling trend for NH winters (longer in some areas) the opposite of the predictions is now being claimed as due to global warming, or rather the enhanced greenhouse effect, which is the theory behind global warming.
If a theory predicts one thing, and the opposite happens, the theory is incorrect. So the the enhanced greenhouse effect theory has already been shown to be wrong.
This will not matter, as the theory will simply be changed to say the enhanced greenhouse effect is causing the colder winters.
Question answered. No, you can not falsify the enhanced greenhouse effect theory. No matter what happens, the theory predicts it.
The same can be said about ocean cooling, which is happening as well. And the same thing can be said about the land to ocean warming ratio, which also has shown the theory to be wrong.
Same for the upper troposphere in the tropics changes, they have not happened as predicted. No problem, the theory is changed to predict an increase in winds from heating instead. So the theory is still correct.
Antarctica was predicted to warm very fast. Now that it is impossible to keep claiming that, the theory is changed again. Greenhouse gases (water vapor and CfCs in this case) destroyed the ozone, leading to warming of the stratosphere, which increased the southern circumpolar vortex, causing cooling of Antarctica.
So a greenhouse gas increase resulted in cooling. But this doesn’t falsify anything. The theory is adjusted.
When you can simply adjust a theory (and the models based on it) after the fact, the theory can never be wrong. If you can control the flow of information, you can even show that the changed theory actually predicted the colder winters. So that all along Global Warming never predicted warmer winters. Who says so? Deniers and fools. Global Warming has always predicted winter cooling.
Sure, sure, but as anyone can notice you completely failed to find a single climate scientist that is on the record to supporting what you claim (and misrepresentations or quoting from researchers that in reality do not support what you claim has been tried before) , so your opinions can be dismissed as usual.
What you’re doing here is what Princhester described as substituting “meta-analysis”, based on certain “signs” in the popular presentation of research, for engaging with the actual science.
That is, you point to the fact that some earlier climate models predicted warmer winters in certain regions, while more recent models incorporating additional data about increased open water in Arctic regions predict colder winters more in keeping with recent observed trends, and jeer “Ha ha, the theory’s unfalsifiable because the scientists will simply change it to agree with whatever’s observed!”
But your meta-analysis is ultimately just armchair psychological speculation about scientists’ behavior, not science itself. Engaging with real climate science, on the contrary, would involve concrete scientific questions, e.g.:
Are the more recent models scientifically better (more precise and detailed, more accurate, based on more data, etc.) than the earlier ones? Can we point to specific improvements that justify us in trusting the more recent models more?
Are scientists changing the features of subsequent models whimsically, as you allege, or are they maintaining an ultimately self-consistent theory?
Essentially, you’re basing your condemnation of mainstream climate science on a psychological hunch rather than on scientific evaluation. You’re saying in effect, “The fact that newer climate models produce different predictions looks fishy to me, so I mistrust the whole enterprise.”
But of course, in science we expect predictions to change as models are refined and modified on the basis of additional data: that’s what science is supposed to do. Whether or not this is what climate science is currently in the process of doing will be determined by actually analyzing the science itself, not by psychological meta-analysis of what does or doesn’t “look fishy”.
I just explained that, with examples of how it’s already happened.
It’s why “the enhanced greenhouse effect” theory can’t be falsified. They just change the predictions after the former predictions failed. Since this is, as you said, " that’s what science is supposed to do.", then the theory can’t ever be falsified.
It’s simple.
If we see 30 years of global cooling, extreme and colder winters, then that is what global warming predicted. It’s science.
It’s not ‘an effect’. It’s the behavior of the entire system. Complexity theory is a big topic, and some of its conclusions are counter-intuitive. And a lot of scientists, economists, and others really don’t like it, because one of the implications is that there is a limit to what we can understand through scientific reductionism and therefore a limit on what we can predict and control when it comes to such systems.
Take an anthill, which is an example of a complex system with emergent properties. What causes the structure of an anthill? What causes all those ants to synchronize their activities in such complex ways?
After all, ants do amazingly complex things: they form armies with defined fronts, they build bridges, they build rafts to float the colony away from floods, they build incubators with temperatures controlled by the decay of precisely controlled amounts of plant matter, they build intricate structures that are quite ingenious in architectural detail, they coordinate the scavenging of food and change the ratios of hunters to food gathers to builders as conditions change. They even raid other colonies for slaves and bring them back to their own colony and make them work. And much more. Very coordinated, very intricate, very complex behaviors.
And yet, what drives it all is not intelligence or any sort of planning whatsoever. It’s just simple rules baked into an ant’s brain - a brain of such low capacity that we can simulate the entire thing in a computer. Everything else, all that complexity, arises from how those rules affect the behavior of the ant and the other ants around it. All the higher level complexity is emergent from those simple rules. The ants don’t even know they’re doing it. They probably can’t even see it.
So we can build computer simulations that do a great job of simulating an anthill. We have dissected ants and studied their behavior for a long time - it’s a perfect system for studying complexity.
But what we still can’t do is predict what form an anthill will take from the start, how fast it will grow, how quickly it will use up nearby food supplies, etc. The reason we can’t is because every single anthill is different, and the differences derive from a combination of random behavior and very high sensitivity to initial conditions.
For example, ants have a simple rule for foraging - an ant starts by moving away from the anthill in a random search pattern. If it finds food, the next simple rule tells it to go straight back to the anthill, leaving a pheromone trail behind it.
If another foraging ant runs into the pheromone trail, another simple rule kicks in - follow the trail until you find food. If you don’t find food at the end of the trail, start randomly searching. If you do, pick it up and follow the pheromone trail back to the anthill, leaving your own pheromones behind.
This set of simple rules turns out to be highly efficient. If you start an anthill, you’ll see thousands of ants stream out and begin milling about randomly. As they find food, other ants follow to the food source. If there’s more food there, they keep adding to the pheromone trail, making it stronger and pulling in even more foraging ants. Soon you’ve got a solid line of ants moving back and forth between the food source and the colony. Then when the food source ends, the ants start randomly searching again, starting from where the food source was. If they find more food, they keep production going. If that was the only food in the area, eventually the pheromone trail fades away, and ants no longer head out to the old source and start randomly searching around the colony again.
Now, you can understand every bit of how this works, and be utterly unable to predict the future of the anthill because part of the process is random, and also because the feedback loops mean tiny changes can turn into huge results. A raindrop hits and kills an ant that was about to discover a major food source, so instead another ant beats him to the race to find food and the entire direction of the colony’s movement changes.
Another example of a complex system is the market. A fire breaks out in a refinery. What’s the result of that? Can you model and predict its impact on the economy? Not a chance. You might predict that the price of the refined goods goes up because supply becomes more constrained. But what then? The price of the goods that uses that goes up. That in turn drives up the price of the goods that use those goods. But then that may stimulate additional production of alternative goods. And sometimes you hit a point where the price of the good climbs higher than an alternative that had no market share before. Suddenly the alternate is superior, and the market in the initial good completely collapses.
Then through feedback, the changes in the ratio of these goods might cause the demand for other refineries to increase - or decrease.
In this way, the closing of a factory that makes brass doorhandles causes the price of pencils to fall, which causes graphene prices to increase, which increase the cost of graphene lubricants, which causes substitutions which cause aircraft engines to fail more regularly, which stimulates demand for aviation mechanics.
This kind of stuff is completely unpredictable by any model, because for any change there were potentially many paths. Like the ants milling around, an economy is full of people randomly searching for new market opportunities. There is competition between them, and tiny changes in the patterns can cause massive swings in the results.
This explains the ‘law of unintended consequences’ in economics and in ecology. It explains how introducing a handful of wolves into Yellowstone park can cause the course of rivers to change. It explains how the financial collapse was completely unpredictable, despite many models being developed to do exactly that.
Climate is no different. There are millions of interactions, and they are complex because the climate is part of a complex system that includes natural and biological feedbacks. The feedbacks we’ve identified are first-order ones - like predicting that the closing of a refinery will cause a price spike in refined goods. So for example we can predict that warmer temperatures will lead to more water vapor in the air, and that should increase temperature.
But then what? What does a warmer, wetter world do to the ecosystem? And how do changes in the ecosystem feed back into the climate?
This is not an argument to say we shouldn’t be worried, or that nothing will happen. And it’s still possible to make predictions based on probability - it’s just that the error bars around the prediction have to take the complex nature of the system into account. And that means the farther into the future we go, the less sure we should be of the prediction because the system is mutating and changing with each iteration of a feedback loop.
Of course, this is exactly the argument the ‘deniers’ make - that every time there’s an extreme weather event or a hot day, people start shouting “Look! Global Warming is harmful!” And then, when other evidence doesn’t pan out the way the prediction said, climate scientists shrug and say, “Well, maybe the heat is hiding in the ocean”, or “Well, the model never incorporated this new effect we just discovered. Now it does, so it’ll be better next time.”
As for backwards-facing models - understand that if you’re building the model based on observed behavior of a complex system, you can always predict backwards. I can come up with a complex model that describes how an anthill got to be the way it is. I can then run the model and come up with the past size or shape of the hill at some point and compare it to the data and come up with a good match. Because the model itself describes what happened in the past. But that tells me exactly nothing about what the colony will do tomorrow.
That’s simply not true. Recognizing a complex system as being difficult or impossible to predict is not denialism and it’s not anti-science, any more than it’s anti science to say that you can’t perfectly predict the location and momentum of a particle. Complexity theorists sometimes take the opposite position - it’s the people trying to deny the nature of those systems because they really want to predict and control them that are behaving unscientifically, even if they are using the trappings of science to do so.
This is a fascinating topic that goes far beyond climate science.
You’re deliberately conflating “refining the theory” with “just changing the predictions”.
Of course, as I noted, what science is supposed to do is to use additional data to make a better theory that will over time improve the success of its predictions. In other words, the whole process of refining a theory consists of using data to falsify the parts of it that don’t work, and replace them with more successful hypotheses.
You’re trying to blur the distinction between that scientific improvement process and ad hoc flailing around to “just change the predictions” after the fact in an endless astrology-style game of increasing pseudo-scientific ramification. But that’s just superficial public-relations “negging”, not a genuine critique of the science.
If we do find serious flaws in the mainstream hypotheses of climate science, it will be done by critically evaluating the actual research. Not by sitting around ignorantly sniggering “Hur hur hur, don’t all this here flip-flopping on climate predictions sure make the whole thing look like pseudoscience bullshit?” Which is basically all that you’re doing.
Once again: The validity of a scientific theory is not determined by whether or not con artists can make some kind of an argument that it superficially doesn’t look valid.
