Transcribe your podcast
[00:00:14]

Rationally speaking, is a presentation of New York City skeptics dedicated to promoting critical thinking, skeptical inquiry and science education. For more information, please visit us at NYC Skeptic's Doug. Welcome to Russian speaking the podcast, where we explore the borderlands between reason and nonsense. I'm your host, Masimo, and with me, as always, is my co-host, Julia Gillard. Julia, where are we going to talk about today?

[00:00:48]

Today, we are delighted to welcome Dr Michael Mann, who is the distinguished professor of meteorology at Penn State with joint appointments in the Departments of Geosciences and the Earth and Environmental Systems Institute. He directs the Penn State Earth System Science Center and is the recipient of many awards, including the Ashgar Medal from the European Geosciences Union. In addition to more than 150 peer reviewed publications, Dr. Mann is the author of the books Dire Predictions Understanding Global Warming in 2008 and the Hockey Stick and the Climate Wars Dispatches from the Front Lines in 2012.

[00:01:26]

He's also the co-founder and frequent contributor to Real Climate. Doug.

[00:01:32]

Michael, welcome.

[00:01:33]

Thanks. It's great to be with you guys. Make the first question that I wanted to ask actually has very little to do with the science per say. And it's kind of personal because I know as a scientist that that often is at least it's interesting. Result answers. Why did you get into climate change to begin with?

[00:01:51]

Well, it was sort of thr through a circuitous random walk, if you will. I started out as a physicist. I got my undergraduate degrees in applied math and physics at UC Berkeley and then started a PhD program at Yale University in theoretical physics. And I realized as I went on that the sort of work that I was doing and was sort of being pushed towards in theoretical physics was actually rather applied. And it wasn't sort of the big picture. Science that I had originally envisioned and that had gotten me excited in physics in the first place, and it happens that I became aware of the fact that there was some really interesting work going on in the Department of Geology and Geophysics at Yale University using math and physics to study the way the Earth's climate system works.

[00:02:47]

And to me, that was a fascinating big picture problem. And I decided that that was something I could sink my teeth into. And I ended up going in that direction, transferring over to that department and doing my PhD on modeling of Earth's climate and studying. Actually, climate variability, I wasn't really focused on the issue of climate change that came later.

[00:03:14]

It's funny that that the quest for big picture is what brings a lot of people into science to begin with. And then you get you often get trapped in the sort of small, very localized kind of issues. And that's the same kind of quest actually eventually brought me from being an evolutionary biologist to a philosopher of science, of all things. So I can can sympathize. So starting with that, why don't you give us sort of the basic facts as we understand them at this point about climate change?

[00:03:43]

Sure.

[00:03:44]

The first thing that is important to understand here, and as an evolutionary biologist by training, I think you can appreciate this, the physics and the basic science is not new. It's nearly two centuries old. And the joke that I sometimes make with my friends in evolutionary biology is that actually our science goes back even further than yours does, which is not a competition. But literally our understanding of the greenhouse effect goes back to the early 19th century, the early eighteen hundreds physicists like Joseph Fourier, the same scientist who gave us the four year series and who gave us the the law of heat conduction.

[00:04:28]

He understood about the existence of this phenomenon known as the greenhouse effect, that certain gases in the atmosphere like carbon dioxide have this warming effect on the surface and the lower atmosphere that was recognized nearly two centuries ago. And essentially what the scientific community has been doing over the past over the past two centuries is refining our understanding of the the details, the specifics, like how how strong is this effect when you take into account so-called feedback mechanisms, amplifying factors like when you warm the atmosphere by increasing greenhouse gas concentrations, CO2, for example, you actually.

[00:05:16]

Create greater evaporation of water from the surface of the earth into the atmosphere, a warmer atmosphere can hold more water vapor and water vapor. It turns out is a natural, very potent greenhouse gas. And so that actually is an amplifying factor.

[00:05:31]

And without without which, by the way, we wouldn't have life on Earth, right? That's right. In the absence of the greenhouse effect, which is a part of it, is due to carbon dioxide, some of it natural. And of course, we're increasing the amount of carbon dioxide through human activities like fossil fuel burning. But some of that natural greenhouse effect is due to the CO2 that exists naturally in the atmosphere, the methane that exists naturally in the atmosphere and the water vapor.

[00:05:59]

Now, the interesting thing about the water vapor is that you can't actually control how much water vapor is in the atmosphere without changing the thermostat, without changing the temperature of the earth, because what determines how much water vapor is in the atmosphere is how warm the atmosphere is. And so unlike these other trace gases like CO2 and methane, which we call forcings, you can turn that knob, you can turn it up by putting more of it into the atmosphere.

[00:06:27]

The water vapor isn't a control knob. It's an interactive component and it's a feedback and amplifies the amount of warming. And as you allude to, if we were to eliminate the natural greenhouse effect altogether, if you do the calculations, the Earth would be about 60 degrees Fahrenheit, colder than it actually is. But that's cold in thirty three degrees Celsius. Colder it would be this would be a frozen planet and we probably would not have life, at least in the sense that we know of it now.

[00:06:58]

That reminds me of a notion that I encounter when I was very young. My hobby was actually astronomy. And for some time I did think of going into physics because of that. And at a time when are talking about now the 70s and early 80s, I read about the greenhouse effect, also being responsible for the some of the major differences between our planet and Venus and Mars. I think the idea was Venus has a really effective greenhouse effect. That's why one of the reasons, not the only, but one of the reasons it's so hot.

[00:07:29]

And Mars, on the other hand, is one of the reasons why it's so cold, because it doesn't have as much of a of an effect. Is that still current science or people change their mind in the meantime?

[00:07:39]

Absolutely. And this is something that I point out to so-called skeptics. Some people are genuinely skeptical, other people or what we call pseudo sceptics or contrarians or deniers. They just manufacture controversy because they like being devil's advocates or they don't like the implications of the science. But regardless, one of the arguments that you hear from skeptics and pseudo skeptics is that they don't believe in the greenhouse effect. Again, that that's somehow, you know, controversial science. And of course, it is.

[00:08:14]

And it's very basic physics and chemistry we've known for nearly two centuries. But suppose that the greenhouse effect didn't exist, as you already alluded to, as we already discussed, the first planet that we wouldn't be able to explain is the one we live on. We wouldn't be able to explain why it is that there's life on Earth. The other thing we wouldn't be able to explain is why certain planets like Venus are as hot as they are or other planets like Mars are as cold as they are.

[00:08:42]

If you just take into account the simplest factors that are unrelated to the greenhouse effect, like the distance from the sun, which is obviously one determinant of how warm the planet should be. You can't get anything close to the correct temperatures that these planets actually have, the correct temperature that Mars has, the actual temperature that Venus has to get those answers, you need to understand the greenhouse effect. So, you know, not only would astronomy be unable to explain a lot of the phenomena that it seeks to explain, if we didn't understand the greenhouse effect, we wouldn't be able to explain our own solar system.

[00:09:23]

So, Michael, we've been talking about the science behind the greenhouse effect and how long it's been established as a scientific phenomenon. But what about the science of the increasing greenhouse effect? At what point do you think we had enough evidence to conclude pretty decisively that the greenhouse effect was increasing?

[00:09:43]

That's a great question, because we are talking about two different phenomena. There's the natural greenhouse effect, which, as we've discussed here, in the absence of it, we wouldn't even be able to explain why the earth is the relatively balmy, you know, roughly 15 degrees Celsius temperature that it is instead of negative 18 degrees Celsius, what it would be if there were no greenhouse effect. So when people say they don't believe the greenhouse effect, they don't accept what they typically.

[00:10:15]

Probably mean is that they don't accept that we are increasing the greenhouse effect through fossil fuel burning and other human activities, but again, with respect to that proposition, the evidence is absolutely overwhelming. We know, for example, that earlier this year we passed the four hundred ppm limit, what that is, parts per million for every million parts of atmosphere. At least four hundred of them were CO2 molecules. We passed that limit for the first time in millions of years because we do have records, paleoclimate records, ice core evidence that takes us back in time, other lines of evidence that tell us what greenhouse gas levels were in the past.

[00:11:03]

And what the evidence tells us is that we just passed the four hundred ppm marker for the first time in millions of years. Now, one could imagine maybe that's a coincidence. But what you can actually do a little bit of further detective work on this. You can look at the composition of isotopes of carbon in the CO2 that's in the atmosphere because there are different stable isotopes of carbon, primarily carbon 12 and carbon 13 and. It turns out that the relative abundance of those two isotopes of carbon is different if you're looking at natural sources of CO2 and unnatural anthropogenic sources, specifically the burning of fossil fuels.

[00:11:58]

It turns out that the isotopic composition of CO2 that makes it into the atmosphere naturally is different from the isotopic composition of the CO2 that we are putting into the atmosphere. So there's a fingerprint to look for. There's anthropogenic signature bass. Absolutely. And what we see is that the CO2 that's building up in the atmosphere has the fingerprint of anthropogenic sources. And in fact, the exponential curve in CO2 concentrations matches a similar exponential curve in the ratio of those isotopes, telling us that CO2 is increasing and we can't explain it from natural sources.

[00:12:36]

It's us so that that concerns the past, the recent past and the current state of things. Now, of course, a lot of climate science is about also projecting into the near future what's happening, and that's done through climate change models that very basically. How do you build a climate change model?

[00:12:55]

Well, you know, this is what got me interested in climate science in the first place, because what a climate model is, is a representation. It's a formalization of our understanding of the relevant fluid dynamics. It's a really interesting fluid dynamics problem. If you like fluid dynamics because you've got.

[00:13:14]

And who doesn't? The martinis count.

[00:13:18]

I mean, yes, among this group, it may be a biased sample, but a lot of us love, love, physics and fluid dynamics. And it turns out it's a really interesting fluid dynamics problem because what you have is a rotating solid body, the Earth. It's being heated externally and it consists of different fluids that are interacting the ocean, which is a liquid, the ocean's liquid water, the atmosphere, which is, of course, a gas.

[00:13:50]

They're interacting with each other physically. They're interacting with each other, thermodynamically, exchanging heat. There is. So, you know, it's essentially about as complicated a physics problem as you could hope to set up. But you know what? It isn't even enough to understand the physics, the physics of radiation, the physics of fluid dynamics.

[00:14:14]

You actually have to understand the biology because the composition of the atmosphere depends on biological processes. Life on Earth has had a substantial, in fact, even dominant influence on the composition of the atmosphere. We wouldn't have an oxygenated atmosphere as we do if it were not for life. So you have to understand the biology. You have to understand the chemistry, because a lot of the processes that we're talking about, the interactions of solar radiation with molecules, the greenhouse effect which we've been talking about, which has to do with the way certain molecules absorb invisible infrared radiation.

[00:15:01]

So you've got everything you could want if you're a science geek, you've got chemistry, you've got physics and you've got biology. And you have to put it all together. It's one of the most complicated and one of the most inherently interdisciplinary problems that a physicist could hope to get involved in. And so that's sort of the way that I came into it. And what a climate model is, is just our best effort to formalize our understanding of all this physics, of all this chemistry, of all this biology.

[00:15:34]

What you sometimes hear people say, well, you know, I don't trust these models, but all the model is is a way to be rigorous about our understanding of the way the world works. It's a way of formalizing, writing down equations that we can, at least in principle, solve. And that's what. So that's what a climate model is. It's an atmospheric component. Looks a lot like a numerical weather forecasting model because it's the same physics, the physics of the atmosphere.

[00:15:59]

And so people who say, again, I don't believe these models, well, you know, the weather forecasting models do a pretty good job and increasingly good job. We take them seriously because we know they have predictive skill. The information that they're giving us is far better than if we had no information at all, if we were just making assumptions that whether it behaved randomly. So a climate model has all the physics of a numerical weather forecasting model as far as the atmosphere is concerned.

[00:16:28]

But you've got to account for all the interactions with the other components, the ice sheets in the oceans. So there are other physical components. And as I said before. There's even chemistry and biology that you have to take into account as well, so there have been different climate models created by climate scientists over the time that you've been looking at this phenomenon. And I assume they've all sort of made predictions and some of them have been more successful than others at making predictions.

[00:16:57]

So how do you avoid the problem of just, you know, making a ton of models and and just by luck, some of them turning out to make more accurate predictions than others, even though they're not true and actually related to this?

[00:17:12]

It's or perhaps even a different way of simply phrasing Julia's question. Is this like any as you say, a model in science is simply a rigorous formalization of the best way we have to understand that system. But of course, every model has uncertainties. And so another way perhaps to ask that question is what are the sources, major sources of uncertainty in climate change models? You know, what's causing the discrepancy between different models, that sort of thing?

[00:17:37]

Yeah, it's a great question, you know, because it gets at, again, some pretty basic issues of sort of the philosophy of science. You know, what is a model? What's the purpose of the model? And, you know, the fact that there are many different models. What are we to make of that? Well, you know, sort of like in physics, you know, there are certain models that do a great job within the realm of the validity of the underlying assumptions of Newtonian physics.

[00:18:06]

You know, Newtonian gravity, Newton's universal law of gravitation does an excellent job in explaining a very large range of observations, know the the orbits of the planets. And so that model we accept is valid over a pretty broad range of assumptions. But we also know that it's wrong. And all models at some level are going to be wrong because you have to make certain simplifying assumptions in order to get solutions to problems. And so we know, for example, that at very small scales or at very high velocities, Newtonian physics fails and it becomes replaced with quantum mechanics and and special in general relativity.

[00:18:56]

And so I think we can actually draw upon that analogy to talk about sort of the hierarchy of climate models that one might use to study Earth's climate. The simplest climate model is what we call zero dimensional. It doesn't know about the, you know, the vertical distribution of the atmosphere. It doesn't know about longitude. It doesn't know about latitude.

[00:19:21]

It's pretty ignorant. It's a very ignorant model.

[00:19:23]

And yet it does remarkably well in making certain predictions that that turn out to be valid. So with a simple model of that sort, which we call an energy balance model, you treat the Earth as a zero dimensional. It doesn't have any spatial extent. It's just a point in space and it's a point in space that has to be in equilibrium with the external heating source, the sun. And so whatever energy is heating that point in space from coming in from the sun has to be balanced by an equal amount of outgoing, invisible radiation.

[00:20:04]

Black body radiation, but it turns out that even the assumption of black body radiation for, you know, the amount of radiation that that that an object produces as a function of its temperature. So you could make a simple assumption that the earth behaves like a black body. And so it has to heat up to whatever temperature makes it produce, the amount of outgoing, invisible radiation that balances the amount of solar radiation that's receiving. And if you do that calculation, the simplest possible calculation is just an algebraic equation that, you know, if you've taken high school algebra, you can solve this equation.

[00:20:48]

It just involves taking a fourth route of of a number it. So if you take the amount of energy produced by the sun, which is approximately thirteen hundred and seventy watch per meter squared, you take into account the average reflectivity of your surface, which is about 30 percent. So 70 percent of that has to be is going to be absorbed by the earth. That means the Earth has to heat up to a temperature that when you raise it to the fourth power, produces enough of this invisible black body radiation to bounce incoming solar radiation.

[00:21:21]

And when you do that, you get a temperature of two fifty five Kelvin. You get a ridiculous answer. You get a solution that the earth is frozen. But you know what? If you just patch that model slightly by saying, well, hold on a second, the earth is not a black body. It's not absorbing all the radiation that's incident upon it. We have this greenhouse effect, some of the radiation that the Earth is producing and trying to send out to space to come into balance with the energy from the sun it's receiving is absorbed by its atmosphere and it's sent back down towards the surface.

[00:21:57]

So it turns out you can patch that model by making the assumption of a gray body earth, an earth that doesn't have a perfect emissive. And if you use the right number, you get an earth temperature of about two hundred eighty eight Kelvin, about 15 degrees Celsius, about fifty nine degrees Fahrenheit, which is the actual average temperature of the earth now. So that model, that very simple gray body energy balance model, not very complicated. Again, a high school student with algebra can solve that problem.

[00:22:33]

It gives you a reasonable temperature of the earth.

[00:22:35]

But if you want to know what you know and you can even use it to predict how much warming you would expect for a given increase in CO2 concentrations, and you can get reasonable numbers, numbers that correspond closely to what more complicated, more elaborate climate models give. But suppose that I want to know. OK, well, how is it how is that going to affect winter rainfall in Pennsylvania?

[00:22:58]

Well, now things got a little more complicated. So you can imagine there's a whole hierarchy of increasingly complicated models that allow you to look beyond just what's the average temperature of the earth, but where are the winds? What are the wind patterns look like? What is the overall circulation of the atmosphere? What are the ocean currents doing? What are the ice sheets doing? You can make these these models as elaborate, elaborate and complicated as you want, depending on what sort of questions you're trying to answer.

[00:23:28]

And it doesn't mean that the energy balance model is wrong within its domain of assumptions. It's just that it's not very useful for certain kinds of questions. We need more elaborate models for other questions.

[00:23:40]

So one of the things that I think it's very difficult sometimes to get across, even when I teach introductory sort of philosophy science classes, as you pointed out a few minutes ago, the question of what counts is and what is a model and how is it supposed to be structured and used?

[00:23:54]

It's it's a very interesting question to philosophers of science. And it's very difficult to get across the idea that there's no such thing as a wrong model unless, of course, that the code is wrong or the questions are badly or whatever. But if everything's done technically correctly, there's no such thing as a as a wrong model. There's a question of how much the assumptions that go into building the model are reflective and informative about the thing you're trying to model.

[00:24:18]

And that is no different at all from, let's say, buying a map of New York City and find out that, oh, it doesn't have all the information I want. And so it's wrong. Well, it's not wrong. It's just that it focuses on certain things rather than others.

[00:24:35]

That's what our map is, trying to model something that is static. I mean, more or less. Yes. But the kind of models that we're that we use in many fields, including climate science, are trying to model something that will continue on in time. And so I would imagine a sensible way to decide what to call a wrong model is a model that, you know, makes predictions about future data, not about current data that turn out to be, you know, systematically wrong.

[00:25:00]

Yeah. And, you know, it's a glib statement that we sometimes. Recite is all models are wrong. It's a famous statement by George Box, statistician, and it turns out has been grossly taken out of context because he was no, I'm not surprised.

[00:25:16]

Some of it was actually talking about statistical models, models for a fitting coefficients, basically to a time series. And from that, making predictions. He was not talking about physically based models of the sort that we're actually talking about here. And yet there is there is one level of truism to the statement that all models are going to be abstractions of the real world at some level. And if you want to call that wrong, well, so be it.

[00:25:46]

But it doesn't mean that it isn't useful and even reliable within the range of assumptions and that you're that you're making. And so, as you said, you can make a prediction. And is the model going to make a correct prediction? Well, this energy balance model, you can use it to make really good predictions about what how the climate should be changing as we increase greenhouse gas concentrations. And they match quite well what we've actually seen. And they're more elaborate climate models, which, again, have made predictions, made predictions decades ago that turn out to have been correct, that match up very well with the warming that we've actually seen since the in the time that the prediction was made.

[00:26:29]

The most famous of these being James Hansen, the NASA Goddard Institute for Space Studies, just retired as director of the NASA Goddard Institute for Space Studies and back in nineteen eighty eight using a fairly primitive climate model. By today's standards, he made a prediction of what warming we would expect in the future, depending on different possible scenarios of fossil fuel burning. The one thing that you can't predict as human beings live with physical laws is human behavior free will.

[00:26:59]

And what he said, though, was that, well, let's consider different possible scenarios for what we might do. One scenario, imagine that we would greatly curtail our burning of fossil fuels. Another scenario imagined a great increase in our burning of fossil fuels. It turns out the third scenario right in between corresponds best to what we actually did. And the prediction for that scenario matches up very well against the predictions that the observations that have played out in the intervening period.

[00:27:33]

Now, you might look at that same model and look at what it had to say about how rainfall patterns would change in central Pennsylvania and find it didn't do a very good job. And so then you're you've got a quandary. You've got a model that made a good prediction for some very generic quantity, like the global average temperature, but not so good prediction about what happened to rainfall patterns in the US. And there would be a very good reason for that.

[00:28:04]

The models that climate modelers were using back in the nineteen eighties didn't produce El Nino events. And El Nino is this natural climate phenomenon that has a very important influence on rainfall and temperature patterns around the globe. And if you have a model that doesn't produce El Ninos, well, then it's not going to be able to capture any climate changes that are related to how El Nino changes over time. And so what you find is that for certain predictions, certain models are valid, but they're not valid for phenomena that are sort of outside of the range of the assumptions or the representations of physics that the model has.

[00:28:48]

Speaking of predictions, valid and especially local. So I own an apartment on the Upper East Side of Manhattan. Should I sell it before it gets flooded by sea level levels or what?

[00:29:03]

That that's a great question. Mean not just because it might be helpful to you and your personal decision making, but it but it gets at a real interesting question here, which is, again, it gets back to the topic at hand. What we can tell you is that there's going to be a certain amount of sea level rise that we can predict pretty well from the warming of the oceans as the oceans warm and the amount of sea and the density of sea water decreases, the water expands as heated up.

[00:29:40]

And so some of the sea level rise is pretty easy to predict. It's just a basic consequence of a simple property of thermal expansion. And we understand thermal expansion really well. We can make that prediction easily. But here's the problem. That component turns out potentially to be the smallest contribution to sea level rise. A far greater contribution is likely. To come from the melting of the major ice sheets, the Greenland and the Antarctic ice sheets, and we don't have a great deal of confidence on the rate at which that will happen.

[00:30:14]

There is a wide range of estimates of how much melting we're likely to see of those ice sheets over the next century. And with that comes a huge uncertainty in the amount of sea level rise that you would expect. And it doesn't even stop there, because if you look at what Hurricane Sandy did and you might have actually seen, you might have been close to the the region that saw flooding. Yeah, yeah.

[00:30:37]

I was right outside of the area that got flooded. Yes. Yeah.

[00:30:41]

And, you know, Battery Park saw a record surge, a 13 foot coastal surge associated with that storm. But it was a consequence of a bunch of different factors coming together. The argument that I would make is that some of that we can say was due to climate change because sea level in that region along the mid-Atlantic coast of the US is about a foot higher than it was a century ago because of climate change and global warming. But the flooding potential was also a consequence of the time of day.

[00:31:17]

The title sort of it was the high tide. It was a combined with also a specific sort of setup of the of the jet stream, which actually favored winds that go in the opposite direction. From what you would usually expect. Most hurricanes head off east out into the open Atlantic Ocean because of the prevailing winds. But Sandy went in the opposite direction. It headed west. It made a beeline for the coast of New Jersey and New York City.

[00:31:56]

And because of the geometry of that basin and the direction that the storm came from, that contributed to that very large coastal surge. Another question is, well, the size of Sandy, it's the largest storm, tropical storm that we've ever seen in the Atlantic basin in terms of it just its sheer size. And it turns out that that's relevant because the the larger the storm, the larger the area of strong winds. We call the fetch a longer length over which the winds are blowing against the ocean surface and piling up a surge, a coastal surge, these things that all came together to give that event.

[00:32:36]

We think climate change definitely had a role with certain components like sea level rise that played a role, but other components like it was the largest storm ever seen is that because of climate change? We don't have the answer to that question. What about the configuration of the jet stream that caused Sandy to take the trajectory that it took? Well, it turns out that there could be a connection with climate change, but it's pretty tentative. We know that the melting of Arctic sea ice in the summer is actually changing the pattern of the Northern Hemisphere jet stream, and it's favoring a configuration of the jet stream that is consistent with the prevailing winds that we saw with Sandy.

[00:33:17]

Now, does that mean that it was that Arctic sea ice melting in retreat that caused the trajectory of Sandy? Well, we don't know that with any degree of confidence, but we know that that may have been favored by those circumstances. And so you can see that there are a whole bunch of things that are coming together to try to address your question of risk, which ultimately we need to make it decisions, cost benefit analysis. What are we going to do based on the risks that we can estimate, the costs of taking action and the uncertainties in both of them?

[00:33:50]

And that all comes together. Forgive the expression and a certain in a type of perfect storm of competing factors here so we can inform that risk. And indeed, New York City right now, under the leadership of Mayor Bloomberg, has a commission that is looking very seriously at all of these factors. What's the best scientific evidence that we can bring to bear on the question of assessing increasing risks, coastal risks and threats to New York City?

[00:34:22]

Well, based on what you're saying, since I love New York, I'm going to stay for you. But what you are saying actually does remind me of a parallel, again, with my own discipline of evolutionary biology, which is, for instance, one of my specialties was studying natural selection. And in studying natural selection is really a statistical issue of detecting patterns in populations of organisms, because although natural selection acts on individuals, you can only detect its its effects at the population level.

[00:34:51]

And so in some sense, it's the very same distinction you were making, I think, a minute ago between being able to predict the general pattern like, you know, does do climate change models predict an increase, let's say? The frequency and or size of storms, well, there may be one answer, the question may may have an affirmative answer. That doesn't mean you're going to be able to actually say, well, this particular storm is actually connected to it.

[00:35:14]

And it's the same idea with studying natural selection. And I assume a lot of other things in science. That is, I can make predictions, fairly accurate predictions. Actually, at the population level. I can tell you that given a certain environment, certain characteristics will be increasing in frequency in time and others will be decreasing. But if then you ask me, well, why did that particular plant or an animal die? What was wrong with them or why did it have so many offspring?

[00:35:39]

What was right with them? I said, well, I don't know. It could have actually been chat's that there's no there's no way for me to tell about specific events. So it's a similar situation.

[00:35:48]

It's what I like to use. The example for the sports aficionados of A-Rod or Barry Bonds, you know, the effect of steroids on a baseball players performance. You can look at their, you know, over time how, you know, the number of home runs that they have been hitting has increased exponentially beyond what you would expect in the absence of them taking steroids.

[00:36:16]

And so at some level, you know, we know that they're you know, their record setting performances were enhanced by taking steroids. Does that mean we can look at any one homerun that they actually hit and say that homerun was caused by the use of steroids? No. Well, we can't, but it's not the right question. And the same holds here with climate change. If we look at statistically the increased incidence of record heat, of record wildfire, of record flooding events and a whole range of extreme costly weather events, and we step back.

[00:36:53]

We can see that as we look at the horizon, that the frequency of these events has increased in a way that's outside of what we can explain from natural causes. We're breaking all time records for heat in the US right now over the past few years, at three times the rate you could you could explain from chance alone. So we know that we are seeing increased risks associated with increasing severity of certain types of weather events, and it's having a huge cost on us economically.

[00:37:27]

Last year, one hundred billion dollars in extreme weather and climate related damages in the US alone, but. In the end, we can't prove that any one event was caused by climate change, the point in the end is that that isn't the right question to ask, because from the point of societal risk and damages, the relevant question is, are we are we loading the dice? Are some of it? Is the random roll of the weather dice, but are we loading the dice?

[00:38:01]

And we loaded the dice by erasing a five and replacing it with a six. So there are two sixes on that dice. And so sixes come up twice as often as they ought do. Well, all time record heat is occurring at three times the rate that it ought to. Right now we're seeing the loading of those, whether dice by climate change. And Michael, how much do we know at this point about things that we could do now going forward and what effects those things could have on projections of of future weather?

[00:38:34]

Sure.

[00:38:35]

I mean, we we know and there's plenty of uncertainty to go around. There's certain things, as we've always said, that you can predict pretty confidently, you know, global warming, the warming of the oceans is going to raise sea level at some rate, even regardless of what the ice sheets are doing, which is the more uncertain part of it. Because of thermal expansion of sea water, a warmer atmosphere holds more water vapour. It means that we're going to see a potential for greater flooding events.

[00:39:03]

And there's evidence we're already seeing that. So, you know, we know that we can decrease the risk from these sorts of events by limiting the amount of warming that takes place in the future. Precisely how much of an effect are we going to have through some specific? A change in greenhouse gas emissions. Well, that's uncertain. And so what we're talking about is making decisions in the face of uncertainty, but it doesn't stop us in any other realm of our lives.

[00:39:40]

A good example, a good analogy being fire insurance. You know, most of us have fire insurance for our homes. And it's not because we think our homes are going to burn down. In fact, we think that that. Is an extremely unlikely event. It's almost astronomically unlikely, but it's not zero. We know that it can happen and we know that if it happens, it's cost to us is catastrophic. And so the product of the very low probability of it happening by the nearly infinite impact it's going to have on our lives.

[00:40:14]

If it does happen, as you folks know, the product of a very small number and a very large number is indeterminate and it can work out to be either a small or a large number. Our best estimates are that when you work out these risks, these extremely unlikely but catastrophic events, they are a substantial contribution to the potential risk of not acting to reduce emissions. And so uncertainty in this case plays out in such a way that just like with our insuring our homes with fire insurance, it makes sense to be taking out a planetary climate change insurance policy because of the potential for catastrophic outcomes.

[00:40:56]

How likely are they? We don't know. But that uncertainty isn't a reason to question that. There is a sizable risk of not acting. I don't know if I answered if that was what your question was getting it. I'm happy to have a follow up with that.

[00:41:13]

Yeah. I mean, it sounds like the answer is it's all very uncertain, but we're better off trying, not trying, which is a fair answer.

[00:41:22]

Make it a little more specific, actually. So how much warming will we get if we stabilize CO2 concentrations below twice pre-industrial levels? pre-Industrial levels were about two hundred eighty. Right now we just breached the four hundred ppm mark for the first time. It's going back down because of the seasonal cycle, but we were basically just about at four hundred. We started out two hundred eighty. If we continue on, the course that we're on will probably hit twice pre-industrial levels, about five hundred fifty parts per million within a timeframe of three, four or five decades.

[00:42:02]

How much warming will that give us? If you look at all the lines of evidence from various attributes, various lines of evidence about how sensitive the climate system is to changing greenhouse gas concentrations, we're pretty certain that the warming that we can expect lies somewhere between two and five degrees Celsius. So somewhere between four and nine, three and a half and nine degrees Fahrenheit warming of the globe. If we pursue that pathway, if we continue with, you know, with with an emissions pathway that stabilizes at twice pre-industrial levels, we know that we'll probably see twice that much warming in the Arctic.

[00:42:46]

And so there's some range of warming that we project. If it turns out, if we're at the low end of that warming, then a lot of the impacts that we talk about will be at the lower end of the impacts. But it turns out that even the lower end of the impacts is hugely costly. We're talking about costs of trillions to the global economy, of even the lower end projections of business as usual, fossil fuel emissions. And so the precise cost is uncertain because the precise amount of warming and the precise amount of climate change is uncertain.

[00:43:22]

But even if we're lucky and you know, you don't generally make a policy based on assuming that you're going to be the luckiest you possibly can be at every stage. But even if we were to do that, to assume that we're going to luck out and that the climate system really is going to resolve itself to be at the lower end of the current range of uncertainty, even in that event, when economists look at the costs and benefits that they say that it makes sense to be reducing our emissions dramatically right now.

[00:44:11]

Yeah, it's a great question and, you know, there are a range of. Options that are out there and that are under discussion. The surest thing is to reduce the levels of these greenhouse gases. The only thing we know for certain is that if we reduce emissions and indeed if we decide, we even need to actively takes you to back out of the atmosphere. Because some scientists look at the impacts of climate change, they have concluded that even if we were to keep CO2 levels at their current level, that's too high, we would still be committing to potentially catastrophic and irreversible changes.

[00:44:51]

If that's true, it means reducing emissions dramatically isn't enough. We have to do that well, actually extracting CO2 from the atmosphere. And there is technology to do that. What we call open air capture is relatively expensive. It's an expensive it's much easier to reduce emissions by keeping the genie in the bottle than by trying to stuff it back into the bottle after it's gotten out. And so it's much easier to just not put the CO2 in the atmosphere in the first place than to literally suck it back out of the atmosphere.

[00:45:23]

But if the costs are estimated to be high enough, it would actually make sense economically to use even this relatively expensive air capture technology. Now, there's a whole nother set of options that are under discussion. That's the simplest example of a broader category of what we call geoengineering approaches. Essentially, the idea here is that we introduce another intervention into the climate system that hopefully offsets the intervention that we're already having by increasing greenhouse gas concentrations. And so, one, the safest intervention is this open air capture.

[00:46:04]

Let's start sucking CO2 out of the atmosphere because that's basically dealing with the problem at its root cause, but other possibilities that scientists are actively talking about. In fact, the latest Harvard Magazine, there's a whole article by there's a scientist at Harvard University, David Keith, who is advocating for other forms of geoengineering, for example, putting reflective particles into the atmosphere, sort of emulating what a volcanic eruption does when a volcano, a massive volcano, erupts.

[00:46:39]

It can potentially put large amounts of volcanic particulates, what we call aerosols, into the stratosphere where they sit potentially for several years before they fall out. And the reflecting some of the solar radiation back up to space while they're there. And you can actually show that a large volcanic eruption, because of that effect cools the global climate potentially by nearly a degree Celsius or more for several years. So one.

[00:47:11]

Approach that has been suggested geoengineering approach is to simulate what those volcanic eruptions are doing or emulate them by taking huge cannons and firing a large amounts of that sort of particulate matter into the stratosphere and doing it every few years so that we do the equivalent of setting off a Mount Pinatubo type of volcanic eruption every few years. And if you do the calculation, if you were to do that, you could offset the warming effect of greenhouse gases. Well, it turns out there are all sorts of potential unintended consequences that shocks me well.

[00:47:49]

And the law of unintended consequences really reigns supreme, in my view, when you start talking about geoengineering. When I was growing up, there was a song we used to sing. You know, there was an old lady who swallowed a fly. Maybe she'll die. And then she swallowed a frog to swallow the fly. And you and you can and it goes on from there. And that's potentially the real potential danger of geoengineering. You know, those of us, again, is a very scientifically literate group here.

[00:48:22]

Errors often don't cancel out. They tend to add in quadrature, which is to say that if you introduce to perturbations, to random perturbations, the most likely effect is that you're going to introduce an even larger perturbation than if you had not introduced that second random perturbation to try to offset the first.

[00:48:42]

So you make the problem worse on average, if you don't quite know what you're doing that I know I'm beginning to sound like a broken record, but that again brings up a lot of parallels with with ecology. For instance, I remember since I was a student studying textbook cases of ecological interventions like started, there was a famous case many years ago of accidental introduction of I forgot what what sort of bug that, of course, created all sorts of problems because it did not have natural enemies and it started multiplying in all that well.

[00:49:13]

So what did we do? Well, let's introduce the natural enemies to control the bug.

[00:49:17]

And sure enough, that seemed to work for a while and then that created more havoc then than before, because, of course, now the new predator didn't have itself unless you swallowed the fly problem, did die, but not because of the fly, but because of everything else that she did and much more painfully.

[00:49:35]

But but but but I want to fire giant cannons that our atmosphere really want this to happen.

[00:49:42]

You know, it's and, you know, it does almost seem I mean, you can it's easy to joke about because it seems so ridiculous at some level. And yet, you know, there was an article 50 of them. And yet yesterday there was an article that I read.

[00:49:57]

I forget where it had it was describing some recent proposals by Nate Myhrvold. He was the former CFO for Microsoft, sort of this iconoclastic pro technology guru who, you know, has all sorts of ideas for geoengineering, ways of of dealing with climate change. And, you know, he's a very prominent individual. He's you get so, you know, when he says something, a lot of people are listening. Bill Gates is talking about geoengineering. He's funding a lot of these geoengineering investigations right now.

[00:50:36]

And so can I point out the obvious?

[00:50:39]

Let's hope that that doesn't that's not going to end up like Windows being right.

[00:50:46]

It could we could get a catastrophic crash with it. Yeah, that's exactly exactly what I worry about.

[00:50:53]

OK, I'm going to wrap things up before we spend another twenty minutes. Windows bashing, that would be. But we are all going to be on my Mac. Yes.

[00:51:03]

So we're going to wrap things up now and move on to the rationally speaking PEX.

[00:51:25]

Welcome back. Every episode, we pick a suggestion for our listeners that has tickled our rational fancy. This time we ask our guests Michael Mann for his suggestion Mike.

[00:51:35]

Yeah, know there's a lot of confusion out there when it comes to the issue of climate change or human caused climate change. Some of it is just intrinsic to the fact that all the complicated scientific issues can be difficult to fully comprehend. But part of that confusion, unfortunately, is due to the fact that there are groups, institutions, individuals who are actively involved in a disinformation campaign to sort of try to confuse the public about the threat of climate change so that we don't do the things that are necessary, like reducing our reliance on fossil fuel energy, the things that are necessary to avoid catastrophic climate change.

[00:52:17]

And so what I really encourage folks who have been confused by the conflicting information they read about this is to turn to some some resources that are there to help you deal with the the conflicting information. And there's a wonderful site. It's called Skeptical Science. And it's a great name because true scientists are skeptical. Those who deny climate change aren't skeptics. They're pseudo skeptics. But there's a site called Skeptical Science, and they have an app that you can actually download on to your smartphone, your iPhone or what have you.

[00:52:56]

I have it right.

[00:52:58]

And it's got every one of those talking points, those myths that, you know, you're your uncle who listens to Rush Limbaugh, you know, raises with you every year at Thanksgiving. Well, what about the fact that blah, blah, blah, blah, blah, blah? Well, it's got a list of all of the leading denials to talking points and the actual scientific responses. And it's at your fingertips because it's right there on your iPhone. And the responses are available at three different levels.

[00:53:28]

You can get sort of the quick, the short, simple response, the beginner level response and intermediate response or an advanced response which really gets into the meat of the underlying science. And so it's an incredibly useful resource. And I would highly recommend it to anybody who's been frustrated every time they hear these talking points, these myths about climate change espoused by friends or family members or neighbors or colleagues, and they don't know how to respond. Well, here's a resource to help you do that.

[00:54:03]

Well, great, thank you so much, Michael, it's been a pleasure having you on the show. Me, too. Thanks, Mike. This concludes another episode of rationally speaking. Join us next time for more explanations on the borderlands between reason and nonsense.

[00:54:23]

The rationally speaking podcast is presented by New York City skeptics for program notes, links, and to get involved in an online conversation about this and other episodes, please visit rationally speaking podcast Dog. This podcast is produced by Benny Pollack and recorded in the heart of Greenwich Village, New York. Our theme, Truth by Todd Rundgren, is used by permission. Thank you for listening.