It is clear that **FXMastermind **does not know that “seemingly” means.
Mea Culpa on that one. I missed that it was referencing stratospheric water vapor. You’re absolutely correct.
According to the IPCC, cloud feedbacks are not one of the larger components of global climate sensitivity, but they have a major effect on the latitudinal distribution of warming, and therefore have a major effect when trying to calculate the impact of global warming. They could make the difference between climate change mostly warming the high latitudes, which might actually be beneficial for moderate warming, or for warming to mostly affect tropical climates, which could be very bad. And there is still low confidence in our ability to model and predict cloud response to warming.
From here:
Bolding mine. It seems to me it’s pretty hard to model the impact of warming in the tropics when one of your major uncertainties could ‘completely alter the nature of the coupled model response to increased greenhouse gases’.
Don’t forget where I’m coming from on this. I’m not using any of this to claim that global warming isn’t happening. The whole point to my message was to show areas where there was still no consensus on what the science says, to break the myth that the entire global warming debate is one that has ‘the consensus of over 90% of scientists’ on one side and ‘deniers’ on the other. What I am trying to show is that the issue is very complicated, with varying degrees of consensus over various parts of it - some small, some large. And also that there is still much uncertainty remaining in the model responses, as is shown by the wide spread in projected warming values that the IPCC is willing to commit to.
My assumption was that ‘Central North America’ meant mid-latitudes, as opposed to southern or northern regions.
I went to some length to say that it is NOT a conspiracy. My point was that when 90% of your members are liberals, you are likely to discount or even deny the cost of higher taxes, giving up national sovereignty to international organizations, etc. It’s bias, not conspiracy.
I happen to believe that political leaning is a big factor in the climate debate. The fact is, the proposed solutions to climate change are all policies that tend to align with what the left prefers anyway. Or do you think it’s a coincidence that the global warming debate seems to have polarized people down political lines and not scientific ones?
This is not conspiracy, it’s a real and valid consideration. If I believe that high energy taxes are dangerous, and you believe that higher energy taxes will, at worst redistribute wealth from oil barons to the common people, then clearly we are going to weight the cost of those tax increases very differently. If you believe that strengthening extra-national institutions will help the cause of social justice and I believe they are a serious threat to liberty, then obviously we are going to weight the cost of those differently.
So even if both of us agreed completely on the science, we could still completely disagree on what should be done about it due to our political differences. And in fact I think that’s what’s really going on in the climate change debate. The debate over the science has been polarized and obfuscated by activists on both sides because it’s become a proxy war in the endless political struggle between the right and left. And that’s a shame, because it’s an issue that is complex enough on its own without being distorted by politics.
And yes, both sides do it. On your side, when the global temperature doesn’t rise as much, or if there’s a quiet period in storm activity, your side is very quick to remind everyone that short-term weather is not indicative of long-term climate changes. But every time there is a drought or a hurricane, activists on ‘your’ side run out and start yelling that this is obviously a result of global warming. And people on the other side do the same thing in reverse.
Now, I’m not claiming that you specifically are doing this, or even that the global climate science community is doing it. I’m talking about the advocates on both sides who are not scientists (and a few scientists who are also activists).
[quote]
You seem to try to refute the greatly improving accuracy of global climate models by claiming “[the scenarios] aren’t science. The IPCC calls them ‘storylines’.” Like “storylines” was supposed to imply “fairy tale”, or something. The concept of RCPs is that one of the major uncertainties in climate change projection is what our future emissions will be. The science can only operate based on specific GHG levels. The RCPs are intended to offer a range of projections based on different mitigation scenarios which helps establish danger levels and mitigation targets. The “storylines” are a holistic view of the entire socioeconomic pictures in the different scenarios, and you’re right, that’s not hard science, and no one said it was. But the underlying models are.
I am fully aware of that. You are conflating the models with the scenarios. The scenarios are things like “In this scenario, the world moves to an information economy, and there is less cross-border migration”, or “In this scenario high economic growth causes X and Y”. They are ‘pictures’ of the future world, and they are highly speculative. The IPCC ameliorates this by having many different scenarios to cover a number of possible outcomes, then modelling the climate response to the CO2 emissions predicted under each scenario.
This type of activity falls into the category of, “Hey, it’s the best we can do.” No one can predict the future, so you predict multiple futures and hope that you’ve covered all your bases. But there’s no reason to believe that any of these scenarios are even remotely correct. Our history of being able to predict the future past a couple of years in the future is dismal.
As I said, where’s the ‘peak oil’ model? Or are we pretending that peak oil is no longer an issue at all? If so, when did that happen?
And no, coming up with scenarios for how society might evolve is not science. It’s not remotely science, even if scientists are doing it. It’s simple prognostication and extrapolation based on the opinions of experts. It’s a set of assumptions to be used to come up with some numbers that can be input into models to predict the future.
Sorry, it looks like that info was in AR3. I searched AR4, and they reference a graph from AR3 and contrasted it with the Stern Report’s impact assessment, which does not show the net benefit. You can see that here: https://www.ipcc.ch/publications_and_data/ar4/wg2/en/ch20s20-6-1.html
Note that the graph marked IPCC 2001(b) which definitely shows net benefits for moderate warming, although only one study on that graph extends it to 2.5 degrees.
While I was searching for the info in AR4 and AR3, I also found this meta-study:
http://pubs.aeaweb.org/doi/pdfplus/10.1257/jep.23.2.29
Figure one shows a graph of estimates impacts from 14 different studies. All but one shows net global economic benefits for warming up to 2.3 degrees.
Those are overall impacts include environment. I was referring to the estimates of human economic benefits and costs, which aren’t referenced on that graph at all.
An example of some of the possible benefits: Opening the Northwest Passage would substantially lower the cost and energy requirements of shipping between many countries. One of the reasons why you can have net economic benefits is that the countries that are hurt the most have lower economic contributions globally, while the countries that might see net benefits have a disproportionate effect on the global economy. A slight inccrease in crop yields in the northern ‘food belt’ would have a large economic impact, even if more people in the tropics might be hurt by global warming.
This was not an argument saying moderate global warming is a net good - just that it might result in net positive economic benefits. A very different thing.
Also, I should admit that the latest assessment is more pessimistic about the effect of global warming on crop yields than earlier assessments. But this is yet another area where there is a lot of uncertainty.
We have no idea if it will drive it in a particular direction over anything but the short term. The transient response is certainly positive, but we have no idea what the result will be after various feedbacks kick in - especially after a period of multiple iterations. Complex systems can respond to shocks by over-correcting, by collapsing into a new state, or by having a series of complex responses. We could see warming, followed by climate responses that ultimately result in cooling, followed by further responses that cause warming again. Or we could see the rise of CO2 sequestration mechanisms (plant growth, algae blooms, etc) that begin to scrub CO2 from the atmosphere at a faster rate. If we stop adding to CO2, those mechanisms could wind up driving CO2 below recent historical levels.
After all, CO2 in the atmosphere has fluctuated substantially in the past, without any human contribution. CO2 is part of the complex climate system and does not exist in a fixed steady state in the atmosphere.
Of course, these responses could be a long time coming and not be useful to humans. Or maybe they’ll happen over a generation or two. Or maybe the long term response will be to correct, but the short and medium term response could be a positive feedback that makes things worse for the next couple of hundred years. This stuff is frustratingly hard to understand and predict, and scientists have a history of underestimating the difficulty of dealing with complex systems. This is a chronic problem in macroeconomics, for example.
Now, I’m not saying that this is going to happen, just that we don’t know enough to validate your claim that a forcing must always push a complex system in one direction.
What it looks like to me is that the very short term is unpredictable (weather), the medium term is more predictable (annual and decadal climate), but the long term becomes less predictable as the long-term global feedbacks enter the mix.
I’m not trying to distort anything. I wasn’t trying to provide a comprehensive assessment of what the IPCC is saying. I was specifically addressing the argument that ‘the science is settled’ or that there is a strong scientific consensus over every aspect of the global warming debate. There is not. There are still serious uncertainties in the climate models, and future projections of human impact require projections of human economy, population migration, technology breakthroughs and other social factors that are totally outside the expertise of climate scientists and for which the track record of prediction is quite dismal.
Sounds more like a strawman, in reality as I point many many times before (and the vast majority of experts and environmentalists I refer to ) we can not talk about an specific hurricane or drought as being caused by global warming, that may came later if worse things than the ones expected by nature come. What many do talk about (like Kerry Emanuel) is that all that excess water vapor in some regions, and the increase in heat already observed is making extreme weather events worse. In the case of the hurricanes, just the rise of the oceans will increase the damage the hurricanes will bring when they eventually come on land.
The point once again stands, you do need to calibrate even your activist balance.
Even conservative scientists do notice who is more accurate.
Incidentally I also did notice many mistakes in your previous post, but I decided to look more into the trust issue rather than the science, I’ll let **wolfpup **deal with those.
Suffice to say here is that this is nothing more than wishful thinking, just about the worst one can do when looking for solutions, and here one has to point out that just about all republicans in power do go for this wishful thinking. The reports I have seen point to an increase in the like hood that the feedbacks will become more positive or the ones you wish will increase will be overwhelmed by the main ones.
The impression I get is that you are going way overboard with the assumed lack of consensus. And once again the science is not settled but it is understood at levels where it is really irresponsible for policy makers to continue to ignore.
Good news, you don’t have to. First, spend 4 years at a university getting a relevant degree in whatever field you don’t trust. Then, spend another 4 researching at that university while studying for a PhD in said field. Then either get funding for your own laboratories or gain access to existing ones with your credentials. Then spend most of your life repeating research other people already did to make sure it’s true. Boom, no more trust required! 
There, that wasn’t so hard, was it?
Unfortunately, “should” is not the same thing as “will”. I had a friend once, he was a smart, upstanding fellow, and no matter what evidence I presented, no matter how I worded my case, he would not acknowledge that HIV caused AIDS. The fact is that sometimes, otherwise intelligent people become dogmatic and fail to understand incredibly simple things.
And yet, I’m pretty sure that if Goldman Sachs, Bank of America, and JP Morgan Chase all simultaneously went bankrupt, it would be bad for our economy. I’m pretty sure 99.9999999% of economists would agree with me. Jeez, how could we make such a prediction about an unpredictable, complex system?
And of course, the comparison to economics is just plain wrong. Economics is dependent upon the capricious whims of several billion humans going about their daily business. Any prediction based upon it relies on understanding the human and cultural psyche. The enrivonment is dependent upon a large number of interrelated but ultimately calculable and deterministic systems. Systems governed fundamentally by laws and rules that we can determine. And the massive increase in CO2 is less in the category “people seek out less loans” and more in the category “every major bank in the country goes belly-up at the same time” in terms of how obvious the effects in many regards are going to be. The details are still fuzzy, but the obvious and inescapable result is baaaaad.
Then there is the other really big problem. All the far too alarmed predictions which didn’t happen. (the mights, coulds, mays and “possibly happen by 2013”) But that turns it into just another debate over current idiocy in the news and journals.
Yes, you would think something that is so important (the biggest threat ever in the history of all mankind) would have multiple shows about it, and it would be clearly explained, over and over. In all media.
Here we see the “answer” that is constantly spewed forth, rather than even attempting to explain things. Why this happens, it’s the topic.
And that is the science people want to know about. Nobody wants to here nebulous claims, or sneering derision from the people they want to explain it. That raises more than just suspicions about motives, it takes it firmly into pseudoscience.
The vast amount of text in this topic alone, that doesn’t explain anything, shows how much time and energy will be spent “not explaining”, when what people want is an “idiots guide to global warming and why it matters enough to change everything”.
I checked and the “For Dummies” information is simply atrocious.
Really? You really are selling that?
Is it any wonder people would ask for a program to simply explain it, and show the evidence? There is just so much bad information. Who wouldn’t want the good info? In a way that anyone can understand it?
Here’s where you need to give citations, sources for such a claim. because that is hilarious, and quite wrong.
Clearly FX did read this tread either.
The sneering comes **after **simple and complex explanations are ignored.
My mistake, I wanted to say that FX did **not **read this tread.
That isn’t the take I got from his post. I got that he wants to understand, in detail, exactly why and how and what to do about it, if found by him, to be true.
He wants it explained, and I can honestly say that I agree.
There is plenty of science about the issue. But what the scientists have failed to do, is get the average Joe to understand the science. Understanding is possibly the first step to belief.
Gigo has posted numerous “facts”, linked to numerous studies and papers (usually long winded ones). There are people who dissect them and find fault with some (cherry pick has been used) but the fact that there are errors at all over something that they say is so important to the habitat that we live in is harmful to their cause.
The explanation is the part they have failed at.
We’re not talking about immediate responses, but the long term effects as the affected system adapts and adjusts to the change. So predict what effect those collapses will have on GDP 20 years from now. And I’ll throw a dart at a dartboard marked with GDP gains and losses centered around the mean for historical GDP growth, and we’ll see who is more accurate.
From Nate Silver’s The Signal and the Noise, which I highly recommend:
Silver goes on to show how many serious predictions utterly fail - not just in economics, but predictions of many complex systems. For example, he looked at the political predictions of The McLaughlin Group’s political roundtable over a period of of many years. He chose that show because it has a section called ‘predictions’ where everyone has to give a firm prediction of some event happening. His panel is made up of people from the right and the left, and they are supposedly among the best political analysts around. He evaluated nearly 1,000 predictions made on the show, and found their accuracy to be no better than flipping a coin. And it made no difference whether the panelists were on the left or the right.
And yet, we still listen carefully to political pundits and their predictions every election season. They are dutifully reported in the media and debated on message boards. And guess what? They’re just noise.
So then he looked at professional political scientists. He looked at the work of professor of Psychology Philip Tetlock, who began collecting expert opinions in 1987. Opinions on a variety of subjects: Domestic politics, economics, and international relations. He looked at almost every major event of the 1980’s and 1990’s. And guess what? That large collection of experts 'had done barely better than chance - regardless of their occupation, experience, or subfield." And they did worse than rudimentary statistical models.
Silver goes on to discuss the flaws in deterministic models applied to complex adaptive systems, and finds them very wanting. One very sophisticated simulation of European weather patterns showed radically different outputs with very tiny changes in input simulating tiny measurement errors. As another example the National Weather Service’s sophisticated software for predicting temperature does little better than just looking up historical temperature averages once it goes past about 3 days into the future.
He then looked at earthquake forecasting models. Earthquakes are devastating, and therefore much effort and money has been spent trying to model and predict them. The models are very sophisticated and incorporate a lot of knowledge about the cause of earthquakes. After all this effort, the official USGS position is that earthquakes cannot be predicted. At all.
Then he looked at economic predictions, which are equally dismal. In fact, says Nate, “Fairly often in fact, these forecasts have failed to predict recessions even while they were underway.”. A majority of economists did not think the U.S. was recession at all at the start of the last three major recessions in 1990, 2001 and 2007.
He looked at the survey of professional forecasts - people who make their living doing nothing but forecasting future economic conditions. In November 2007, their median prediction was that the economy would grow by 2.4% in 2008. This was after the crisis had already started, BTW. Not only that, but they assigned very high confidence to their prediction, and the consensus was that there was only a 3% chance that the economy would shrink in 2008. They said there was only a 1-in-500 chance that the economy would shrink by more than 2%. But that’s what happened.
Silver then looked at the past 18 years of their forecasts for GDP in the next year. Guess what? The actual value fell outside their 95% confidence interval six times in 18 years. Going back to 1968 their estimates fell outside that range half the time. Those people still have jobs, and their predictions are still taken seriously. We have a burning need to believe that we can predict the future of chaotic systems.
And it goes on and on. Flood predictions, medical research, psychology, finance… All complex systems. And the success rate of prediction is about the same in all of them - almost nonexistent.
Actually, Silver says that economics should be one of the easier of such systems to predict because we have tons of data right down to the micro level.
It most certainly is not. The environment is a complex adaptive system with nonlinear feedbacks. Such systems are not deterministic at all, because their outcome is highly sensitive to tiny changes in initial conditions.
A simple example of a nonlinear system is a pile of sand. Start dropping sand on the pile, and try to predict what the height of the pile will be with each new grain. Good luck.
This is just wrong. You should do some reading. An anthill is easily modeled and is definitely structured around some very simple rules. And yet, predicting the future size, shape and direction of an ant colony is virtually impossible.
Don’t forget that these systems generally have very random elements. Lightning strikes are a chaotic feature that can alter forests dramatically. That in turn can change drainage patterns and cause permanent changes to large areas of land. Had the charge potential at that moment been just a tiny bit different, the lightning might have struck in a different area and had no effect. There are millions of random events that happen all the time, and since systems like this are sensitive to initial conditions, they can become unpredictable. I’m sure you’ve heard of the butterfly effect.
Just a few days ago tens of thousands of antelope in one herd dropped dead. Occasionally we find hundreds of dolphins and whales beached. Algae blooms can appear that contain as much carbon as some countries produce in an entire year, and we still can’t predict them. Six wolves managed to reshape the ecosystem in Yellowstone park through a complex series of cascading interactions that were utterly unpredictable.
Now obviously some changes can have obvious effects. I think it’s safe to predict that major bad things would happen if the temperature went up by 20 degrees. We can predict what will happen to an anthill if we take a torch to it. The question is what happens to them when small changes are made to input.
You know, that’s not what the IPCC says. They refuse to state that any particular outcome is ‘obvious and inescapable’. Everything is expressed in probabilities and levels of confidence - as it should be. And many of the possible outcomes are far from being catastrophic.
I’m afraid I have to say that you are relying on more ignorance here. As pointed to Sam you need to calibrate your balance a lot the “skeptics” that dissect them do not tell you how wrong they are. You are missing that a lot of research was done recently to check if those “errors” do allow us to dismiss one of the main theories.
The theory does stand as Professor Muller, former skeptic, found out:
I read about it a while ago, but I believe this is the paper in question: http://www.pnas.org/content/112/2/436
Sam we already dealt with your ants before, suffice to say Silver did miss somethings in his book and it is clear that much more on it is not really good news for the skeptics:
I’m not sure what point you are trying to make. I don’t disagree with anything you bolded above. I’ve never used the argument that scientists used to predict cooling, for example.
But the authors seem to think that Silver’s argument comes down to whether the models have accurately predicted current temperature, and that’s a red herring.
See, the problem with using the traditional method of testing models by sequestering past data and then seeing if the model can predict that data has a serious flaw - at best it tells you that the model could accurately describe the system as it existed in the past. But a complex adaptive system adapts. As they say in the financial biz, “Past performance is not indicative of future performance.”
This is also a problem for climate science when it seeks to examine historical evidence for climate sensitivity, as if there is one fixed number that can be discovered by studying enough tree rings or other proxies. Again, just because the climate responded to CO2 in a certain way in the past does not mean it will do so in the future.
There are a lot of scientists who refuse to accept the implications of complexity theory on their own work. Economists, for example, are very stubbornly clinging to the old equilibrium models that are based on 19th century physics, despite the fact that it’s eminently clear that these models are at best an approximation of something much more complex and at worst completely wrong, and despite the fact that their predictive models routinely fail and that two economists using the same models on different data can and do come to opposite conclusions.
The same goes for policy people - especially on the left. It really sucks to be a central planner and discover that not only can you not predict what’s going to happen in the future, but that your grand plans to ‘fix’ the economy will result in little more than more adaptations and unintended consequences. So they are ‘deniers’ as well.
Silver’s point about complex adaptive systems is that they are not predictable in the long run, period. It doesn’t matter whether the models have succeeded in predicting past performance, or even that they can predict climate in the short to medium term (say a decade or two or three). The longer you get from the baseline, the more the system veers away from the old trend lines and the more unpredictable it becomes.
However, this is a fair point:
This is absolutely true. There is a fair point to be made in saying that, since the future climate cannot be predicted, man-made forcings have an unknown effect and therefore could be very dangerous. I believe Nassim Taleb makes the same point about climate, and supports action to fix the climate on that basis even though he does not believe that climate models are going to be accurate.
By the way, the inability to predict the future of complex systems is the consensus position of complexity theorists. You wouldn’t want to deny the consensus of experts in the field, would you?
Okay, to be fair that’s a little glib, if fun. There are obviously things we can learn about complex systems, and there are predictions we can make in broad strokes and within the range of certain parameters. I said before that the direction and future size and shape of an anthill can’t be predicted, and that’s true. But I CAN predict some things - for example, if I spray ant pheromones all over the place it will screw them up. If I kick an anthill ants will come swarming out. If I destroy a food source it will cause more ants to begin milling about.
The trick is to know what’s predictable and what isn’t, and under what constraints. I actually think climate is one of the hardest complex systems to understand and predict for the simple reason that the timescales are so long, and therefore it’s very hard to test theories and models. When iteration cycles are long and you have to wait years or decades to find out how well your model fits, it’s a real problem. We have a hard enough time understanding complex systems that operate fast enough that we can test and re-test hypotheses against future changes very fast, and attempt to refine our understanding.
It’s like looking at clouds. In real time they look like puffy, discrete objects slowly moving across the sky. But if you look at time lapse footage of the same clouds, your perspective shifts and you start to see that clouds are just the result of moving air masses. The clouds appear, but they’re just a symptom of something else.
So how come they can be used in other planets?
As I pointed before you are trying to use complexity as a show stopper, and it is not really.
Forgot the link in the last quote: Science of global climate modeling confirmed by discoveries on Mars
BTW you are indeed once again going into debating the science, a big no-no as you even said, but as usual you do not keep it that way.
As Silver noted, what we can expect in a warming world does not depend solely on computer models. Paleoclimatology pointed before at what the oceans, for example, are bound to do with all that CO2 that is being released into the atmosphere.
https://scripps.ucsd.edu/programs/keelingcurve/2013/12/03/what-does-400-ppm-look-like/
This points to the current estimations of how much of a sea rise we can expect in the future to be very conservative ones.
AFAIK many estimates of the costs to protect our coastal cities are based on the lower estimates. (And as pointed before in another thread, it was not cheap at all). The problem now is that the republicans in the USA are bound to not even look at prevention because they do not believe that there is a problem, because they do not trust science indeed.
If you think investing in prevention and adaptation is expensive consider the cost of not investing. That is what even Wall Street is beginning to notice (in the already cited Citibank report).
Watch, here’s the contradiction:
The science behind CO2 being, essentially, the thermostat of the planet is well-known and well-established. Pump more CO2 into the atmosphere with a constant solar output, and the planet gets warmer. It doesn’t matter how many chaotic events can affect this; this is the signal, and it’s not as open to initial conditions as you think it is. To appeal to the system being chaotic given the information that we have about CO2, solar output, and temperature is simply not reasonable. We know that pumping massive amounts of CO2 into the atmosphere causes warming. We have tons of historical and experimental data establishing this.
So I read your link, and I don’t see how it contradicts anything about global warming.
To summarize the points made in the article:
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CO2 warms by absorbing infrared radiation that would otherwise escape into space. So as CO2 is increased, you would expect to see less infrared radiation escaping the atmosphere.
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Current models of CO2 behavior at the planetary level don’t see the decrease in longwave radiation, but rather an increase in absorption of shortwave radiation, which doesn’t seem to make sense since CO2 is trasnsparent to shortwave radiation.
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As it turns out, it all makes sense after all, because the absorption of longwave radiation causes heating, which adds water vapor to the air. Water vapor absorbs shortwave radiation, as anyone who tries to look far on a humid day can tell you. I’m an amateur astronomer, and it’s well known that when it’s humid atmospheric transparency falls. Visible light is shortwave radiation. Hell, I can see it through my telescope.
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In the meantime, the hotter earth emits more infrared or longwave radiation, only some of which is absorbed by the CO2, so it looks like the mechanism is backwards. But it’s really not. It’s just that there’s a secondary effect that sort of causes a flipping of the polarity of the longwave/shortwave signal.
The reason this doesn’t really matter is because the old scenario and the new one both require the same warming effect of CO2.
Again, the fact that CO2 behaves this way is uncontroversial. You can put a mix of atmospheric gases in a test tube and measure the heat transfer, then add a little CO2 and measure it again and see the change. You can measure it. You can watch such an experiment right here
Another easy-to-do experiment is to simply shine sunlight through a prism and examine the spectra, then do the same while passing the light through some CO2. You will see the absorption lines of the CO2 in the spectra and tell exactly which frequencies are being absorbed. This is basic science.
At a planetary scale we can also test this by looking at the temperature of other planets with CO2 in their atmospheres and compare them to what they should be from surface heating alone.
Here’s a paper that compares Earth, Venus, Mars and Titan, all of which have CO2 in their atmospheres, and finds greenhouse effects on all of them attributable to CO2 and aligned with theory:
http://www.astro.washington.edu/users/eschwiet/essays/greenhouse_ASTR555.pdf
What is incontrovertible is that CO2 is a greenhouse gas, that CO2 is increasing, and most of the increase is coming from human activity. Therefore, all else being equal, we would expect the temperature to rise as a result.
Now, the real sticking point here is the ‘all else being equal’. And in the short term, it is. It’s the long term that’s more iffy, as an increase in CO2 triggers many other effects. It causes an increase in plant growth, warming causes albedo changes. Warming also changes ocean currents, which move the heat around and affect sea life. There are many other changes that occur - some of them causing positive feedbacks, and some causing negative feedbacks.
Having looked at many adaptive systems, the feedbacks are often not very intuitive. Who would have guessed that adding six wolves to an ecosystem would change the direction of rivers? Because complex adaptive systems are not ‘designed’ but instead evolve, the mechanisms can be bizarre. Bees keep temperatures constant in hives because many species mix together, and each species beats its wings at different frequencies at different temperatures. Put enough of them together, and they switch on and off in proportion to temperature swings and regulate it smoothly. Certain parasites cause ants to climb grass stalks and wait for birds to eat them, then the birds poop out the remains including the parasites, and that’s how they spread and survive.
This is a bit of a digression, but it’s incredibly cool. Lately I’ve been fascinated by slime molds. Slime molds are actually huge single-celled creatures. No brain at all, no nervous system, nothing. And yet, they can solve complex problems like the travelling salesman problem. They can find the shortest paths through mazes. And watch as one figures out how to efficiently plan the Tokyo rail system:
Urban planning slime mold. The oat flakes in the video are placed in the same relative location as the towns in the area around Tokyo. At the end of that video you can see a lot of paths between the flakes. If you overlay a map of the Tokyo rail system over it, it’s almost a perfect match. The mold solved a wickedly complex problem that civil engineers have to work on for a long time, and it did it in a few hours with no brain.
That’s an example of how sophisticated adaptive systems can be, even at the simplest levels. Imagine a globe full of them all through the ecosystem. To believe that we can predict what’s it’s going to be like 100 years from now after we apply a shock to it takes a real leap of faith.
By the way, when those slime molds start to dry up, they have another trick up their sleeve. Their behavior changes entirely and they start to grow ‘pods’ full of spores, then they sacrifice themselves to elevate the pods to the point where the wind can catch them. They fly away over long distances, then the pods break open and disgorge the spores, spreading the slime molds to new regions where there is more moisture. There they eat decaying vegetation. So nature has evolved a creature that moves from area to area feeding on dead vegetation. That’s yet another feedback - if climate change causes an area to dry, slime molds will suddenly lift off and fly around, descending on other ecosystems and changing them. Other animals that eat the same decaying vegetation have their own complex responses, and so it goes.
We have only begun to scratch the surface of our understanding of how all this interrelates at a global level. And these effects are not necessarily small - The 1997/1998 El Nino/La Nina event caused algae blooms that resulted in the trapping of 700 million metric tons of CO2 - an amount equivalent to half of the U.S.'s annual carbon emissions. If we use a ‘social cost of carbon’ of $50 per metric ton, that one event equates to $350 billion dollars in carbon reduction (assuming all of it was permanently sequestered, which it wasn’t, but I’m just pointing out the scales here).