Good afternoon, everybody, and welcome to the Oxford Climate Research Network's annual lecture, co-hosted by the Oxford Martin School.
The Oxford Climate Research Network aims to pull together all those across Oxford whose research touches on climate,
and the goal broadly is to help us be more than the sum of our parts. So over the last year,
we've brought in several million pounds worth of grants focussed on understanding the climate system on impacts policy mitigation.
Several members of the network contributed to the IPCC 1.5 degree report.
Others provided data for the BBC's Earth from Space programme.
We continue to engage with the Met to co-fund many of our activities and with the United Nations Framework Convention on Climate Change Process,
as well as reaching out to local organisations and schools. So it's really great to see so many people from a wide range of departments here today.
If you're a research student or a member of academic staff at the university and not already a member of a vocal,
and please do go to our website, which is Climate Doacs, Dot Dot UK and sign up for our mailings.
And after today's talk, there'll be a reception just next door and hope they're all able to stay on a network over a drink.
Get to know each other better. It's a huge pleasure for me today to introduce today's speaker, Professor David Battisti.
Is the Tomoaki Endowed Chair of Atmospheric Sciences at the University of Washington in Seattle.
David's research focuses on understanding natural variability of the climate system.
He's especially interested in understanding how interactions between the ocean, the atmosphere,
land and sea ice lead to variability in climates on timescales ranging from months to several decades.
He also studies the impact of climate variability and climate change on global food security.
He served on numerous international scientific committees, co-authored many international science plans and reports,
published well over 100 papers in atmospheric sciences and oceanography.
Davies is also very passionate about training the next generation of climate scientists,
and as such, he's received many awards for both his research and his teaching.
In addition to his responsibilities at the University of Washington,
David is a fellow at the Food Security Institute at Stanford University and an adjunct professor at the University of Bergen in Norway.
And it's a real honour to have him with us today to deliver our annual lecture.
Today, he's going to talk about the relationship between global climate sensitivity and regional warming.
So if you join me in welcoming David to the state. Thanks, everyone, for coming today, and thank you for inviting me to your community yet again.
And one of these days, you'll probably get tired of me, but I'm never going to get tired of you and.
This talk was kind of inspired by last year I was asked to go to him at this meeting at MIT and talk about the value of regional downscaling,
I don't do regional downscaling, I do a little bit of it, but it's not my it's not my forte.
And so I kind of flipped that around and say, Well,
where could you actually use regional downscaling to kind of either improve your predictions or reduce the level of uncertainty in the predictions?
And I was a little bit surprised by this, and it gives a sense insight on what's controlling regional climate variability and help.
The one thing you come away at the end of the day is that most of what controls regional
climate variability everywhere on the planet is one thing which I won't tell you yet.
OK, OK, so there's the outline. You've been staring at it for a while, so this is going to go ahead.
Climate sensitivity it's defined for the purpose of today is the global average temperature
change due to a doubling of atmospheric carbon dioxide and any weight long enough.
So the climate system is equilibrated. And the answer, according to the last IPCC report, was the best guess.
The answer is three point two degrees Celsius. But there's a huge range of uncertainty in that there's climate models that
give you as low as little bit over two and some that give you almost fives.
The range is basically 100 percent three point two plus or minus 50 percent.
And I used to think. Who cares? Nobody lives on the global mean temperature, so what what how useful is that piece of information?
And let's go back to this at the end of the talk. So if you're a climate specialist, do not answer this question.
What's the major uncertainty in the global average climate?
If you look at the global average temperature change you do, what's the main reason for uncertainty?
There were no, not a climate specialist. There's one thing I mean, obviously, we're seeing the CO2 is doubled,
so it's not a human problem, they're not worried about how much emissions is going to have. Let's say it did double.
There's this factor of two uncertainty and how much warming you're get the global average with clouds, clouds, right?
And in particular, tropical clouds. Right? OK, good.
So it's tropical clouds. Here's a figure from the last IPCC, and he's one of these dots is for climate modelling.
Just tells you like how much feedback you get? Yes. Oh no.
How much feedback do you get under different things? Planck is just the black body radiation from the surface.
There's very old models do that the same. Then there's the long wave and water vapour feedback combined give you very little uncertainty in the end.
But the biggest uncertainty here, the biggest spread is in what happens to clouds and in particular, claptrap of clouds.
And there's a little bit of uncertainty due to libido.
OK, so if you say what's the common pattern across all the models, you take all the models responses to an increase in CO2.
What's the common response? Here's a picture from the last IPCC.
I grabbed 18 climate models and just said,
What's the difference in the annual mean temperature at the end of this century compared to the end of last century?
So this is one hundred year difference. OK, and we'll call the global average change this bracketed Delta T here, and that's the answer.
Global average is three point seventy three degrees C for this group of 18 models at
the end of this century due to a kind of a business as usual emissions scenario.
Not every place forms the same. The main thing you see in this thing is the tropics don't warm as much as the Arctic right,
and the land warms more than the ocean, and that's kind of washed out there.
But the typical global average three point seventy three to typical number in mid-latitude land area is more like five degrees C over.
The ocean is more like three degrees C, and the polar regions is 12 degrees C, OK?
Wise or wise or polar amplification, why does it why did the poles warm more than the oceans?
There's a common answer that everybody says, which is actually not the right answer.
The answer is there's the ice albedo feedback, right?
You get warm, you melt ice and and the melting ice allows more sunlight to be absorbed.
But that's not it. OK. There's actually two.
The two most important things here are the fact that when you have this plank feedback, the BlackBerry feedback,
you're starting from the very cold temperature in the Arctic in a very warm temperature, high temperature in the in the tropical regions.
So if I just need to warm a little bit,
the tropics to get the same amount of energy back out is the amount of warming I need to do in the polar regions.
So that's that's about a quarter of the answer.
The other quarter of the answer is I put the same amount of energy down everywhere on the planet due to increase in CO2 and in the tropics,
it's really warm. I get some of the energy to evaporate, which means I don't warm as much because I use some of that energy to evaporate.
That vapour goes to the atmosphere and the atmosphere moves that vapour to the pole, and that's where it condenses.
So I have this transfer of energy. It's been absorbed by Joseph, you're on the microphone is cracking, can I swap?
Oh yes, sir. It's a transfer of energy that's been absorbed in the tropics,
that's been moved to the poles by atmospheric circulation since another quarter of the answer.
And I want to tell you what the other half is. Yeah, what I could tell you, but it's not obvious.
OK, so that's that's the main reason for polar amplification. And there's a there's a clue in this.
I'll show you a winter versus summer in a second. Why does land warm more than ocean?
It's a little bit more complicated, but the basic answer is, is a similar answer is I put the amount of energy down over the ocean energy of the land.
And I need to use more of it or use more of it to evaporate over the ocean, over the land.
It's a little bit more complicated that Mike is here, right? Mike, Mike, Mike.
Bring some nice work on this. A little more complicated now, but basically it's pretty well understood why the land warms more than the
ocean and effectively the winds will kind of distribute the stuff around now.
OK, so I won't say so far, yes. OK, good.
So if I look at winter versus summer, there's a winter picture northern hemisphere, you can see now the scales are different here.
So the amount of warming you have in the polar regions about twenty four degrees warmer.
And if you look at the summer here, the amount of warming in as you go over here,
the amount of warming you have here in the Arctic is only about three degrees.
So if it's ice albedo feedback, you're kind of stuck with this strange situation.
But ICE will be the feedback has to be working in the summertime, yet the polar ramifications in wintertime.
So, you know, fundamentally, it's nice a bit of feedback. OK.
And in both winter and in summer, the land warms more than the ocean, if you just go along a latitude line, you just say ocean land, ocean land.
The land is always warmer than the ocean, so that's a basic pattern there.
And it comes about because of this absorption of energy that's used to evaporate the tropics.
That the atmosphere diffuses by winds just mixes that energy to the polar regions where it's colder and it condenses, right?
So that's that's a good part of the answer. As long as you have those winds that are doing this mixing, you're going to have polar amplification.
OK? OK, now let's look at departures from this multi-model mean this is average across all models.
There's the picture of you with that before, and this is just a measure one sigma right?
One standard deviation difference. So every model take a model ensemble right away.
You have the differences and just calculate the standard deviation is a measure of the uncertainty.
And you see the uncertainty is greatest in the Arctic, and it's also great over land and over ocean.
And if I take the ratio of those two things called the coefficient variation,
it gives me kind of the relative uncertainty compared to the ensemble mean change.
And if you look at that ratio here, I don't know if you can see this, but the answer is about twenty five percent.
So one sigma standard deviation twenty five percent and this is consistent with the global mean.
It means you have a plus or minus 50 percent is kind of the extremes plus the models
or the difference from the lowest model on the highest model is one hundred percent. Factor of two.
OK, so regional uncertainty is typically plus or minus twenty five percent of the ensemble average average overall across all models.
By the way, ask me a question any time or if I say something that's really offensive, I call me out on it.
OK. All right, good. So if you do the same thing for wintertime, look at this coefficient of variation.
The the spread. The models divided by the model mean you find that the same answer.
It's about twenty five percent uncertainty plus or minus. And the same thing is true in the summer.
The places with the biggest uncertainty are places here around Antarctica, where you have sea ice edges in in the sea ice.
Projections say what's going to happen in the future is pretty different across models.
Do you have a pretty different? The big uncertainty there and in the North Atlantic, here in the ocean, just south of Greenland,
there's a lot of uncertainty across the models, but the actually the absolute change is very, very small.
So models just differ a lot. But the the the difference is actually tiny because the absolute change is small.
OK. Now, this is every model has these differences subtracted from the the ensemble mean?
And what I did was just said, let's find the pattern that explains most of the variance in this.
If you look at the differences, the residuals,
let's find the patterns explains most of the differences between the models and if if you're if you're into it,
this is just called a NeoGAF analysis of the residuals.
So I just took the ensemble means every model you had the residual you have 18 of these fields,
you calculate the empirical orthogonal functions and you pick out the leading pattern.
And I did this for monthly mean seasonal means annual means.
And the answer is pretty simple I what to you, sir?
And if I lose you, the answer is the results.
If you interpret the most of the regional uncertainty and temperature projections
is related to one generic pattern of non-local uncertainty and feedbacks.
That is, the most of the uncertainty in your place in space is not due to uncertainty in your place, in space, in the physics.
So I'd say that's I immediately got implications for regional downscaling.
There's only so much you can do in your place in space if you want to do a regional downscaling to improve temperature projections.
In fact, pretty much nothing you can do on improvement. OK, let me show you that.
So this is the pattern that explains most of the differences across the models.
And if you look at this pattern, it looks like a pattern we've seen before.
Colour scales a little difference. It's it's all the same sign.
It's warm everywhere. It's more warm in the tropics or in the polar regions and in the tropics.
It's more over the land that is over the ocean. It's the same exact pattern.
It's the ensemble mean pattern and the physics is the same.
And it explains 60 percent of the variance across all the models, so once you take this pattern away,
you're just left with, you've got the uncertainty down by more than a factor of 50 percent.
OK, now and let's see what I want to do this.
OK, so this pattern has its place.
60 percent of the variance is only homogeneous over the ocean land, with more warming over ocean, over land than ocean and its polar amplified.
And so you can think of the physics as being the same physics. It's tropical clouds.
They're explaining the uncertainty and the atmosphere as it's distributed in this heat differently.
Now, if that's the case, let me try to explain this before I show you the answer.
That's the case.
It turns out also that the way the atmosphere equilibrate, this is mostly to bleed heat to space in the tropics, not in the high latitudes.
So let's take the extreme case, here's my tropics. Here's the.
Arctic, here's the Antarctic, and I'm just going to put a lot of CO2 in the atmosphere in the tropics are going
to heat up and it's going to just pretend it is basically come to an equilibrium.
If I just if I just stop the circulation so I can't move energy to the poles,
I'm going to one the tropics by a certain amount amount so that the radiation to space
is equal to what's what's coming back down the surface because of increased CO2.
Now and the rest of the planet hasn't done anything.
If I now take my atmosphere of speculation and move it to the polls now I can warm every place on the planet a lot, right?
And polls more than the tropics. I still have to run the tropics up enough to bleed the same amount of energy to space to come into an equilibrium.
So the global average temperature will be higher in the case where I'm be able to distribute that heat to the poles faster.
OK. And that turns out to be the case. So, first of all, point out that the global average is one degrees Celsius.
If you then say, if I project this pattern to each one of my models, residuals just and plotted versus a climate sensitivity, you see that here.
This is the model of weights. So if a model, for example, has the global average plus twice this pattern, you're out here.
It's got a climate sensitivity of five degrees that increases the temperature of five degrees to the CO2 doubling.
And if the answer is my model gives me my ensemble average minus, say, one stick of this pattern, basically every climate signal sensitivity of two.
So in other words, this pattern is explaining climate sensitivity. The uncertainty in why is there global climate uncertainty?
Again, someone said it over there. The main reason for uncertainty in time models, global average clouds, tropical clouds,
and what this is telling you is that also explain the global uncertainty everywhere, not just in the tropics, but everywhere.
OK, so now that being the case, you have the correlation between climate sensitivity and how much your model projects
under this pattern is 0.9 to four equilibrium climate sensitivity for transient,
as 0.6 of that actually makes some sense.
I won't go into it now, but you expect that result that would be more correlated with equilibrium than transient.
And here's the other thing that based on that argument, I just told you,
the more efficiently your model moves hit the polls, the stronger the warming you should have.
So it turns out that not all climate models move heat to the polls at the same rate.
And this plot here shows how much the model,
how much your model projects onto that pattern and how much energy your model
moves from the equator to the pole in the climatology that you start with.
And there's a positive correlation is that is the more efficient you're the your model in today's climate moves heat from the tropics to the poles,
the stronger your climate sensitivity is. And that's the physics behind this basically more diffusion to the poles, more warming.
A view of clouds that are less bright, you get more warming,
but this diffusion actually moves the stuff from the equator to the poles and gives you this pattern of Arctic amplification.
This called pattern scaling, it's been known for a long time,
but I guess a new part is coming up here is what's left over when you actually subtract out this climate.
If we could solve the tropical cloud problem, what's left to explain your place in space?
And I'll try to show you that here before I go there, though, if you do the same thing and you say, what's the common pattern in the annual mean?
That's the top panel which we just looked at. If the common pattern in the wintertime looks like the wintertime ensemble average,
the common pattern of differences in the summertime looks like the ensemble average in the summertime.
So basically just telling you the same physics is going on and all of these models
and the climate sensitivity is just set by uncertainty in tropical clouds,
but explaining this pattern of variability across the whole planet.
OK, so conclusion is most of the local uncertainty and temperature pressure is
not due to differences in local feedbacks or to uncertainty inside to prices.
It's due to tropical clouds. Now.
Let's take that at the results and say, let's say we solve the problem and I'm not going to remove, so I have a residual that's from each model.
And I mean, remove the global climate sensitivity model and say, what's left over?
So what's left to explain after the leading difference pattern that is climate sensitivity is is solved.
And that's this picture here. And so what this is is for each climate model.
I've subtracted ensemble mean, I've subtracted out the pattern that goes with the climate sensitivity.
And then I said,
I have a residual I don't explain and I have that for each model and I take the standard deviation of those residuals and this is the plot.
And you can see there's still most uncertainty left over to explain in the sea ice region of Antarctica,
actually in the sea ice region now in the Beaufort Sea, which is a place where we've got really stunning changes in sea ice today and temperature.
And if you take that and you and you divide it by the ensemble, mean change again,
so you can you're getting a coefficient of variation after you remove climate sensitivity to this picture.
And let me just put up the original coefficient values that one. Can you see the difference in colours?
You've reduced the uncertainty from twenty five percent down to about 10 percent.
So in any one place in space, once you remove global climate, once you fix the tropical cloud palms,
now you're down to a local uncertainty of plus or minus 10 percent, which is pretty good.
OK. So after accounting for climate says the regional strategy is cut in half to around 10 15 percent,
with the exceptions being the Atlantic, North Atlantic and the Southern Ocean.
Because of sea ice differences across the models. OK, now we can put this back in the original.
Here's the original plot here, which is the ensemble average estimates from the climate models.
And I'm going to put this residual pattern over here on the same scale as this,
so you can get a sense of just how different they are, and that's the answer right there.
So basically, there's really nothing left to explain locally. There's no point in regional downscaling for temperature.
And in fact, I think it's probably going to make things much worse, if you look at the uncertainty even in regional models,
when you drive them with the observed boundary conditions, you look at the differences across the models.
The errors are actually much bigger than this in the climate, in the regional models.
So in terms of regional downscaling of temperature, you can only do worse by doing that.
I mean, the best thing to do is to leave the climate models alone and not touch it. OK.
Why does this work? This is a little technical for a minute or two.
It works because of the atmosphere is basically diffusive.
The circulation has storms in the mid-latitudes and must move heat from the from the tropics to the poles.
Basically, an illustration of that is, Oh, this got washed out and.
And both of them. So this is the South Pole, the North Pole, the black line here is the ensemble average feedback from all of the IPCC climate models.
How much maybe local response in temperature or radiation you get due to an increase in CO2 and all the squiggly lines you can barely see?
Here are the feedbacks from each particular model. And so you can see every model does something pretty different.
All right. There's a lot of uncertainty in the individual model,
but that the ensemble average and you can define a feedback is the amount of force in at that latitude divided by the temperature response.
OK, now. If you just take a simple diffusion model and I say about two parts per million
squared or whatever it is to a WCO to two that I put it in my atmosphere everywhere,
and I have this distribution of feedbacks I can solve for the temperature change.
And there's nothing fancy and this is just pure diffusion. And then I can say, OK, that's my that's my ensemble mean response to the increase in two,
and then I can put her uncertainty in the local feedback and say, OK, in the tropics.
I don't know exactly what the answer is. I so I put it a little uncertainty in and I can say, Well, what?
What does that do to the global average temperature or due to the temperature distribution?
So there's this didn't come out of it. That's the feedback.
So we put in little delta functions are close to delta functions at each latitude of uncertainty and a feedback, and they're all positive feedback.
So every one of these simulations is going to be warmer than the ensemble mean simulation.
And so here's an example of that, so that the black line here are the Black Red Line, is the ensemble mean from the climate models?
And that little red dash, the coming up is just a perturbation we're going to add on at that latitude band and that's the only place to get it.
So we just make it more positive there. So for the same amount of force in locally, expect warm more in that region in that particular model.
But the atmosphere situation is going to move that heat away and distribute it in other places.
They can take that equation there, and you can add a perturbation and uncertainty in the feedback.
A delta lambda.
And you can subtract out the ensemble average change and come up with an equation for the responsive temperature that perturbation temperature.
That is the uncertainty in one model due to that perturbation at that latitude.
And if life is really good for you and you have constant diffusivity, which you don't and constant feedbacks,
you just end up with base exponential decay around where you put in that uncertainty so locally have a warm,
but the circulation will take this stuff and distribute it away. And this is extremely powerful.
So let me show you what the answer looks like. That black line is the equator.
The equator to pole to pole south is poles on the left.
Sorry, this didn't come out very well. North poles on the right equator is in the middle of the black line is the ensemble average change you
get from using the ensemble average feedbacks from the climate models from the scene at five miles.
And each one of those other lines is if I put a perturbation in place, for example, here, let me just show this.
Here's a perturbation that we put in in 20 degrees here. When we put it on the equator is 120 degrees north is 30 degrees north.
Here's 50 degrees north. So we put perturbations in these.
One of these lines is the solution for what the temperature is with a model that has a perturbation at that latitude.
And you can see the all the lines are all above it. It's a positive feedback perturbation, so they all have to be warmer.
But the distribution the shape is exactly the same as the ensemble mean shape.
Does this is the the equivalent of pattern scaling?
It's just there because of diffusivity, the atmosphere is just distributing this heat by storms and moving it from the tropics to the poles.
OK, so these are really hard to resist. Let me just show you this part, an honest man.
So if you just subtract, if you just like normalise each one of these curves,
you take out the ensemble mean so that you adjust the mean temperature of the global mean temperature.
And these kids all have the same thing. They all kind of collapse under the same curve.
So it says no matter where my uncertainty is, if it's if it's at 20 degrees south in one model,
20 degrees north, one model and it's like a more positive in Model A and less positive in Model B.
The result is going to give me the same shape in the end of the temperature. Profile change, it's going to be polar amplified, right and right.
Yeah, and that's all because of diffusion. It's because the fusion is roughly the same in every model for circulation.
Every model has one strong track in the mid-latitudes moving heat and water vapour from the tropics to the poles.
OK. Anything else I want to say about that? So I'm going to skip the next one because it's going to be impossible to read.
And this is true for any uncertainty between 50 degrees south and north.
So it's really powerful weapons, daily works, because the atmosphere is basically Diffusive,
and you have this need to move vapour from the tropics to the polar regions, which give you that polar amplification.
And that's that's a two dimensional, more static energy and small is nothing fancy.
There's no land in it, and it tells you exactly what the climate models are doing.
OK? So certainly in regional feedbacks, give rise to uncertainty in global global average global temperature distribution implications.
If you're interested in, say, temperature change in Oxford in the future,
then you can't gain much by resolving take a regional model and say, downscaling all the GC-MS for for Oxford.
Because basically the uncertainty Oxford is determined by by tropical clouds, not by the stuff going on around here.
That's true of every place in the space, pretty much, except for the sea ice ages where sea ice actually plays a pretty big role.
OK. So whatever whatever gives you large and certainly a climate sensitivity will give you large distal regional uncertainty.
It's the largest uncertainties and tropical feedbacks.
Hence the largest uncertainty in list of reasonable locations is due to uncertainty in the tropics.
OK. This doesn't work for precipitation, not a surprise,
because precipitation is controlled by circulation changes, so let me just show you how bad this is for.
This is again, this is not great colour scheme, but anywhere it's blue.
This is the ratio of the precipitation at the end of the century compared to the precipitation at the end of last century.
In the end, it's blue in the high latitudes.
These are regions where it's going to be wetter in terms of more rainfall at the end of the century and then the regions where it's wet,
which you can barely see. I'm sorry, drier, which is it's hard to see, but there is subtropical regions here, so well,
for example, the regions where it will be drier in the future compared to just today.
And if you do this same thing and you look at the standard deviation of differences across the models,
you find out the numbers about the same models differ by about 20 to 30 30 percent in terms of what they say about precipitation.
That's the leading pattern of differences across the models is pretty messy.
It's tropics centric, but it only spend 17 percent of the variance.
So there's not one pattern that says, OK, climate sensitivity is can is is controlling the amount of precipitation here or there.
Every model does it differently. So that's one problem. It's not explained by simple diffusion and it's not related to climate sensitivity.
So that's the raw uncertainty we looked at before the standard deviation of this precipitation ratio.
And that's what you see after you remove the common pattern of differences in the answer is you've made no improvement whatsoever.
So precipitation is one of these things that if there's going to be improvement and it's going to be, I don't know what it's going to take.
It's not going to be good, though. I mean, it's a hard problem.
And and if you did have to downscale, then there's an issue of do I trust a regional model or just I empirically down scale?
It's it's it's a it's a it's a tricky trade-off.
OK, so so where we at, we're at that most of the uncertainty in the climate system is due to tropical clouds.
Most of the uncertainty at your place in space is due to tropical clouds wherever you live.
And the physics of this is is pretty simple, just the fusion.
OK, now this actually makes life easier if you're actually asking what's the impact of the sort of temperature changes in the future?
You don't have to downscale or you don't even have to look at all the different climate models, you just have to look at two, right?
You just have to look at a model with high sensitivity and model with low sensitivity,
and that should bracket pretty much the whole uncertainty in space.
Now let me show you an example that works pretty well, OK?
And this is examples in heat, and there's a lot of ways you can measure heat stress.
No measures if by some combination of temperature and relative humidity.
If it means anything to you and it's always out to be wet bulb temperature for Noah,
but there's other indices called humidity humid, humid x, and there's various things that I'll tell you roughly the same thing.
The in the Noah language, basically, when you see it, a wet bulb temperature of twenty seven degrees,
you're in this danger zone where you have heat cramps and heat exhaustion, the like.
The heat stroke is probable and with continued activity.
And the list of symptoms are really horrendous experiences.
I've experienced this. It's not fun.
You get fatigue, nausea, headache, excessive thirst, muscle muscle aches, confusion weakness is slowed heartbeat, dizziness, fainting.
And it's pretty hard to work under these conditions. OK.
And then you can go to extreme danger where heatstroke is imminent. This is you take all the symptoms that I just read and you add to that vertigo,
shortness of breath, vomiting, blood in the urine, delirium, loss of consciousness.
I mean, I don't know anyone who experiences convulsions. It's bad.
OK, so let's look at the last thirty five years from era and say under these two definitions of danger and extreme danger,
how often we see these conditions. So this is actually taking the the era data,
the last thirty five years of six hourly data and saying How often do we exceed the danger category today and guys who didn't come out very well?
But there's you can see some three days per year. So on average is off places like Oman and Bangladesh.
We we're like three weeks per year.
You exceed the danger zone where you we really have to be really careful about being outside and being active in any way.
And I want to see that right. There's a few other places like the town here where you were green.
So like a couple of days per year, there's there's Eastern China,
where you're up to maybe two to five days per year and then you can say, well, how often the extreme extreme danger in this?
Actually, there's only two points on this one here.
And whatever this place is, you know, it's it's this little thing that sticks out of Central America.
You know, I mean. I've never been there, so I don't know.
But yeah, and then there's one place here, I guess you like near Oman,
where basically one day in the last thirty five years hit the extreme danger category where you basically if you are out, you deck your dead.
OK, so that's today. And then what I did is I said, OK, let's look at the end of this century using the business as usual mission style ensemble mean.
So we're just averaging all the time miles. We're getting a new annual cycle in temperature.
We're assuming the same weather. So we're just adding that change in the annual cycle temperature to the same weather.
From that, we've experienced the last thirty five years,
which is probably a pretty conservative estimate of of how often you extreme exceed this extreme danger because there's reasonable evidence,
which is just as you warm up, the very variance of temperature should increase,
which means you're going to exceed extremes that warm extremes more often than not.
OK. So same weather, plus this change in the annual cycle of temperature and is going to look again how often
you see these things and now remember this is the same picture I showed you before.
So the global average temperature change is three point seven degrees, but you have an annual cycle associated with that.
And this is what you get with end of the century and the numbers here have changed.
Then the scale goes up to 320 days per year. So basically, it's our year.
So if you look at the Amazon, it's all a year when you're in the danger zone. If you look in the southeast US here, you're in yellow and orange.
So you may be like a month a little bit more than a month a year that you basically have to be really careful about being outside northern Australia.
You're looking at almost there's those of the coastal rim here is about a half a year.
You know, you have to be careful. India, obviously, you're looking at one or two months per year, sometimes one or two months per year.
And then how often the extreme, the extreme danger. We're basically heat kills you very quickly.
It's maybe a week per year in the southeast US, a few like a month per year in the Amazon, maybe a month per year in northern India.
It's not good. OK, now now I'm going to take this pattern of change and just add to it the thought that
the common pattern of difference and just multiply it by the most extreme climates.
So I'm just saying, let's just assume this pattern exists.
I have the ensemble mean, I just add that change to it, and I just do that calculation again.
Yeah. OK, good. OK, so that's what you get here, and you can see the numbers go up pretty significantly here in the southeast us,
you're now into orange, so you're looking at maybe three months per year, basically all summer.
You have to be really careful about the amount of time you spend outdoors.
There's a lot us to [INAUDIBLE], basically here in Europe over six months per year, there's there's Southeast Asia,
India, where you're like five months per year, where you're exceeding the danger category and then eastern China.
Here we're in the yellow, so maybe six weeks per year and then extreme danger here this now it's getting
pretty significant where you're maybe three weeks per year in the southeast US,
you cannot go outside for more than an hour, basically, and you want to be active.
So these are pretty extreme conditions. Now to illustrate how this how this is useful, this pattern is useful.
What I did is I went through every single climate model and I did this calculation how many days per year you exceed this danger zone?
And I said for any one, places places big Oxford where you never exceed it.
Nice, you right? I mean, you're on an island, for God's sakes. OK, but let's talk.
I think someplace where no one want to live, like Atlanta may be like, OK, like down here.
OK. So you picked that place. You say, like, OK, I take my ensemble.
Mean, I've added just like that extreme climate sensitivity, and I've got a number this whatever it is,
it looks like it's maybe orange, maybe one hundred and seven days a year.
So three and a half months per year, you can't be you have to be really careful about being outside.
And what I did is I went to every single climate. I'll forget about these pattern things.
I just said, here's the climate change this model predicted at the end of the year.
I'm going to do this calculation and figure out how many days per year exceeds it.
And I'm going to take the model with the most at that point in space. And compared to that number,
some of the words if I just take this shortcut is one one pattern downscaling one model fogerty's by the heat application.
How far off I am by. Instead of using all 18 models and probing the model and say, just like what's what's the worst case scenario?
And that's this, this is the additional days you get if you actually probe every climate models say,
is there ever a climate model that gives me more than the ones on the right hand side that one estimate the right hand side?
And the thing to point out here is the scales change. So basically in the southeast U.S., you get an extra month.
OK, OK. So there was a climate model that gave you an X.
Yeah. I'm just wondering just looking at this. So I'm just looking at a lot of like desert areas.
Yeah, Western states, Western Sahara.
Yeah, this is this because it's exactly they're dry.
They're really dry. So it's very hot, but it's extremely dry. Good point.
Yeah. So like western us, a lot of Central Asia is is white, and that's because they're really dry areas.
So the heat stress is not as great, even though in temperatures higher. Yep.
OK, good one. OK. But basically, you know what?
What more do you get like if you're in Europe? You can see maybe there's like a light green there, which we see an additional week.
You know, there's some model in that in that close to 80 models that gave you one more week of extreme danger compared to to see a guy can go.
I can like, Oh, look at this. Yeah.
So a [INAUDIBLE], Africa, for example, Orange. One hundred and fifty days and on the right hand side, I had maybe another 40 days.
The point here is that, OK, yeah, it's worse. But I mean, it's already when you're talking about three months of camp uninhabitable.
What's another month, right? It's it's like you've got a vision here, which is, you know, using all 18 months doesn't give you much more information.
OK, and then here the extreme maximum temperature that kills people.
I don't know if you can see it, but there's basically there's less than five extra days,
so there might be one Mali can find that gives you like five more days of extreme dust.
That's five days on top of a month. Right?
The bad number is it's a month.
OK, now, since the 1.5 degree report just came out, let me show you the answer for a world is one point eight degrees warmer and that's here.
All right. So I think that maybe this was in the report, but I haven't read the report yet.
Sorry, Miles. OK, I read that. I read some of it.
I am totally OK. But basically, you know, here's the difference between eight point five high climate sensitivity.
So this is pretty extreme. And here's that one point eight degrees.
This is the RCP four point five scenario, and you still end up with a lot of yellow here, which is on the order of six weeks per year of danger.
Right. So one point eight degree warmer world is still a real problem when it comes to heat stress for people.
OK. All right, so the summary here, there's a common pattern in the uncertainty and temperature change due to anthropogenic
forcing more warming over land than over ocean global and extend polar amplified.
And that pattern is really basic physics. It's strongly correlated to climate sensitivity, which means it's related to tropical clouds.
Oh, let's see. Go here for change, it indicates that 60 percent of the global regional differences are uncertainty is due to one generic
uncertainty due to tropical clouds in pop tropical feedbacks sea because the atmosphere is really high.
Diffusive After accounting for this comparative uncertainty, the regional scale uncertainties is greatly reduced.
Local prices account for less than I've got to say in an absolute sense.
The uncertainty in your place in space is less than a degree at the end of the century,
a lesser degree compared to the ensemble mean of typically five or six degrees over land by 12 degrees over the pole.
The indications are that in most cases, numerical,
downscaled and sea five models can't reduce regional uncertainty in mostly most likely is make it worse.
I have a couple of slides that just have this list of.
Problems that come with regional can think there's a point here in the rare instance in numerical downscaling is required and efficient
approaches to just about any regional sun is just they use the common pattern and just scale up the ensemble mean change with that pattern.
And so I guess going back to that first statement is like, why would you care about the global?
Meanwhile, turns out that all of the uncertainty in your place in space is related to that global mean answer.
Because because of the atmospheric dynamics. All right.
And I think I'm going to stop there. Thank you, David, for a great motivators, also think about tropical clouds chess club, yeah.
So David is happy to take questions, but if I can just ask that you do wait for a microphone because this is being filmed,
so just to make sure that the audio is captured by understand, right?
If you do pattern scaling based on one target time, but a lot of takeoff,
Kyle Chalmers were indicates that as the ocean comes into equilibrium, the patterns corresponding to a given warming actually change.
And I wonder to what extent you've explored what happens if you go, you know, say,
five hundred years out and so forth or in terms of the just a change in the pattern scaling.
I don't know if you remember, but there's the scatter plot of transient and equilibrium time sensitivity.
And the closer you get to equilibrium, the better this works. And that's simply because the yeah, the I mean,
the uncertainty on short timescales is the uncertainty and ocean heat uptake or the uncertainty where sea ice is going to go.
But it's just going to completely go and there's no uncertainty anymore.
And basically, when the ocean up to heat up, which is different, every model actually does come to adjustment.
It doesn't matter anymore. So in equilibrium,
you get your the pattern works best right and it doesn't work great in that in the transient correlation was only zero point six two.
So that's that's an issue where but I'd say that so so places where you still I'll have to explain the North Atlantic,
where the changes are small anyways, where you have sea ice that's retreating is very different needs model that
doesn't come back too much to haunt the tropics or the rest of the poles to be.
Because if you think about those two uncertainties are really high latitudes and all the diffusion is kind of more equator.
Where to this?
So in fact, you can you have to really get big uncertainties here before you can believe that that temperature and back into the tropics,
we try this a little bit. It's really difficult.
I mean, you get huge uncertainty in polar temperatures, but it just doesn't communicate to the rest of the world.
Thanks, David. I guess proponents of downscaling,
if they were in the room might suggest that one of the main reasons you want to do it is actually for the hydrology down scale,
you know, better topography and, you know, better weather rainfalls and therefore,
you know, better which catchments got the water in which soil is moist.
So it is kind of a two part question for us is would you disagree that it's useful to do dams getting for that for hydrology?
And the second part is, is there any likelihood that there's actually a possible feedback if you get the hydrological balance better at small scale?
Will that feedback into the large scale climate of the temperature that you've questioned? Flexible, OK, which of the two questions first?
Yes, if you really want hydrology, you have the down scale because it's about precipitation.
And none of this is getting worse than the issue, of course,
is the rainfall is done pretty poorly in climate models and any place where you have really stable precipitation,
mid-latitudes high latitudes, then regional downscaling to get sharper, greatest say due to mountains.
But that is a sensible thing to do. Next question would be do you want to do it with numerical modelling and empirical modelling?
My choice will always be empirical. Just because the regional models can give you big uncertainties, in fact, I could show you slide.
But the regional models for Europe, for example,
they've been mining them for a long time and and actually the uncertainty if you drive them with the modern day boundary conditions.
So we actually know what's coming in.
There's no problem with the GCM having the storm track the wrong places are driving it with the primary analysis and say,
how well do the models reproduce the seasonal average precipitation? Well, the answer is plus or minus 40 percent?
That's actually worse than the GCM, much worse than the GCM.
So, so I'd say like, yeah, if you want to do better for changes in precipitation in places where precipitation is strata form,
precipitation is not dependent on your prioritisation of convection empirically down scale, if you can.
If you got the data, if you don't have the data becomes something else, that would be my response.
Now is there? Is there a feedback? There probably is, but it's probably not fundamental.
I mean, I think it's probably a a second order process. That would be my guess.
Yeah. But if you look at it, I mean, there's these big office of regional models and how well they produce the modern climate.
And they're generally the uncertainties the because of the well, these are not built for climate purposes, right?
If you look at the uncertainties in precipitation like says plus or minus 40 percent in the air,
not even in the that's the ensemble mean, that's the climatology. And then the interannual variability in these models is horrendous.
Temperature uncertainties are the same order as the GCM, so I don't I don't think in general, unless you happen to be in a place like Norway,
maybe or you even do that because every model puts a storm track in a slightly different place and they change it in a different way.
So I mean, I know people want to know regional precipitation changes, but I just don't think it's something we should be promising to deliver.
It's asking me the question. Hello. I live it, thank you, thank you very much indeed.
This is fascinating stuff. I'm trying to reconcile it in my head is Thomas Hoenig goal?
Exactly. It's on behalf of. Yeah, he was pretty one of the people who failed to bribe the doorman.
But some of which I believe there are quite a few. Thomas, well, I'm going to ask you, what would you want?
Ask questions, shall I? I mean, if you say so, Thomas has done a bunch of experiments.
Yeah, it's always done stripey sunshades. Why doesn't? Why doesn't this apply to stripe his conscience?
Do you want to question in what respect does it not apply to them? Wait, wait, apply to what?
OK, so so Thomas is imposed if I get this right,
Thomas stripey geoengineering experiments where he puts his or sunshade across the planet and finds quite a lot of structure in the response,
at least. Yes, yes. But over time, the temperature response that you get is presumably behaving in a similarly diffusive way.
I imagine in the residual as well. Yeah, I mean, it's not just one percent, yes, but it's over the course of the first decade.
It starts to diffuse out a little bit over time. OK. Do you do you have a question?
I apologise. It was. It was going to be important, but apparently it isn't.
OK. But OK, well, I'd say, would this apply?
Would this apply to a geoengineering a regional geoengineering plan?
It should and actually, Malta Stuka has done these prescribed CO2 bans, and it actually works pretty well in those things.
And the idea, of course, if you apply a CO2 warming to the polar regions for the reason we were just talking about the second ago,
it's hard to get that warming out of there. So you really have to warm it up a lot to diffuse that to get the energy diffusion back.
But in other bands like one, when multiplied in bands of CO2, basically it's distributed really nicely by distribution of more static energy.
If you wait long enough, you have to wait long enough, right? Do you have any idea how long you'd have to wait 100 years, 100 years?
OK? Maybe less, but eventually you have a slab model of a GCM with an expectation.
So if and a GCM, if you apply this in a way, there's hope if you applied it right in a place where there's a very,
very deep ocean mixed layer, but you don't have to wait for a long time for this thing to happen.
But once it warms up enough, it'll diffuse just like this, right? So I think that might be the difference.
If you play a strip at 50 degrees north, right, you have the mixed layer that's 4000 metres deep in North Atlantic.
It's going to take a long time to get up to an equilibrium in one spot. But as it's coming up, basically that he's going to be diffused the way.
On the my questions about the the human health effect.
Have you looked at in addition to the absolute stress levels that you showed as the extra stress that people suffer in different areas,
the point being that people who are living on the equator are already used to living it?
Yeah, which is a great and high levels of humidity and the absolute stress that you
show might not be much of an increment over where they are at the moment,
right? The extreme danger one is a physiological thing.
So even if you live in the tropics, you're dead. But the danger zone is is not.
I mean, that's a little bit like where you live. So a lot as you probably know more about this than I do, but a lot locally,
what's considered hot is varies from place to place and depending on how much have you adjusted to it, how much society's adjusted to it.
So this is just a ball. I think the purpose here wasn't to say that's the answer for heat stress in the future, you know, under RCP 8.5,
but but is there a can you can you take shortcuts rather than saying, I need to downscale all my climate models?
Can I just down scale one that has high sensitivity and that's going to give
me like an upper bound in one with a low sense of you give me a lower bound. I think the answer is, yeah, you can get away with that.
But what you what you call critical place. There's lots of ways you can do this and we're starting to look at this.
We're actually working on how heat as stress affects agricultural workers in the US, mostly migrant workers.
You can imagine how popular we are in the US, and that's what we're working on.
And it's it's a tough problem. But that's yeah, it's a question of time over here.
Hi, David. Erm, can you sort of compare the temperature of precipitation effects by saying that the precept was very circulation bound,
whereas temperature is not? I mean, that's only true to some extent.
And for example, here in the UK, heatwaves are clearly linked to circulation anti-psychotics, circulation patterns.
And so any attempt to estimate, you know,
whether the number of of of dangerous or death just deathly heatwaves has got to take into account the propensity for changes in circulation,
for sure. This is something which seem that models do not do a good job.
So I guess my question is you have assumed implicitly that uncertainty is actually represented well by the same models.
But I would suggest that they're at the regional level that could be called into question.
OK, so because I know there's a problem with that in the models, what we did for the future one is we assume the same weather is today.
You know, that's an assumption. But but again, that assumes, you know, that cyclones occur with the same frequency, but that's a crucial question.
Yeah, that's an open question. Yeah. And see, the smart thing is they want to hold on home.
Uh, oh, yeah. The other thing which is we're just counting days like we're not defining heat waves as four days in a row,
so that's another issue that makes this complicated.
But yes, the frequency of blocking how that's going to change the future, those are all really cool science problems.
And you can add that on top of this doesn't change any of the implications of this.
But but because each one different,
whether you want to and it might be interesting to see what's the range of uncertainty and blocking climate models,
and that might be different from the range of uncertainty. Climate sensitivity certainly will be just like that, like in precipitation.
They should say that we didn't try too hard to do patterns of precipitation.
There certainly should be ways like there's some common things like, you know, the rich get richer, poor, get poorer kind of arguments.
People may and every climate, but every time I'll put precipitation in a different place, the tropics.
So just kind of sweeping this under the rug is a little bit unfair because every model,
for example, might do the same thing is an increase in precipitation.
But the ITCZ might be in different place started in each model, which case it's show that there's more uncertainty than there really is.
We didn't try to do that, but there's ways you could maybe eke out more information from from the models in terms of scaling for precipitation.
I've not tried it. It's hard. Yeah, we just take well, this is a question from a layman.
Do your conclusions reinforce the idea that it's less important whether you have relative success in
some regions versus other regions of reducing CO2 emissions and the overall success around the world?
Who was that audience even before the U? OK, wait. Say that again.
How do you can't you come up to the point that it's the global mean change the same?
That's the relevant thing. Some countries might be more successful, or some regions in reducing CO2 emissions are yes than others.
What does that not really matter? Because at the end of the day, overall, what is the total accumulation?
At the end of the day, the atmosphere is really good at distributing. The stuff was that was not known before.
Oh yeah. Yeah, yeah, yeah. Thank you. Way before I was born. Even before me.
Yeah, no. The atmosphere doesn't. I mean, that's what makes us a hard problem.
The atmosphere just accumulates CO2 and distributed.
So the difference in concentration and CO2 around the planet, you know, varies by like eight percent.
And so it doesn't matter who puts it and where it all accumulates, and it comes out thousands of years later.
And if we. If we can wait that long. All right.
I think you have the time before I thank David again. One, one one.
Oh yeah. Oh, I don't know. Can we do it over this?
Do I suggest we do have a drinks before we thank David again for a French talk that has generated lots of discussion,
which will no doubt spill over into into the drinks? I'd like to thank the Oxford Martin School again for hosting us.
And remind everybody and thank you all for coming and remind everyone that there is a drinks reception now just next door.
So please don't dash off. Please come and continue the conversation. Catch David if you haven't had a chance to ask you a question.
But now, as you can just join me again in thanking David for a great.