Thank you very much, Tim. And it's a pleasure to be here and to speak to you.
So just to quickly say a lot of what I'll say here, those are a couple of background papers here.
Review prospective papers, one a couple of years ago on circulations, climate change in general,
and then one just this past spring on event attribution and how we think about circulation there are
so you can find these on my website and so if you don't catch up with them here and follow up there.
Okay. When we talk about climate change and and anthropogenic climate change, this is, if you like, this the poster for that.
We know that there's long term warming of the climate system and we know it's the issue and the anthropogenic causes.
And so this is one of the map that's been updated a few times, but this is from the last IPCC report, the Intergovernmental Panel on Climate Change.
What you see are a bunch of a postage stamps and they either represent the the red ones.
Here are a surface air temperature over all over a continent, an entire continent.
So South America, North America, Europe, Asia, Africa. And the blue ones are the the the ocean heat content theory.
That's the the upper part of the ocean over the over the ocean basins, North Pacific, South Pacific and so on.
And then there's some ice as well, the sea ice and the in the in the polar regions.
And in each case, what you see is a band that's in purple, which is the as a climate models without anthropogenic climate change.
And maybe I'll focus on temperature here and pink as the models with climate change and blue are the observations.
And in each case, you see it's clear that you can only explain the observed record if you include the effects of climate change.
And the observations are incompatible with with the model without climate change.
This is the simplest attribution statement and it holds if you look at the scale of entire continents or ocean basins.
So this is, you know, from a classical causation point of view, this is what you would call sufficient causation.
They remember this is necessary and sufficient will get to necessary in a moment.
So sufficient causation, if you just consider this is a cartoon,
if you think of a of a geophysical quantity like surface air temperature, of course there's natural variability.
So if you talk about temperature over a certain continent or let's say you have a PDF,
and so this is just a cartoon and we would represent a distribution under a world without climate change,
with a peanut and a world with climate change with P one.
So Pinard is strictly a song is called The Counterfactual.
It's the world that would be if it wasn't for climate change course.
We often use the pre-industrial period as a proxy for that counterfactual so that the thing about these PDFs is that they're well separated.
So if you if you take a single realisation, you know, with with pretty high confidence, which of these two distributions you're you're you're in.
And this is of course you could say an extreme case where we've really shifted the PDF and this value out here,
which was extreme in the counterfactual climate, is now the new normal.
So this is a case where it's actually manifest.
This is a very but this is what we had on the previous plots, right, where the PDFs get well separated.
It's called predictive power for individual realisations and for climate.
A Germany applies if you look at at land surface temperature over sufficiently large
areas and sufficiently long times like a climate over one year or something like that.
And so this is just an example. I like this figure. It's a, it's a graphic of, of the hottest the decade of the hottest summer.
And this is using climate proxy records. So it's a 500 year record.
And when you add ten years to that record, so the, the, the vertical scale is the temperature and the colour is the decade.
And so what happened is that basically the whole content becomes much more red, which means that the records happened in the last ten years.
And of course, if we updated this now, this is only up to 2010, it would become even more red.
So basically the whole map got redrawn in ten years, which couldn't have happened by chance.
So this is a great example of sufficient causation, but the problem is that it's not only about temperature.
And so this contrast of precipitation and temperature, precipitation, of course, is very, very important because we need for water.
And you can have, of course, too too much as bad and too little as bad.
So from an impact point of view, precipitation is often one of the key variables.
So these plots, you may not have seen these before, but these are common in the IPCC.
So what's plotted here is a change in on the left. Surface temperature on the radius of precipitation.
Precipitation is always in percent, so blue and green means wetter in red and in yellow means drier.
Temperature is is just a scale of warmer or colder. This is at the end of the century under one of the one of the climate change scenarios.
So and then on top of that are the symbols.
And you can see that there's either stippling, which is the dots, and simply means that the models all agree on the sign of the chain.
So it's a robust change. Hatching means that the change is small compared to natural variability.
And if it isn't, if anything, if you don't have either hatching or shading, it means that the models are disagreeing.
So if you look at the temperature, it's stippled everywhere, except for a little patch in the North Atlantic Ocean.
Because of circulation issues, there changes, but basically everywhere else and certainly over land it's stippled.
But if you look at precipitate, so this is the case of a sufficient causation.
But in the case, if you look at a precipitation,
you see that the stippling is confined pretty much to a high latitudes where not many people live or over the ocean where not many people live.
So if you look at the land areas in the in the temperate latitudes or the tropics,
you find a lot of catching and a lot of areas without any any stippling at all.
So this is basically saying that the model that we don't have robust predictions either
because of just the climate noise and natural variability or because the models don't agree.
So it's quite a contrast between what you get for precipitation and temperature and going to be really the main theme of my talk.
So we'll talk first about natural variability. We know, of course, the variability is a coupled have a sort of ocean phenomena.
I'm interested in the atmospheric manifestation of that. But of course it should be remembered that the ocean is part of that whole system.
So one of the people talked about most modes of variability, and one of them that the climatologists talk about is the North Atlantic oscillation.
And it basically affects whether the jet stream is is is heading over Britain or heading for the south.
And so that will affect whether the northern part of Europe, let's say, is is dry or wet.
And so certainly ever since I've been in this country, everything's blamed on the jet stream.
So if you want to explain why things are the way it is, it's because of the jet stream.
And so that's basically this is one of them, one of the ways of characterising it.
There's other other ways, too. But this just shows one of the classical variations.
It's a convenient one because it's it's some the index actually is based on sort of surface pressure measurements over Iceland in the Azores.
So we have a pretty good time series of that. So that shows the record of the observations.
That's about 110 years. And you see that this North Atlantic Oscillation Index goes up and down and up and down.
So this is the problem that we're faced with, with chaotic variability.
And it's what very long timescales. You can see that there's there's year to year changes, but there's also changes that are multi decadal.
And of course, this makes it very hard to pull out a climate change signal because there's this whole trend that you can pull out of this time series.
In the early two, thousands people were quite excited about this this apparent trend here, and there were papers published.
Even the IPCC attributed this to climate change, but I think no one would would make that claim anymore.
And actually, no one actually believes that an EL probably should have a trend.
So this is really a confounding issue when we talk about trying to identify climate change in the circulation.
So of course, precipitation, as you can imagine, is controlled by temperature because the warmer the air,
the more water vapour you can have because our closest cooperation.
So that's kind of a thermodynamic control, but it's also controlled by the circulation.
And if you read the IPCC reports, basically all the statements about precipitation that have any level of conflict,
any any reasonable level of confidence behind them are based on thermodynamic arguments.
Basically, just as you warm up the air and holds more moisture and then you can make some arguments around that.
And the if the the way it's sometimes summarised to me and my guess is like the weather and the dry good dryer.
But if you look at many and I think we're realising now that this just doesn't hold over land,
which is kind of most important place for rain, but in many.
So it's say it's controlled both by circulation and by temperature. So this is what this is showing is simple model.
Of course, we only have a single realisation in the atmosphere, but with with a model you can generate an ensemble.
And this is one of the first what was called, I guess at the time, a large ensemble.
It's about 40. Combo members, which isn't that large, I suppose, but at the time it was large.
This is with me with the anchor model from the US and it's PDF.
So the winter time. So is December, January February widget on trends over 55 years.
So going in the future in a pretty large area, the Eurasian North Atlantic sector, each part has two PDFs.
One, the great is the control that's without climate change and the red is with climate change.
So for example, if we look over here and this is surface air temperature,
you see that the grey has a mean that's within noise of being zero because there shouldn't be any trend.
There shouldn't be any mean trend in the in the in the in the control simulation.
But you can see that you can certainly get members that will have trends that are either positive or negative over 55 years.
But if you look at the at the PDF with climate change, you can see there's a well separated.
So from a single realisation, you know, you'll know whether you're climate change or not.
Again, this is consistent with the kind of figures I was showing at the beginning.
But if you look over here, this is sea level pressure. So that's an index or a measure of the circulation and you see that the PDF.
So there is a change here, there is a shift.
It's significant because you've got enough ensemble members to see it, but it's basically about one standard deviation.
And so if you only have a single realisation, you're not going to be able to tell which these two populations are from the strongly overlapping.
And if you look at a precipitation, it looks a lot more like sea level pressure than it does like surface area temperature.
So in this case, a precipitation is really being controlled by the circulation and we've got strongly overlapping PDFs and the signals noise is small,
but the fact that is so IPCC of what we call this hash because it's small compared to climate change, but of course it's it's small in some sense.
But if you look at the risk of and these are not even extremes in a tail sense, but just on the shoulders of the distribution,
the chance in this case of being dry has gone down by about a factor of two, and the chance of being wet has increased by both by a factor of two.
So the change in risk is is not small yet. We're dealing with a prediction with only one, one realisation.
You can never of course validate this with a single realisation.
So this is the challenge that we're facing. And what do you do with, you know, of a winter like 2013, 2014?
This was not long after I arrived and and you may recall there was all this flooding in January.
This was the This is the Time series from the Met Office of the Precipitation in southern England and Wales for all of January over 130 years.
So good long record and you see that that winter was or was a record amount and there was all this flooding.
Now, this is a weather map it's I guess you don't at writing this is the standard theta on V2 which I won't try to explain,
but it's basically a map of the weather systems and what some people would now call the polar vortex, the tropospheric polar vortex.
And and the blue is cold air. And so actually on this is on January 5th, 2014.
And actually, you can see this big cholera outbreak all over the central U.S. And if you look at the newspapers at that time,
there were all these cold, cold snaps and everything. It was a lot of a lot of talk at the time.
And you see this big storm travelling towards Britain. This was the first of many storms that came in.
None of the storms were particularly extreme. But what happened was that the jet stream got quite stuck.
So the proximate explanation for why there was so much rain was just that the jet
was stuck in the same place and the storms just kept rolling in one after another.
But if you ask is is a stock jet just re more or less likely under climate change?
There's no no accepted view on that.
And certainly there's no accepted view on on how much that would change by.
So this is the kind of challenge that we're facing with a lot of extreme events that that are controlled by by the jet stream.
So if you have a small signal to noise ratio, then we're talking about not not sufficient but necessary of causation.
So this is a this is a case where we have strongly overlapping PDFs.
So if you have an extreme, you already have to be extreme in the counterfactual climate because you have to be out in the tail of the distribution.
So actually for all of these extremes, it's correct to say it's mainly natural variability.
So you have papers, I will say this and they'll say it's mainly natural variability.
And that's true in the sense that you have to be out in the in the tail to actually get the thing in the first place.
So a stuck jetstream would be an example. Of that. So that means we're dealing with with multiple causal factors.
We can't just say climate change. We can't say climate change caused it. In a sufficient sense, it just was one of many factors.
So it's a bit like an accident investigation, and you really should look at it with all these multiple causal factors.
It's got no no predictive power for single events because if you just have a single event,
you don't know if you're in the counterfactual or the factual distribution.
As I said, the anthropogenic effects are in some sense small,
but the the impacts depend in a very non-linear way on the on the hazard, as it's called.
So, for example,
flooding will depend will the flooding damage is a very steep in terms of cost is a very steep function of the flood level and that kind of thing.
So small effects really do matter for impacts,
but it means that the attribution problem is very different from what we talked about with climate change in terms of temperature.
It's fundamentally a probabilistic you're just talking about a change in likelihood.
So if you want to quantify this, you're going to, if will with a model, you're going to need very large ensembles of simulations.
So this is what I call the risk based approach. And and Oxford really pioneered this, I would say.
But there's a number of issues with that that I want to highlight.
One is that the event needs to be generalised because in fact every event is unique, right?
It's like you'll never have exactly the same event.
So if you want to talk about statistics, you need to sample population.
And to get a sample of population, you have to blur the event. So, for example, we're talking about a heat wave, you might say,
exceeding a certain temperature threshold over a certain area, over a certain length of time.
But of course, as many other events that might cause it to do that for other reasons.
So you blur the event to get enough of the sample on, and that will certainly lose local features and may lose connection to the actual event.
So you're not actually asking the question, you're not addressing the question that people are probably asking about if there's, say, a heat wave,
which was really because of, you know, warm temperatures in the city for the or for for the hottest day,
you're going to take aa5 day average or something like that.
The definition, the event is generally arbitrary. I mean, there are categorical events like a hurricane.
But in general, if you're talking about temperature or precipitation or something, you're talking about a continuous variables.
And so you just have to pick a threshold, you have to pick an averaging time, you have to pick an averaging domain.
Those are arbitrary. And the quantification, the actual number that you get can depend a sensitivity on that.
As you can imagine, if you try to estimate the change risk here,
if you're in this part of the curve or way out here, you're going to get a very different ratio.
So the numbers that you get are very sensitive to this definition.
There's a question that Tim Tam certainly asks all the time if you want to get these good statistics,
even if you have to run the model for a very long time. Well, if you have to run the model for a very long time, the model has to be cheap.
If the model is cheap, is it doing a good job? Cheap in the sense of computationally.
So the problem is you may not have the spatial resolution that you need if you want to do thousands of years of runs,
which is what you need, you need to do to get out the tail. So that's a very serious question.
Also, this construction of the world without climate change can be a major source of uncertainty.
The so-called counterfactual, and in particular, since I've already raised this issue of circulation,
if the circulation changes were important for your result, why would we actually believe those if we don't have any?
And this is what I'll now talk about, is if we don't have any basis for that, why would we trust that result?
This is an example from a from Oxford Lee scholar who isn't here anymore.
I really like this paper because I think it highlights the other issues very clearly.
So this is getting back to January 2014. And as I say, there was this very persistent jet stream or stuck jet.
So what's done here is this is with the weather at home here with very large simulations.
And what they did was they was they estimated they made different estimates.
So to get the counterfactual, you have to estimate how much of the sea surface temperature changes were anthropogenic.
And we don't know, of course, how much was anthropogenic and how much might be variability.
Plus, I guess there's observational uncertainties. So you use a model, the climate model, to determine what the half of the time part of that is.
And the answer that you get depends on the climate model. So what they've plotted here is this is zero is the ozone hole.
I'm not not with your stands for but it's this so the was only a persistent state and versus
the number of days in the month on the vertical axis and the return period out here.
So further to the right means it's more rare. And then these are offsets just so you can see them in the red.
In each case, is the is the. Present day condition and the light blue are the different models.
So you see, just to take this case here of 22 days in the state.
Some of these models say that there's been no change in the likelihood of the state.
Some of them say there's been quite, quite a big change. In other words, moving from the right to the left, from blue, blue to red.
So in some cases, you find that there's there's been a big change in the persistence of the jet.
In other cases, not. And then if you if you propagate that to actual flood risk in terms of numbers of properties at risk, the answer depends.
The sign of the answer depends on that state.
And what they did in this paper was also show that the which sea surface temperature changes that that you change really affected the jet.
Wally Well, you can see it here. So basically the circulation changes make the difference between increased and decreased flood risk.
So it means we can't really make a statement about this this risk unless we can get some confidence in which of the circulation changes is correct.
So you won't read all this, but what's there's been a lot of interest, of course, in extreme events.
And the American Meteorological Society publishes a report every year on the on the extreme events in the previous year.
It's a report of opportunity. People send in papers on almost different extremes.
This is the one for 2013. And what's what's done is there's the there's a statement, but then there's in this column,
it's the case where you can attribute the the event or at least an increase in risk due to climate change.
And it's a decrease in risk. And this there's no answer basically null result can't tell.
And for hot events, everybody finds there's an increase in likelihood of hot events.
That's not too surprising for cold events. Several times there's a decrease in cold events.
That's not too surprising. Once you get to heavy precipitation and drought, you can see that things are mixed, maybe up, maybe on no result.
Once you come to storms, you basically can't conclude anything.
So what this is saying is that when you try to do these studies, and especially if you really try to take the part of the uncertainties,
which I just I think most of the studies probably didn't do because they're often just using a single model, so on.
But what you're going to find is that if the attribution really depends on the circulation,
you're probably not going to be able to make make a statement.
But that, of course, doesn't mean that there was no effect. On the longer time scales.
This uncertainty also affects, well, long term climate change.
So, for example, this is of matrix, I guess of of this is contributions to uncertainty of the wintertime changes for ten year averages.
So, you know, so it's a climate variable. It's a ten year average of the wintertime precipitation per breadbox.
And there's basically three sources of uncertainty and what's going to happen in the world.
One is internal variability. We just have chaos and the butterfly effect and we and we always have to live with that.
There's also the model uncertainty, some models, the models of errors, and that that means that we can't that there's going to be issues there.
And then we have the scenario uncertainty, which is we don't know what the greenhouse gases are going to be in the future.
This uncertainty, of course, is one that we can do something about.
So the policy angle is to focus on on the CO2 levels and in the other climate forces and focus on the scenario.
So if you do this for temperature, what you find is that the and I trust that this is a fraction of the uncertainty
that these authors have partitioned into these three different sources.
For all the different climate models that are used and blue is it's a small fraction and red is a large fraction.
It goes up 200%. So if you do this for temperature, what you find is that it's a diagonal.
And so if you look at mine decades ahead, it's basically dominated by the scenario uncertainty and the fact the models disagree.
The fact we have internal variability doesn't matter. The leverage on the uncertainty is, is from how much the CO2 is there.
But if you look at a precipitation, you see that it stalls.
And if you were trying to set your your carbon targets based on precipitation,
as some people talk about a two degree target or something like that or what, one and a half degree target, that's what temperature.
If you tried to do it for precipitation, you couldn't because the models are just too uncertain.
So when you look at the IPCC, this lack of confidence in the future circulation changes propagates to the impacts.
So this is just because the air 51i couldn't fit on the page anymore, but this was from a slightly older one on extremes.
But if you look at droughts or floods, which are two of the important circulation related impacts, these are observed changes.
This is the Archbishop observed changes and the projected future changes.
And you see that there were some medium confidence statements are actually downgraded to low confidence.
So we got in the error five. So we have low confidence in the past changes.
We have a low confidence in the reasons for that and we have low confidence in the future.
But maybe this is the wrong way to ask the question.
So the normal side of a prior in our business and physics is is no, no effect at least, right?
Well, maybe yeah. I guess that's the normal scientific prior.
So you you try to reject the null hypothesis, but of course, given the magnitude of the natural variability, given the model uncertainty,
given the limited observational record because it's often fairly short, even for the best things,
that's 100 years or so, which is not not nearly enough for the really multidecadal variability.
It may be that you can't reject the null hypothesis, even if even if there was a real signal there.
But of course, we must remember that failure to reject,
reject the null hypothesis doesn't mean that the null hypothesis is true, and that's a mistake.
But I think a lot of people in climate science who seem to make.
But maybe that's the wrong null hypothesis.
We know that there's climate change, so maybe that the null or the prior hypothesis should be the robust aspects of climate change that we know about.
We are very confident about those aspects, the warming and the poisoning.
It seems a bit strange to basically throw it all that knowledge each time that we do a new analysis.
And from a practical point of view, I've just shown examples of time series of things.
If you wait till the observed changes are unambiguous, like they are for temperature, we wait for that for storms, it'll be much too late.
And certainly a reinsurance firm would take a precautionary approach.
So should we shouldn't we be thinking about that for climate science?
So there's what I've called the the storyline approach in this in this paper, and it's what you might call dynamically conditioned.
Attribution, which is a growing theme in the field.
So basically this is a formulation of the National Academy of Sciences in the U.S. produced an extreme report last year, this past year, I should say,
where this is the formulation of a different one in this paper, which is a little more clumsy, actually, this is more elegant version of it.
So you have a a joint probability. So most on, you know, a circulation of a related extreme.
So there'll be, say, a heat wave.
It's normally connected with an insect on a circulation or in the case of the flooding and the heavy rain that was connected with this structure.
So you can express a joint probability of the extreme that you care about flooding or rain or heat or something.
And then C is the C for circulation, but C is the synoptic or the weather situation that's conducive to that extreme.
So this is very trivial.
The the probability of a of A, the joint probability B and C is equal to the conditional probability of A given C times, the probability of sea.
That's just that's just sort of straightforward. And we do this for the counter.
If you do this for the factual of the current climate, P wanted to do this for the counterfactual of you,
not you just take the ratio, you get this very nice Factorisation So what this is saying,
if you want to look at the change in risk of this combination of the of the circulation, that's a conducive to the extreme and the extreme.
And people often look at these joint joint probabilities. It's composed of a product of two terms.
The first is the change in the probability of the event given the synoptic situation.
And then the other is the change is the risk ratio or the change in probability of the circulation.
So this first one is a is a conditional probability ratio and it's really got the purely thermodynamic effects of climate change.
You're saying given the synoptic a situation, how much did the event change?
Maybe because of having more warmer temperatures or more moisture?
And what happens then is that you really tighten up. This is a cartoon, but it's been shown in examples.
Now we get if we're way out in the tail here, you you can't separate these guys in grey in the unconditional.
But now when you a condition all of a sudden you get high signal to noise and
actually what you restore is sufficient causation but in a conditional way.
And then there's this other term, which is the ratio of the circulation.
So the real point about this is that now you can probably do a you could probably compute this first or ratio with with a fair
amount of accuracy because when we have a lot of confidence in the thermodynamic effects of climate change and this other term,
it may be small or it may be highly uncertain, and you should not necessarily ignore it, but you should certainly be treated differently.
And you shouldn't mix it in this because you shouldn't be combining things with different uncertainties.
That's what we argued for in this paper last year.
Now, why would you defend the the argument that we might want to have a null hypothesis of no circulation change?
Well, if you go to the IPCC and you basically that any statement that you pick about a circulation is very, very weak.
This is for it acts to tropical storm tracks, which is what we would care about in terms of, you know, winter time flooding in the Britain.
This low confidence in near term projections of low confidence.
Over the longer term, the global number of cyclones is unlikely to decrease by more than a few percent,
likely to be small compared to a interannual variability. So basically IPCC is saying we have no basis to assume anything other than no change.
And if you assume it, if you find if you have a model that does give you a change, you would have to ask yourself, do I believe the model?
Because basically there's no no confidence in any of those changes.
So I'll give a few examples of how this kind of reasoning works.
This is from a paper a few years back by a French group, so there was a cold winter in 2012.
I guess the climate sceptics said, What about climate change?
So what they did here was they said, Well, how cold would it have been under the circulation regime?
So they used a relationship historical between circulation and temperature and they computed what they call the analogue state,
which is the is the the temperatures that they would have had under the same circulation regime in the past.
And what you see so this is the the observe state,
this is what they estimated would have happened 50 years earlier if they'd had the same configuration.
And the difference shows a warming. So that's an attributable warming.
If you just look at the Time series over 50 years, you certainly can't pick out a trend there.
But if you look at the difference between this analogue state and the actual state, then then you can pick up the trend.
It's showing this signal to noise gets very, very high.
Once you condition on the circulation, this is another example, a recent study of Hurricane Sandy which led to flooding in New York City.
So, of course, a hurricane is something that Clinton models calculated.
But the weather models can you you may know that Hurricane Sandy was forecast quite accurately about ten days ahead by the European Centre.
So this is what the American model called morph, which is a weather model.
And you can forecast the hurricane and then you can ask how would a hurricane have been if the sea surface
temperatures had been cooler as they would have been in the past or warmer as they would be in the future?
And this is the Time series. This is an hour of a run over over fourth of four days.
So you see that the surface pressure goes down. This is the minimum surface pressure.
This was the actual well, the observed state when in the in the Dawson model ensemble under president conditions.
This is rerunning under a conditions of 100 years ago and this is 100 years in the future.
And you can argue that the hurricane has was a bit stronger than it would have been a hundred years ago and it will be much stronger in the future.
This is another conditional attribution where you do it using forecasts so you stay close to the observed state.
Another example the California drought has been a very source thing in the U.S., has been a lot of papers on this, a lot of debate.
Of course, it's a big issue for poor California.
I like this paper, which are basically you can you can argue that drought depends on a combination of temperatures and and precipitation.
You have, of course, low precipitation, but it tends to be exacerbated by hot high temperatures.
And in California, that's because of the melting of the snowpack.
So if you look at the at a precipitation time series over 100 years, you can see that it's up and down and up and down.
And these are low values here and there's not no apparent trend. If you look at the temperature, it's clear that there is a trend here.
This Palmer Drought Index is is some synthesis of these two different factors.
People argue over it, but it's it's a combination of the two and you can see that there's been going down means more drought.
So the argument here is that if you look at the long term record over a hundred years,
you see that if you ask whether the temperature was high or so warmer or cooler and the precipitation that your high or low basically,
of course, it's evenly spread around the mean. But if you look in the last 20 years,
you can see that the that the probability of both being both dry and warm at the same time has increased a lot because of the warming.
So if you ask what's going to happen to a precipitation in California, it's highly uncertain.
It depends on the storm track that that comes in. There's a lot of uncertainty about the storm track changes.
You really you really wouldn't want to trust models for that.
So what you can say is that we don't know what's going to happen to to to precipitation storms, but we do know it'll get warmer.
So the risk of drought has has increased. So again, it's a conditional statement.
This is another example that's a little bit different.
But L.A., because it really to me emphasises the signal to noise when you condition there was a course, a terrible heat wave in France in 2003.
These are images, maps that this scale is 500 metres.
These are three kilometres by three kilometres in central France.
The top panel shows a vegetation index from satellite.
This the bottom is surface temperature. So this is a grid box of three kilometres by three kilometres.
So what you see is this is one day in August 2000, one day in August 2003.
And you would say, how can you compare one day in 2000 with one day in 2003?
Of course, there's synoptic variability, but the synoptic variability is zero over a three kilometre scale.
So you can think of it as is basically that we have a uniform state over the grid blocks and
what happened the red means that there's a vegetation and the remains of that there is an.
So what happened in 2003 is all the passengers and crew all claims to have died out.
So the red became green, but the forest is still there, so the red is still red.
And if you look at the warming between here and here in the forest, it's 11 degrees C,
but in the pastures it's 20 degrees C, and if you isolate to avoid the hedgerows, it's 24 degrees.
So this is the impact of the vegetation on the surface temperature.
And I'm just it's the signal to noise as you go across the sky is to me amazing.
So it's the same kind of philosophy that we're talking about here.
A few condition on a synoptic regime. You can get very high signal to noise.
In this case, it's not the climate change that we're talking about.
We're talking about the the land use effects on the on the extreme.
Okay. So getting back to the to to to the stock jet stream and the flooding Commonwealth is
actually do predict a strengthening of the winter time jet and the storm track over Britain.
This is plots from up ahead. So if you take all the different climate models,
what they predict is going to happen at the end of the 21st century under under a particular scenario, this is a number of storms, basically.
And what you see, blue means less. And so we what this is predicting is that the Iceland gets some relief in a bit.
But if you're storms and and also fewer storms in the Mediterranean but more more storms over Britain.
But the problems here, first of all, we have very large model biases.
So this is the the bias in the models. And you can see just the different scale here.
So the bias is four times as large as the signal.
So you generally would be very nervous about as a signal.
That's one quarter of the size of your bias in a nonlinear system.
There's no mechanisms for for the strengthening jetstream.
No, no, no one really has any theory for that. Certainly there's no theory that everybody would agree and it's not seen in the observations.
So if we don't have an observed signal, if we don't have a theory, if models have huge biases, of course we have low confidence.
And that's why the IPCC gives us gives us this prediction, very low confidence.
Now, this this has an extension. So the strengthened storm track is reflected in extension of the jet over Central Europe.
And if you think about a signal to noise that's often quantified in what's called the time of emergence,
so it's when does the when does the climate change signal become manifest in the observable record?
When will with a single realisation. So this will be the sufficient causation and you can see that.
So this is by year and you can see the most of this is, is, is white.
So it's basically well, well past the end of the century. So temperature changes are manifest very quickly everywhere.
But circulation changes are actually seem like they're going to be very small, almost everywhere.
And there's a couple of and they're going to be very localised.
So what you see over Europe is that there were there's there's a predicted changes in the winter time over Central Europe and over North Africa.
And so I think that's the first important point is that the the circulation changes that are going to merge are probably very localised.
If you look at the Central Europe case, although the models agree that there will be a strengthening of the jet, when a well will happen varies a lot.
So some of the models,
the models with the strongest response say that we might begin to see this signal emerging and the observations lie about in about 50 years.
Others say it'll be after the end of the century. So there's a huge uncertainty in in the strength of that response.
One place where you do see a strong circulation, that response really the biggest is in this area is is is to do with the Mediterranean.
And so drying the models do I won't go back to it.
But if you look at the precipitation maps that I showed in the first second slide probably or something,
the one place where there's a stippling over populated areas is the Mediterranean.
And so the models are consistently predicting a drawing of the Mediterranean, which of course will be very serious for Europe.
So if you look at the time of emergence in the precipitation,
now you see that that is that it's that the time of emergence may be much earlier, like even within the next ten years.
And even the weakest response is the middle of the century.
So this is a place where circulation is really the big story.
And if you look at the at the drawing, it really is is associated with a circulation change.
So this shows the precipitation response and this is the wind.
This is the other 50 is the solar wind in the lower troposphere so close to the surface.
And if you look so this is the average of all. The models. These are the models with the strongest drawing.
These are the models with the weakest drawing. And you see it's highly correlated with with the circulation.
And we use the the window of a North Africa as an index for that.
And so if you look at the relationship between this circulation index, the wind over North Africa, solar,
wind and the precipitation, if you look in the in the observations there, they're highly correlated.
So the year to year variability is a very strong relationship.
If you look at the models, the the black is the models under under historical conditions, the red is the models under future climate change.
And basically what's happening is the models are just moving down this down this line.
So what that saying is that they're forming the same relationship between circulation and precipitation.
It's just becoming more extreme and it's controlled by the circulation.
There is a slight drying as well.
And it depends a little bit on these are individual models, but it's mainly coming from the shift in the circulation.
And so if you correlate the precipitation, the response across the different models with which which is always a drying with the wind change,
you see there's a very strong relationship. And so if it turns out that the models with the weakest response are correct,
the by the end of the century, it turns out we'll have a shift that's like one standard deviation.
But if the models with the strongest response are correct, they're going to be very well separated.
So it turns out that 85% of the mean precipitation change and 80% of the model spread is related to changes in the circulation.
So it's the same we're not going to be able to place any constraints on this cold season drawing unless we get to grips with the circulation response.
So why do the models differ so much in the in this response?
Those this is just a clue. It's something that we're working on at the moment.
This is an analysis of the different models. The sign up is the archive used for the IPCC.
How much done here is to partition? This is the mean sea level pressure.
So it's a circulation index and basically say how much of the model uncertainty comes from uncertainty and the tropical warming,
which has to do with with the climate sensitivity,
how much has to do with the Arctic amplification, how much has to do with the stratosphere, which is also changing under climate change?
And you see that, first of all, the conch, it's the same number of contours, more or less on each plot.
So it's a relative plot, but it's saying that these three factors are equally important in terms of the model spread.
So not necessarily for the total change, but for the uncertainty.
And the tropical warming tends to push the jet poleward as people have seen in lots of studies,
the Arctic warming tends to push it back and the stratosphere, if it warms, tends to push it back as well.
And these these kind of patterns people have found in single forcing studies,
in the case of the stratosphere, it's it's also what happens in the risk of some warming.
So I think this all makes a lot of sense. So you can argue that there's a tug of war.
These are telling actions really between that the the mid-latitude circulation is strongly affected by but by telling connections from other regions.
And these are uncertainties in the climate response which are exerting a tug of war.
And whenever you have two things acting in opposite directions, you can get uncertainty.
So just just to give an example, I think this is why we have such differences on what the models are, projects under climate change.
So this is just for different models of the end of the century.
And you see that this is this this is this 852 scale, this lower troposphere, it's all wind.
And you see some models show a strengthening over Central Europe.
This model that doesn't show anything of that, the shows a strengthening over the Mediterranean and so on.
So there's models that are doing very different things, which is probably because of this tug of war.
So to summarise the growing emphasis on first of all,
there's a growing emphasis on regional climate and there's, of course, on also near term projections.
People want to know what might happen in 20 or 30 years is really exposing the limitations in our understanding of atmospheric circulation.
And if you go to the IPCC, you start to find very weak statements with very large uncertainties and very
low confidence once you get to to to the regional scale and the short term.
So that put the signal to noise of a projected changes in circulation is small in general, but that doesn't mean that the risk is small.
So we actually can't ignore this and we have to develop a scientific language,
something incorporating risk that will express these uncertainties, but without losing sight of what we know.
So this is what I for example, this factorisation of the uncertainties is a way of doing this.
If you just put everything in the bin, then you just say, well, we we don't know.
We have low, low confidence, but that it may just be that we don't have confidence about circulation, but we have confidence about other things.
So we somehow want to be able to talk about.
I think a lot of climate scientists are afraid to talk about uncertainties because they figure it'll give the sceptics too much from the material.
But we have to find ways to talk about uncertainty without losing sight of what we know.
Wintertime circulation chains over Central Europe are highly uncertain in this,
probably because of a tug of war between these different drivers and certainly has
implications for important climate change impacts like windstorms and Mediterranean drying.
And in a way, to me, the fundamental challenge of climate science is that we need to talk about we need to we have model projections of the future.
A projection is just a conditional prediction based on an assumed scenario of greenhouse gases.
So we need to use those models to get information.
But the only observations we have are in the past.
The classic scientific paradigm is your theory. You make experiments, you test the theory, you go back and you go back and forth.
We can't do that in a simple way because the space that we're making the observations in isn't the space that we're making, but the predictions.
And so I think this really challenges the classic deduction and side of the paradigm.
And certainly I think one of the key fields for for the future will be trying to how do
we get information out of the short timescales in order to constrain long timescales?
Thanks very much.