0:06

The reason to talk about risk is two fold.

First of all, risk provides a construct to make rational decisions and

I'm gonna describe that in just a second.

And the second reason is that I think we all understand intuitively what risk is.

We understand it in the context of buying car insurance to avoid the risk, or

to mitigate the risk of an accident.

We may not avoid it, but [COUGH] if we were to get [COUGH] in an accident

presumably, if our car is damaged, we'd be able to get it fixed and so on.

Avoid some of the liabilities and things like that.

So, I think we intuitively understand risk.

So if we can start talking about climate change in the context of risk,

then maybe we can also start having a more intelligent

conversation by avoiding all that sort of ideological odor

that seems to accompany any discussion of climate change.

So those are the two reasons that I wanna talk about risk.

So before I actually talk specifically about some of the characteristics of

risk in the climate system, which is where I'm going with this,

what I wanna do is talk about risk in general.

So what I'd like to do is ask you to imagine two cases.

So in this first case, now imagine that this farm is in

a floodplain of a river and that river floods frequently.

It floods every two or three or four years, something like that.

And further, imagine that because this floodplain is really, really wide,

that river can flood, but the water never gets really, really high.

It never gets say, more than a meter high.

Now this farmer has been living here for a long time.

Maybe he's been living here and farming here for 30 years and

he's seen this over and over and over again.

And in fact, his family had been here for a long time and

they had a smaller house and they had seen this.

And the mitigating action, the insurance,

if you will, was pretty obvious.

Build a berm around the farm.

So this guy takes out his bulldozer and he builds a berm around his farm.

And it's not very expensive, but it's the insurance,

it's the mitigating action that avoids the risk.

So he builds a berm that's say, two meters high.

And voila, his farm doesn't flood.

2:49

So he understood, he saw this, he experienced this event over and

over and over again.

The mitigating action was pretty obvious and it wasn't very expensive.

Here is a farm in a floodplain, and this is that proverbial 100-year flood.

And so this guy's been living here for another 30 years and

it's never happened before, never had a flood of this magnitude.

And also this farm is in a river valley, a much narrower river valley, so

that potentially the water could rise significantly.

And he's never occurred to him, because he's not experienced this flood.

He had no concept that this could happen.

It never occurred to him to take mitigating actions.

And even if he were to take mitigating actions, I think you can see that whatever

those actions would be, they'd be very expensive.

So the result is this catastrophic damage

to his farm, and I raise these two issues,

because they illustrate this concept of risk.

So risk depends on the probability of an event occurring and

it also depends on the magnitude of the potential consequence.

4:41

As the mechanism for making sensible decisions,

we can talk about probabilistic risk assessment and equate that with,

as the product of the probability of an event occurring and

the magnitude of the potential consequence and that's what that is.

So that's what provides us with a construct for rational choice.

So on the left here is a graph, we've got consequence increasing.

On the far left, probability on the increasing on the abscissa.

So a high-risk event would be one that is likely to occur and

the consequence would be dire.

And a low-risk event would be just the opposite.

The consequence would not be dire and the probability would be low.

And that gives us choices.

So if on the right, we got probability times consequence.

In other words, the level of risk.

Now imagine, if there is an event that, for

whatever reason, is a high-risk event and

you don't take any action and the event occurs, you're screwed.

On the other hand, if you do take action and the event occurs,

then there's no impact and we're very smart.

6:39

So that's what I mean by a construct to make sensible choice.

And I've used these rather unscientific terms, if you will,

to illustrate again, the fact that this is not science.

There's a lot of judgment and

there's a lot of personal beliefs embedded in

making choices as far as risk is concerned.

So there are several things that I wanna talk about.

First of all, I wanna talk about why the future is unknowable.

And the reason that I want to talk about this in the context of

climate is because risk is the mechanism that we deal with an uncertain future.

If we knew the future, we wouldn't have to deal with risk at all.

Well, we know this is gonna happen.

Either we deal with it or we don't deal with it.

That's not a problem.

But the fact is that if there's an uncertain future,

the risk is the way that we deal with it.

And that's why I wanna make this point about the uncertainty in

the future in terms of climate.

I wanna talk about why risk differs for

different classes of impacts in the climate system.

How risk is governed by extremes, I wanna talk about something called tail risk.

And finally, I wanna talk about the special characteristics

associated with really large events, climate catastrophes.

The future.

So in this diagram on the vertical axis or the ordinate,

we have probability of exceeding two degrees above

preindustrial global surface air temperature,

preindustrial temperature by the year 2100.

So this is probability goes from 0 to 100%.

And on the abscissa, this is a plot of

the cumulative carbon dioxide emissions for

the year 2000 to 2049 and

that's in gigatons of carbon dioxide.

In other words, billions of tons of carbon dioxide.

So that goes from 800 to 2,500 gigatons of carbon dioxide,

just to put that in perspective.

We today, we're producing about 30 gigatons

of CO2 per year in emissions from the burning of

fossil fuels and agricultural activity.

So we have this colored band and

what that is the climate sensitivity for

a whole bunch of climate models for actually 23 climate models.

Most of them fall in that band.

So what that means is that for certain emissions,

the climate model indicates a certain probability of exceeding this

two degree centigrade temperature above preindustrial.

So these climate models then, using all these climate models,

this is in essence some measure of the uncertainty of the science,

because all the climate models are different.

Now it's not precisely a measure of the uncertainty in the science,

which we don't actually know.

And there's something not included in this, which I'm gonna get to later on.

But it is some measure of the uncertainty in the science.

10:34

So let me expand on that a little bit.

So in other words, let's say in this first 50 years of the 21st century,

14 years of which is already gone, we emit a 1,000 gigatons of carbon dioxide.

So the climate models indicate that there's some range

of probability of exceeding this 2 degrees above

preindustrial temperature ranging from 10% to 42%.

Do you see that? Just to make things simple.

So that's this measure of uncertainty in the science.

This qualitative measure of the uncertainty of the science.

Just to make this a little bit simple, instead of taking this range now,

I wanna consider this black line, which is an illustrative case.

Let's just forget about the range and

just now take the black line an illustrative case.

And let's do that, so

that we can consider other possible emission scenarios, if you will.

So you can see that if instead of 1,000 gigatons of CO2,

we cut global emissions by 80% from the 2010 level.

The probability of exceeding two degrees of preindustrial by 2,100 would be 20%.

On the other hand, we see that if the developed world cut emissions by 80%,

but the developing world actually increased emissions by 1%.

We would have a 34% chance of exceeding that 2 degree temperature.

On the other hand, if we continued emissions at 2008 level,

we would have a 42% probability of exceeding

the 2 degrees centigrade temperature.

And finally, if emissions grow by 2% per year, which is more or

less the path that we're following these days, there's an 89%

probability of temperatures exceeding the 2 degree above preindustrial.

So what I hope is an obvious point is that I've just given you a whole bunch of

scenarios and we don't know which scenario is gonna happen, we have no idea.

There's no way that you can tell me that it's more likely that we're

going to move along constant 2008 emissions or grow by 2% a year.

And so there is this uncertainty in emissions and

the reason for that is pretty obvious.

The uncertainty in emissions is because emissions and

thus, energy use depend on technological developments.

They depend on political developments.

They depend on socioeconomic evolution.

They depend on all these things that none of us really know anything about in terms

of being able to predict, so that's why the future's uncertain.

14:32

What are the consequences to society?

Well, I would argue that at the moment, they're mild.

The consequences of loss of biodiversity,

the consequences of loss of sensitive ecosystems,

while certainly not desirable by any stretch of the imagination.

They're not bringing society to its knees.

And so we can list a number of potential impacts

of climate change that range from 100% probability,

because they're occurring now to risks

that become progressively less probable.

And that's what this list is suppose to be.

So I'm on the first column from occurring to less likely,

that's this decrease in probability and

the next column happening now to happening decades out.

So there are some impacts that as I say,

are happening now, such as loss of biodiversity.

But there are other impacts such as significantly

more severe droughts, which are probably a decade.

Maybe a couple decades out to fewer water supplies,

which are probably even further out to really catastrophic

events such as Huge sea level rise, which are even further out in time.

18:16

event, namely the 2003 summer heat wave

that struck Europe.

This is a very, very intense heat wave, I'll just point out in a second,

it was responsible for some estimate of 35,000 to 50,000 deaths,

it was most severe, basically over France.

Now this plot, the vertical blue lines

are a mean summer temperatures from

a number of sites in Switzerland and

these data are from 1864 to 2002.

So every summer you've got an average temperature, right, for a number of cities

in Switzerland, and of course that's where they keep excellent records so

that's probably why this is on this plot.

And so that's what all those vertical blue lines are and then there's,

the average, by the way, of all those is 17.2 degrees,

and then there's a normal distribution around that 17.2 degrees.

Now a normal distribution is a distribution of events that

is a consequence of a perfectly random phenomenon.

And so 68% of all those events are in plus or

minus one sigma of the mean, and

95% are within two sigma 99.7 of the events

are within three sigma and so on and so forth.

So what you can see here is that distribution of summer

temperatures is not a perfect normal distribution, but

it's not too far from a reasonable normal distribution.

The red line labeled 2003, of course, is the summer temperature.

It's 5.4 standard deviations from the mean,

which if you know anything about standard deviations,

the likelihood of that happening in [LAUGH] any kind

of a random circumstance is basically zero.

So it was a very, very unusual event.

Now what's gonna happen?

Let's talk more about extremes in the future, so

[COUGH] let's look at this diagram.

The top panel, now, is a model and

what we wanna do is ask ourselves or a given emissions scenario,

what will the summer temperatures in Europe look like?

Okay, and so let's find a model that actually works.

So first thing we want to do is test that model as best we can and

that's what the upper panel shows.

So we run this model between the years 1961 and 1990,

given the atmospheric CO2 contents,

and it returns a summer temperature for every year, and

that's the purple vertical lines.

The average there is 16 point something or other degrees, so

it's producing an average that's a little cooler than the observed, but

it's not too different from the observed.

Okay, now we've established that we have a model

that is giving some reasonable results.

Let's choose an emissions scenario, that is to say,

let's make an assumption about what future emissions are going to be.

This particular assumption is a high mission scenario.

And let's now ask that model to predict,

to calculate what the temperatures are gonna be for the last 30 years of

the 21st century, and that is the second panel from the top.

So, those are the vertical red bars, okay, so the first thing you can see is

the distribution's far, far wider so there's much, much more variability.

The second thing that you can see is that the average summer temperature

in the last 30 years of the 21st century

is now not too distant from that summer temperature in 2003.

And the third thing you can see is the heat waves in the last 30 years

of the century are gonna be far hotter than the heat wave in 2003.

So that's what we mean about extremes.

We've got to consider the extreme rather than the average conditions.

Tomorrow's extreme could be much larger than today's.

And third and very important point is that the extremes are typically

local in these kinds of events.

We've gotta talk about local extremes not global extremes because

you can talk about two degrees centigrade rise above pre-industrial, but

that's a global thing, right?

That averages everything, so

it's not very meaningful in terms of talking about risks.

So now, what we have plotted here, on the ordinate,

is the probability density versus the climate sensitivity to

doubling of the CO2 content Of the atmosphere.

Now there's several curves here.

Let's just consider that green curve for the sake of convenience.

All right?

Now I want to describe what that means.

Okay, so we're using a model.

And we're saying that we're going to double CO2 content and

the model returns a global temperature.

Okay?

And you do that over and over and

over again, and it returns different global temperatures.

So what the probability density means is that the sensitivity of CO2 three degrees,

so what that's saying is that the probability is 50%.

This gives a 50%, let's say it's right there, this gives a 50% probability.

Okay? So 50% of the time,

the climate model is returning three degrees.

Okay.

2% of the time, the climate model is returning about 25.

Excuse me.

25% of the time, the model's returning two degrees.

10% of the time, the model's returning five degrees.

But the reason I point this out is this huge,

huge tail, this long tail on the positive side.