WEBVTT 00:01.300 --> 00:02.830 RONALD SMITH: Yeah, so this is a long list of things 00:02.833 --> 00:06.733 that one might do to reduce global warming. 00:06.733 --> 00:08.633 But they're all very difficult, if not impossible. 00:11.633 --> 00:18.073 So are there any points that you want to make about these? 00:18.067 --> 00:20.467 Any questions about what I mean by them? 00:20.467 --> 00:21.967 I think we did go over this last 00:21.967 --> 00:23.997 time, if I'm not mistaken. 00:26.567 --> 00:30.997 There are lots of other recommendations or suggestions 00:31.000 --> 00:33.770 that have been made under the general title of 00:33.767 --> 00:35.967 geoengineering. 00:35.967 --> 00:41.797 But one I indicated there is to install an umbrella-like 00:41.800 --> 00:47.570 object out in space, somewhere between the earth and the sun, 00:47.567 --> 00:51.697 so that it casts a shadow on the earth. 00:51.700 --> 00:54.670 And, therefore, reduces the total amount of sunlight 00:54.667 --> 00:57.227 hitting the earth. 00:57.233 --> 01:01.073 A related suggestion is that we put a layer of small 01:01.067 --> 01:06.167 particles, kind of like volcanoes have done naturally 01:06.167 --> 01:09.767 from time to time, put a layer of small particles in the 01:09.767 --> 01:13.427 stratosphere that will increase the albedo of the 01:13.433 --> 01:17.903 earth and reflect the Sun's radiation back into space, and 01:17.900 --> 01:19.930 thereby cool the earth. 01:19.933 --> 01:23.533 The question for something like that, though, is what 01:23.533 --> 01:25.503 material do we put in there? 01:25.500 --> 01:28.070 They would have to be very fine particles or they would 01:28.067 --> 01:30.997 just fall out. 01:31.000 --> 01:33.730 If they have some kind of weird chemistry, like sulfate 01:33.733 --> 01:37.603 particles, what would they do to the chemistry of the 01:37.600 --> 01:38.170 atmosphere? 01:38.167 --> 01:41.967 So whenever you think of something, some little clever 01:41.967 --> 01:45.697 idea pops into your mind, unfortunately there's probably 01:45.700 --> 01:51.170 100 unintended consequences on these geoengineering ideas. 01:51.167 --> 01:53.997 So they have to be thought through extremely carefully 01:54.000 --> 01:56.700 before any of them could be implemented. 02:03.567 --> 02:09.397 While climate research is quite a mature area now, there 02:09.400 --> 02:13.570 are some areas within it that require further thought. 02:13.567 --> 02:17.497 For example, we're still missing a complete theory of 02:17.500 --> 02:18.670 the ice ages. 02:18.667 --> 02:22.127 We think that maybe this Milankovitch forcing has 02:22.133 --> 02:22.773 played a role. 02:22.767 --> 02:27.897 But that Milankovitch forcing is very weak actually. 02:27.900 --> 02:32.300 The obliquity change, the tilt of the earth's axis, is only a 02:32.300 --> 02:36.000 couple of degrees one way or the other. 02:36.000 --> 02:40.700 The procession theory, which changes the orientation of the 02:40.700 --> 02:43.970 tilt of the earth's axis, changes the way the seasons 02:43.967 --> 02:46.297 work, but oppositely in the two hemispheres. 02:46.300 --> 02:50.000 So if you're looking globally, that influence cancels right 02:50.000 --> 02:51.470 out completely. 02:51.467 --> 02:53.567 So you have to make up some argument about why one 02:53.567 --> 02:55.927 hemisphere is more important than the other. 02:55.933 --> 02:58.133 So we're missing a complete theory there. 02:58.133 --> 03:02.533 We're also missing a theory of Venus' climate history. 03:02.533 --> 03:06.033 How it got from being an earth-like planet to something 03:06.033 --> 03:07.803 that's much, much hotter. 03:07.800 --> 03:10.730 And that's important, as I mentioned, because there's 03:10.733 --> 03:14.803 some worry that the earth's atmosphere might follow a 03:14.800 --> 03:16.170 similar path. 03:16.167 --> 03:20.497 Theory of solar variability, now we know that the sun has 03:20.500 --> 03:25.030 gotten hotter over the millennia, over 03:25.033 --> 03:28.233 the age of the earth. 03:28.233 --> 03:32.133 And we know that there's a sunspot cycle, an 11 year and 03:32.133 --> 03:36.003 a 22 year cycle, on the number of sun spots on the 03:36.000 --> 03:38.100 surface of the Sun. 03:38.100 --> 03:40.330 And we think that that may have had a very minor 03:40.333 --> 03:44.173 influence on the amount of radiation reaching the earth. 03:44.167 --> 03:47.297 But that theory is not well fleshed out. 03:47.300 --> 03:49.600 There are some statistical relationships 03:49.600 --> 03:51.900 that have been proposed. 03:51.900 --> 03:55.800 But the physical mechanisms for this make almost no sense. 03:55.800 --> 04:00.570 And so we need to further understand why the sun might 04:00.567 --> 04:03.427 vary in its strength and what influence that 04:03.433 --> 04:05.773 would have on the earth. 04:05.767 --> 04:07.727 The theory of positive feedback I'm going to be 04:07.733 --> 04:09.873 talking more about that in just a minute-- 04:09.867 --> 04:13.697 the idea that there are these processes on earth that will 04:13.700 --> 04:16.030 tend to amplify climate change. 04:16.033 --> 04:18.703 The ones that we've talked about already have been ice 04:18.700 --> 04:21.030 albedo feedback. 04:21.033 --> 04:21.173 Right? 04:21.167 --> 04:25.067 More snow in a colder climate makes the albedo larger, 04:25.067 --> 04:29.497 reflects more radiation, cools the earth more, and that might 04:29.500 --> 04:31.670 spiral on itself. 04:31.667 --> 04:34.267 There's the water vapor feedback. 04:34.267 --> 04:37.967 A warmer climate would put more water vapor in the 04:37.967 --> 04:39.667 atmosphere certainly. 04:39.667 --> 04:42.597 Water vapor is an important greenhouse gas that would warm 04:42.600 --> 04:44.900 the climate further and so on. 04:44.900 --> 04:47.700 And then one that's been talked about more recently, 04:47.700 --> 04:51.100 seems to be going on with the last 100 years or so, is 04:51.100 --> 04:59.230 carbon dioxide feedback, where a warmer climate is beginning 04:59.233 --> 05:05.703 to allow carbon trapped in permafrost to decay and be 05:05.700 --> 05:07.030 added to the atmosphere. 05:07.033 --> 05:12.003 Also ocean water, when you heat it, it can hold less 05:12.000 --> 05:15.870 carbon dioxide dissolved in water. 05:15.867 --> 05:17.667 So that CO2 is going to be added. 05:17.667 --> 05:20.827 So this acts on a slower time scale perhaps, but there's 05:20.833 --> 05:24.533 some worry that there's a carbon dioxide feedback that 05:24.533 --> 05:26.833 may be starting to happen. 05:30.400 --> 05:32.700 That one has to do--that last one has to do with this next 05:32.700 --> 05:35.770 one, how greenhouse concentrations are controlled. 05:35.767 --> 05:37.967 We don't have a complete understanding 05:37.967 --> 05:38.967 of the carbon cycle. 05:38.967 --> 05:43.567 Or how some of the other greenhouse gases, like 05:43.567 --> 05:50.297 methane, ozone, oxides of nitrogen there's not a 05:50.300 --> 05:53.470 complete understanding of how they are controlled in the 05:53.467 --> 05:54.727 earth's atmosphere. 05:54.733 --> 05:58.473 The last IPPC report, the one that you've been looking at, 05:58.467 --> 06:05.897 the AR4, is full of apologies for how little they can say 06:05.900 --> 06:09.030 about regional climates. 06:09.033 --> 06:13.973 The problem is that these global climate models roughly 06:13.967 --> 06:17.927 agree in their predictions of how the global temperature 06:17.933 --> 06:18.403 will change. 06:18.400 --> 06:22.370 But they disagree, to a large extent, about how the climates 06:22.367 --> 06:25.167 of different continents and different sub regions within 06:25.167 --> 06:27.167 continents will change. 06:27.167 --> 06:29.097 So this is a big area of research. 06:29.100 --> 06:32.800 In fact, my group is interested a little bit in how 06:32.800 --> 06:38.670 global changes will influence local and regional climates. 06:38.667 --> 06:41.827 We don't understand very well how clouds would change, 06:41.833 --> 06:45.303 aerosols, how those two things would impact the earth's 06:45.300 --> 06:47.130 albedo in a different climate. 06:47.133 --> 06:50.873 And this question of whether we can re-bury carbon dioxide 06:50.867 --> 06:53.997 into the earth is receiving a lot of funding and a lot of 06:54.000 --> 06:56.230 research at the moment. 06:56.233 --> 06:58.033 Those are some of the active areas. 06:58.033 --> 07:02.933 If any of you were to get involved in climate research, 07:02.933 --> 07:05.703 there's a good chance you'd probably be working on one of 07:05.700 --> 07:08.670 those problems. Although, maybe I've left off a couple 07:08.667 --> 07:10.767 of newly emerging ideas. 07:13.767 --> 07:16.997 So the last thing I want to talk about in this set of 07:17.000 --> 07:22.000 lectures is something called climate sensitivity. 07:22.000 --> 07:28.470 And it's basically an attempt to simplify and quantify how 07:28.467 --> 07:32.167 earth will respond, how the earth's climate will respond, 07:32.167 --> 07:35.367 to changes in radiative forcing. 07:38.067 --> 07:42.267 I'll define radiative forcing in just a minute. 07:42.267 --> 07:48.797 There are two different common definitions for climate 07:48.800 --> 07:49.600 sensitivity. 07:49.600 --> 07:51.400 So I need you to be aware of both of these. 07:51.400 --> 07:55.630 So when you pick up a paper on climate sensitivity you can 07:55.633 --> 07:59.533 spot which definition the author is using. 07:59.533 --> 08:04.303 One definition is, how much would the earth's climate warm 08:04.300 --> 08:07.800 if there was a doubling of CO2? 08:07.800 --> 08:10.570 That's a commonly used definition for climate 08:10.567 --> 08:11.867 sensitivity. 08:11.867 --> 08:14.997 The other one is, how much would the earth warm if you 08:15.000 --> 08:19.030 increased the radiation hitting the surface of the 08:19.033 --> 08:24.333 earth by one watt per square meter? 08:24.333 --> 08:28.633 Now these two things can be related, roughly, because a 08:28.633 --> 08:33.233 CO2 doubling when you add CO2 to the atmosphere you increase 08:33.233 --> 08:35.103 the absorption and the 08:35.100 --> 08:38.530 re-emission of longwave radiation. 08:38.533 --> 08:45.003 And a doubling of the CO2 is approximately adding 3.7 watts 08:45.000 --> 08:48.570 per square meter of radiative forcing back to the 08:48.567 --> 08:50.097 surface of the earth. 08:50.100 --> 08:53.400 So you trap the outgoing longwave radiation, you warm 08:53.400 --> 08:56.230 the atmosphere, and then that readmits back to 08:56.233 --> 08:58.933 earth about that much. 08:58.933 --> 09:02.303 So you can go back and forth between those two definitions 09:02.300 --> 09:07.800 using that kind of relationship, that quantity. 09:07.800 --> 09:10.000 Now the reason I like it, the reason I want to stick it in 09:10.000 --> 09:13.600 here, is that it's being used more and more as a compact way 09:13.600 --> 09:15.330 to compare different models. 09:15.333 --> 09:18.203 For example, the National Center for Atmospheric 09:18.200 --> 09:20.600 Research has a global climate model. 09:20.600 --> 09:23.370 The Department of Energy has a global climate model. 09:23.367 --> 09:26.867 NASA has a global climate model. 09:26.867 --> 09:32.027 Several in Europe, the Russians have one. 09:32.033 --> 09:33.073 How do you compare them? 09:33.067 --> 09:39.227 One way is to use this measure of their sensitivity. 09:39.233 --> 09:42.003 It's been found in that way that there are some 09:42.000 --> 09:44.270 significant differences between these models. 09:44.267 --> 09:46.667 And it's been a help to find out why these 09:46.667 --> 09:49.197 models are so different. 09:49.200 --> 09:52.470 Also, it's a nice way to summarize some paleoclimate 09:52.467 --> 09:56.697 data of the type that you and I have been looking at a 09:56.700 --> 09:57.830 couple of weeks ago. 09:57.833 --> 10:02.333 And I like it because it illustrates the role of these 10:02.333 --> 10:03.233 climate feedbacks. 10:03.233 --> 10:06.303 So it turns out that these climate sensitivity values, 10:06.300 --> 10:11.330 almost no matter how you compute them, are larger than 10:11.333 --> 10:16.203 you would expect if you left off the feedbacks. 10:16.200 --> 10:19.500 And so by that increase in sensitivity you can show what 10:19.500 --> 10:22.630 role these climate feedbacks are having. 10:22.633 --> 10:25.833 So for all these reasons, we're going to 10:25.833 --> 10:26.703 take a look at this. 10:26.700 --> 10:28.970 Now, so the definitions. 10:28.967 --> 10:31.497 Radiative forcing, extra radiation at the earth's 10:31.500 --> 10:35.100 surface relative to preindustrial time. 10:35.100 --> 10:37.530 That's the way radiative forcing is defined in the 10:37.533 --> 10:39.373 global warming debate. 10:39.367 --> 10:41.967 I wouldn't call that a universal definition. 10:41.967 --> 10:43.097 But in the global warming debate 10:43.100 --> 10:44.600 that's how it's defined. 10:44.600 --> 10:47.530 These are the primary feedbacks we're talking about. 10:47.533 --> 10:51.733 And occasionally you'll find a discussion of a runaway 10:51.733 --> 10:55.073 climate where the feedbacks, the positive feedbacks, are so 10:55.067 --> 10:58.567 strong that they would take over and drive the climate to 10:58.567 --> 11:02.467 an extreme state, like has happened on Venus. 11:02.467 --> 11:05.597 Unlikely for earth, but it's part of the conversation. 11:12.333 --> 11:17.833 I've got to define okay, now there's another variable that 11:17.833 --> 11:22.473 comes in, in how you define climate sensitivity. 11:22.467 --> 11:25.167 And that has to do with, what feedbacks do you include in 11:25.167 --> 11:27.097 the calculation? 11:27.100 --> 11:29.570 So I'm going to define something called the Black 11:29.567 --> 11:34.227 Body sensitivity, which has no feedbacks in it. 11:34.233 --> 11:36.733 It's based on the Stefan-Boltzmann law only, 11:36.733 --> 11:40.203 which you remember, from earlier in the course, is the 11:40.200 --> 11:43.730 fact that the radiation emitted from an object's 11:43.733 --> 11:48.833 surface goes like the fourth power of the temperature. 11:48.833 --> 11:50.103 That was the Stefan-Boltzmann law. 11:52.700 --> 11:54.670 There's something called the Charney sensitivity. 11:54.667 --> 11:59.597 Charney was a scientist who chaired the first 11:59.600 --> 12:02.970 investigative panel on global warming. 12:02.967 --> 12:08.397 And he tried to base their final report on a particular 12:08.400 --> 12:12.930 definition of climate sensitivity that included some 12:12.933 --> 12:16.973 short-term feedbacks, but excluded some of 12:16.967 --> 12:18.067 the longer term ones. 12:18.067 --> 12:21.427 For example, he included water vapor feedbacks, snow/albedo 12:21.433 --> 12:23.503 feedback, and cloud feedback. 12:23.500 --> 12:28.830 But he excluded any feedback connected with the oceans, the 12:28.833 --> 12:31.873 ice sheets, or carbon dioxide changes. 12:31.867 --> 12:35.197 And that's become known, in the literature, as Charney 12:35.200 --> 12:37.200 sensitivity. 12:37.200 --> 12:45.670 It's probably valid on periods of time of, say, well 20 to 50 12:45.667 --> 12:49.597 years, something like that. 12:49.600 --> 12:53.070 When you go to longer terms, however, maybe 100, 200, 300 12:53.067 --> 12:57.327 years, you have to realize that other slower adjustments 12:57.333 --> 13:01.303 in the climate system are going to be happening, such as 13:01.300 --> 13:05.130 changes to deep ocean temperature, changes in the 13:05.133 --> 13:06.203 ice sheets. 13:06.200 --> 13:08.470 Sea level will be rising. 13:08.467 --> 13:11.267 Biomass on the continents may be changing. 13:11.267 --> 13:17.867 And the carbon dioxide may be responding to changes in the 13:17.867 --> 13:21.727 ocean and the land surface, the land biomass. 13:21.733 --> 13:24.103 So that would be called long-term sensitivity. 13:27.900 --> 13:32.700 Makes you realize this subject is not dirt simple. 13:32.700 --> 13:35.900 I mean, it's got some complexity to it, depending on 13:35.900 --> 13:39.300 the time frame that you're talking about and the 13:39.300 --> 13:42.470 feedbacks that you're allowing in the calculation. 13:42.467 --> 13:45.267 So here's the easy one, Black Body sensitivity. 13:48.033 --> 13:50.533 If I've got an object, like a planet, receiving a certain 13:50.533 --> 13:54.403 amount of radiation from the sun, you know that it'll 13:54.400 --> 13:58.570 increase its temperature, or adjust its temperature, so 13:58.567 --> 14:03.767 that it radiates to space just as much as it's 14:03.767 --> 14:05.727 receiving from the sun. 14:05.733 --> 14:09.173 So it'll adjust it's own temperature to come into that 14:09.167 --> 14:11.667 kind of radiative equilibrium. 14:11.667 --> 14:14.767 Well then, if I were to increase the amount received 14:14.767 --> 14:20.067 from the sun by 1 watt per square meter, how much must 14:20.067 --> 14:23.197 the temperature rise in order to establish a new 14:23.200 --> 14:24.100 equilibrium? 14:24.100 --> 14:26.670 Pretty simple idea, right? 14:26.667 --> 14:29.927 So there's the Stefan-Boltzmann law. 14:29.933 --> 14:33.273 I'm going to do a quick little calculus trick here that some 14:33.267 --> 14:34.727 of you may not be familiar with. 14:34.733 --> 14:37.673 But if I take the derivative of F with respect to 14:37.667 --> 14:41.067 temperature, I get 4 times sigma T to the third times the 14:41.067 --> 14:43.227 change in temperature. 14:43.233 --> 14:48.773 And if I rearrange, bring the dF under and that down under 14:48.767 --> 14:50.897 here, I get that formula. 14:50.900 --> 14:57.270 The change in temperature for each change in flux is given 14:57.267 --> 15:02.367 by 1 over 4 sigma T to the third. 15:02.367 --> 15:03.467 I can plug numbers in. 15:03.467 --> 15:05.967 That Stefan-Boltzmann constant is a fixed constant. 15:05.967 --> 15:10.627 I can use the temperature of our planet, of 288 Kelvin, 15:10.633 --> 15:12.703 plug it into that formula. 15:12.700 --> 15:16.970 And I immediately get this rather interesting number. 15:16.967 --> 15:21.497 For every watt per square meter that I add to the earth 15:21.500 --> 15:28.300 in the form of radiative energy, it'll warm by 0.2 15:28.300 --> 15:31.100 degrees Kelvin. 15:31.100 --> 15:38.130 That is the simplest way to compute climate sensitivity. 15:38.133 --> 15:41.303 That's a pretty small number. 15:41.300 --> 15:44.570 And the reason why, as we will see the reason why it's fairly 15:44.567 --> 15:46.397 small is we have not included any of 15:46.400 --> 15:47.670 these positive feedbacks. 15:51.467 --> 15:55.097 Now this is a calculation you can do for yourself. 15:55.100 --> 16:00.830 Just try putting in different values of F or T and computing 16:00.833 --> 16:05.273 it and you'll see that that is the rate at which the two 16:05.267 --> 16:07.767 things are--the slope at which the two things are related to 16:07.767 --> 16:09.327 each other. 16:09.333 --> 16:11.803 Any questions on that? 16:11.800 --> 16:17.200 So we use this value as kind of our foundation, right? 16:17.200 --> 16:21.600 Now, if you look at the IPCC report let me get the lights 16:21.600 --> 16:29.400 down this summarizes the radiative forcing, using the 16:29.400 --> 16:31.170 definition that I gave a moment ago. 16:31.167 --> 16:36.067 This is since the beginning of the industrial revolution. 16:36.067 --> 16:39.867 It shows how much the radiation reaching the surface 16:39.867 --> 16:44.367 of the earth has changed and for what reason. 16:44.367 --> 16:47.997 Carbon dioxide is the biggest reason. 16:48.000 --> 16:51.500 That increase in carbon dioxide, we've seen, caused 16:51.500 --> 16:59.330 about 1.7 watts per square meter added greenhouse forcing 16:59.333 --> 17:00.773 at the surface of the earth. 17:00.767 --> 17:05.097 However, methane, N2O, and the halocarbons have added 17:05.100 --> 17:07.200 something too. 17:07.200 --> 17:11.130 Tropospheric ozone has added more. 17:11.133 --> 17:13.873 And then on the negative side and I talked about this a 17:13.867 --> 17:19.197 couple days ago aerosols, mostly sulfate aerosols from 17:19.200 --> 17:23.530 coal burning, has had a negative effect. 17:23.533 --> 17:27.673 Well when you add all these together, these 17:27.667 --> 17:28.927 roughly cancel these. 17:28.933 --> 17:31.673 And you get a value that's about what CO2 would 17:31.667 --> 17:33.167 have been by itself. 17:33.167 --> 17:37.567 That doesn't mean that CO2 is alone in importance. 17:37.567 --> 17:38.567 The others are important too. 17:38.567 --> 17:41.697 But generally these cancel those and you get a value of 17:41.700 --> 17:47.730 about 1 and 1/2 meters per second for 17:47.733 --> 17:49.173 the radiative forcing. 17:49.167 --> 17:53.897 And so if you then just use the Black Body sensitivity for 17:53.900 --> 18:00.270 that, that would give you about 0.3 18:00.267 --> 18:03.827 degree Kelvin warming. 18:03.833 --> 18:07.303 And we know, in fact, from our data, that the warming of our 18:07.300 --> 18:11.100 planet since the industrial revolution began is about 0.8. 18:13.833 --> 18:16.733 So it's quite a bit larger than this value. 18:16.733 --> 18:19.603 We can formalize that. 18:19.600 --> 18:23.970 So let's try to compute radiative forcing based on the 18:23.967 --> 18:26.597 data that we have. 18:26.600 --> 18:31.530 Over the last 100 years we've had about 0.8 degree warming. 18:31.533 --> 18:35.503 From the last slide, we've got about 1.5 watts per square 18:35.500 --> 18:38.000 meter radiative forcing. 18:38.000 --> 18:42.670 So if I divide one by the other, I get a sensitivity 18:42.667 --> 18:47.067 based on the radiation definition of 0.5 Kelvin. 18:47.067 --> 18:56.567 That then is about 2 and 1/2 times the sensitivity there. 18:56.567 --> 19:02.667 So the sensitivity that we're seeing in our planet is about 19:02.667 --> 19:05.167 2 and 1/2 times what we compute from a simple Black 19:05.167 --> 19:06.567 Body forcing law. 19:06.567 --> 19:10.127 This then, is evidence that the feedbacks are working, the 19:10.133 --> 19:13.703 positive feedbacks are working. 19:13.700 --> 19:18.200 You could re-formula that as a warming per CO2 doubling. 19:18.200 --> 19:22.530 And you get about 2 degrees Kelvin for every CO2 doubling. 19:22.533 --> 19:24.803 I want to do this one other way. 19:24.800 --> 19:28.300 I know I'm beating this to death, but you remember this 19:28.300 --> 19:33.900 plot, from the Pleistocene, the advance and retreat of the 19:33.900 --> 19:37.670 glaciers over the last 400,000 years. 19:37.667 --> 19:39.797 Well, here we have a temperature signature on the 19:39.800 --> 19:44.570 bottom and a CO2 signature on the top. 19:44.567 --> 19:48.097 We can convert the CO2 signature 19:48.100 --> 19:51.370 to a radiative forcing. 19:51.367 --> 19:52.867 And here's how that argument goes. 19:52.867 --> 19:57.927 So that's a warming of about 5 degrees between glacial and 19:57.933 --> 20:00.933 interglacial times. 20:00.933 --> 20:05.073 We estimate a change in the longwave forcing of about 7 20:05.067 --> 20:07.427 watts per square meter. 20:07.433 --> 20:14.833 Divide one by the other, I get about 0.7 degrees Kelvin per 20:14.833 --> 20:16.473 watt per square meter. 20:16.467 --> 20:18.967 Now that's slightly larger than the one we had on the 20:18.967 --> 20:21.967 last slide from the last 100 years. 20:21.967 --> 20:26.897 And most people believe that that number is larger than the 20:26.900 --> 20:33.730 what did we have for that 0.5, 0.7, because you get these 20:33.733 --> 20:38.133 extra long-term feedbacks coming in. 20:38.133 --> 20:41.133 So there's still a lot of argument in the literature 20:41.133 --> 20:41.733 about that. 20:41.733 --> 20:44.803 But the general interpretation is that when you go to this 20:44.800 --> 20:48.470 longer time scale of a couple hundred thousand years, you 20:48.467 --> 20:51.627 get some longer--some other positive feedbacks that make 20:51.633 --> 20:54.473 the climate even more sensitive 20:54.467 --> 20:57.767 to radiative forcing. 20:57.767 --> 20:58.997 Any questions on that? 21:04.100 --> 21:09.070 Yeah, so I'll just wrap that up by saying, as expected, our 21:09.067 --> 21:12.567 climate is about 3 times more sensitive to radiative forcing 21:12.567 --> 21:17.197 than Black Body would suggest, due to positive feedbacks like 21:17.200 --> 21:19.870 water vapor feedback and snow/albedo feedback. 21:19.867 --> 21:22.497 And when you go to longer time scales, it becomes even 21:22.500 --> 21:25.930 slightly more sensitive due to, 21:25.933 --> 21:28.533 possibly, some other feedbacks. 21:28.533 --> 21:29.403 Yes? 21:29.400 --> 21:30.130 STUDENT: Was the 5K increase from like the lowest 21:30.133 --> 21:31.373 temperature to the highest temperature on that graph? 21:37.433 --> 21:39.373 PROFESSOR: On the glacial one? 21:39.367 --> 21:39.967 STUDENT: Yeah. 21:39.967 --> 21:42.367 PROFESSOR: Yeah, I tried to well, I didn't go to 21:42.367 --> 21:45.467 the very max and the very min, but I tried to take an average 21:45.467 --> 21:48.127 of the minimum and kind of a general value 21:48.133 --> 21:49.133 for the higher ones. 21:49.133 --> 21:50.933 I didn't push it to the limit. 21:50.933 --> 21:52.233 You can't read that scale there. 21:52.233 --> 21:55.203 But that would be slightly larger than 5 degrees if you 21:55.200 --> 21:57.570 went from minimum to maximum. 21:57.567 --> 22:00.497 So I've reduced it a little bit. 22:00.500 --> 22:02.130 I didn't want to let the noise, you 22:02.133 --> 22:04.403 know, drive my answer. 22:04.400 --> 22:08.000 I wanted to use average values. 22:08.000 --> 22:08.830 Yeah, okay. 22:08.833 --> 22:11.003 So that's the summary. 22:11.000 --> 22:13.500 So this is everything then. 22:13.500 --> 22:15.100 We did all of these subjects. 22:15.100 --> 22:18.670 And today we just finished up the concept of climate 22:18.667 --> 22:20.397 sensitivity. 22:20.400 --> 22:21.770 Are there any general questions 22:21.767 --> 22:23.067 about global warming? 22:28.567 --> 22:31.527 Okay, if not, we'll leave it there. 22:31.533 --> 22:35.573 And I want to do one other thing today. 22:35.567 --> 22:37.427 I think we can get it in. 22:51.333 --> 22:55.873 Yeah, because population is a driver in global warming, I 22:55.867 --> 22:59.697 wanted to just to spend the rest of the period I'm not an 22:59.700 --> 23:04.370 expert in demography or population studies. 23:04.367 --> 23:07.267 But there's a few things that are quite obvious, that even I 23:07.267 --> 23:07.767 can understand. 23:07.767 --> 23:09.667 And I wanted to lay those out for you. 23:12.267 --> 23:14.397 Because it's intimately connected with all these 23:14.400 --> 23:17.130 environmental issues, including the air pollution 23:17.133 --> 23:21.373 which we'll be studying next time and on Friday. 23:21.367 --> 23:24.827 So these are the terms you're going to have to know. 23:24.833 --> 23:29.103 The fertility rate is the number of children that a 23:29.100 --> 23:32.070 woman would bear in her lifetime. 23:32.067 --> 23:35.197 The growth rate of a population would be the rate 23:35.200 --> 23:41.800 of change of the population as a fraction of the existing 23:41.800 --> 23:42.470 population. 23:42.467 --> 23:45.497 In other words, a relative rate. 23:45.500 --> 23:49.630 Not a number of people per year, but like a percent per 23:49.633 --> 23:51.973 year, or something like that. 23:51.967 --> 23:55.097 We'll talk about the special case of exponential growth, 23:55.100 --> 23:58.170 where the population would follow a pure exponential 23:58.167 --> 24:02.667 curving rising curve. 24:02.667 --> 24:05.797 I'll talk about population density, urbanization, the 24:05.800 --> 24:07.600 so-called demographic transition, and 24:07.600 --> 24:08.800 the population pyramid. 24:08.800 --> 24:11.770 So in the next few minutes, I'll introduce each of these 24:11.767 --> 24:14.267 terms. 24:14.267 --> 24:18.567 Population versus time, going back 50,000 years, human 24:18.567 --> 24:24.267 population rose very slowly over most of the period of 24:24.267 --> 24:31.067 time when humans were evolving, because, well, life 24:31.067 --> 24:33.697 is difficult. 24:33.700 --> 24:38.370 The death rate was high due to disease, due to accidents, due 24:38.367 --> 24:40.027 to all kinds of things. 24:40.033 --> 24:43.433 And it isn't really until you got in the very recent period 24:43.433 --> 24:46.833 of time where you began to get this rapid, very rapid, 24:46.833 --> 24:48.833 increase in human population. 24:48.833 --> 24:55.033 Probably the biggest change in the rate of growth had to do 24:55.033 --> 25:01.573 with the changes in medicine, I would say the germ theory of 25:01.567 --> 25:05.567 disease and inoculations. 25:05.567 --> 25:07.197 Both of which are rather recent. 25:07.200 --> 25:10.630 We're talking about the 19th, in some cases the early part 25:10.633 --> 25:12.073 of the 20th century. 25:12.067 --> 25:18.997 1920, 1930, 1940 when mass inoculations began to be used. 25:19.000 --> 25:23.500 Anyway it's a very curious curve, so slow and so flat for 25:23.500 --> 25:29.030 so long, and then rising so rapidly now just in the last 25:29.033 --> 25:32.403 100 years or so. 25:32.400 --> 25:36.470 That is partly controlled by the fertility rate. 25:36.467 --> 25:41.997 And here's a diagram from Wiki about that. 25:42.000 --> 25:45.930 What you have for each country and this is by country is a 25:45.933 --> 25:49.873 fertility rate from 0--or from 1 to 7. 25:49.867 --> 25:52.327 So the United States would have a value 25:52.333 --> 25:54.533 somewhere around 2. 25:54.533 --> 26:01.003 Remember this is the number of births per woman in her life. 26:01.000 --> 26:06.070 And a lot of other countries have that medium gray color, 26:06.067 --> 26:09.327 which puts you around 2. 26:09.333 --> 26:13.633 The Soviet Union, especially Ukraine, is even less than 26:13.633 --> 26:15.973 that, however. 26:15.967 --> 26:19.727 And most of Africa and the Middle East are dramatically 26:19.733 --> 26:24.173 higher, generally in the range 4, 5, 6. 26:24.167 --> 26:26.567 That doesn't necessarily mean that their population is 26:26.567 --> 26:30.067 increasing rapidly, although that's true also. 26:30.067 --> 26:33.467 But the fertility rate is one of the components that goes in 26:33.467 --> 26:39.267 to determining the rate of population growth. 26:39.267 --> 26:44.597 Everybody understand this definition of fertility rate? 26:44.600 --> 26:47.970 It's controlled by a number of factors, but a 26:47.967 --> 26:51.097 lot of it is cultural. 26:51.100 --> 26:56.570 A lot of it has to do with expectations, having children 26:56.567 --> 26:58.697 to take care of you in your old age, to help with the 26:58.700 --> 27:04.930 farm, or because you don't have a way to control 27:04.933 --> 27:07.103 population. 27:07.100 --> 27:08.570 A number of--a wide variety of things come into 27:08.567 --> 27:09.867 the fertility rate. 27:09.867 --> 27:14.397 The role of women in society is a big one as well. 27:14.400 --> 27:19.530 Here's the fertility rate plotted versus GDP, the Gross 27:19.533 --> 27:23.303 Domestic Product, that makes it pretty clear there's a 27:23.300 --> 27:27.130 relationship between how much money people have, their 27:27.133 --> 27:29.973 standard of living, and the fertility rate. 27:29.967 --> 27:32.367 USA is here. 27:32.367 --> 27:36.297 Generally, for the developed world the fertility 27:36.300 --> 27:40.300 rate is 2 or less. 27:40.300 --> 27:44.500 Whereas for the underdeveloped world the fertility rate can 27:44.500 --> 27:45.570 be very much higher. 27:45.567 --> 27:51.527 And this magic value here, of 2.3, is the replacement rate. 27:51.533 --> 27:55.233 You have to have a fertility rate of about 2.3 in order to 27:55.233 --> 27:56.633 maintain a steady population. 27:59.500 --> 28:04.770 There are a few outliers that have to do with the special 28:04.767 --> 28:08.127 cultural conditions of those countries. 28:12.467 --> 28:16.067 Stop me if there are questions on this. 28:16.067 --> 28:19.867 So let's look at the fertility rate for the United States and 28:19.867 --> 28:23.697 Canada since 1940. 28:23.700 --> 28:27.930 There was a big bulge in fertility rate, reaching 3.7 28:27.933 --> 28:31.333 for the USA around 1955. 28:31.333 --> 28:34.073 And that is really what's responsible for what's called 28:34.067 --> 28:36.467 the Baby Boom in the United States. 28:36.467 --> 28:39.727 And I guess you are the children of the Baby Boom 28:39.733 --> 28:43.973 generation or something approximate to that. 28:43.967 --> 28:48.097 And then immediately after that came a movement called 28:48.100 --> 28:52.470 ZPG, Zero Population Growth. 28:52.467 --> 28:55.067 Maybe that movement caused this drop, or maybe it was 28:55.067 --> 28:58.397 just changes in background cultural or economic 28:58.400 --> 28:58.800 conditions. 28:58.800 --> 29:02.230 But there was a dramatic drop in the fertility rate in the 29:02.233 --> 29:09.973 United States from 3.7 down to less than 2. 29:09.967 --> 29:12.867 And now it's come up a little bit for the United States to 29:12.867 --> 29:16.567 just about the replacement rate. 29:16.567 --> 29:18.197 Canada is roughly similar. 29:18.200 --> 29:20.770 The details may be a little different, they haven't jumped 29:20.767 --> 29:21.427 up at the end. 29:21.433 --> 29:25.303 But they followed roughly the same curve. 29:25.300 --> 29:26.530 Any questions on that? 29:28.700 --> 29:30.800 So this is a wild swing, right? 29:30.800 --> 29:34.370 This is a huge variation in just a few years in one 29:34.367 --> 29:37.467 particular country. 29:37.467 --> 29:41.427 Europe is famous for having a low fertility rate. 29:41.433 --> 29:43.073 And here are some of the numbers. 29:43.067 --> 29:44.767 Remember the replacement rate is about 2.3. 29:48.133 --> 29:50.673 Italy has 1.3. 29:50.667 --> 29:54.297 And some of the Eastern European countries are even 29:54.300 --> 29:56.200 lower than that. 29:56.200 --> 29:57.200 France is higher. 29:57.200 --> 30:00.200 They've tried to keep their population, their 30:00.200 --> 30:01.400 fertility rate up. 30:01.400 --> 30:05.130 But they don't have it quite at replacement levels. 30:05.133 --> 30:07.273 And Spain is down at 1.3 as well. 30:07.267 --> 30:11.227 So Europe, as a whole, generally has a very low 30:11.233 --> 30:13.403 fertility rate. 30:13.400 --> 30:21.000 We saw that in the earlier map as well, here, as opposed to 30:21.000 --> 30:23.630 some other developing parts of the world. 30:26.367 --> 30:29.297 And China has gone through wild swings, as 30:29.300 --> 30:31.270 has the United States. 30:31.267 --> 30:33.797 For somewhat different reasons, perhaps, but the 30:33.800 --> 30:38.570 curve is somewhat similar. 30:38.567 --> 30:41.967 It climbed even higher than the United States, much higher 30:41.967 --> 30:44.497 in fact, up to 6.5. 30:44.500 --> 30:49.170 And that, I think, was part of the cultural revolution where 30:49.167 --> 30:53.427 it was every woman's duty to have as many children as she 30:53.433 --> 30:57.433 possibly could for the strength of the state. 30:57.433 --> 31:02.073 And then talk about a complete turnabout they've now dropped 31:02.067 --> 31:03.897 down to less than 2. 31:03.900 --> 31:07.600 And, of course, that is largely due to China's one 31:07.600 --> 31:12.470 child policy, kind of flipping the political pressure on 31:12.467 --> 31:14.427 families and individuals. 31:14.433 --> 31:17.403 So wild swings in fertility rate. 31:20.167 --> 31:22.697 Now we'll move to this fractional growth rate then. 31:22.700 --> 31:31.100 So, of course, this is now a combination of birth rate and 31:31.100 --> 31:32.630 mortality rate. 31:32.633 --> 31:34.033 How many children are born every year? 31:34.033 --> 31:35.633 How many people die every year? 31:35.633 --> 31:38.873 You have to know both of those to then get the total 31:38.867 --> 31:42.427 fractional growth rate of the population. 31:42.433 --> 31:49.003 And there again, these colors follow the same trend. 31:49.000 --> 31:54.300 As the Soviet Union has the lowest growth rate, well 1-- 31:54.300 --> 31:55.770 well below 0. 31:55.767 --> 31:57.527 They're losing population. 31:57.533 --> 32:03.573 And all the other countries are gaining population, but at 32:03.567 --> 32:10.297 a rate ranging from 3% per year to just a small fraction 32:10.300 --> 32:14.030 of a percent per year. 32:14.033 --> 32:17.373 So that's the total fractional growth rate. 32:17.367 --> 32:19.297 Here is the way you would compute that. 32:19.300 --> 32:20.130 Here are some numbers. 32:20.133 --> 32:26.003 For example, China population in 2010 and 1990. 32:26.000 --> 32:29.730 Well that's a 20 year span, so you can get an approximation 32:29.733 --> 32:34.703 by just taking the percent change over that period of 32:34.700 --> 32:38.030 time and dividing that by the number of years. 32:38.033 --> 32:41.633 So divide that by 20 and you get a value that's just 32:41.633 --> 32:45.003 slightly less than 1% per year. 32:45.000 --> 32:48.200 And that would be that percent, or that fractional 32:48.200 --> 32:51.700 growth rate for population in China. 32:51.700 --> 32:53.170 That's how you do the calculation. 32:53.167 --> 32:57.627 In some cases taking a 20 year estimate might be blurring 32:57.633 --> 32:59.203 some details. 32:59.200 --> 33:02.370 If the growth has been changing rapidly over time, 33:02.367 --> 33:06.227 what you would be getting here is some kind of average over a 33:06.233 --> 33:09.233 fluctuating quantity. 33:09.233 --> 33:12.433 But nevertheless, you see how easy it is to do that 33:12.433 --> 33:17.133 calculation, if you don't mind losing some of the details, 33:17.133 --> 33:18.933 some of the rapid changes in time. 33:23.567 --> 33:30.027 So globally then, this is what the fractional growth rate has 33:30.033 --> 33:33.433 been doing and is projected to do. 33:33.433 --> 33:35.233 But these projections are quite uncertain. 33:35.233 --> 33:37.933 I wouldn't pay too much attention to the blue curve. 33:40.567 --> 33:44.067 Globally we've had, over the last couple of decades, we've 33:44.067 --> 33:46.527 had growth rates-- 33:46.533 --> 33:49.203 this is not fertility rate, this is growth rate-- 33:49.200 --> 33:56.130 of about 2% per year, which is quite a large growth rate. 33:56.133 --> 33:57.403 It has been decreasing. 33:57.400 --> 34:02.070 At the present time, it's more like 1% per year. 34:02.067 --> 34:06.297 But that's still quite a large growth rate. 34:06.300 --> 34:08.170 And then it's predicted that will continue to 34:08.167 --> 34:10.267 drop as we go forward. 34:10.267 --> 34:14.667 But that will depend a lot upon the future economy, the 34:14.667 --> 34:18.967 future cultural development of the countries that have the 34:18.967 --> 34:20.227 highest populations. 34:23.733 --> 34:27.433 Now this concept of exponential growth, you've 34:27.433 --> 34:30.873 probably heard it. 34:30.867 --> 34:34.267 If the fractional growth rate is constant, that is, if this 34:34.267 --> 34:39.427 quantity were constant in time, over some period of 34:39.433 --> 34:46.073 time, then the population will grow exponentially. 34:46.067 --> 34:48.027 Now what does that mean, to say the population grows 34:48.033 --> 34:48.803 exponentially? 34:48.800 --> 34:53.300 The exponential function defines a system whose annual 34:53.300 --> 34:59.400 increase is proportional to the population itself. 34:59.400 --> 35:03.830 In other words, its rate of increase is proportional to 35:03.833 --> 35:06.403 the population that it has. 35:06.400 --> 35:09.930 That is what is meant by an exponential increase. 35:09.933 --> 35:12.473 Sometimes that word is misused. 35:12.467 --> 35:14.567 Sometimes in the popular literature someone will be 35:14.567 --> 35:20.027 saying, well, you know I'm rich, my bank account amount 35:20.033 --> 35:21.433 is growing exponentially. 35:21.433 --> 35:25.173 And by that he might just mean rapidly. 35:25.167 --> 35:27.197 But that's not what exponential really means. 35:27.200 --> 35:32.300 Exponential means it's growing at a rate proportional to how 35:32.300 --> 35:32.900 much you have. 35:32.900 --> 35:37.630 Now, if you're getting a fixed interest rate in your account, 35:37.633 --> 35:40.633 then it would be exponential. 35:40.633 --> 35:43.373 It'll grow at a rate that will increase as you get more and 35:43.367 --> 35:44.597 more money in the bank. 35:44.600 --> 35:46.530 So that might be an accurate representation. 35:46.533 --> 35:52.003 Remember, exponential doesn't refer to some rate of 35:52.000 --> 35:55.970 increase, it relates to the relationship between the rate 35:55.967 --> 35:59.267 of increase and the amount of the standing population that 35:59.267 --> 36:02.227 you have at any particular moment. 36:02.233 --> 36:05.433 So in other words, births minus deaths, really, 36:05.433 --> 36:07.733 proportional to population. 36:07.733 --> 36:09.503 So we write it this way. 36:09.500 --> 36:12.030 Population, as a function of time, is given by the 36:12.033 --> 36:19.133 population at some reference time, call it 0, and then P to 36:19.133 --> 36:25.103 the plus alpha t, where alpha is the growth rate and t is 36:25.100 --> 36:28.230 the time that's elapsed since your 36:28.233 --> 36:31.873 reference time has occurred. 36:31.867 --> 36:37.427 So if the growth rate, for example, is 1% per year-- 36:37.433 --> 36:41.503 and that was a number that we have about 36:41.500 --> 36:45.870 now for planet earth-- 36:45.867 --> 36:48.327 and if that were to remain constant. 36:48.333 --> 36:52.973 in other words, if this curve was level at 1% per year, then 36:52.967 --> 36:56.427 here's how you could do the calculation. 36:56.433 --> 37:03.933 After 100 years, putting in 100 years into there and 0.01 37:03.933 --> 37:09.673 year to the minus 1 in for alpha, the units will cancel. 37:09.667 --> 37:16.467 And in this case, the product is equal to 1, in this special 37:16.467 --> 37:17.627 simple example. 37:17.633 --> 37:21.833 So it's going to be exp to the 1, which has a value-- you can 37:21.833 --> 37:23.233 check it on your calculator-- 37:23.233 --> 37:25.103 of 2.7. 37:25.100 --> 37:30.900 So a growth rate of 0.01 over 100 years would give a growth 37:30.900 --> 37:32.530 rate factor of 2.7. 37:32.533 --> 37:35.633 So however many people you had at the beginning of the 100 37:35.633 --> 37:40.173 year period, put that there, multiply it times 2.7, and 37:40.167 --> 37:40.697 you'll get-- 37:40.700 --> 37:43.770 it's roughly a tripling, roughly a tripling of the 37:43.767 --> 37:48.767 population over 100 years if you have a growth rate of 1% 37:48.767 --> 37:50.767 percent per year. 37:50.767 --> 37:54.927 Remember it's compound interest. The rate increases 37:54.933 --> 37:56.273 as the population increases. 37:58.900 --> 38:01.300 Questions on that? 38:01.300 --> 38:04.070 Now this is a bit of a fiction, because, as you've 38:04.067 --> 38:07.697 just seen, population growth rates change 38:07.700 --> 38:10.400 quite a bit over time. 38:10.400 --> 38:16.470 And so, while you can do rough calculations with this, a real 38:16.467 --> 38:19.597 population demographer would probably never use this. 38:19.600 --> 38:22.430 He would go step by step and calculate it assuming a 38:22.433 --> 38:26.703 variable growth rate rather than assuming a constant one. 38:29.700 --> 38:33.400 So here are the current UN population 38:33.400 --> 38:37.170 projections for our planet. 38:37.167 --> 38:40.727 Data up to this point, and then uncertain projections, 38:40.733 --> 38:43.203 depending on what you think the growth rate will 38:43.200 --> 38:44.770 be around the world. 38:47.667 --> 38:52.327 So we crossed 6 billion back in 2000, roughly 2000. 38:52.333 --> 38:56.433 We crossed 7 billion just, what was that, 3 38:56.433 --> 39:00.773 weeks ago, I think. 39:00.767 --> 39:05.227 And in the future, well the projections vary 39:05.233 --> 39:06.873 all over the place. 39:06.867 --> 39:12.127 But by, let's say, 2080, this looks like 2080, some 39:12.133 --> 39:15.673 calculations would have us at 12 billion. 39:15.667 --> 39:17.467 Some would have us at 9. 39:17.467 --> 39:20.467 And some would have us declining, population already 39:20.467 --> 39:24.867 getting back towards 6 billion. 39:24.867 --> 39:27.167 So great uncertainties in this. 39:27.167 --> 39:33.297 But still great potential for environmental difficulties, if 39:33.300 --> 39:36.700 either of those curves are realized. 39:41.867 --> 39:45.127 Another issue that comes up is population density. 39:45.133 --> 39:50.373 How many people per square kilometer do you have? 39:50.367 --> 39:53.967 And this is a map of population density. 39:53.967 --> 39:56.897 Units are people per square kilometer. 39:56.900 --> 39:58.830 And there are some countries-- 39:58.833 --> 40:02.373 and this is by country so this can be quite misleading, 40:02.367 --> 40:07.167 because for a given country they put a uniform color over 40:07.167 --> 40:08.967 the whole country. 40:08.967 --> 40:13.727 So it's the total population divided by the total area of 40:13.733 --> 40:17.433 the country, then painted as a uniform color 40:17.433 --> 40:18.273 over the whole country. 40:18.267 --> 40:19.427 That's quite unrealistic. 40:19.433 --> 40:22.473 But it will give us a place to start to talk 40:22.467 --> 40:24.097 about population density. 40:24.100 --> 40:27.900 India, as a country, has a population exceeding 1,000 40:27.900 --> 40:32.670 people per square kilometer, which is enormous. 40:32.667 --> 40:35.297 Whereas something like Australia is in the lightest 40:35.300 --> 40:39.030 shade that they have, less than 10 40:39.033 --> 40:41.703 people per square kilometer. 40:44.700 --> 40:47.100 Wide variation in that quantity. 40:47.100 --> 40:49.870 But again, this is quite misleading because it's done 40:49.867 --> 40:51.927 by country. 40:51.933 --> 40:55.873 United States, this is per square mile. 40:55.867 --> 40:59.167 Remember, the United States unfortunately is still on an 40:59.167 --> 41:00.827 old English system of measure. 41:00.833 --> 41:06.233 So very often you'll find population per square mile 41:06.233 --> 41:07.603 instead of per square kilometer. 41:07.600 --> 41:12.100 You can do the conversion easily to go back and forth. 41:12.100 --> 41:15.270 But once again, doing it by state is misleading, because 41:15.267 --> 41:17.327 there are parts of California that are 41:17.333 --> 41:18.773 nearly empty of people. 41:18.767 --> 41:21.367 And there are very densely populated areas of 41:21.367 --> 41:23.527 California and so on. 41:23.533 --> 41:26.673 But even averaging over states, you'll find a big 41:26.667 --> 41:31.097 difference between Montana, for example, and New Jersey 41:31.100 --> 41:32.600 and Rhode Island. 41:35.633 --> 41:38.633 Now, if you want to read about the role 41:38.633 --> 41:41.503 of population density-- 41:41.500 --> 41:45.730 not all demographers would put a great emphasis on population 41:45.733 --> 41:49.833 density, but Jared Diamond, in this book, Collapse-- 41:49.833 --> 41:51.233 how many have read that book? 41:51.233 --> 41:54.673 I thought a few of you would have. He, in places in that 41:54.667 --> 41:57.867 book, makes a pretty strong case for the importance of 41:57.867 --> 41:59.327 population density. 41:59.333 --> 42:02.773 He really thinks it matters that populations have enough 42:02.767 --> 42:04.767 space to live. 42:04.767 --> 42:06.167 And I partly agree with that. 42:06.167 --> 42:08.967 But he maybe takes it-- 42:08.967 --> 42:12.667 some examples he gives wouldn't carry over to other 42:12.667 --> 42:15.367 civilizations that he didn't talk about in his book. 42:15.367 --> 42:17.697 But anyway, he makes a strong case for the importance of 42:17.700 --> 42:18.900 population density. 42:18.900 --> 42:20.270 And I party believe that too. 42:20.267 --> 42:23.067 So I encourage you to take a look at that book if you get a 42:23.067 --> 42:27.067 chance, because it makes the case very nicely for the 42:27.067 --> 42:28.597 importance of population density. 42:31.333 --> 42:34.973 When you think about what the limiting resources are for 42:34.967 --> 42:41.297 living on our planet, water, food, energy, living space, 42:41.300 --> 42:44.600 even these other ones, like water and food and energy, to 42:44.600 --> 42:50.670 some extent they all depend on having enough area as well. 42:50.667 --> 42:56.097 For example, energy from hydroelectric requires a large 42:56.100 --> 43:00.770 catchment area of rain in order to get hydroelectric. 43:00.767 --> 43:04.967 Farmland, obviously food from farms requires lots of area. 43:04.967 --> 43:08.727 So just scanning this list and realizing that many of those 43:08.733 --> 43:16.333 have a relationship to population density would, I 43:16.333 --> 43:22.503 think, partly confirm Jared Diamond's idea that living 43:22.500 --> 43:26.200 space is an important part of the success of a culture. 43:29.567 --> 43:34.027 Okay, now to correct the misimpression from those 43:34.033 --> 43:37.773 earlier country-based and 43:37.767 --> 43:40.327 state-based population densities. 43:40.333 --> 43:44.133 People don't live uniformly distributed over a 43:44.133 --> 43:46.333 country or a state. 43:46.333 --> 43:50.903 In fact, a higher and higher fraction of populations are 43:50.900 --> 43:52.270 moving to cities. 43:52.267 --> 43:54.897 They're abandoning the land and moving to cities. 43:54.900 --> 43:59.830 So this is a plot, on the x-axis 1950 into the future, 43:59.833 --> 44:04.673 2050, the percent of the population that's living in 44:04.667 --> 44:07.027 urban versus rural areas. 44:07.033 --> 44:12.303 Back in the 1950's, 70% was rural, 30% urban. 44:12.300 --> 44:15.870 And we've crossed that boundary, we've crossed that 44:15.867 --> 44:17.227 crossover point. 44:17.233 --> 44:19.903 And in the future, we'll have more people living clustered 44:19.900 --> 44:21.470 together in cities. 44:21.467 --> 44:23.427 That doesn't mean they don't need the land around the 44:23.433 --> 44:26.433 cities to grow the food and provide the energy and so on. 44:26.433 --> 44:30.273 But at least their living density is much larger than 44:30.267 --> 44:34.197 those country-averaged or those state-averaged values 44:34.200 --> 44:36.070 that I gave you. 44:36.067 --> 44:39.467 And these are some of the growing megacities, where now 44:39.467 --> 44:44.127 more than half the people in the world are concentrated 44:44.133 --> 44:45.733 into these large cities. 44:45.733 --> 44:46.133 Yes? 44:46.133 --> 44:47.403 STUDENT: [INAUDIBLE]? 44:55.367 --> 44:56.827 PROFESSOR: I don't know. 44:56.833 --> 44:59.603 When you do calculations-- 44:59.600 --> 45:03.770 I saw an article recently about how much energy people 45:03.767 --> 45:07.667 used that live in New York city versus the rest of New 45:07.667 --> 45:09.497 York state. 45:09.500 --> 45:12.470 And the people who live in New York city use less energy per 45:12.467 --> 45:16.527 capita then the people that live in upstate New York. 45:16.533 --> 45:19.233 Probably because of the dense way that they live and the way 45:19.233 --> 45:23.103 that transportation is so much easier when you live close. 45:23.100 --> 45:25.630 So I think it could be argued that this might be a more 45:25.633 --> 45:27.373 efficient way to live. 45:27.367 --> 45:28.697 But that's only one measure. 45:28.700 --> 45:32.770 And I haven't studied the issue in it's full breadth. 45:32.767 --> 45:34.167 So that would be worth looking into. 45:34.167 --> 45:37.027 To see whether this might actually be an advantage for 45:37.033 --> 45:44.133 the environment, to have people clustered in cities. 45:44.133 --> 45:46.373 Other comments on that? 45:46.367 --> 45:47.397 We need to wrap up. 45:47.400 --> 45:48.630 I do need to give you this. 45:48.633 --> 45:51.003 So there's something called the demographic transition. 45:53.700 --> 45:57.300 And it goes basically from a third world country converting 45:57.300 --> 45:58.830 to a first world country. 45:58.833 --> 46:01.433 What happens to its population? 46:01.433 --> 46:04.933 In the beginning, you've got a high birth rate, a high death 46:04.933 --> 46:06.603 rate, and a small population. 46:09.700 --> 46:14.630 Standard of living begins to increase, health is improved. 46:14.633 --> 46:18.533 The first thing that happens is the death rate drops. 46:18.533 --> 46:21.003 The birth rate stays high. 46:21.000 --> 46:22.770 Because the death rate has dropped and the birth rate 46:22.767 --> 46:26.397 stays high, population begins to increase. 46:26.400 --> 46:29.470 Then, at some point culturally, the birth rate 46:29.467 --> 46:31.227 drops as well. 46:31.233 --> 46:34.033 You end up with a lower birth rate and a lower death rate 46:34.033 --> 46:35.903 and a larger total population. 46:35.900 --> 46:38.700 We've seen this happen in country after country. 46:38.700 --> 46:41.370 And if you look at that map of the world, clearly some 46:41.367 --> 46:44.567 countries in the west, like the US and 46:44.567 --> 46:46.367 Europe are in this state. 46:46.367 --> 46:47.967 And a lot of the third world countries are 46:47.967 --> 46:48.967 still in this state. 46:48.967 --> 46:51.997 And a few are in the middle, doing the demographic 46:52.000 --> 46:53.570 transition. 46:53.567 --> 46:57.467 So this is an important concept of how you go from one 46:57.467 --> 47:01.467 type of population situation to the other. 47:01.467 --> 47:02.667 And the other one I want to leave you with-- 47:02.667 --> 47:04.467 I'm out of time-- but you have to know about 47:04.467 --> 47:05.767 the population pyramid. 47:05.767 --> 47:12.067 So a country that has a stable population, here's a plot of 47:12.067 --> 47:14.197 how many people you have, male and female, in 47:14.200 --> 47:17.370 different age groups. 47:17.367 --> 47:20.127 A country that has a stable population usually has a 47:20.133 --> 47:22.703 population pyramid like this. 47:22.700 --> 47:25.400 A country that has a rapidly growing population has a 47:25.400 --> 47:28.600 pyramid like this, a lot of young people. 47:28.600 --> 47:31.870 And as they get older, of course, the overall population 47:31.867 --> 47:34.127 will increase very rapidly. 47:34.133 --> 47:36.173 Well, you can see this immediately, because if you 47:36.167 --> 47:40.697 drive into a small town in the Middle East or in Africa, the 47:40.700 --> 47:43.800 number of children that come running out of the house to 47:43.800 --> 47:47.300 see who's visiting their village tells you you've got a 47:47.300 --> 47:50.170 population curve like that. 47:50.167 --> 47:52.597 And that will do it. 47:52.600 --> 00:00.000 Okay, we're finished.