OK. Welcome everyone.
My name is Tim Palmer, I'm at the physics department here, and I'm going to say a few words of welcome to our speaker, Typekit Schneider.
I should explain that we've been having, I think, a fairly successful workshop on the application of artificial intelligence,
machine learning all that stuff into to improving climate modelling and Typekit, as well as real well experts at that.
So the workshop was kindly, very kindly co-sponsored by the Oxford Martin School.
So we kindly agreed to give a talk here this afternoon after the workshop finished.
So it's happened in his Ph.D. at Princeton University.
He had a spell as professor at Swarek, but has returned to the US,
where he's currently a professor of environmental science and engineering at
Caltech and also a senior research fellow for NASA's Jet Propulsion Laboratory.
Tucker, I was, you will find out, is very much one of our world experts in the interaction between clouds and more generally,
the hydrological cycle with the atmospheric circulation patterns.
And if there was a single problem that really is critically important for understanding
climate change and what direction we're heading to and how bad things will be,
it's understanding this interaction between clouds and circulation.
Typekit has got numerous awards, which I went I went embarrassing with entirely embarrass, embarrassing with one, actually.
He was named as one of the 20 top brains of people under the age of 40 by Discovery Magazine.
So that isn't embarrassing. I don't know what is, but so.
And he's also won the James Holtmann Award for the American Geophysical Union and various other awards, which have, as I say, I won't go into.
So can we just give a quick round applause and welcome to temptation?
Thank you, Tim, and thanks for coming. So indeed, I'll tell you the story of clouds and climate,
and I'll talk a fair bit about some of my favourite clouds, which should be your favourite too,
because of the most frequent on earth, stunning cumulus clouds, the ones that you see on this, the satellite image from a space station.
And I'll come back to studying cumulus clouds in just a little bit.
But the story really starts in the cold place and Ellis Island.
This is an island in the Canadian Arctic archipelago. It's an Arctic island.
There are. There are flowers like the river beauty flower growing there.
But the largest plants you'll find on this island right now is about two foot tall.
Two Willow. I think Tyler grows there and it's fairly barren, but it was.
It wasn't always like that about 50 million years ago.
This is roughly what Ellesmere Island looked like. Once upon a time, it was lush and warm.
There were a hippopotamus grazing on large firms that were top years peak like herbivores in the corner of the image.
They're grazing along their deciduous trees, conifers and the like.
And we know that because scientists have found fossils there, fossils of leaves larger than 10 centimetres tall or the jaw bones of tough years.
They are from crocodile bones. There are crocodiles and Ellesmere Island.
Fifty million years ago and don't need to know much about crocodiles or climate, but you probably can appreciate that they don't like it cold.
In fact, they can't deal with frost. So we know that Ellesmere Island, the Arctic was frost free and that you've seen from 50 some million years ago.
Now, 50 million years ago is a long time. And maybe sometimes hard to put these geological timescales in perspective.
So does this post dinosaurs, but well before humans? And you can ask, Oh, maybe I was.
It was a tropical island, and it wasn't. So here is a reconstruction of what continent look like at around the time.
Ellesmere Island was a red star.
It's right next to Greenland, as it is now is as much an Arctic island in the Eocene 50 million years ago as it is now.
So there were an trees and ferns and all these things because the island migrated.
There were trees in Ferguson and crocodiles because it was very different.
We have some idea of how climate was different from.
Looking at the shells of tiny marine organisms that fossilised marine sediments and from decomposition of the carbonate and the shells,
we can tell to some degree how warm it was.
So here is an attempt to reconstruct the last sixty five million years of the temperature history of Earth and.
First thing, maybe you noticed this in week also that the climate has varied a lot.
If you look at the scale on the vertical axis, assisted temperature change relative to today.
So in the U.S., in early Eocene, fifty five million years ago, it was about 10 degrees warmer than it is now.
We don't know the numbers yet, very precisely, but about 10 degrees is a pretty good estimate.
And by and large, climate has been calling for the last 65 million years I've been gradually sliding downward.
The temperature scale was a gradual, slight levelling off.
There are some, some bumps and spikes in between. For example, there's this one spike you see at the forty three million years ago.
It's that you're seeing climatic optimum.
The temperature spiked for geologically brief period, which is still millions of years, and then went down again or even earlier.
There's the Palio seen using thermal maximum of five or six degree temperature spike on top of an already very warm climate.
The reasons for these spikes are for thermals, as they're called are not very well understood.
It is clear they have something to do with greenhouse gases, but how exactly they came about is unclear.
Just to orient you where we are Homo sapiens, we we appear on the far right of this plant about 300000 years ago.
Temperatures have been decreasing at some point.
Ice sheets started to form for a standard article because there is a land Antarctic ice sheets started to solidify around 10 million years ago.
Ice sheets in the northern hemisphere are more recent.
Around three million years ago that we started to form ice sheets in the northern hemisphere simply because there's no continent at the North Pole,
and we don't know a lot about what drives these temperature changes,
but we do know that the principal driver over these timescales are changes in the composition
of the atmosphere and principally in the greenhouse gas concentration in the atmosphere.
So here is a reconstruction of carbon dioxide and the atmosphere over these timescales.
And well, first thing to note is this temperature was weakly and is somewhat uncertain.
But the CO2 reconstructions are really uncertain. We don't know them very well.
And that even if you look at any given time, you see a lot of scatter and the reconstructed CO2 values they come from such things as fossilised soils,
where you can try to infer how much CO2 was an atmosphere based on the composition of the soil.
It doesn't work all that well, but it works well enough that we can say that again, if you go back to the crocodile in the Arctic period,
fifty five million years ago or so, CO2 concentrations were maybe 6500 parts per million at most.
Most data seems to suggest they were lower than that, but there's a lot of uncertainty in it.
But you can see that CO2 concentrations went down as the climate cooled from perhaps 6500 or so parts per million to around 400 parts per million.
The Pliocene so three to five million years ago, 410 parts per million is just about where we are now.
Come back to that number. So in the early years, maybe there was four times as much CO2 in the atmosphere.
Maybe not even we don't quite know. But it was about 10 degrees warmer.
And again, this number, we don't know precisely.
But we do know that the Arctic was frustrated because there were crocodiles and to make the Arctic frost free,
the entire globe would have to have been warm. So there are two numbers we can use here to do some sort of magic physics, if you like.
So there's about four times as much CO2 in the early Eocene than in a Pliocene, and I'm picking the Pliocene here advisedly.
I'm not comparing with the present day for a reason I'll come to come to in a moment.
And temperatures were about 10 degrees warmer in the early U.S. and then apply as seen.
I'm picking the Pliocene because I want to estimate roughly how sensitive the climate
system is with respect to perturbations and greenhouse gas concentrations CO2.
And if you pick a later time, you have ice sheets changing change the albedo or is that make these estimates a lot more complicated and the Pliocene?
They play some role, but they're relatively minor role compared to the greenhouse gases.
So we can take these two numbers about 10 degrees warming for quadrupling CO2 and combine them into something we call the climate sensitivity.
The climate sensitivity is the way it's defined as the global mean temperature change you get in response to doubling CO2 concentrations.
So quadrupling as twice and doubling and for twice doubling.
We had about 10 degrees global warming or so. And all these numbers to be taken with a grain of salt is really the order of magnitude.
Physics is not accurate even to the first digit and definitely not to any digits after the point here.
But when you get out of this, if you combine the two, is that the climate sensitivity to get from the Pliocene to the U.S. from the U.S.
into the Pliocene must have been something like five degrees or two by 10 degrees,
but two doubling by two and must be around five degrees. And that's what the data seem to suggest.
It might well be higher because CO2 may well not have been four times as high in the U.S.,
but less than that, but at least around five degrees it has to have been.
And here's the problem. If you look at the climate models today,
and here are the twenty nine climate models for which the data were available from the most recent IPCC report, here is their climate sensitivity.
It ranges from two to almost five degrees, not quite five degrees, but there is no model with the climate sensitivity higher than five degrees.
And that assessment that's about to change with the next assessment that's coming out.
But what this means is that these models just looking at these data alone,
you think have a hard time reproducing and using climate if you just give it the
right continents and the composition of the atmosphere as best as we know it,
and even change the ice sheets as best as we know them. You would think it will be difficult for these models to reproduce.
The first three are taken. In fact, that's the case a number of people have tried to do.
That were at Purdue University, for example, but whenever they have tried to simulate and eocene climate,
they could succeed, but only if they increase CO2 concentrations to something like 4000 parts per million.
It was less than that there was frost in the Arctic. It was not consistent with the data, but we know it wasn't for a thousand parts per million.
It was somewhere maybe 600 parts per million, maybe less, but not for a thousand.
We can't exclude that. So the models are not sensitive enough to perturbations as a suggestion here that you get from looking over Earth's history.
And the other thing you see from this graph here is that this kind of sensitivity varies.
A lot varies by almost a factor of two from model to model, meaning there are large uncertainties.
And the primary driver of these uncertainties are those clouds.
And I think they may also hold the answer to what that missing X Factor and global warming and do is Eocene loss to the clouds.
I'm talking about these guys on the left or strata cumulus clouds that cover vast areas of tropical oceans.
It's the most frequent cloud type on Earth. They cover about 20 percent of the tropical oceans.
This is off the coast of California, Baja California. I live in the upper left quadrant of this image and.
If you fly from, say, Los Angeles. Goes here to the Hawaiian Islands, which are on the right.
You look down and this is, by the way, how I got interested in this problem.
Looking out of aeroplane windows and thinking we ought to be able to explain a wet blanket over the ocean.
What happens is as you fly towards the Hawaiian Islands at some point, actually fairly suddenly,
these whites try to cumulus clouds give way to more scattered cumulus clouds.
These white clouds you see around the Hawaiian islands and where you have these cumulus clouds,
there's more dark ocean surface exposed to less sunlight is being reflected.
Meaning it's warmer the way strutting cumulus clouds reflect a lot of sunlight.
They call the ocean underneath.
And the fundamental problem we have inside modelling is that we don't know if we get more strident cumulus or cumulus or neither of the above,
as the Climate Forum's climate models give wildly divergent answers.
So if you go back to this graph shows the climate sensitivity for twenty nine models,
the models on the left that have a relatively low climate sensitivity tend to produce more low clouds as the climate warms,
meaning they reflect more sunlight. And that reduces the warming you get,
whereas the models on the right tend to produce fewer low clouds that reflect less sunlight and that amplifies the warming.
So that's the principle difference or the principle driver of uncertainties in climate predictions, even for the next few decades,
and it's certainly one big uncertainty when you go back millions of years to where clouds hard, the various ways of thinking about it.
I think one way I find intuitive and helpful is this If you take all the water in the atmosphere,
everything vapour condensed water does bring it to the surface as a liquid layer of water.
You get a liquid layer that's twenty five millimetres stakes for America to spot an inch thick, right?
It's easy to remember. So all the water in the atmosphere is the layer that thick on the ground.
That's not much the water is a trace constituent. But now the the fact that surprises many people,
even in our field is if you ask how much of that water is actually in clouds as opposed to being vapour.
It is exceedingly little like clouds. It's about two and 50 times more water in vapour form than it is in condensed form droplets or ice crystals.
So you can take all the cloud water and bring that to the surface as a liquid layer.
You get a liquid layer at 100 microns. Then it's the thickness of a human hair of a coat of paint here.
So now imagine you're a climate model and you have to predict the tiny, tiny residual of a trace constituent of the atmosphere.
Predict how much of that condenses as air moves around. That's a really challenging task.
Of course, clouds form as air masses rise. As they rise, their cool water reaches, saturation condenses,
and you need to predict that small residual of the total amount of water in the atmosphere that's condensing.
That's hard. Climate models are not made for it. They don't do it well.
Another way of looking at it is that climate models try to simulate the motion in the atmosphere using Newton's laws of motion,
the loss of some dynamics with grids that are typically 100 kilometres wide.
Maybe you're going towards twenty five 10 kilometres and some simulations and these
clouds say strata cumulus cumulus damped dynamical scales of 10 to 100 metres.
They literally fall through the cracks of the climate. Models fall through the mesh.
It's no way to resolve it. We need to represent these clouds empirically in some fashion, and that's hard.
And in fact, that's not working very well. It's just one climate model that I think is representative of all climate models.
What is shown here is the cloud cover relative to observations so widespread that means the model has fewer clouds and observe it's blue,
which means it's more about 10 than what's observed.
And you see these vast red stretches in the subtropics or regions with a lot of low clouds of cumulus clouds.
This climate model, as well as every climate model we have vastly under,
predicts the amount of clouds by almost a factor of two is this colour Skelly goes to minus 40 percent.
It's saturated, so it's even even more than 40 percent under prediction.
So that's the problem with climate models that these uncertainties going forward and that leads to questions
how reliable they are if you go back for millions of years and try to explain climates of the past.
So we cannot resolve clouds, we cannot simulate clouds explicitly in a climate model and will not be able to do it any time soon, not for decades.
Bigger clouds we can resolve. But these small clouds, you would need a computer billions of times faster than the fastest.
We have to actually resolve them. This is not going to happen, but clouds are still physical objects.
We know the equations governing them. We can solve the equations quite accurately and it can solve them just not on the globe.
It needs to be in a smaller box. So here's a simulation of the cloud we did to cumulus clouds and the Caribbean over some ocean surface,
and it's a computer simulation that looks pretty lifelike. We can compare those simulations with field data.
They reproduce well. We have great confidence that we can solve the equations in small boxes quite well.
The boxes just aren't as big as a globe.
In fact, we have so much confidence in the simulation of this cloud that was cast in stone, says Karen LeMond,
a sculpture who approached me with a question I have about this and saying she
wanted to make a sculpture that weighs as much as a cloud and marble and said,
well. You need a very big block of marble and you're very busy.
You'll be very busy for a long time, but I didn't know at the time is that sculptures of robots and they can deal with cat files.
So there was a robot sculpting for for a good six weeks and Karen than finishing the work by hand.
It became the sculpture called Cumulus that was exhibited at the piano in Venice in 2017.
So this this is this weighs about as much as a real cumulus clouds.
It's a few times before in there. We can also simulate stroke cumulus clouds quite well.
This is a simulation of strutting cumulus clouds. With this,
you're looking down on if you wish to try to calm the clouds or you can think of looking up into
the blue sky and what you see and there's wide scale is how much water there is in the clouds.
See, is this just measured as a thickness of a liquid layer? It's about these 100 microns or so the dimensions.
So an individual cloud that's about how little water there is to try to cumulus clouds.
I'm particularly interested in, well, either the most frequent,
but b they have very special dynamics that I find intriguing and that led us to questions what happens to them in a global warming scenario?
So A. They're really important.
And for Earth's climate, they cool Earth by about eight degrees globally as the subtropical strata cumulus clouds simply by reflecting side.
What makes them pretty interesting is that.
Every cloud in some fashion is driven by turbulence to run emotions that lead to air masses rising and air condensing.
And in most clouds, that turbulence is driven from below by heating.
Send a tropical cumulus clouds you heat the surface air masses rise. Eventually, water vapour condenses.
You form a cloud of Australia. Cumulus clouds of turbulence is driven from above.
It's driven by a cooling instrument by radiative cooling. So these clouds does.
The 100 micron thick layer of of liquid water is an incredibly good absorber
of infra-red radiation that's basically opaque to infra-red radiation means,
which means they emit thermal infra-red radiation upwards, and that's how they cool themselves.
And the question I had a number of years ago is, well, if not, you put more greenhouse gases in the atmosphere.
This cooling of the clouds should become weaker. Well, the cooling does is it leads to cooling of air mass at a sink to the surface.
They pick up moisture from the ocean surface, bring the moisture back up, and that nourishes the clouds in a convective cycle.
If this cooling gets weaker is convective. Cycles will get weaker and the clouds can get thinner.
And the question was, well, now you put more greenhouse gases in the atmosphere that cooling off to get weaker and much the
same way that the humid summer night is warmer than the clear summer night and humid summer night.
The Earth's surface can't cool radiative lee as well as it can in a clear summer night and hence the human nights of warmer,
which is why I prefer Los Angeles summers over in New York summers.
Same for the clouds, so you put more greenhouse gases, carbon dioxide, water vapour, anything else in the atmosphere above it?
They won't be able to cool themselves as well. And the question is what would happen?
So we asked the question by building a computer model to answer it.
And a global climate model can simulate the clouds seen.
So what we have to do is build a computer model that simulates the clouds in a box like the box you see.
But then we have to represent the rest of the climate system somehow.
So in a usual climate model, what you do is you resolve all the large scales of the motion quite accurately.
We have great confidence we are doing this well.
And then you represent all the small scales semi empirically and we don't have confidence we are doing that well.
So we did two years. Just do.
The exact opposite is similar to exactly small scales accurately, but that represented all large scale semi empirically, meaning the large scales.
There's a lot of uncertainty in what we do there, but the small scales, if you have a good simulation,
so in a cartoon, what it looks like, what we did is something like that.
You had a box sphere. You simulate part of the subtropics, a small patch of ocean sea off the coast of Angola, Namibia, Peru, California,
and that's coupled to the rest of the climate system to some column representing tropical dynamics to energy moisture transports in the atmosphere.
It's coupled to the surface temperature and energy budget for the ocean to radiative fluxes of solar radiation, along with variation and the like.
And then we the simulations. We did a lot of simulations, and what comes out in the end is a simple looking plot that is maybe too simple looking.
This is a few million hours of computing to get a few dots of.
I don't want to convert it into dollars or pounds for this plus plot costs, but it's expensive.
And what do you see as the cloud fraction in the simulation and in the subtropical patch?
So we start out at 400 parts per million CO2, about present day climate and this red upward pointing error that you can call our present day climate.
So this is a situation where we have sort of cumulus clouds covering the entire subtropical part,
simulating 100 percent cloud cover, and then we started increasing CO2 concentrations.
And that's. As far as cloud cover is concerned, boring for a long time,
you go from 400 to 600 to 800 and and 2000 parts per million and cloud cover is at 100 percent.
But then there's a point where cloud cover falls literally off a cliff.
Here it goes from 100 percent to around 30 percent. As happens between twelve hundred and thirty million parts per million in the simulation.
The precise CO2 concentration where that happens is not crucial because it depends a bit on what you assume for the rest of the climate system,
but that it happens is crucial. So we start from 400 parts ppm having strong cumulus and then we go into something like 30 percent cloud cover.
But we just have scattered cumulus like around here. Why? And then he can increase CO2 concentrations even more.
You lose some more cloud fraction. Otherwise, nothing too exciting happens.
But here's what's interesting now let's go backwards. So we figured out how to scrub the atmospheric CO2 and our computer model.
That's just literally a novel term. So you remove CO2 from the atmosphere and then you're walking back along the blue areas here.
And the interesting part is that we are not reforming shredded cumulus clouds where they first broke up.
But here you have to go to two below 300 parts per million CO2 before you get back to 100 percent cloud cover and start to cumulus.
So we say there's hysteresis, meaning that this the state of the system depends on its history.
And I'll explain it in just a moment how it comes about, but that's just cloud cover.
So I have these clouds so enormously important for climate because it reflects so much sunlight.
So you, you perhaps are asking and should be asking what happens to temperature.
And he said, what happens to temperature in these simulations? So this is the temperature in the tropics,
which is the temperature change in the tropics as a reasonably good approximation of global mean temperature changes here.
So again, we started out at 400, we're going up and 2100 parts per million or so.
The cloud cover drops and temperature goes up in this case by about eight degrees centigrade.
So this is roughly the cooling subtropical marines try to cumulus clouds currently provide.
So you lose that cooling, you get to about eight degrees warming in addition to the global warming that was gradually happening before.
So it's to go from, say, from four to eight and reports familiar and you see global warming of this model of three and a half degrees or so.
So the fairly canonical climate sensitivity that you see in climate models.
But then when these clouds break up and that happens quite abruptly as a function of CO2,
here you see an additional jump in temperatures and then as you go down, remove CO2,
you stay on the warm branch of the climate system and in this model system and temperatures
stay seven degrees or so above the temperatures and this cooler range until the clouds reform.
And in this case, 200 parts per million. So the lower concentrations then we currently have.
That's just a simulation, and you could ask all sorts of questions.
How relevant an idealised model is, but one one reason we have confidence in idealised models is when we understand physically what's happening,
and the physics is pretty straightforward. So the way you lose the clouds,
the principal factor responsible here is that greenhouse gas concentrations increase that long with cooling of the cloud tops gets weaker.
That drives turbulence down, but the driving of the turbulence down gets weaker.
Up to a point where the clouds, we say decouple from this surface moisture supply,
basically even when the turbulent air masses are driven down by the hook,
by the cooling are not reaching the surface anymore, then cloud cover bends as cloud cover thins.
The surface warms because less sunlight is reflected,
less sunlight being reflected means there's more water vapour, any atmosphere as the surface warms.
That in itself is a greenhouse effect gas amplifying the effect of CO2.
You have a feedback cycle that leads to the break up of the clouds here.
There are other processes that are important, and one is counterintuitive.
Evaporation is important, and you might think that there's more evaporation.
You would get more clouds. But that's not correct for these clouds with more evaporation, you actually get fewer of them.
And that has to do with whatever variation does in the clouds. Water vapour condenses in the clouds.
If you have more of it, that can convince you get more latent heat release that enhances the turbulence and the clouds,
and that leads to more mixing of dry air into the clouds, and it works against their existence.
That's another process that plays a role here, and it's somewhat counterintuitive. But now you can see how has two reasons why stability comes about.
Once you lose the clouds, it's warm. When it's when it's warm, there's more water vapour, for example in the atmosphere.
It's an additional greenhouse gas. Now, when you just ramp down CO2, well, there's still more water vapour in the atmosphere.
That's still a greenhouse gas that still prevents the long way of cooling and still prevents the clouds from reforming.
Similar story Applying to evaporation evaporation jumps by 70 percent when these clouds break up.
It also works against the existence of the clouds, and that's how you get the sense to resist look out.
So I said everything about large scale and the rest of the climate system here is idealised and to be taken with a grain of salt,
which means that things like the precise CO2 concentration where that happens,
that's not necessarily quantitatively accurate account in that quantitatively.
Here's an example of how we can.
You can make different assumptions, different reasonable assumptions what the large-scale atmospheric circulation does,
whether it weakens weakly as the climate warms or weakens more strongly as the climate warms.
These are the results again for the tropical temperature as a function of CO2.
And you see that the point where these clouds break up shifts quite a bit.
It's a function of CO2. The points where they reform also shifts the width of this as Teresa's loop shifts and the like, which is all to say.
We can say with precision whether these clouds would break up a twelve hundred ppm CO2 or 600 or whatever it exactly is.
We don't know that, but that it could happen seems pretty clear. Simulations show it, and the physics of it is clear.
So back to back to the Eocene and the past was the mean.
So one possible explanation of how you could have gotten climates as warm as the Eocene was,
as we know it was because there are crocodiles in the Arctic. Is that a cloud cover might have been a lot thinner?
Maybe there were no strutting cumulus clouds at the time, subtropical strutting cumulus clouds at the time at all.
And that would certainly suffice to create a very warm climate globally and make a lush Ellesmere Island possible.
It's one possible explanation.
There are other possible explanations of generally climate as much more sensitive than models suggest without dramatic changes in cloud cover.
That's also a possibility. But at least that's one possible explanation for a climate state that we know occurred.
That has confounded explanation for a while. And what does it mean for the future?
So here is CO2 and a bit more. More recently, it's just the last thousand years, rather than millions of years over those timescales.
We know CO2 precisely.
We know it by just taking air bubbles frozen into ice from Antarctica and looking at the CO2 concentration of the air that's preserved there.
And we know that CO2 was fairly constant at two hundred eighty or so parts per million for thousands of years into the past.
And of course, it has been speaking up for us at the beginning of the industrial revolution.
And the red curve are actually instrumental measurements also in Antarctica.
So they consist of a person leaving a station and taking a flask of air and then going to allow but measuring how much CO2 is in there.
And you see, now we are at around 410 parts per million CO2.
How significant the change is that I think it's useful to put this again into a perspective of the past so years,
is Zoom in now the last 50 million years again and and then projecting out in the future what might be happening so far in 10 parts per million?
That's that's a CO2 concentration we haven't seen in at least two million years on Earth.
So this is getting us back to the Pliocene.
Somewhere between two or three or four million years ago and a Pliocene Earth was around three degrees warmer and three degrees.
I don't know if that sounds significant to you or not the other two, but there is agreement and to ease and come back to it.
Three degrees may not sound like much, but three degrees of warming sustained over thousands of years is enough to melt a lot of ice.
So during the Pliocene bit about three degrees higher temperatures, sea level was a good and good 20 metre or so higher than it is now.
Now that is significant.
Life then, was perfectly possible for all sorts of animals, and there were no Homo sapiens around at a time, but they could have lived just fine.
Just the challenges,
the challenges that we have built cities and say this is what the artist called London be under with this case will be 40 metres sea level rise.
That's melting two thirds of all ice sheets. London would be flooded as much as much of L.A. and New York and the like.
So the problem here is that if you sustain a warming of that sort for a long time, for thousands of years, you're just not adapted to that situation.
We have built cities and places where they perhaps wouldn't be in that case. Now, I don't.
I hope you won't get to anything resembling the scenario.
But here's where we are. So we had about one degree global warming over the last 100 150 years.
That's not uniformly distributed as this in temperature change between 2000, around now in the 1850s.
And the grey areas are just areas where we don't have enough data to say much. This is in the mean.
It's about the degree there's more warming over high latitudes,
more over continents for reasons we understand having to do with how much water is available to evaporate.
Number to remember is a degree of warming we have had,
and I've probably heard about the Paris Agreement that had two hundred and ninety six signatories a while back,
and now it's down to one hundred ninety five.
And the Paris Agreement stipulated that we should limit warming to two degrees above pre-industrial levels.
So we had about a degree, so there's one more degree to go. Now you can take climate models we have and ask how much more CO2 can we put in
the atmosphere before you get that extra degree before we reach this Paris target,
the threshold is fairly arbitrary, but it's useful to pick a threshold to illustrate the point I want to make here.
So here's the answer you get from climate models.
How much CO2 can you put into the atmosphere before we reach two degrees warming above pre-industrial levels?
And it's the same twenty nine models I've shown you before. The answer varies between close to 600 parts per million and 480 parts per million.
The models here are ordered in order of increasing climate sensitivity,
so the models that are right are more sensitive just to say that the models on the radar right,
the more sensitive models are right, then we'll reach those two degree warming within 20 years or so.
And that's pretty much irrespective of policy interventions and the like. That's energy infrastructure, for example,
is so long live that this would be an unavoidable with the models on the radar right for the models on the left.
All right. Well, we would have the twenty six days or so even under high emission scenarios before these two degrees of warming are reached.
So just a full human generation difference between the members of the predictions here, and I find that wide range of predictions unsatisfying.
And it's perhaps even worse than what this graph says, because climate models now coming out are even a lot of them right,
even to the right, the most sensitive models we have here. So maybe that's not even spanning the range of what could be happening.
The point is the uncertainties are large and there are there are enormously large.
If you want to plan rationally for human generation difference when you hit a certain temperature target,
temperature affects everything in the climate system, precipitation and the like, so all the other predictions will be equally uncertain.
You want to be able to plan infrastructure in one storm water management infrastructure and want to do so rationally.
A number of cities are building seawalls in New York, for example, and spending close to a billion dollars on that.
It would be nice to know how to build the seawall that New York City is protected from, say, a 100 year flood 50 years from now.
And we don't have the information to say accurately what would what that would take Sam for wildfire risk.
Assessing risks for vulnerable communities, droughts and the like.
So the cost of climate change adaptation, which will be unavoidable, is estimated to be above $200 billion annually by 2050.
People have tried to calculate what the socio economic value would be of having the uncertainties
in climate sensitivity within 10 years and the number of they come up with this from a paper.
Chris Hope was 10 trillion dollars.
I don't have a good intuition for $10 trillion and maybe it's only one trillion dollars, but it's a big number is a point.
And and the other point is.
Note note how the second point is phrased, having the uncertainties within 10 years,
it's important to get better predictions fast because climate is changing and at some
point the predictions won't be that useful anymore because we see what what is happening.
So the name of the game here is to come up with better predictions and come up with them fast.
You don't want to wait 10 years, and there are things we can do assign side.
And here's a shot you simulations of clouds and small boxes so we can do them in one small box that can do them in two small boxes.
Well, we can do them in tens of thousands of small boxes. You can embed in the climate model.
We can do it everywhere. That's where you need factors of billions and extra extra in computing that we don't have,
but we can do it in thousands and maybe 10 thousands of places.
And these simulations are quite good, at least as far as emotion of the clouds is concerned.
So that's something we should be able to use. And the other thing we have, of course, is a wealth of data.
This is what's called the train of satellites.
It's it's satellites flying and constellation crossing the same spot on Earth's surface within seconds of each other.
It's called the A-Train because they cross it in the afternoon after afternoon.
The measure all sorts of things in a coordinated fashion.
For example, in front is OCO two is measuring carbon dioxide for the actress calypso and clouds that they measure.
A cloud properties, the lighter radar, a number of other Aquos measuring water vapour, for example, if you have a wealth of data and.
These data, they can inform climate models, we have used them to evaluate climate models,
you have used them in a way I've shown you before to say that there's a large bias in cloud cover, but you can do more.
You can directly learn from the data. They have seen so many advances in the data sciences in the last few decades.
And what we wanted to do is bring these advances in a data scientist to bear on the climate problem by building a model that
learns from these high resolution simulations we can do in many spots and learns from from the data we have could be space based.
But the same is true, say, for autonomous vehicles in the ocean and the like.
So we formed versus a number of people getting together over time, and companies come to call ourselves the Climate Modelling Alliance.
And some are people at Caltech,
my institution and the Jet Propulsion Laboratory at MIT Naval Postgraduate School and an increasingly increasing network of other collaborators.
The idea of this group is let's just try to. Bring climate model.
Put it on a data driven footing, and weather forecasting has happened decades ago.
Weather forecasts have become very good and perhaps better than their reputation, in large part thanks to extensive use of data.
What we want to do for climate modelling is, in some ways the same that has happened for weather forecasting,
make much more extensive use of data to make the models better. So we are doing is built a model that is in some ways a traditional model.
If you visit it as an atmospheric model and ocean model and land model, size models and the like, but wrapped around everything,
so is a layer of machine learning data simulation tools that allow the model at the core to learn from observational data
and from high resolution simulations of clouds of sea ice of ocean turbulence that you can spin out where you need them.
When you need them. You can spin them out. Part of what's attractive, it lends itself very well to cloud computing.
You can do this in a distributed computing framework. And of course, it's irresistible to simulate clouds on the cloud, which is what we're doing,
what we want to do if we want to achieve predictive advances that are practical.
In the end, we want to get better predictions of of rainfall, for example,
rainfall extremes and things that really matter for infrastructure planning,
for example, high impact weather, accurate risk assessment, heat waves, droughts, storms and the like.
High latitudes are important and like to be able to say what happens to Arctic sea ice.
You'd like to be able to assess risks for vulnerable communities.
And what I'm hoping for is that a few years from now that most of you will have apps for climate just as much
as you have them for a weather where you can get accurate climate information for whatever interests you say.
If you want to purchase a house and a coastal area,
the likelihood that it will be impacted by the storm surge or in California if you purchase a house near near brush land,
what the likelihood is that there will be wildfires and the like.
I'd like to have people to have apps that provide that information, much like you get weather information now, but now.
Things a few decades ahead that are just as actionable for your decision to now say,
if you plan infrastructure or purchase property or whatever it is as weather information is for you,
currently, you decide whether to bring an umbrella or not.
But for these billions of dollars, a decision that we have to make, it seems even more important to have accurate information.
And hopefully you get there within within four or five years is our goal.
So the rev up, let me say a little bit of the people who were involved here Colleen, Colleen,
press or two research scientist or now with the Pacific Northwest National Laboratory,
as they did these simulations of the straight cumulus clouds I showed you. And the Climate Modelling Alliance.
That's most of us down there. Not quite all of us at the meeting a few months ago.
This is our office space. It's the garden of the Joint Office space.
We have it pretty luxuriously nice and part of what makes us exciting for us.
Aside from the direct usefulness of it, is that the group of people you see,
other oceanographers or atmospheric scientists are software engineers who applied mathematicians and computer scientists,
Caltech, who are all in one building occupying joint office space.
And the hope is, and we have seen that hope being materially assisted by fostering interactions
between the disciplines in a much tighter way than what has happened before. But we can make progress.
I should acknowledge people who funders Eric Schmidt, provide the bulk of our funding,
the recommendation on the French futures programme and to a number of other philanthropic organisations and the public public foundations involved.
Earthrise Alliance, the Family Foundation and National U.S. National Science Foundation is funding a
software institute for us and Charles Trimble and Maxine Linder of trustees of Caltech,
who provided seed funding for us and got us started. And especially Charles Trimble early on was a great inspiration.
You may not have heard his name,
but he changed your life in case someone who figured out how to miniaturise jobs and that he is the reason that you don't get lost anymore.
And so now we want to make sure that in the future we won't get lost as far as climate change is concerned.
So I'll stop right here. I'm happy to answer questions. OK, thanks to here.
So the plan is to ask hopefully some questions or comments.
I guess the plan is to use the microphone, is that right?
So maybe what I'll do since the microphone is here is sort of start in this area of the room.
And yes, I worked my way. Though it is quite interesting, right at the beginning of your talk,
when you said of all the water vapour that's in the atmosphere, only a very small fraction of its clouds.
And it made me think, what is the contribution of vapour trails from high, high flying aircraft to global cooling or global warming?
So clubs have two important effects. One is reflecting sunlight, and the other is that they have a greenhouse effect.
And these low clouds they've talked about at the beginning, the greenhouse effect is quite small because they're so low.
So they mostly reflect sunlight on high clouds, contrail cirrus clouds.
Their main effect, as is the greenhouse effect, and they contribute to warming.
I mean, it is a measurable effect of what comes from aircraft, but it's a small effect.
That's for a very interesting talk with the curve that you have on the top right, so the looking back at that patio,
Seville Maxima and one of the things about the oceans then is that you had quite a deep thermocline and as that as a cool,
the thermocline came up and you could shoulder thermocline and bring cold water to the surface.
So in those experiments that you've done, I wondered how sensitive others shot shoddy chemist regimes to the ocean surface temperature.
So we have motion dynamics and those experiments.
I did suggest really it's an ocean layer as an energy balance, and the surface temperature is is not independently determined.
It's determined through an energy balance. So you can't really ask the question how how sensitive are they to surface temperature alone?
But what you can ask is say what surface temperature increasing surface temperature does as, for example, lead to more water vapour in the atmosphere?
And you can ask how large is the effect of the added water vapour on the long wave cooling of the clouds?
In addition to the CO2, and that's about half. Okay, so over here.
Okay, thank you to you for great talk.
So one of the big differences between weather forecasting and climate prediction is if you have a lousy numerical weather prediction model,
you find out pretty quickly climate forecasting. We may not find out for several decades.
So could you say a little bit about the challenges of, you know,
what data is available and to what extent can you really constrain the climate forecasts with data?
Yeah, I mean, that's an obvious challenge. A couple of answers.
So a in many aspects, climate models are doing so poorly right now that simply having a better present,
better representation of the present climate will be progress. A seasonal cycle.
Cloud cover. Seasonal cycle in the land biosphere and the like.
So there there is just a lot of areas. For example, photosynthesis in a biosphere models differ by an order of magnitude and that seasonal cycle.
So just simply getting a better present climate with a model that didn't talk about is so process oriented.
So it's not going to be model where fit millions of parameters will be sparse and parameter space.
So getting getting a better simulation of a better climate with a sparsely parameter model, that challenge and if you.
Some confidence that you have gotten a better model now you want to verify that.
And that's where things get harder and you don't want to wait decades.
I think what you have to do is find Short-Term prediction targets, say, predict the response and try to do that accurately.
It would actually be interesting to use a climate model in a weather forecasting setting and see if you can see progress there as well.
Even though that may not be the success metric that's most important for climate, but you have to find some,
some targets that can build confidence over time, and the targets have to be short term because we don't want to wait 20 years.
But it's something you probably right, that's a problem, and we have the climate modelling ready. Thanks.
That was really interesting. If with increasing temperatures, you continue to get more water vapour in the atmosphere.
What happens to types of clouds that aren't destructive cumulus? Do we see a compensation with more cumulonimbus clouds or something else?
Where's that water vapour going? Mostly in vapour and not so much rain clouds, right?
Because clouds are just this tiny, tiny residual of water that that condenses there.
So. You you cannot make any direct connexion between the amount of vapour and the atmosphere and and how many clouds you have.
If anything, what you would need to do is think about how far from saturation you are.
How much do you need to move an air parcel to reach saturation, and the interesting part is as you warm the climate,
you have more water vapour in the atmosphere in such a way that the relative humidity doesn't change very much.
But what that implies is that the what you might want to call the distance to saturation actually increases some.
But that metric alone, it would be harder to form clouds. But I'm not saying this is actually how it works.
Just to say that the intuition more water vapour translates into more clouds can easily lead to Australia.
It's more complicated than that. For.
Thank you. You've pointed out that we trust these high resolution models, and therefore they're a good tool to build a climate model.
I mean, you've heard earlier this week already, I'm much more cautious about models of deep conviction, also in high resolution settings.
And we have very limited observational constraints. So I wondered how long do we have to wait until we have some constraints on these models
that we trust them more and how this industry of time scales that we're worried about now?
I mean, a couple of aspects, right? So there's you're talking about the micro physical issues that we discussed.
So yes, that's what Philip is referring to, is that even human, you can resolve the dynamics of motion in the clouds.
There's still the physics of how droplets and ice crystals form or micrometre scale and those who can still resolve.
So that that remains is a challenge. So I think there are two pieces that help, or maybe three.
I mean, the first one is. Even if he couldn't do anything about the micro physics, I think they can, but even if he couldn't.
Wouldn't it be nice if all uncertainties are reduced to that and the dynamics as we have confidence and right?
So my first answer would be, well, let's just deal with the pieces we can deal with.
That might be something left.
And I think, however, what we can do in the micro physical side or how we can reduce high resolution simulations in that context.
So what do you need to make sure that your global model,
your high resolutions are consistent with one another so that whatever is wrong with the
micro physics is consistently wrong so that you can still infer dynamical properties?
But then we do have data and we do know a lot about the how.
The deep convective clouds and main issue is the ice face a mixed phase, right?
We have a lot of data about.
The properties of clouds higher up and to what degree they're liquid or ice, and we do know that models are doing this poorly.
And I think those data alone paired with whatever you can get measuring effect of radius of droplet droplets and the like.
I think they do provide constraints we haven't even begun to exploit.
Now, whether they do, I don't think they will do some scientists zero, but I think they can reduce uncertainties in that area as well.
Josh, did you? As you said, you can't run the pretty high resolution simulation or the grip boxes,
but have you got a sort of sense as if you did couple it to a full model,
whether that might kill your hysteresis loop, whether it might make it wider if you got any sort of sense of what you'd expect to see?
Ask me again, and I mean, six months, OK, I am trying to do it.
They're challenging simulations. It's computationally expensive to do, but yeah, it's the obvious thing to do, right?
And it's a good point. OK.
Anymore for any.
So you mentioned some of the funding sources and also the all the costs that could be avoided if we actually have a better prediction of climate.
And I was just wondering why not? Well, a signal of the big tech giants that we have in the world like Google, Facebook and so on.
Why are they not on board with these kind of projects? Because I mean, yes, making money and avoiding costs.
Still something different. But in the end, it's in the information that they could really well well use for any kind of future investments, right?
Short answer, as people are on board. I mean, if.
It's a huge business opportunity, it's a reality, right?
And there is a lot of interest, it's just that this problem is not going to be solved by having 100 software engineers.
That's a challenge. OK.
Just maybe take two, but we leaving the front first.
I wonder whether there is anything that we can do like the in this room after today to help the climate or decrease global warming and vote.
And the next best thing is come up with a really clever way of producing electricity from sunlight and say half the cost of what it currently is.
Then you will also be rich. OK.
Just one last one was. Being greedy on the questions here, the bogeyman in all the climate change seems to be CO2.
We're really worried about CO2. But in the real world, CO2 doesn't just sit there as CO2.
We know, for example, there's ammonia in the atmosphere, and as soon as it mixes CO2 molecule in some water, you get ammonium carbonate.
So in all these models, are you simply modelling it with CO2 or modelling with carbonic acid ammonium carbonate or want to as a proxy?
And what I talked about that took CO2 as a proxy for all greenhouse gases, including methane and to and you name it, in the models.
CO2 is it's very inert, which is part of the problem here.
It stays in the atmosphere for tens of thousands of years. So the models there, we don't tend to model reactions of CO2, but we do model reactions,
for example, of methane, which is much less than our average oxidise is much more easily. And other greenhouse gases as well.
So it's just a convenient proxy and talk like that. You don't want to go through all complexity and take it as a proxy for greenhouse gases broadly.
OK, another one or two more questions, but I think we'll we'll sort of call to halt and just say there will be,
I think, a drinks reception around the corner. So correct.
Yes. So any of those who didn't get a chance to to ask tough here, you can colour him over a glass of wine.
So let's finish by giving a big round of applause to separation, either.