Experts Debate Climate Change Science, Policy

ANDREW DESSLER: All right, so I talked a lot about why climate change concerns me. And I like to begin with this plot. This is something of an iconic plot. And it shows the temperature of the last 150 years measured by thermometers on the surface. And this is year on this axis running for the last 120 years. And temperature anomaly, which is the departure from the mean. And what it shows is that temperature's been going up. It's been going up by about 0.74. And there's some error bar, of course. There's uncertainty. Now, if this was all we had, you would have to be very skeptical. [MICROPHONE FEEDBACK] Wait, am I doing something? What? Oh, really. All right. Let me switch to-- is that better? Ah, it was the clicker. That's weird. All right, I've never run into that before. All right, so this is the important point. If this was the only thing we had, there are lots of ways these data could go bad. And so what scientists do is we look for what's called coherence, or concilience. Lots of different words for it. We want to find lots of data that are independent that tell you exactly the same thing. So if you ask the question, why are scientists so confident the Earth is warming, it's because we have lots of data. We have surface thermometers. They show the temperature's warming. We have satellites. You can measure temperature from a satellite. That shows the Earth is warming. Glaciers are melting, sea ice is receding. We know ice melts reliably at 32 degrees Fahrenheit. So with a warmer planet, you expect to see less ice. And indeed, we do. Ocean temperatures-- we're not talking about the surface. That's including the surface thermometer record. We're talking about the temperature the bulk of the ocean. That's going up. And finally, sea level's rising. And the best part about this is this creates this really integrated system. It all fits together perfectly. So for example, glaciers-- so glaciers are receding, or glaciers are melting. And that water is running into the ocean. That's causing sea level to go up. In addition, ocean temperatures are increasing. And when ocean temperatures increase, water, like most things, expands. And that causes sea levels to go up. And so it all fits together. It's this coherence of data. And even if any one of these data sets is wrong, it really wouldn't affect your confidence, because we have so much other data that suggests it's warming. And because of this, the IPCC calls this unequivocal, which means essentially beyond doubt. And so for example, Professor Lindzen, who'll be talking after me, he wrote in a Wall Street Journal op ed, there is general support for the assertion that global average temperature anomaly has increased about 1.5 degrees Fahrenheit since the middle of the 19th century. So essentially, nobody argues this. And again, I don't want to waste too much time on this. I want to get to the interesting thing. But the key thing to look at is look for coherence. Look for lots of evidence supporting a point. And you'll clearly see why scientists are so convinced that the mainstream view of climate science is right. So the real question I want to address here is this question of, how much does carbon dioxide warm the climate? This is really the key question. And it's not a question of, does carbon dioxide warm the climate? I think Professor Lindzen and I will agree it does. It's a question of magnitude, a question of how much it warms the climate. And what we're going to talk about here is this question of climate sensitivity. That's often referred to as climate sensitivity. And we're going to use a standard measure, which is how much warming would occur if we doubled carbon dioxide, OK? And so we're going to go through the math. And we're going to do a very simple calculation that indicates that we might be screwed. [LAUGHTER] All right, so I'm going to start off with an equation that I suspect Professor Lindzen will also show you. So there's really no debate about this. And this is a-- I use this slide at presentations at NASA headquarters, so you know it can't be very complicated. [LAUGHTER] And so, OK, so Tf, which is the final temperature, this is how much warming you get if you double carbon dioxide. Now, Ti is how much warming you get if you add carbon dioxide to the atmosphere and nothing changed. So no change in clouds. No change in humidity of the atmosphere. No change in ice. No change in the lapse rate, which is the temperature structure. Everything remains fixed. And so let's go to the source, Dick Lindzen's Wall Street Journal op ed, it is generally accepted that a doubling of carbon dioxide will only produce a change of about 2 degrees Fahrenheit if all else is held constant. And again, that's that term right there. So we can convert 2 degrees Fahrenheit to 1.2 degrees Celsius. And we know what this term is right here. Now, the key part of this is if all else is held constant. Now, it turns out that all else is not held constant. And the things that change are this f right here. And that stands for feedbacks. So let's talk about what a feedback is, and what this does to the problem. All right, so start off with, let's say you add some carbon dioxide to the atmosphere. Now, Professor Lindzen and I agree that if you did nothing else, that would give you about a degree, 1.2 degrees of warming. But other things do change. So we know, for example, and I'll show you some data showing this in a second, that as the surface warms, the atmosphere becomes more humid. Now, water itself is a greenhouse gas. So the increase in humidity causes additional warming. And that additional warming causes more humidity. And that causes more warming. So you have this infinite loop that goes. Now, a lot of people think because the loop is infinite it has to imply a runaway, that the warming has to be infinite. That's not right. If you remember back to probably ninth grade math, you can have infinite series that converge on a finite number. And that's essentially what's happening here. So you get additional warming from this water vapor feedback. Now, let's go back to our equation. And what you see here is that if f is greater than zero, then this number is less than 1. And you're dividing by a number smaller than 1, and the final temperature is greater than the initial temperature. And we call that positive feedback, where these processes are amplifying the warming for carbon dioxide. And if f is less than zero, then this number is less than this number, and you get a negative feedback. And that means that the feedbacks tend to damp out, damp out the effect. All right, so let's go to-- let's look at what f is in a little more detail. So we can essentially write f, which is the total feedback, as the sum of various feedbacks that are operating. So this first one is water vapor. That's was wv stands for. And that's the one I just talked about. As the Earth warms, the atmosphere holds more water. And that gives you additional warming. And so you'll hear a lot of people say, if you read the blogs, that there's no evidence for this. It's just assumed by the models, or it's hypothetical. In fact, that was true 20 years ago. But we now have data. And so let me show you some data that I think is actually quite clear. All right, so this axis is temperature, surface temperature. And that's the black line. And this is surface temperature-- this is of the tropics. And there's a reason for that, which I can explain if someone's really curious. As you can see, the temperature in the tropics over the last eight years has been going up and down. These are mainly ENSO events, El Nino, La Nina. So there's a big La Nina event right here. And you can pick out other little events. And the temperature goes up and down. The red line is water vapor. And this is measured by a satellite. That's one of the red lines. The other red line is what we call a reanalysis system. And I won't go into the details of that. But feel free to ask me if you want. And what this shows, in fact, there's even a little lag. This is the time scale for water vapor. This is the time scale for temperature. You see the water vapor actually even lags the temperature by a few weeks, which shows that water vapor is reacting to surface temperature. Surface temperature changes, then water vapor. You see, the water vapor follows the surface temperature exactly. I mean, this is really-- next time a skeptic tells you that the water vapor feedback doesn't exist, or it's purely hypothetical, you have a decision whether you can believe them or you can believe your own eyes. It's quite clear that water vapor follows temperature. Now, we can then put this into a radiative transfer calculation, because we know the spectroscopy of water vapor very well, just like we know the spectroscopy of carbon dioxide. And-- I won't go into that. So you can get a number in these units, in the units appropriate for the equation I gave you, of about 0.6. And so we can fill in the first term, 0.6. And what we're going to do now is we're going to go fill in all the rest of the terms, and we'll see what we end up getting. All right, now, I'm not going to go into great detail about the other two. Let me go back for a second. So this is the lapse rate feedback. It's a negative feedback that simply reflects the fact that a warmer atmosphere radiates energy more. If you took physics, you might remember sigma t to the fourth. So that's essentially what the lapse rate feedback says. The ice albedo feedback is ia, and that simply reflects the fact that if you melt ice, ice is very reflective, so as ice on the planet goes away, the planet actually absorbs more solar radiation. And that's a positive feedback. And so we can actually put terms on this. And again, these are based on data. You can estimate these from satellite data. And this is a negative feedback. It offsets about half of the water vapor feedback. And then the ice albedo feedback is a small positive feedback. It's big in the Arctic, but the Arctic doesn't cover a huge region of the globe. So when you average globally, the ice albedo feedback is not terribly important. So we get to the cloud feedback. And in many respects, this may be where Professor Lindzen and I disagree most vehemently, on the cloud feedback. So let's talk about clouds. And I'll give you a little primer about clouds. OK, so there's a little puffy trade cumulus maybe. And clouds do two things to our climate. The first thing clouds do is they reflect solar photons back to space. So that orange beam is some sunlight comes in, hits the cloud, and goes back out. And that effect tends to cool the climate. So in that way, clouds cool. But at the same time, clouds absorb infrared radiation emitted by the surface, upwelling, that would escape to space. And because clouds are colder-- remember sigma t to the fourth-- clouds emit less radiation. And so they also tend to warm. And the net impact of clouds is the difference between these two counteracting effects. Now, in today's atmosphere, clouds tend to cool. So clouds are cooling by about 20 watts per square meter. So it's actually quite a big amount. But don't get confused, the elementary grad student mistake of confusing the magnitude of the function of its derivative. Just because they cool now does not mean that the derivative of the function is also cooling. And in fact, as the climate warms, we really don't know what's going to happen. Do clouds cool more? That would imply a negative feedback. Or do clouds cool less? That would imply a positive feedback. So as before, let's go to the data. Let's see what the data tell us. And this is some work I've recently done. And this is-- so on this axis right here is how much energy clouds trap. And this comes from measurements of an instrument called CERES, which is flying on the NASA Terra satellite. And also use some reanalysis data. And this is global average surface temperature anomaly. So these are anomalies, deviations from average. If you average all this data, it'd be zero zero. And what you end up seeing is, first off, there's no strong trend in this. So you don't see clouds actually changing a whole lot as the climate warms. Yeah, so the number doesn't actually seem to change. But we can do better than that. You can look with your eye, but people who do, [INAUDIBLE] by eye in science, typically get what they deserve. So let's do some statistics on this. And we can put a best fit line through this. And you get a value about 0.15. And these are the two sigma uncertainties. And you can do statistics. You can say there's an 80% chance that the slope through this is positive. So what this means is that it is-- borrowing the parlance of the IPCC, these data suggest it is likely that the cloud feedback is positive. And again, our best bet is 0.15. So if we go back to our equation, we can plug 0.15 in. But before we do that, let's-- actually, in the interest of time, I'm going to skip ahead a little bit. All right. So we'll go here. So here's our climate sensitivity. Here's the value we just derived. So you add these numbers up, you get this. And we'll plug this into our equation. And what do we get for a doubling of carbon dioxide? 2.7 degrees Celsius. And if you add some uncertainty, as I pointed out, there's a lot of uncertainty in the cloud feedback I derived. And if you put that uncertainty in, you pretty much get the canonical IPCC range of if you double carbon dioxide, you get 4.5 degrees of warming. Now, one point I want to make. This was not derived from a model. This was derived from data. And I'll come back to that point repeatedly. So get used to it. But the other point I want to make is, if this is all we had, we wouldn't have great confidence. Again, scientists look for concilience. We want to look for all-- we want to look for lots of data that support what we think is happening. So let's go to the ice ages. Ice Age is another great example of climate change. And so here's our favorite equation. And we know a few things about the Ice Age. First of all, we know the ice ages were about five degrees colder, maybe five to eight. But five in the context of this calculation is conservative. And we know how the ice ages were different. There were big ice sheets. And the ice sheets tended to reflect sunlight back to space. If you averaged that over the globe, the ice sheets reflected about 3 and 1/2 watts per square meter. We know there were less greenhouse gases in the atmosphere at the last glacial maximum. And those allowed about 2.6 watts per square meter of energy to be radiated to space. We know the planet was drier and that it was dustier. And these aerosols also reflect sunlight back to space. And so we know what we-- these are what are called climate forcings. And we know what the climate forcings were. And so we can calculate what ti is. If there are no feedbacks, we know the forcings and there are no feedbacks, it's trivial to calculate what the temperature change would have been. All you have to do is know the specter of the atmosphere. And we know that. We know the specter of the constituency atmosphere extremely well. And so you have two-- these two are know. You can solve for f. And you get 0.58. And if you remember, we got 0.55 for the last one. And in climate change, that's identical. So the ice ages feature the same climate sensitivity as we derived from the last 10 years of data. But let's go further. Two points isn't enough. We need lots of data to confirm this. So let's go back. Let's look at the last 420 million years. Now, it is difficult, but you can infer how carbon dioxide varied over the last half billion years. Similarly, you can infer how temperature varied. And if you do that, you see temperature and CO2 are generally correlated. Now, there are obviously other things going on. Continents are moving. Asteroids are hitting the planet. Volcanoes are going off. So carbon dioxide's not the only thing driving climate. But carbon dioxide and climate are associated. In times when carbon dioxide is very low, you usually have ice. When carbon dioxide is very high, you usually don't have ice. And so you can use these data, and they infer that climate sensitivity greater than 1.5 degrees C has probably been a robust feature of the Earth's climate system over the past 420 million years. Now, I actually could go on, believe it or not, and show you more examples of data that show that climate sensitivity is in the canonical range of two to four degrees. But in the interest of time, I won't continue to go on. But instead, let me go to another question, which is how much warming are we going to experience? Because at some level, no one really cares about the past. You're interested in climate change for the future. And so let's go through a quick calculation that doesn't require climate models. So we have an estimate of what we think climate sensitivity is. And again, there's a mountain of data to support this. Given carbon dioxide reaching 500 to 900 parts per million this century-- it's about 390 now. So we know carbon dioxide is approximately going to double, maybe come close to tripling. And also given that other greenhouse gases are also increasing-- we know methane, methane's sort of flat, but other greenhouse gases are also increasing. Ozone is certainly increasing, tropospheric ozone, things like that. We can therefore expect several degrees Celsius warming, OK? And again, this is not a model calculation. This is based on data, and an estimate of how much carbon dioxide is going to go up. Now, this happens to agree with models. And this, by the way, is why I believe models. Not because I have some religious belief in models, but because the models agree with the data. I believe in data first. Models come second. But the models look good. If you compare the models to the data, and all the things I've looked at-- and I won't show any slides, but perhaps I will in my rebuttal-- the models look great. Models do quite a good job of simulating things. Now, it's not to say you can't find some statistic where the models don't do well. That's pretty easy. So this is interesting. I did some googling on Professor Lindzen before this, and I found this interesting thing to see, what has he said about warming? And I thought this was kind of interesting. So this is about betting on climate change. And apparently, Professor Lindzen said at one point that he'd be willing to bet that it was cooling in 20 years. And it turns out that when-- I mean, anybody would take that bet. I would bet anybody that's going to be warmer in 20 years than it is now. And so a blogger contacted Lindzen. It would only take 50 to 1 odds that temperatures are going down. So he obviously agrees, temperatures are going up. And that's consistent-- I should point out, that's completely consistent with his view that carbon dioxide warms the planet. So there's really no disagreement there between the two of us. What I though was interesting was the magnitude. So Professor Lindzen offered an alternative bet, which was that if the warming over the next 20 years was less than 0.2 degrees, he would win. If the warming was greater than 0.4 degrees, he would lose. And there'd be no payout for warming from 0.2 to 0.4. And he wanted 2 to 1 odds. So this was twice as likely as that. And when I saw that I was sort of struck. I say, what do the IPCC models show? And that's basically what it is. So apparently, at least when that was written, he was unwilling to put any money on the fact that the IPCC models were overestimating climate change. And so perhaps there's less disagreement between the two of us on future warming. Maybe the disagreement lies elsewhere. I'd be interested to see his talk. OK, and again, I will say again, 2 to 1 odds, which means he views warming up here to be twice as likely as warming down here. All right, so let me tell you about why I'm concerned. So I've shown you data about the warming is. Now, concern is fundamentally a moral judgment. But let me give you some idea of why I think we should be very concerned about warming of a few degrees. And you're probably sitting there and saying, a few degrees, who cares? Doesn't sound like a lot. But this is a really interesting plot. And these are model runs. And the different colors show a range of model predictions. You can see the models pretty much agree with our back of the envelope calculation. I predicted a few degrees of warming. And sure enough, you see the models predict a few degrees of warming. And this shows what the historical record. And I know there's a lot of debate. This is going out. I'm not having good luck with AV equipment. Is there another-- oh, wait. All right. There's debate down here about-- can I borrow [INAUDIBLE]. I'm just trying to get the laser pointer. Maybe it's-- I can see the laser point-- [LAUGHTER] This is just not-- yeah, [INAUDIBLE]. I'm having technical difficulty. But the point I want to bring is this. See this period right here? This is a period called the Little Ice Age. Little Ice Age is about one degree cooler than it is now. Now, I think about that for a moment. One degree doesn't sound like much, but one degree cooling is enough to make the Earth go into a region that people refer to as a Little Ice Age. Your intuition about what's a big change, which is based on global conditions, one degree of change here doesn't change much. But in the global average, one degree is a huge change. And if you cool the planet by one degree, you enter a Little Ice Age. Now, another thing is this is about the amount of warming we've had since the last Ice Age, about five or six degrees. Now, think about how different the planet was during the last Ice Age. And that was only five, six, seven degrees cooler than it is now. So you have to look at temperature increases of a few degrees very seriously. If we have warmings of, say, three or four or five degrees, it's going to be a different planet. We're not going to be living on the same planet. And you might say you don't care about that. Well, that's a moral value judgment. And I can't argue with that. But I do care about that. And that's why I'm concerned. Oh, the other thing I'll just say very quickly, it's kind of funny to see how people look at climate change before and after. So this is a quote from Vladimir Putin. "Russia is a northern country, and if temperatures get warmer, it's not that bad. We could spend less on warm coats." And yeah, you think about it. Global warming, great. It's like you think barbecues and go to the beach. But in reality, things aren't that bad. This is after the heat wave they had this summer. Practically everything is burning. The weather is anomalously hot. What's happening with the planet's climate right now needs to be a wake up call to all of us. Climate change is not all fun and games. It's not short sleeves and barbecues. It's drought. It's forest fire. I looked up the weather forecast for Moscow during this, and the forecast was smoke. I've never seen that. I mean, think about that. Smoke, that's what climate change is. It's not great things. So where's Susan? How much time do I have. OK, good. Perfect. All right. So I want to talk a little bit about skeptical science, because you can't understand the debate unless you understand the context of this. Now, this is a memo that was written by some cog at Brown and Williamson in the late '60s. "Doubt is our product." It says, "the best way of competing with a body of fact that exists in the mind of the general public. It is also the best way of establishing a controversy." And you think about all the memos that are written. I'm quite certain the person that wrote this at the time didn't realize that there'd be books written on it. Merchants of Doubt, quite an excellent book. Doubt is Their Product. And it's completely established through the release of tobacco documents. They knew cigarettes were killing people. They absolutely knew it. In fact, they knew it before the surgeon general had proven it. And yet they embarked on a policy of debating the science to produce doubt. That was their strategy. And so let me give you an example of what the tobacco strategy would look like. So when it comes to tobacco, the facts aren't all in. Science can't tell us how cigarettes cause cancer. They can't give us the mechanism. They can't tell you how many cigarettes you need to smoke before you get cancer. They can't tell you why some smokers get lung cancer and others don't, and why some nonsmokers get cancer. Think of all these things we don't know. How can you possibly try to link cigarettes with health impacts when you can't tell me any of those things? You don't know the mechanism. You have nothing. And the answer is that by focusing on things we don't know, you obscure what we do know. And even though these are all true, I don't know how many cigarettes you have to smoke before you get cancer. I don't know why some people don't get cancer and other people do. But I do know that if you smoke cancer, you increase your chance of getting these diseases. And so this doubt strategy is incredibly effective. And this is funny so I'll go over it. I saw in the New York Times the tanning, Indoor Tanning Association, tried to adopt the doubt strategy. Tanning cause-- and this is the New York Times. "Tanning causes melanoma-- hype." And they go on to say, "recent research indicates the benefits of modern exposure to sunlight outweigh the hypothetical risks. Surprisingly, there's no compelling scientific evidence that tanning causes melanoma." And you know, this is wrong. But again, they're trying to introduce reasonable doubt. And it's time to rethink sunbathing. Find out more at sunlightscam.com. And a few months ago, I was trying to update my slides. So I went to sunlightscam.com and it was gone. And so I did some googling. And every once in a while the universe metes out some well deserved justice. And this was one case. Indoor Tanning Association settles FTC charges that it deceived consumers about skin cancer risk from tanning. So doubt is their product. And you see this in tobacco. You see this in tobacco with secondhand smoke. You see this an ozone depletion. You see this in acid rain. I'm the parent of two five-year-olds, so I'm very familiar with debates over vaccine. If you listen to the debates about people who say you shouldn't get your kids vaccinated, it's the doubt agenda. Tanning beds, quite ridiculous. And now climate change. And so let's go through what I mean-- OK, great. I'm on time. Let me go through what I mean when I talk about the uncertainty agenda or the doubt agenda when it's applied to climate change. So I googled Professor Johnston, and I found this document he'd written, "Global Warming Advocacy Science, A Cross Examination." And I just want to highlight a few points. A, what do we really know about global mean surface temperatures? And can we really be so sure about the reported warming trends? And then you go to the next page. C, the existence of significant alternative explanations for 20th century warming. So what he's arguing is that the climate's not warming and the warming is natural. And this is what a lawyer does. And a lawyer, a defense lawyer, does not look for coherence. They don't want to look for an argument that completely makes sense. Instead, they're trying to make lots of arguments, because a defense lawyer's product is reasonable doubt. It's exactly the same strategy that tobacco companies use. And that's essentially what's being involved here. And I give Professor Johnston credit. He didn't try to hide this. He calls this a cross examination. When a lawyer cross-examines people, he's not looking for both sides of story. He wants the person to say what the lawyer wants to hear. And so this is-- climate change skepticism as it's generally practiced is one of the defense attorney agenda, one of doubt is their product. And so I don't know what Professor Lindzen's going to say. His arguments tend to be better than most skeptics. But during my rebuttal, I'll try to bring up points that I think support this. All right, so let me just finally-- I'll sum up the following. Elements that should provoke suspicion. All right, so you're going to hear a lot-- when we talk to a skeptic, you hear a lot about model bashing. Models don't do this. Models don't do that. Well, let me show you. I didn't talk about models in my talk. Maybe I mention them once or twice. But it's data. Data is what carries the day. And data is why scientists believe climate change is an issue. Alarmists, elitists, and Al Gore. Anybody who talks about alarmists, elitists, or Al Gore is not interested in a serious discussion. You're essentially using rhetorical tools to try to paint your opponent as biased or confused. And that's not-- when I hear that, I kind of turn off. Conspiracies, people that invoke conspiracy theories, what that really means is nobody believes what they're saying. And the conspiracy theory's there to explain why nobody agrees with them. So if somebody walks into a room and says gravity doesn't work, a bunch of physicists will laugh at them. So they have to generate a conspiracy theory about why they're being laughed at. And so when people invoke conspiracy theories, like scientists are just chasing grant money, I mean, there's no evidence for that. I mean, I'd like to see some evidence that that's actually the case. And there isn't any evidence because it's a conspiracy. So you know, citing natural variability without a mechanism. Natural variability is not magic pixie dust. There is a physical mechanism for every climate change. And you have to determine what that is. And so people that say natural variability, but they don't cite a mechanism, you should view them very suspiciously. Not cause for alarm-- this is not a scientific argument. It's a value judgment. And absolute uncertainty, no error bars. The IPCC is very upfront about the uncertainty. And you can say you don't agree with how they do it. But for example, the iconic statement that the warming is 90%. We're 90% sure it's due to humans. It has 90% in it. I mean, so there's a 10% chance that maybe the warming is not due to humans. And so, you can argue that, but at least they do that. Skeptics rarely give you error bars. They believe what they say. And so I'll be interested to hear in Professor Lindzen's talk if he's willing to give some uncertainty. How sure is he that he's right? And if he's not, if he's 100% certain he's right, that's suspicious. And so I'll sum up there. I'll save nine minutes for my rebuttal. Thank you. [APPLAUSE] RICHARD LINDZEN: OK, thank you. Pleasure being here. I'm going to stay here so I can see at least what my slides say. But I think I'll stick to the format of only casually responding to guilt by association and so on, and leave it to you to decide. And I'll give my own view of how you approach science in this issue. What I've come to realize is most people, including policy makers, don't actually know what the debate is about. We'll see. A number of the points that Andrew referred to, as he said, there's no general disagreement on. But it's equally true they are not germane to the concerns that you have. And that's-- I'll try and clarify that in this talk. You know, Andy was on both sides of this, but I think some of you may be surprised to hear that the debate is not about whether it is warming or not, or even about whether man is contributing some portion of whatever is happening. And here we agree. The issue is how much. And is it a matter, or should it be a matter of concern? Now, unfortunately, a lot of the confusion is not simply the lay public's fault, but it is the fault of scientists who have made it sound as though the question is, is it warming or not? And so people, without looking at the data, or looking at the scale, use that as a basis for their decision. Here are a couple of statements. And again, Andy has covered some of this ground. A doubling of CO2 by itself only gives limited warming. All models project more warming because they require a positive feedback. This is something Andy went over. That is required in order to produce more warming. Now, let me clarify something. Positive and negative feedback is not a statement of cooling or warming. A positive feedback takes whatever change you're trying to impose on the system and amplifies it. A negative feedback resists change. It happens to be the case that most well-designed systems, including your body temperature, are characterized by negative feedback so they maintain a certain stability. The second thing is, if one assumes all warming over the past century is due to greenhouse forces, then the derived sensitivity to a doubling of CO2 is less than a degree. The way you make models consistent, consilient, or whatever phrase Andy wishes to use, with the positive feedbacks, is by introducing other negative force things, like aerosols, which are fundamentally unknown. And the people who are the experts in aerosols-- Schwartz and [? Rhoda ?] and so on-- recently written on this thing that allows for virtually any range of temperatures to be consistent. Given the above-- and here I will use the word alarming, because one has to distinguish between small and large changes. They may be personal choices. But that its settled science should be offensive to any sentient individual. And that despite the fact that this is not emphasized by the Intergovernmental Panel on Climate Change. I agree with Andy on the notion that models are not our only tool. Even if it were true, it would depend on the models being objective and not arbitrarily adjusted. They are. However, models are not our only tool. One good thing that models do is show why they get results they get, through sometimes it's hard to dig through. And the reasons involve physical processes that can be independently assessed by both observations and basic theory. And here I would differ with Andy. I think many of the results are suggesting that the models are exaggerating warming, trying to show some of these results. Even without the scientific breakdown, there are reasons why-- and here it's interesting that come to rather different conclusions from Andy on this-- why you should be suspicious about the presentation of the [? lie. ?] First of all, the claim of incontrovertibility is far more suspicious than the claim of doubt. Arguing from authority is commonplace in this field. And obviously, you should look at the scientific data and reasoning, and even elementary logic. More to the point, and this is something Andy did not stress, the use of the term "global warming" is generally done without either definition or quantification. We'll come back to that. Andy touched on this, but I think it pays to have a better idea of what's going on. There is also something that Andy did in terms of consistency. Many of the phenomena he referred to, including ice, glaciers, and so on, the Moscow fires, are complex phenomena having many, many causes. To identify phenomena with multiple causes with global warming, or even as proof of global warming should be ab initio suspicious. And certainly, again, something you've seen many times, the conflation of the existence of climate change, which after all is unquestionable-- it's always occurred-- with anthropogenic climate change is, of course, misleading. There are some salient points. By definition, nothing in science is incontrovertible. And especially in a primitive and complex field like climate. Incontrovertibility belongs to religion, where it's referred to as dogma. The value of authority in a primitive and politicized field like climate is of dubious value. Said this already. Respect to the last item, however, the situation may not be as difficult as it sounds. You do have a way, as layman, of checking science. So for instance, this letter appeared last spring in Science. And it was signed by 250 members of the National Academy. Most of the signers had no background in climate science. There were the usual suspects, Paul Ehrlich, the late Steve Snyder, George Woodwell John Kennedy, John Schellnhuber. But a few were indeed active contributors to the science. Now, here are two of the assertions. Of course, you can't read this letter here. But let's focus on these assertions. Natural causes always play a role in changing Earth's climate, but are now being overwhelmed by human induced changes. And warming the planet will cause many other climatic patterns to change at speeds unprecedented in modern times, including increasing rates of sea level rise and alterations of the hydrologic cycle. Now, one of the signers was a colleague of mine, Carl Wunsch. And here's what he said in a recent paper in Journal of Climate, and repeated just a week ago in the departmental lecture. The syntax is, of course, convoluted, as it usually is. "It remains possible that the database is insufficient to compute mean sea level trends with the accuracy necessary to discuss the impact of global warming, as disappointing as this conclusion may be." And so when you hear anyone speak about sea level rise accelerating, doing anything else, there's no basis for it in the current measurements. They are sets which cannot be directly compared. But all I'm saying to you is, if you go to the scientific literature and look at it carefully, you often see that the authoritative assertions are no more credible than the pathetic picture of the polar bear that accompanied the letter. Now, global warming, what is it? What are we talking about? It actually refers to a fairly obscure statistical quantity, globally average temperature anomaly. What this is is you take a given station. You have a time series of temperature, let's say annual mean. You take, let's say, 1950 to 1980, following that average. Look at the deviation from that average each year, and just the deviations at each station. And then average the deviation. Now, it's a quantity that is hardly causal of climate change. So you have to be very careful when somebody says, look, this major ice age occurred, and there was only five degrees change in this quantity. This quantity is a small residual. It's not a force [INAUDIBLE]. But when you come to look at it, it depends on ENSO and Pacific decadal oscillation. It depends very much on the fact that the climate system is never in equilibrium. The oceans are always carrying heat to and from the surface. Where it's below the surface, it's not affecting the surface budget directly. And so the surface is out of equilibrium. And so you do have ENSO. You do have Pacific decadal oscillation. Andy's statement that you have to find a cause for it is nonsense. Of course, we know numerous causes, and we speak about them in the normal phenomenology. But it's the fact that it's small and the error bars are large that means the quantity can be readily abused. Now, the last time somebody actually showed what was done was around 1990. It was a fellow named Stan [? Groch. ?] It was the Lawrence Livermore Laboratory. And what he showed here were the points that go into this quantity, namely the temperature anomaly at individual stations. It's the average of these as a function of time that is the graph of temperatures that Andy showed. Now, what are you supposed to look at when you look at the graph? Well, I suppose the first thing to look at are the coordinate absence, right? You don't do what the newspapers do to the stock reports. Whether it went up 5 points or 1,000 points, it always looks the same. So the scale here is each unit is 2 degrees centigrade. And what you're seeing is fundamentally, on this scale, there are roughly as many stations that have negative anomalies each year as positive anomolies. [INAUDIBLE] dense coverage of that. And indeed, when you average these, it confirms that. You get a little noisy line that is hovering around zero. Now, how did that become the graph that Andy showed that you've seen many times? Very easy. Unfortunately. Oh, my gosh. What's happened here to the graphics? I'm afraid it's a little bit puzzling. What was done-- I'll have to describe it by words-- is you change the intervals on this graph from two degrees to 2/10 of a degree. You stretch this graph out, as the stock reports do, so that it now looks more significant. And then you get the graph that you were shown. And with its tenths of a degree change instead of the normal variance, which is about two degrees. Ah, here it is. If you look here, and again, I am wondering, do I have anything here? No, this doesn't show the point-- oh yeah, here. Use the pointer. So here you have the usual picture. But if you look, this is minus 0.2 and 0.2. This is 2 and 2. OK, so to give you a sense of this, this is something that appears in The Boston Globe every day. What they show is the temperature. I don't know how much of this is visible. It's not bad, OK. You have the blue bars. And they show for each day the high and low temperature for the day. You then have the dark gray center, which is the average high and low for the day. Then you have the light gray bars, which are much bigger. They go from the record high to the record low for the day. And what you see is, of course, there's an increase in temperature. After all, you're going from winter to summer, since April. And if you look at the record highs, they come from all over the past. And the record lows as well. In this case, the record low was 1909, the record high was 1974 for the day that they published this. And then you have that red line in the middle. Just for perspective, the thickness of the red line represents the range of global mean temperature anomaly over the past century. So that's in the perspective of your experience. The only reason I mention that is people readily say, I agree the Earth is warming. And you may, and you may have good reason for it. But it can't be a personal experience, because your personal experience is huge compared to the scale of the change [INAUDIBLE].. OK, so the claim that the Earth has been warming and there is a greenhouse effect, and that man's activities have contributed to warming, are in fact, trivially true statements. They are not argued. They're basically-- and they also have nothing to do with the policy issue of whether we should be worried, regardless of how you feel that. They are nonetheless, as you know, frequently trotted out as evidence in support of treating it as an alarming issue. The issues that are essential, and here, one of them we agree on, one has not been mentioned much, the magnitude of the warming you expect and the relationship of warming of any magnitude to various projected catastrophes that are presented. Now, when you get to the catastrophic pictures, forest fires, those sorts of problems and so on, you're guilty of what sometimes is referred to as the prosecutor's policy. And what that confuses is the near certainty that if A shoots B, there will be evidence of gunpowder on A's hand, with the assertion that if C has evidence of gunpowder on his hands, then C shot B. Clearly, the second does not follow. With global warming, the line of argument with many of these events, including sea ice and so on, is even sillier. It amounts to something like-- and I have said this in the Wall Street Journal article some time ago-- if A kicked up some dirt leaving an indent in the ground into which a rock fell, and B tripped on this rock and bumped into C, who was carrying a carton of eggs which fell and broke, then if some broken eggs were found, it showed that A had kicked up some dirt. Which would be bad enough in terms of logic, but these days, we go even further and decide the best way to prevent broken eggs is to ban dirt completely. Now, there are some problems with the science. Andy keeps referring to data. And this is a field where the data is really hard. And I'll show you some examples where we can be absolutely confident the data is wrong. Data is not a gold standard in this field. It's not like going to the National Bureau of Standards or NIST, as it's now called, and looking at some precise measurement. Their measurements are fairly sloppy. You would not have from climategate various biases dealing with temperature anomalies. I found this in some ways to be a complicated ethical issue, because small temperature changes are not abnormal. And they claim changes are consistent with low climate sensitivity. But the public has been misled to believe that whether it's warming or cooling is a matter of vital importance. So tilting the record is not so much important to the science but to the public perception. Also, there's a tendency in this field to hear the word validation used. A lot of data is being analyzed with the aim of supporting rather than testing models. And that's certainly been my experience serving on the IPCC and the National Climate Assessment Program. It's also evident in recent scandals concerning Himalayan glaciers. All right, here's an example of where I know the data, something in the data, is wrong. And that's a little bit surprising. But again, it shows there are ways of doing that. So for instance, there is one thing that is well understood in tropical meteorology. It's dominated by convention. And the temperature tends to have to be approximately following something called the moist adiabat. It's a particular temperature distribution. It doesn't have to be exactly, but it's roughly. And one consequence that is robust is all temperature changes have to be about two to three times larger at-- let me just point to this if I can find the pointer again-- in what is called the upper troposphere than they are at the surface. What these four pictures are are four climate models that, in fact, show this. And in this sense, the models have to be correct. They show different sensitivity, but they all show this hot spot. The hot spot is not a sign of greenhouse warming. It is a property, a basic physical property, of the moist adiabat. The trouble with it is that the observations here, and this is trends as a function of altitude are shown by the solid line, and they don't show the surface warming much less than the upper troposphere. The only thing one can say from this is basic physics that is widely accepted says that either this data must be wrong, or this data must be wrong, or both are wrong. But we know something is wrong with this picture. If it turns out this data, the upper part, is correct, then it says that the surface trend, this is basically since 1979, has to be reduced [INAUDIBLE].. Now, you say, can that happen? There's ample reason to believe it can, because there are all sorts of corrections applied, and all sorts of scandals, and all sorts of other things going on. But the data itself is not something rock solid. As Andy and I agree, sensitivity is a crucial issue. And as he said, it refers to what you expect in equilibrium from the doubling of CO2. It's terribly important to say in equilibrium, because the more sensitive your climate is, the longer it takes to reach equilibrium. By the time you get to some of these really large outliers, it takes centuries. The problem with using paleo data is it assumes you know what caused the climate change. And it is certainly not, in the case of the last glacial maximum, a change in the global mean. It is the Milankovitch orbital hypothesis that doesn't operate by changing the mean. It operates by changing the insulation at high latitudes in summer, with very little change to the mean. In any event, you also can't test models by comparing models with models. They're not basic physics. We can skip this. These are statements that the IPCC claims are authoritative. They have been endorsed by national academies and numerous professional societies. But again, you have to look at these endorsements [INAUDIBLE].. Here's a recent letter signed by the presidents of both the Royal Society and the National Academy of Sciences. I think it tells you a lot about the current state of the science. Again, you can look at it at your leisure if you wanted, but let's focus on a few sentences. The first one is, "however, as your editorial"-- and it's responding to an editorial in the Financial Times-- "acknowledges, neither recent controversies nor the recent cold weather negate the consensus among scientists something unprecedented is now happening. The concentration of carbon dioxide in the atmosphere is rising, and climate change is occurring, both due to human actions." Note that this statement seems to go well beyond the IPCC statement that claim that only more than half the temperature change over the preceding 50 years could be attributed to man's emissions, with aerosols included, in order to cancel much of the excess warming the models produce. Moreover, the assumptions underlying this claim, namely that the models adequately dealt with natural internal variability, have been shown to be false. They say they acknowledge that they did not properly handle long term phenomena, like ENSO, Pacific decadal oscillation, and the Atlantic multi-decadal oscillation. And the assumption that they had was crucial to their conclusion. Of course, one could carefully parse that sentence from Rees and Cicerone. Perhaps what they meant is there was increasing CO2 due to man. That there was warming due to this, though it might only be a small part of the already small observed warming. If this is what they meant, then the statement is trivial and suggests no basis for law. However, there is no doubt that this is not what they intended the reader to infer. They then continued, "uncertainties in the future rate of this rise, stemming largely from feedback effects on water vapor in clouds, are topics of current research." Who would guess from this throwaway comment that feedbacks are a critical issue. Without strong positive feedbacks, there would be no cause for alarm and no need for action. What Rees and Cicerone are actually saying is that we don't know if there is a problem. A third statement they made is, "our academies will provide the scientific backdrop for the political and business leaders who must create effective policies to steer the world toward a low carbon economy." Rees and Cicerone are saying that regardless of the evidence, the answer is predetermined. If governments want carbon control, that is the answer the academies will provide. And I would argue that nothing could better epitomize the notion of science in the service of politics, something that, unfortunately, has characterized so-called climate science. Where do we go from here? Now, I would suggest that since this has become a quasi-religious issue, it's hard to tell. My personal hope is that we'll return to normative science and try to understand how climate actually behaves. Certainly, one step in this is our present approach of dealing with climate is completely specified by a single number, globally averaged surface temperature anomaly, that is forced by another single number, atmospheric CO2 levels, for example, but it could be solar output as well. Clearly limits real understanding. So does the replacement of theory by model simulation. In point of fact, there has been progress along these lines. And I would suggest none of it demonstrates a prominent role for CO2. It's been possible to account for the cycle of ice ages simply with orbital variations. It was so thought to be the case before global warming began. Tests of sensitivity independent of the assumption that warming is due to CO2, which is a circular assumption, show sensitivities lower that models show. The resolution of the early faint sun paradox, which could not be resolved by greenhouse gases, is readily resolved by clouds acting as negative feedbacks, and rendered impossible to solve if you have positive feedbacks. We don't have time to go through this, and it would be tough even for grad students, but let's begin. Let's see what we mean by the feedback [? systems. ?] If you have a system that is unperturbed, then you have a balance between incoming solar radiation and outgoing thermal radiation. If you then add greenhouse gases, and look at it before it has time to re-equilibrate, what you will see is that the greenhouse gases limit the outgoing radiation, because they admitted the colder temperatures. And so in order to reestablish balance, the climate system must warm. If you have feedbacks, then on top of this, you will have additional feedback greenhouse absorption from, let's say, water vapor or clouds. And they interact with each other, so you can't really look at them independently, because the water vapor feedback only works where you have clear sky. Then you get the less radiation. So the idea is, let's look at space and see, for temperature perturbation, how does the outgoing radiation respond? If you find that you get more out, more cooling per given increase in temperature than you'd expect from zero feedback, then you have negative feedbacks. If you see less, you have positive feedbacks. Now here, Andy showed something. And this is this diagram, so I'll skip it. You can compare this with observations. But the crucial thing here is when Andy showed his radiation from CERES and the trend analysis, he did not look at lags and leads. Much of the outgoing radiation comes from things like volcanoes, what I call non-feedback cloud variations and so on. So you have to emphasize the lags, and separate lags from leads [INAUDIBLE].. When you do that, there is an unambiguous negative feedback. And this is shown by the red lines. When you look at the models for the same temperature, they unambiguously produce positive feedbacks. But the situation is a bit worse than that. And you can see this here. When you look at the data for the models, and the models have all sorts of cloud variations that are not feedbacks, you get values for the sensitivity that, in many cases, don't look like what you get from running the models for a long time. For instance, here the IPCC reports essentially 2.3. We get from the model 22.4. The thing to remember is when you take the uncertainty in the radiation budget, even in the models, the range that you have at the 90% confidence levels ranges all over the place. This is not true for the observations. For the observations, the uncertainties leave even when you include the error bars, the sensitivity tightly constrained. The reason is this equation. This equation is a funny equation. You notice what happens when f equals 1. Can anyone tell me what is the answer to this when f equals 1? I'm not asking you, Andy. [LAUGHTER] [INAUDIBLE] Tell me. No, infinite. Infinite, OK? Now, that's the tricky part about this. Because it's saying when you have positive feedbacks, and you're starting out, as Andy says, around here, you add a little bit and you're up here. Add a little bit more and you're out of sight. And so it's always extremely uncertain. If you have negative feedbacks, then you're in this part of the graph. And it's tightly constrained. In other words, it's the existence of positive feedbacks in models that leads to the uncertainty. Now, recently there has been an observation. And again, data is data subject to all sorts of uncertainties. But when one is talking about forcing of climate, one is considering the flux of heat or radiated forcing. Now the thing about this flux is, at the top of the atmosphere, viewed from space, the flux is in the form of radiant energy, infrared flux, visible flux. But as you go down in the atmosphere, although the flux remains reasonably constant-- that's what one means by non-diverging-- what makes up the flux changes. So at the surface, the main component of the flux is evaporation, which is called the latent heat flux. That is to say, when you evaporate, that is a change of state. It evolves energy. Now, some scientists, Wentz, et al, were looking at something that's commonly been reported, that the models show much less change in precipitation when the temperature changes than nature seems to. And precipitation and evaporation should be equal over reasonably short time spans. So Wentz was actually thinking how to produce something more dramatic for climate, wanted to say that maybe the models should be showing much bigger rainfall changes. But he was ignoring the fact that this is a flux of heat. And in point of fact, what Wentz was doing, he was giving a rough measurement of time and sensitivity. Sensitivity is a ratio of flux to temperature change. Let me do that more carefully. Let's call ec the change of evaporation with temperature. And he uses units of percent change per degree. Now, let's call cf the radiative forcing due to a doubling of carbon dioxide. That's 3.6 watts per meter squared. Let's call fl the heat flux associated with ec, a percent change. And that's 0.8 watts per meter squared per percent change in evaporation. As a result, the climate sensitivity will be cf divided by fl. So what Wentz et al found was with models, evaporation changed between 1% and 3% per degree, depending on the model he used. For the observations, the evaporation, as confirmed by precipitation-- they were independently studied with satellites-- gave you about 5.7% change per degree. If you calculate the sensitivity, you get the model 1 and 1/2 to 4 and 1/2. For observed, you get 0.8. Moreover, you can consider, if you read the subsidiary material that Wentz supplied for his paper, that 0.8 is almost certainly an overestimate for the sensitivity, because as Wentz openly acknowledges, when the observations differ too far from the models, he threw out the observations. OK, we can discuss some of the other points perhaps later. I mean, glaciers and sea level, so on, ocean are cases where, for instance, sea ice in the summer depends on wind more so than temperature. One can go over all this corroborative evidence that Andy mentioned. And you'll see it quickly either falls away. But we can leave that to Q&A. I mentioned the early faint sun. I don't know if any of you know what this paradox is, but just in case you're wondering about climate sensitivity, the sun is the star which brightens with time. A few billion years ago, the solar output was almost certainly 20% less than it is today. Remember, doubling CO2 is a 2% in the radiated [INAUDIBLE].. So here we have 20%. Most models would say the Earth would be frozen solid with that change. 10 times bigger than doubling CO2. And yet, the geological evidence is the Earth was not frozen, and temperatures were not noticeably different from today's. There have been 30 years of attempts to resolve this paradox by asking for CO2 or methane, other gases, and none of them have [INAUDIBLE]. What we recently showed is that, whether it's the right answer or not, it's perfectly possible for cloud changes to compensate for 20%. At any rate, you now have some idea why I think there won't be much warming due to CO2. And without significant warming, it's impossible to tie catastrophes to such warming. I would even suggest that if you had significant warming, it would still be extremely difficult to make the connection [INAUDIBLE] catastrophes. Thank you. [APPLAUSE] SPEAKER 1: I know some people have class at 1 o'clock, and so if you do, don't feel awkward about needing to leave. ANDREW DESSLER: Thanks a lot. It turns out when I was doing debate in high school, I was really good at flow charting people's arguments. I've apparently lost that. My notes are a mess. But obviously, I can't respond to everything he said. There are a lot of points we agree on. And there are a few points we don't. So I think I'll go just over a few points that I think maybe a different interpretation is in order. First, I just want to say one thing. I was quite shocked when Professor Lindzen said that he didn't think data was the gold standard. I mean, if you say you don't believe models, and you say you don't believe data, what do you believe? And to some extent, I think a nuanced version of what he said is correct. You don't believe every data set, because some data are wrong. What you look for, again, is you look for lots of data sets that agree. And if all the data shows the same thing, you can be pretty sure that's right. Now, if the data don't show the same thing, then you've got a problem. And then you have to decide which data set you believe or not. And hopefully, I'll have time to get to that, although I'm not sure I will. The other thing I want to say before I start to get into some detailed critiques is this question about no cause for alarm, Professor Lindzen repeatedly said that. And I think it's important to recognize, that's a value judgment. That's not a scientific judgment. And before the lecture, he was smoking. And so that's a risk. He's decided that that's a risk he's willing to take. But not everybody would take that risk. And so-- [CLEARS THROAT] I'm losing my voice. When he says that there's no cause for concern, he's giving you his value judgment on that. OK, so I want to get to a few points. Let me find the slides. Let's start off with-- sorry about that. It's a lot harder to prepare for a rebuttal than it is to prepare for your actual talk. Let's start off with the work he showed that the climate models were overestimating. He actually didn't spend a lot of time talking about it. But it's basically this paper. And I know he's revised it. I have not seen the revision. So I have to talk about this. But this is a paper that he published in GRL about a year ago. And it was picked up. You probably saw it on Fox News or the Wall Street Journal. And basically, it's the argument that he gave very briefly that the climate sensitivity is very low. And there are a couple of problems with this. This work has been severely criticized in the peer reviewed literature. So the first thing is it only looked at the region from 20 north to 20 south. And it turns out that clouds are important at all latitudes, not just 20 north, 20 south. In fact, you cannot, I would argue, infer a climate sensitivity by looking at 20 north to 20 south. So my analysis included all latitudes. It was a global average. I mean, there may be a few degrees at the pole where the satellite doesn't go over. But not many. I'll skip this plot. All right, and this has been very-- this has been criticized. This is a paper that came out by Dan Murphy at the Aeronomy Lab. And he basically said, the analysis of Lindzen and Choi erroneously applies global concepts to a limited region. I guarantee if I had published a paper that looked at 20 north to 20 south, he would be all over me about that. That's irresponsible. That's not science, blah, blah, blah. Secondly, if you look at the details-- this is actually a slide from a talk he gave not long ago. He talked about lags. And he said I didn't take lags into account. And the way Professor Lindzen takes lags into account is-- can you see that. All right, I have to go like this-- is he looks at periods of time, so periods of warming. So this time series. This is surface temperature. And this is a period of warming. Here's a period of cooling. Here's a period of warming. He takes these changes and uses them at a unit in order to get around lags. And what you notice if you blow this-- RICHARD LINDZEN: The paper is not [INAUDIBLE].. ANDREW DESSLER: All right. OK, so-- no, no. All right, this is a plot from his analysis. And it shows that he used-- I mean, I probably am explaining it differently than he would explain it, but this is what's in the paper. And so it looks at these temperature increases and it looks at temperature decreases. Temperature increases are red and temperature decreases are blue. And if you read the paper, it says, we want to look for places where the temperature's increasing or decreasing by more than 0.2 degrees Kelvin. And we're going to look at it as a unit. And that's in the paper. I've got a PDF on my computer. I'm sure he does. Now, if you blow it up, you see some really odd things in it. You see what look like some really arbitrary choices. So for example, he doesn't include that temperature increase. RICHARD LINDZEN: [INAUDIBLE]. ANDREW DESSLER: Yeah, you talked about Lindzen and Choi. You said as corrected by-- RICHARD LINDZEN: [INAUDIBLE]. ANDREW DESSLER: Well, it's not published yet. RICHARD LINDZEN: I do have [INAUDIBLE].. I showed you what was done. ANDREW DESSLER: All right. I'm going to explain, though, why people don't believe this. And this is published literature. RICHARD LINDZEN: [INAUDIBLE]. ANDREW DESSLER: All right. Well, I think it's important, though, for people to see the criticisms. Just because you've corrected them, why did you publish this in the first place? This is obviously wrong. And people have to see why it's wrong. Look-- RICHARD LINDZEN: [INAUDIBLE] to acknowledge it. ANDREW DESSLER: But I mean, this is obvious. Look, why did you pick the warming to stop right there? Can you explain that? RICHARD LINDZEN: Very easily. But the paper's come out [INAUDIBLE].. ANDREW DESSLER: Well, they picked a lot of places that you can't figure out why they picked the results they did. So here, why did they not extend that? And I'll go over this quickly. Apparently Professor Lindzen disagrees with me presenting this. But this is published. This is work he's done. And this is why people don't believe his work. You see it where it goes over the top there. Now, why would you do that? You see here, it doesn't quite go to the top. And right here-- and so basically, what they've done is, imagine you have some data. And you have a model that goes through the data. But what you do is you go through and you pick the data you want, and then you basically throw out all the other data. And then you say, well, the models are wrong. And I'm not the one-- and this is published. I mean, you could say, well you're attacking him. But this is actually in the peer reviewed literature. And let me quote what Kevin Trenberth wrote. "As shown here, the approach taken by LC09 is flawed and its results are seriously in error. Their choice of dates has distorted the results and underscored the defective nature in their analysis." And I apologize if this sounds harsh, but you know, science is hard. And people look at this and they say, he's thrown out the data that doesn't agree with what he wants. And I have another example of that, actually. So you say, OK, well, that's one example. But let's go on and talk about the hot spot stuff he showed. Yeah, I could find it. Oh, I see the problem. All right. And, of course, I can't find it. Oh, wait. Am I using up all my time? Probably. So he talked about-- I'll just say, he's talked about the hot spot. And what's important to recognize is that there are actually several data sets. There are several data sets that you can use to determine the tropical upper troposphere. And it is true, the satellite and the balloon data show less warming in the upper troposphere than expected. But regardless of what's warming the surface, we agree that the upper atmosphere should warm faster. It should follow what we call a moist adiabat. And if you're a grad student in atmospheric sciences, you will derive that. And he talked about basically-- I think he should one data set, which is a balloon data. And the balloon data agree with the satellite data. But there's another data set where that people used wind data to infer the temperature. And that's a straightforward analysis. And that's this paper by Alan Sherwood. And they show that warming patterns are consistent with model predictions. So you have this thing. We have three data sets. You have the satellites and you have two different balloon radiosonde data sets. And they don't give you the same answer. Now, Professor Lindzen didn't mention this data set. But it's equally valid. And again, if you're a defense attorney, you don't show data which disagrees with your hypothesis. And the key thing here is we don't really know what the trends in the upper troposphere are. The satellite data are plagued with problems. There's no question about that. And future science will resolve this. All right, I will end there. [APPLAUSE] RICHARD LINDZEN: Yeah, I feel that you were finally reduced to a personal attack rather than work, on the business. There were numerous criticisms made of the paper by myself and Choi. And we realized that many of them were correct. And we spent a year correcting everything, because we knew most of those changes, as Trenberth had pointed out, would not lead to terribly different answers. And so virtually everything you said has been corrected. And the answers that I presented here are specifically from the corrected work. The use of tropics primarily is perfectly OK if you share the effect with others. And there may be still other feedbacks. But they establish a particular feedback. With respect to the hotspot, I hope you understand something that Andy said, because it's a very problematic issue in this field. Whenever you have a disagreement between model predictions and data, you can be absolutely confident in this field that there will be somebody who will publish a paper saying we've changed the data. And in this case, you have what Andy calls a straightforward measurement of temperature. I think a few people here might understand how it was done. What Sherwood and Allen did was say there is a relationship between temperature gradients in the vertical, or rather in the horizontal, and wind shear, changes in wind in the vertical. So let us take balloon measurements of wind, derive horizontal gradients of temperature, and then use those to calculate temperature changes. Christy has done this. A number of other people have checked this. The answers come out absurd. But it gives you so much scope in its uncertainty. And remember, uncertainty is something Andy throws at skeptics. When you have this much uncertainty, you can get any answer you want. And so the answer was used to say the troposphere is actually changing much more than the direct measurements of temperature or radiance suggest. ANDREW DESSLER: [INAUDIBLE] the data disagree. RICHARD LINDZEN: Who's using weak uncertainty to muddy the waters? ANDREW DESSLER: This is a case where the data don't agree. Data don't agree [INAUDIBLE]. RICHARD LINDZEN: I know, but I'm saying using wind to measure temperature. I think Mike [INAUDIBLE],, and old tropical meteorologist here, is smiling. I mean, this is an absurd way to do it. SPEAKER 1: We can get into that more in the question and answers portion of the debate. Now we have Professor Cannon. JONATHAN CANNON: Is it impossible for me to move this? Is this yours, Andy? Is this your laptop? ANDREW DESSLER: Your presentation's not going to be on this. JONATHAN CANNON: No, no, no. But I'm going to put my paper there. ANDREW DESSLER: Thank you. JONATHAN CANNON: Thanks, great. So this is an exciting day. [LAUGHTER] Thank you all for coming. And thanks to Professor Lindzen and Professor Dessler for giving their informed views and their passionate views. I mean, one thing I think this shows is that science is an intellectual endeavor, but when you become committed to a point of view, it becomes a passionate endeavor as well. And the stakes are big here. And I think there's no surprise that people feel passionately and think passionately on one side or the other. I also would invite the law students to note the analogies that have been drawn by both our scientists to the legal process. With the proponents of anthropogenic climate change and the need to do something about it cast as the prosecutors, making prosecutorial errors, and the defendants, those that are skeptical of those claims being cast as defendants with strong and capable representation by folks like my colleague, Jason. So what I'm going to do here is outline a policy approach, assuming basically the science AS understood by Professor Dessler. That is, the question is, what policy should we adopt in the face of potentially serious, but uncertain, adverse consequences from human induced climate change? And I'm going to give an answer that's a conventional answer, quite current in policy circles. Certainly not a consensus answer, but it's one that should be familiar to most of you who are familiar with these debates. Here the science doesn't drive the policy, of course, as both of our speakers have made clear, but it certainly informs the policy. And depending on which view of the science that you accept, you would be more or less concerned about future risks. When we talk about a policy, we talk about several components of policy, I'll just roughly outline those and then get into the discussion. We want, first of all, to set some of policy goal to orient our efforts. We may set more specific objectives under that goal. In the case of climate change, those would be emission budgets or something of that kind to give us a concrete target to shoot at. We'd want to select some instruments to use if we decided to reduce greenhouse gas emissions. What instrument or instruments would be the best to do that? And then we want some provision for monitoring and following the implementation of our policy to make sure that we were doing OK. I think the right paradigm for making the initial decision, that is, what our policy goals should be, is a risk management paradigm. We try to do our best to assess the risks, taking into account the uncertainties as we are able to understand them. We make a judgment about what risks we are willing to accept. And we work within that judgment to try to establish a policy that will limit risks to that level. Information on costs and benefits is relevant and important to these judgments, but I don't think, in the case of climate change, they are determinative, or should be determinative, because there's such a huge range in estimates of both costs and benefits related to various climate options. So we already know how to do risk management. This is not an arcane methodology. We do it when we buy insurance. We do it when we decide what the deductible on our insurance policy should be. We do it when we decide to drive a car rather than walk. And we do it when we decide how fast to drive the car, particularly when we're going up 29 and we have to assess the risks that we'll be pulled over and fined for driving too fast. So this is not something that we're not used to doing. The question here is whether the risks that are posed by continuing climate change under a business as usual scenario are acceptable to us, or whether they are not. And if they are not, what should we do about it? What changes should we make in the business as usual scenario, which projects in increasing greenhouse gas emissions over time? What changes should we make to bring risks to a level where we would feel comfortable? So accepting Andy's view of the science, I would characterize it generally as a substantial risk, that they are significant adverse consequences that will flow from continued warming. And some risk of serious, severe, and irreversible consequences that may flow from continued warming. I think the data that Andy and others are familiar with shows that the likelihood of these impacts increases significantly when you move more than 2 or 3 degrees Celsius above pre-industrial levels. The risks become more serious then. That's about 3.6 to 5.4 degrees Fahrenheit. That suggests the wisdom of limiting, of adopting a policy that would try to limit temperature increases, increases in the Earth's temperature, to that 2 degrees, 3 degrees Celsius range, right? Some people argue we should be even more aggressive and set a goal that limits warming to 1 degree Celsius. I think we're right up on 1 degree Celsius already in terms of change that's already committed in the system. And therefore, I think that's not particularly feasible or useful as a goal. And I don't advance that. Importantly, there are risks on the other side. There are risks that we will spend a lot of money pursuing such a policy. And that it will turn out, as Professor Lindzen has said, that warming is small or even nonexistent. And that we will have spent money for nothing. There's also a risk that by entering onto a program like this, we will create severe dislocations to the economy that will cause way more harm than we could be expecting to reduce through reducing the risk of climate change. That suggests the wisdom of waiting, at least until we know more, at least until some of the uncertainties that are clear from the debate are resolved. We assume that they will be resolved over time because we trust science and the progress of science. And in the meantime, we also may develop technologies that we don't have, that are not currently available, in order to allow us to do reductions more cheaply than we were able to do them now, and therefore, avoid some of the economic risks. How do you resolve this? It's a matter of judgment. In my own sense, I think there are likely fewer risks to overreacting to the threat of climate change than there are in underreacting. And we can frame our policy in order to minimize the risks of overreaction to further protect ourselves. First, dealing with the risks of overreaction. The risks of engaging in an overly-- what turns out to be down the road an overly aggressive climate change policy will likely be easier to undo than the opposing risks. Why? Because we will have made investments in non-fossil fuel technology. If we decide those turn out to be not necessary, we can disinvest. We can phase them out. We can reinvest in what presumably remain the cheaper or more efficient fossil fuel alternatives. We can also avoid serious adverse consequences to the economy that we don't anticipate, but that materialize over time. We can avoid those by building into our policy checkpoints and safety valves, so if the price of carbon, for example, goes much higher than we anticipated with significant threat to the economy, we can drop that price down, or move back in other ways to avoid those serious effects. So what about the other side, the risks of underreacting to climate change? If it turns out that we do have serious and irreversible impacts from ongoing climate change, those are, by definition, irreversible. They can't be undone. And they present, at least some of them present, very serious difficulties and expenses in adapting. And avoiding those impacts, if it becomes clear-- if we wait and it becomes clear that-- yes? I have five minutes. --and it becomes clear that we have to speed along here-- [LAUGHTER] --then it becomes-- then the issue is how quickly can we react? And because there's such long time lags in the climate system, if we've already put the greenhouse gases in the atmosphere that are going to cause that, it's very difficult, if not impossible, to correct in response to that new knowledge, and prevent the irreversible or serious impacts from occurring. Effectively, we will have locked in long term impacts. So my conclusion-- I'm sure Jason will have another view of how you balance out that, and clearly there are other factors to take into account. But just as a rough sense of how I think this kind of judgment would be made, and some of the factors that go into it. So the conclusion, my conclusion, is we should act to reduce greenhouse gas emissions consistent with the long term goals of limiting climate change, or limiting warming, to the 2 or 3 degrees Celsius that I've indicated. And I think we should begin soon. I mean, soon. I mean, within five years. Sooner than two or three decades down the road. And why is that? Because in the absence of a limiting policy, our emissions will continue to increase. There will be-- the concentrations of greenhouse gases, particularly CO2, in the atmosphere will increase. And that increases the risks of climate change down the road. And if we begin now, this is a recent-- I know the National Academy of Sciences isn't always accepted as the gold standard, but a recent National Academy of Sciences study showed that limiting warming within this 2 to 3 degree target range is feasible if we do begin now. May be feasible if we do begin now. Likely feasible if we begin now for the 3 degrees. But if we wait, either of those goals becomes not feasible. We can't expect to be able to make them on reasonable estimates of our technological capability and development. So that commits us, again, to-- or exposes us again to further risk of climate change if we don't start to do something now. Now, by saying start now, I'm not saying blow the guts out of the economy. I'm saying, set a price for carbon. I think that's the appropriate instrument, either through a cap and trade program or through a carbon tax or a carbon emissions tax. And begin to ramp up that price so the economy can begin to respond. And you do it in a measured way so that you minimize the dislocation caused by the economy. But you get on the road and you create the institutions that are going to be there if, over time, it becomes clearer still that the risks are real and serious. The institutions that you can use to further advance the policy. I have only a brief time. I'm just going to mention-- let me mention geoengineering is an option here. I think it should be considered carefully. There's some collateral risks that need to be considered. But I'm not committed to reducing greenhouse gas emissions as the only way of limiting or controlling climate change if other useful and safe alternatives. The final thing is, of course, the international setting, the global nature of this. Many people argue, with force, and they're right, that this is a global issue. And that a policy by the United States that substantially reduced greenhouse gas emissions would not be sufficient to make a substantial change at all in the worldwide trajectory for greenhouse gas emissions. Is that a reason for the United States not acting? That's a close question to me, and I think in the minds of at least 98 senators at one point, that was a real question. But I mean, my view would be, let's start. Let's make a commitment. Let's make a modest commitment that could be followed by an increasing commitment, and at the same time less aggressively engage the world community, hoping that our commitment can be some catalyst for further developments in the international setting. But I'm also realistic enough to know that may not happen. And if it doesn't, we ought to adjust-- as part of our adaptive management, we ought to adjust accordingly. Am I within time? Thanks. Thank you. [APPLAUSE] JASON JOHNSTON: Just gonna straighten up here a little bit. I've entitled these remarks provisional. Some provisional remarks are an uncertainty in climate change policy. And I very much have my economist hat on today in thinking about this problem. It's true, as Andy said, that I have a long-- well, I don't know if it's long, but I have a paper kicking around in the internet entitled "A Cross Examination of Global Warming Advocacy Science." That's a bit more of a legal hat. But of course, it's entitled "A Cross Examination." And you're all happy to go and find that. You can find it right away and Google it and get me some more downloads. [LAUGHTER] But all I tried to do in that paper is look through the-- first learn a little bit more about climate science, and then see if I could ask some questions and see if there were maybe questions to be asked of the version of climate science presented by the IPCC. And that's all that paper does. It asks questions. Can't answer questions. Good heavens. I'm not a climate scientist. I could, at best, ask questions that are not crazy and that are actually reasonable and plausible. And I think I have. There's a very well-known climate scientist at Georgia Tech, and Professor Curry is one of the leading people in the world studying the relationship between global warming and hurricanes. And she is, by no means, a skeptic. If anything, she would be one of the establishment climate scientists. And recently on her blog that she just started up, she thinks that maybe something like a kind of cross examination approach might be a way of giving the IPCC, or restoring some of the credibility that the IPCC has lost as a consequence of climategate. So in any event, on to today's talk. Why do I call it provisional? Well, because the kind of uncertainty-- when economists think about uncertainty, they're used to thinking in terms of standard risk analysis, the way actuaries do it. The probability distribution over potential realizations of harm, PI, HI's, and then you can figure out things like expected harm, and figure out how much you should spend to avert a particular amount of expected harm. Economists have come to think that with climate change, we can't really do that. Because we look to the science, and it seems like what we've got is what, in economic terms, is known as Knightian uncertainty. We don't know the probabilities that different things might happen. And we really don't know the magnitudes. There are ways that economists have tried to estimate the magnitude of harm in the event of-- especially in the event of what? Really big, catastrophically large temperature increases. I'll show you what economists have done. You won't be very impressed. And you'll be probably pretty shocked to see what the state of the art is. The state of the art is very bad. That's what makes this a great problem, and interesting from my point of view. We don't know how to do this. There is no accepted economic framework for dealing with what we call Knightian uncertainty, which is a situation where we don't know the probabilities of harm. We don't know the magnitude of harm in the event of, in particular, certain catastrophic climate change outcomes. Catastrophic means a certain thing an economic land. It means extremely large losses that occur with a very low probability. Now, there are some problems with traditional economic analysis when you try to figure out how much to spend to avert a potentially catastrophic loss if people have what economists think are reasonably looking preferences. Basically, people's preferences are such that they wouldn't spend an infinite amount today to avert some kind of harm tomorrow. And that seems like reasonable preferences. Because if you spend an infinite amount today to avert harm tomorrow, it means you'll like terminate the present generation to protect the future. That just doesn't make sense. It's inconsistent. If people have these reasonable preferences which you end up with in traditional economic analysis is to spend an infinite price to reduce a catastrophic-- potentially catastrophic risk, even as the probability goes to zero. Well, that can't be right. Marty Weitzman, who's an economist up at Harvard, generated that result in a paper relying on some work done by a truly brilliant economist named Michael Schwarz, who's now at Yahoo.com. And in any event, he found this result and he gave a name to it. But everybody looks at this and says, well, this is wrong. We've got big problems. We can't apply standard economic analysis to this problem. So what do we do? Well, we've got to figure this out. This is probably one of the most interesting and important problems in economics. If we think we can use tools of economic analysis to provide some policy guidance, then we've got to try to figure out this problem. What's the problem? Potentially catastrophic loss, low probability of harm, but we don't even know the probability of harm. We can't even really attach numbers to the probability of harm. So I'll try to suggest during the quick talk today, I'll talk a little bit about what actually has happened. And there seems to be some hope that we can get a handle on this problem and maybe come up with some concrete policy recommendations. So where does the probability of catastrophic temperature increases come from? There are several papers now. I think there's five or six in the literature. Roe and Baker got this ball rolling. They're climate scientists at the University of Washington. And they showed-- in a very elegant way that economists like because it's a neat solution You don't need a model or crank through like a computer for this, you can just solve it-- that if you assume a predominance of positive feedbacks in these models, you're always going to get the possibility, some positive probability, of really monstrous temperature increases, bigger than like 5 degrees centigrade, 5, 8, 10, 12 degrees centigrade, so that we couldn't even understand what the world looked like then. Dick Lindzen had a graph which showed you graphically what happens when you have uncertainty over the feedbacks, and how, then, that is going to generate because of that curve-- you might remember that curve that went up very steeply as you went over to the right-hand side as f approached 1. When you start to have uncertainty and f gets big, it just blows up the potential temperature increases. Now, do we know anything? We assume that we just don't have that good information about the feedbacks. Here's a quote from a very recent paper that came out in the Proceedings of the Royal Society by, I think, a colleague of Dick's, and from MIT and a person from Harvard. "Modelers of global climate know that feedback effects are crucial. They observe simulation outcomes that are skewed to high temperatures, hence the aerosol effect that all the models assume that cancels those. The existence of fat tail"-- fat tails we've all heard about from what? The financial crisis. How could that have happened? Gary Gorton, who's an economist at Yale says, there's no way that could have happened, that all the mortgage backed securities could have tanked at the same time. Well, it happened, OK? Even though you had no probability attached to it, it happened. So something must have been wrong. Well, Gary's running around trying to explain how, well, he wasn't actually wrong. And it did happen, but he was right. Which is hard to explain because it happened, but he said it couldn't. In any event-- [LAUGHTER] --the point here is this is the fat tail. The fat tail means there's more probability in these potentially big temperature increases than you'd think if you just had, say, a normal-- based on other things. "The existence of a fat tail is not an artifact of computational general circulation model simulation that will disappear with repeated Monte Carlo trials"-- Monte Carlo trials, you just run the models with different parameters and see what happens-- "but rather an inherent consequence of the presence of positive feedback." This is a very recent paper. As they say, "PDFs, Probability Density Functions, with fat tails present formidable problems for conventional expected value cost benefit analysis, because of the relatively higher probability of high cost outcomes. And here they're talking about Roe and Weitzman. Simple consequences that damage functions, that is, we're trying to estimate the damage from global warming, because the damage from global warming is the benefit of what? Spending money to do things today to reduce the probability of that damage from happening. We'll have unbounded expected value at all times. And any practical policy designed to reduce the likelihood or consequence of loss probability outcomes must address not only the shape, but its evolution over time. Now, this is an interesting question. What do we know? What are we going to learn over time-- and hopefully we'll learn something-- about the likelihood of these really catastrophic outcomes? And I think the-- I'll conclude with this. I think the optimal policy, I think these guys are right that the optimal policy has to take into account our learning over time about the probability of these catastrophic outcomes. Well, what have economists been doing? We don't really know the harm from any-- we don't have very much information about the expected social cost of any change in temperature, let alone a catastrophic one of 8 or 9 or 10 degrees centigrade. Nordhaus is pretty much the state of the art here. He's been doing this since, well, since long before this book came out. So since the mid-'70s. That's 35 years. He's a careful economist. He builds models that try to take into account how markets really will respond to different changes in the environment, and the policies that people interact in. Economists, though, can't predict very well anything that's relevant to this problem. That's the bad news. Example, you think one thing economists predict is how people adapt to changing prices. Well, back in 1979, Nordhaus did a book, really state of the art in terms of energy economics and predicted changes. He was trying to predict how the demand for energy would respond to price changes, which is a pretty basic economic question. Well, I've got up here on the screen, what? The efficient forecast, the market forecast, those are two forecasts that Nordhaus generated. One assuming the market was efficient, one assuming the market just was as it was. And then over on the right-hand column, these are 1979 forecasts, are the actual oil consumption. So what did he forecast? 1985, 101.4 quadrillion BTUs would be consumed. That was the efficient forecast, 86.6. The point is, compare for both years, 1985 and 1995, which were his projections, the projected demand under both the efficient forecast scenario and the market forecast scenario with the actual consumption. The actual consumption was much, much lower than both. Why? He tried to figure out and use actual demand and supply elasticities in the relevant markets. But what economists cannot predict, even in the short to medium term, is how people will adapt to changing-- to large price changes. For the same reason, economists cannot predict what? How we would adapt to large changes in climate. Now, this cuts both ways. You could say, well hey, we could have a pretty high carbon tax and people might adapt to that in ways that minimize the cost, the total cost, of actually reducing and decarbonizing the economy. That's possible. But a corollary consequences is you also can't predict very well how people would respond to a completely different climate, a much warmer climate. Take, again, Nordhaus. Nordhaus's model, it's called the DICE Model. It's probably pretty much the state of the art in trying to think about the economic consequences of global warming. Where do his numbers come from? Numbers for what? Numbers for the economic consequences of global warming. What's it going to cost, and therefore, what should we spend today to try to prevent it? Here's what he says. "If necessary, use subjective or judgmental probabilities and analyses of climate change, that is, not real frequentist probabilities," because there aren't any, of course, historical observations, or limited historical observations. We can't estimate the economic impact of a 3 degree rise in global temperature from historical data, because nothing resembling this kind of global change has occurred. What do researchers rely on? A variety of techniques, including personal judgments, betting markets, and surveys of experts. As recently as 1995, virtually all of the economic numbers for the costs and benefits of climate change were based on surveys of economists. So what do we get? He went out and surveyed people and said, what do you think the probability is of a catastrophic change in temperature that would cause a 30% decline in global GDP forever? That's ramping us back at least a better part of a century in terms of economic conditions. That's what he did. He did this survey. People gave him their opinions about the likelihood of this. And then what did he do? Then he and his co-author, to come up with the social cost of carbon, assumed-- they came up with an estimate for the global damages from this. In their estimate of the global damages from a 2 point-- well, now 3.0-- degrees centigrade warming, the total damages they estimated we're going to be 1.5% of GDP continued forever. But 1.02% of that GDP loss, or 68% of the total that they published in the book, which is the gold standard for coming up with these numbers, is due to survey-based catastrophic impact scenarios. It's based, basically, on nothing. Nobody has any experience with this. And if you survey a bunch of economists and ask them what's going to happen based on stuff they do know about, there's enough problems with that. Let's ask a bunch of economists what's going to happen when the climate changes by 3 degrees centigrade, which is something they have no knowledge of, what are they going to do? They're going to herd. They're going to try to guess what other people are going to say, and then you try to say what other people are going to say so you don't look like what? Look like you're an outlier and a crazy person or an idiot. So that's-- actually, there's a big literature on that. [LAUGHTER] And by the way, we do all have to remember, when the Civil War started and Sherman was asked how long it was going to last, he said, this is going to be a long and bloody war that's going to last four years, at the time when everybody in the North said, this is going to be short and over in months. They thought Sherman was crazy. I guess he wasn't. So anyway, how can we think about policy with this kind of fundamental uncertainty? Here's one interesting idea set out in this recent paper by Mahadevan and Deutche. It's possible to determine-- this is mathematically-- all the combinations of F1 flux and positive feedback that give certain probabilities of a temperature increase below a set amount. So in other words, if you set a threshold for the temperature increase, then you could basically figure out all the different parameter values, at least for flux and feedback, to get you a probability that are consistent-- to give you a probability below that. And then if you know the cost of the policy options that are available to lower either the emission rate or reduce the feedback parameter-- I think this is an interesting issue, because it goes to land use patterns. It goes to a lot of things that we have control over-- change the feedback parameter, then you could design an optimal economic policy. Do I know if this is true or not? I don't know if this is true. These are two climate scientists who came up with an interesting policy recommendation. Here's what I'd be more concrete about and finish with. What do we know? We know that all population projections of global population generated by United Nations endorsed groups over the last 30 years have been wrong. They have vastly overestimated population increases and population growth. Why have they been wrong? They have failed, until now recently, to understand the enormously powerful relationship between education levels, especially educational levels of women, and population growth rates as operates through the fertility rate. In other words, if you want to do what? If you want-- if you really care about future generations, if you want people to be wealthier and healthier, and you want to see a decrease in global population by 2100, which is now seriously talked about by demographers, what do you do? You spend your money today to educate women in developing countries. It's not real complicated. You generally increase education levels. But it also is consistent with governance structures that treat women and men equally, and of course, in the world today, that is certainly not true that every governance structure would-- that every kind of political system that we have in existence today would even come close to agreeing with that goal. So what do you do? You spend money today to maximize, I think, economic growth, to especially increase developing countries by increasing educational levels, and you try to minimize vulnerability to climate change no matter what the cause of climate change. Anthropogenic or non-anthropogenic. And there's things we can do that really would have concrete impact in minimizing vulnerability to climate change, especially in developing countries, but also in the United States and other development. So that's it. [APPLAUSE]

Channel: University of Virginia School of Law

Views: 72,063

Rating: 3.9702277 out of 5

Keywords: climate change, global warming, Andrew Dessler, Richard Lindzen, Jonathan Cannon, Jason Johnston, UVA Law

Id: l9Sh1B-rV60

Length: 114min 11sec (6851 seconds)

Published: Tue Oct 12 2010