Thank you very much.

And thank you very much indeed for the invitation back.

And I was delighted to accept an invitation back to Ireland.

Yes, my mother’s family hails from County Mayo,

so I heard all about these storms when they were going on.

And in fact I have to thank some of my cousins

for contributing some of the pictures I will be talking about this evening.

I think it’s helpful to think about climate change in the local context,

because we often get the impression going to climate change talks

that climate change is something that happens to polar bears

and to our grandchildren.

And the kind of point of this lecture is to try and explain to you

how we can relate the global forces shaping our planet

to the weather events that shape our lives.

And that’s the objective of this lecture.

It’s a relatively delicate task.

So when something happens, like the storms last year,

this is Lahinch in the height of one of the storms,

which did a very considerable amount of damage.

I think County Clare estimated the initial damage at €17m.

The question, came up, fairly rapidly,

whether this exceptional winter was somehow related to climate change.

At the same time we were seeing, just a little bit further north,

thanks to my cousin Julian,

this is a photograph he took of his barometer actually hitting the bottom of the scale.

I thought this was really interesting, …

it’s an old barometer and he had never known it to actually bump the bottom of the scale

when he was measuring the pressure.

And it wasn’t just the intensity of the storms, it was their persistence.

There’s a remarkable snapshot of the app…

so this is sort of the opposite ends of weather applications here,

the barometer on the one side, the app on the other.

And the app showing an entire ten day forecast with triple rain drops every day.

So it was the persistence of this extreme weather

as well as its intensity that was really exceptional during that winter.

I like this picture…this is from last summer…

but it shows a nice example of the timescales

we have to think about when thinking about weather and climate.

In the foreground there you see a sea wall,

probably built about 100 years ago,

which was taken out for the first time by the storms last winter.

In the back you see a little bit of infrastructure

which has been there for rather longer than that.

So on the basis of eyeballing this picture you would say,

well this was clearly …roughly a one in a hundred year storm

but not a one in a thousand year storm.

Although it’s probably fair to say that the fortress in the background

was probably build not to withstand the weather but to withstand some other things.

But it does illustrate the kind of planning time horizons,

when we build infrastructure

we build it for the weather not of tomorrow

but the weather of 50 to 100 years hence.

And therefore understanding how our weather is changing

and what kind of risks the infrastructure we are building today

will have to put up with over the lifecycle of the buildings

is a crucial part of planning for a climate safe future.

At the same time that all these dramatic events were happening in Ireland

we had perhaps rather more prosaic events going on in southern England.

We had a lot of rainfall.

This is the iconic picture of flooding in the Somerset Levels.

And we also had heavy storms which damaged the rail network,

in particular communications down to Cornwall.

And then the government really took notice

when the flooding extended down towards London.

There’s a bit of a joke at the time in Britain that

it didn’t really get on the political radar until flooding started in the home counties.

And I really like that picture,

because somebody must have paid a lot of money for that house

with the swimming pool in the Thames Valley

and they have got a little bit too much water at the moment in their garden.

So at that point it became a political issue, a big political issue.

Well before that point we were very excited about this as meteorologists.

And we are proud that we maintain

the world’s longest continuous daily weather record in Oxford.

Those are some of the records that were taken in 1767.

I don’t know if you can see, but the date at the top of that sheet there is February 1767.

That’s when we started taking these weather records in the Radcliffe Observatory Site,

which you can see a picture of at the beginning of that period.

And that’s what the Radcliffe Observatory looks like today.

And that is Ian Ashpole pouring out the measuring flask

to record the wettest winter ever.

And it’s not often that you break a record

that has stood since men were wearing tights.

So we thought this was really quite a remarkable event.

And of course, it raises the question, well what’s going on here?

The storms in Ireland were also record breaking.

There was a nice paper by Tom Matthews,

published in Nature Climate Change last year,

which looked at the overall level of storminess,

if you like what’s shown here is a measure of

how much structure there is in the pressure field,

which is a way of measuring just how intense the winds were.

And you can see there that the red line there is extending up to break a record

previously set almost exactly 100 years before in the winter of 1913/1914.

And so this was clearly an unusual event.

And in southern England rainfall likewise it was a record-breaker.

But it was only just a record-breaker.

One of the things you should notice here,

if we look at the season totals on the bottom draft there,

the season total rainfall was,

just over what it was in the winter of 1913/1914 100 years before.

So even though rainfall was in parts of the UK over double what it normally was,

for that time of year, we were still only just breaking the record.

Weather is very variable in this part of the world.

You expect to break records every now and then.

So just because you break a record

doesn’t mean that the climate is changing.

So that’s an important thing.

Every time a record gets broken people think something must have broken that record.

That’s not true. In a random system records get broken.

You just have to wait.

And so teasing out the role of external drivers in this system is not such an easy task.

Which is why, when these things happen,

the question which comes up, people like myself

get phoned up by journalists saying, is this climate change?

I notice that Robert Devoy at University College Cork was asked this question.

I think people may be listening in from Cork.

And the Irish Examiner reported what he said as,

‘Severe weather is attributed to human impact’.

That was the headline they got.

I did feel for Professor Devoy on this,

because if you actually read the article,

what he said was,

"well, this is the kind of event which we might expect to become more frequent under climate change".

It’s the sort of event that we have been saying for some time

might well become more prevalent.

Which is a little bit longer, a bit more nuanced than the headline

‘Severe weather attributed to human impact’.

And that’s the frustration, that’s the difficulty we face as scientists,

that the link is a subtle one.

And I am going to be taking you through in this lecture how that link works.

But it’s an important one, because it’s important for people to understand it.

I myself was very much put on the spot last winter,

because we had organised a science briefing,

a briefing for science journalists, on the floods,

and then the day before…two days before

we had this briefing David Cameron stood up in Parliament,

entirely unprompted, as far as we could tell,

and said that he suspected a link between climate change and the floods.

And suddenly it became a hot political story.

And at the time it would have been really good

to have had some numbers, to have said,

did we think he was right or not.

And I am now going to tell you….

we have been working on this ever since,

and we do now have some numbers

we can discuss at the end of the lecture,

whether or not we think that my numbers

which we have now bear out what Cameron suspected at the time.

But it is very difficult when these problems arise,

because everybody tends to use these events to make a point.

So people who want to advocate action on climate change say,

"this is climate change - without climate change this wouldn’t have happened".

Those who don’t want action on climate change stand up and say,

"don’t be ridiculous, this is weather not climate".

And in the meantime the climate scientists get caught in the middle.

And it’s a very uncomfortable position for us.

So what I am talking about here is

how we set about the problem of linking

these extreme weather events and global climate change.

So there are sort of three goals of this talk.

The first part is unashamedly techy.

I want to show you some of the work we actually do as climate scientists.

Frank sort of said anxiously to me earlier on, don’t get too technical.

It’s OK. Well I will explain to you what we do,

what the background to this information is.

Bear with me on that.

Because I think it’s important that you do understand

where this evidence comes from.

It’s the evidence that we are influencing the climate on a global scale

- the nature of this evidence, which I want to explain to you.

And then we are going to drill into the main point of the talk,

which is quantifying the implications of that global scale change,

these global scale changes for UK and Irish weather.

And in that part of the talk I will be presenting our findings,

which we are presenting for the first time this evening,

on the impact of human influence on climate, on the high storms….

on the storm events of the west coast of Ireland.

It’s important to stress in the second part of the talk …

the first part is all published scientific material, IPCC assessed and so forth.

The second part is still unpublished.

It’s under peer review, some of it, and not yet even submitted for peer review.

So you should bear that in mind.

We scientists love to sort of not say anything

until everything has been through several rounds of peer review

and it’s several years later and everybody has long forgotten.

But I thought, we are compromising here,

so I am telling you …giving you some up-to-date results.

And I will just ask your forbearance,

that I am therefore showing you some results which haven’t been through peer review yet.

And then finally I think in the hope of getting some interesting questions going,

I want to talk about the implications

for the attribution of harm to human influence on climate.

Because I think that’s a really important and profound

problem we need to struggle with.

So to begin with,

let’s step back to look at the global climate problem.

And one of the headline conclusions of the IPCC’s 5th Assessment Report

was that it was extremely likely that human influence on climate

has been the dominant cause of the warming observed since the mid 20th century.

This is one of the headlines.

And ‘extremely likely’ there had a very specific sense.

It meant that we had better than 95% certainty,

better than 95% confidence that that statement was true.

And I was rather sort of put on the spot on this

because I was the one who had to be sitting next to the presenter

during the press conference at the end of the release of the report.

And the presenter was talking to me while the camera was off,

and they were talking away in Stockholm,

and then the red light came on, so you knew that it was all live on national television.

And he turns around to me and says,

"so scientists are 95% confident that most of the warning is due to human influence".

"What do the other 5% of scientists think?"

And it was a clever question.

Because of course most people viewing television do get very confused about that sort of thing.

They don’t really know what this sort of 95% confidence language means.

And it’s fair enough. It is quite obscure.

And it’s not helped by the fact that occasionally

we get studies coming out saying 97% of scientists agree that…whatever.

And you sort of get the impression that

climate research is almost like a big opinion poll -

that you sort of ask enough scientists and then average up the results.

That’s absolutely not how it works.

And I am going to try and tell you a little bit about how it does work…now…

and so bear with me on that.

So that you get feel for…this is not just a matter of opinion.

This is a matter of what the data are telling us.

Crucial to this…obviously the starting point for all this is

observations of global temperatures which you see here,

the observed temperatures over the past 150 years, up to 2012.

And they are coloured in,

just by temperature so that you can sort of keep track of

where the temperatures are going in the future.

And what we do to work out what’s ….

we obviously see this very pronounced warming

of roughly 0.8 degrees since the 19th century.

But to work out what’s causing that warming

we need to compare these observations

with what climate models say we would have expected to have happened

as a result of the increase in greenhouse gases

and other forms of pollution in the atmosphere.

That’s the orange line here.

And we also need to take into account

what the models also tell us about

what we would expect to be the response to natural factors affecting climate.

We know that other things affect climate as well as greenhouse gases,

the big spikes there you see are the cooling responses to large volcanic eruptions.

But then also going into that blue line is the impact of the sun,

the power output of the sun goes up and down over time.

And that also causes long term fluctuations in climate.

So a lot of people think that the reason we think human influence on climate is ….

the reason we are confident there is human influence on climate

is because when we combine models with human influence

and natural factors we get the black line here.

And it more or less matches the observations.

And if we leave out human influence, you have the blue line,

and it doesn’t match the observations.

So there you are. That’s the evidence.

This is actually not the way the evidence works.

So I am going to take five minutes of your time to explain that it’s not that way.

And the reason that’s important is,

you don’t have to be all that hawkeyed to recognise

that the fit isn’t that great,

particularly at the end here, the climate models seem to be too warm.

So does that mean the whole conclusion should be thrown away?

Well it doesn’t. And I am going to explain to you why it doesn’t now.

Instead of comparing just the observations with the models directly

what we actually do is …

instead of just sort of plotting them against each other

and standing back and looking at it and saying,

oh that looks alright as a fit.

What we do…to do it systematically,

we do what every epidemiologist or every doctor would do

if you are trying to work out the effectiveness of a drug,

or the impact of something like smoking on cancer,

you plot one thing against the other.

You plot the thing which you think might be causing the change

against the data you have of cancer risk or cancer cases or whatever.

And you try and look for relationships between them.

So what we are doing here is plotting the observed warming,

in the vertical, against what the models tell us we should have seen,

as a result of human influence on climate, in the horizontal.

So now you see that orange line is just a straight line,

because it’s just one to one, models predict what models predict.

You can see why the orange line is a straight line here.

But because we have got these multiple factors playing a role,

both human induced warming and natural factors,

in order to see how these different factors are affecting the observations,

we need to plot the thing in three dimensions.

So this is where you really have to stay focused.

I hope everybody down in University College Cork is very focused at this point.

So what you see here is,

I have got the observations in the vertical,

I have got the response to human influence in one direction

and I have got the response to natural factors in the other.

Now if that seems like a strange thing to do,

imagine that in the vertical it’s incidents of lung cancer,

but I have got a population with various ages.

So your risk of cancer might go up with age.

So I would have to plot age in one direction

and then number of cigarettes smoked per day in the other one.

If I leave out any of these things I will draw the wrong conclusions from my sample.

You see how I do this?

So what I am doing ….I am looking for a relationship between these three quantities.

And only if I find that I can’t explain my data

unless I bring in these individual factors

will I conclude that these factors are driving the changes I am seeing in my data.

So this is what we do.

We essentially rotate this cloud of points around to look to see

if there’s a consistent relationship between human-induced warming

and natural factors in the observations.

And we see there is. All the points lie more or less on a plane.

And we can work out the uncertainty in this relationship

by looking to see how much we can vary

the slope of the plane and still fit the observations.

So notice here, I am not assuming the models have got

the size of the response to human influence right at all.

I am just inferring that from the observations.

I am fitting to the observations here.

I am working out how big the human influence is from the data,

not by relying on my models.

And that’s a really important distinction.

It’s a really important point to realise

because it may be that all our models are wrong.

It may be that, a priori,

we couldn’t assume that all our models get the response to human influence right.

What we do is, we go and check the models against the data to work out if that’s true.

And this is what we find.

We find that the models actually consistently slightly over-predict the response.

They are a little bit too sensitive to rising greenhouse gases,

by about 10% or so.

They also over-predict the response to volcanoes.

So you will notice that in the best fit line the volcanic spikes are rather smaller.

And so when we put all this together

we find that the best fit to both human influence and natural factors

gives us now a much better fit to the observations.

Notice now we don’t have that discrepancy at the end.

And most of the warming over the past 60 years is attributable to human influence on climate.

Forgive me for taking you through this,

but I do feel strongly that the idea

that you should just trust us or trust the scientists

to tell you how much the warming is a misunderstanding of the way science works.

You don’t have to trust any scientists. You can go to the data.

You can look at it yourself.

You can try and work out whether you can explain

what’s happened without a substantial contribution from human influence

onto the warming which has happened over the past 60 years.

And unless you are more ingenious than anybody in the climate science community

over the past 50 years, you will….well past 15 years or so, you will fail.

We can’t explain what’s happened without a substantial contribution from human influence

to the warming over the past 60 years.

And that’s what that 95% confidence means.

When a doctor tells you,

the medicine has been tested in a clinical trial,

and the impact is significant at the 95% level,

the doctor is not giving you his personal opinion.

He’s telling you what the statistical results of the trial were.

So when IPCC tells you we are 95% confident

that the warming over the past 60 years

is attributable to human influence on climate,

it’s not an expression of our collective opinion,

it’s a statement of what the data tell us.

So far that’s focusing on the global picture,

the fact that we can see that this warming which has happened over the past 60 years

is attributable to human influence on climate.

But now let’s get down to what people are really interested in,

which is what’s happening to the weather

in different parts of the world, particularly in this one.

Now we all know that extreme weather events have happened before

that human-induced warming.

Back in the mid 19th century in 1839 there was a very famous storm,

the night of the big wind, across the whole of Ireland,

which was very substantially more powerful…

as far as we can tell from all the records…

of course we don’t have detailed meteorological observations back then.

But certainly all the records suggest this was

substantially more powerful than any storm

that happened in the winter of 2013/2014.

So it’s clear that we don’t need human influence on climate

for these extreme storms to occur.

Likewise we don’t need human influence on climate

for high rainfall to happen in the UK.

This is a nice plaque on a wall in Shillingford,

just south of Oxford, on the River Thames.

And the highest point here actually

is a flood that occurred on 27th January 1809.

That’s when the Thames was clearly well over head height.

That’s a bike track. Whoops. So…I have just spoilt my joke.

Never mind. Can I go backwards? Yes, I can.

So that’s a bike track there.

So you are just seeing how much higher than head height it was.

So we have had some pretty catastrophic flood events in the UK in the past.

So quantifying human influence is about

understanding how large scale drivers of climate

are affecting the chances of these events occurring.

A nice analogy is loading the weather dice,

that’s what I used in the title of this talk.

And if you were shown a handful of dice

thrown in this way you should, if you looked at it for more than a couple of minutes,

be a little bit suspicious.

With this number of dice in front of you,

you should be seeing a couple of sixes….

you are actually seeing six…far too many sixes anyway.

And so if you saw that you should be suspicious about the dice that’s being used.

But crucially, as soon as I flashed up this slide it wasn’t immediately obvious.

It was like, you had to look at it twice to realise

that there was something wrong with this group of dice.

Now imagine…so this is the challenge we face

in working out how climate change is affecting extreme weather risk.

The weather events we are interested in,

as I emphasised at the beginning of the talk,

don’t happen very often. That’s why they do damage.

Because the buildings we build,

and the sea walls we build and so on,

are typically not engineered to withstand them.

The weather events that happen every year or every other year we are used to them.

So we are interested in the changing odds of relatively rare events.

So this is where the dice analogy isn’t so good.

Because of course if you roll the dice you have got a one in six chance

of getting a six in the first place,

you don’t need to roll it very often to realise you are getting too many sixes.

Yes? Now imagine….so to take another analogy,

if you have got a horse that’s a consistent winner,

and then suddenly it starts losing,

then you would be suspicious that it might have been doped.

Your suspicions would be aroused very quickly.

But in trying to work out if we are doping the weather

we are in a situation where the horse is a rank outsider.

It’s only got a one in a hundred chance of winning in the first place.

So if you imagine you are in that situation,

you would have to go to an awful lot of races

to work out if this rank outsider

was actually a little bit more of a rank outsider

than you would have expected it to be.

Are you with me?

So if there’s only a one in a hundred chance of something happening in the first place,

imagine how many times you have to watch it happen

to see whether those odds are changing.

And that’s crucial about understanding the link between climate and weather,

because we can’t just sit back and watch the weather

and see whether the odds of extreme weather events are changing.

Because if we are looking at changing odds of one in a hundred year events

we would have to watch the weather for 10,000 years

before we would see whether those odds had changed.

So the only way of doing this is using computer simulation.

We have to…the only way of getting this information is by modelling the climate system.

And of course with computers we can simulate the world,

we can repeat history, as many times as we like.

And this is what my group in Oxford spends a lot of its time doing.

We actually don’t do these calculations ourselves.

In fact one of the crucial….a crucial part of our work is ….

these calculations are essentially too expensive.

The cost of simulating the weather so many times

would put it beyond our resources,

or indeed beyond the resources of most institutions,

to do these with a super-computer.

So instead we put the model up on the web,

people download it, they run their simulations.

And so I will be showing you a lot of results this afternoon

which have essentially been contributed to us by the general public.

They run these simulations for us, they send us their results,

we compile them, and that allows us to build a picture

of the influence of climate change on our changing weather.

That black map there is where the participants are.

There are tens of thousands of them around the world…quite a few in Ireland.

So if there’s any participants here in this room or watching online,

then we are deeply grateful…we remain deeply grateful to you

for your computing time, because it allows us to do these unique experiments.

First of all, of course, you are using a model.

So you need to check that your model is capable

of simulating what actually happened.

Is the model relevant to the real world?

Is it showing you the same processes

that were important in the real world events?

So this just shows a comparison of the observed sea level pressure

on the left and rainfall in UK and Ireland….

sorry we haven’t got…the UK and Northern Ireland…

we haven’t got the Irish data on there…for January 2014.

And on the right the top 1% of our simulations.

So if we assume this is a one in a hundred year event,

it’s a sort of fair comparison to compare the top 1%

of our simulations with the observations.

And you can see that it’s a pretty good agreement between the two.

So our models seem to be able to capture

the big picture of what was going on.

We also see in these models…

this is a view of the world looking down over the North Pole.

So hopefully you can see here North America, Greenland, Europe, UK down here.

And last winter the weather was characterised

by this strengthening of a band of very high winds,

known as the Jet Stream, over the North Atlantic,

that was significantly stronger during the winter for…

at the time it was noted that it was stronger at the time.

Nobody was quite sure why.

We find, in our simulations, the same pattern emerging.

So we see a strengthened Jet Stream, across….

this is now averaging over all of our simulations…

we see a strengthened Jet Stream over this region.

So this band of high winds which is likely to be

associated with bringing extreme weather into the UK.

So that part of the picture is all fitting together.

The Jet Stream is part of the story.

Now I have moved the model simulations.

The wettest 1% of our simulations of the world are now moved over to the left.

In the computer we can do things that we can’t do in the real world.

We can magically take ourselves to a hypothetical world

that might have been if we hadn’t raised greenhouse gases and caused global warming.

So we take that signal out of the model simulations.

And then ask, how would the weather have been different?

And this plot here on the right hand side

shows how much more rain we expect to have occurred

in the winter of 2013/2014, specifically in January 2014,

in the top 1% of our simulations,

compared to this hypothetical world that might have been,

without human influence on climate.

And you can see it’s generally blue,

which is that our model is telling us that

human influence on climate made the top 1% of simulations,

the one in a hundred year rainfall risk, a little bit higher.

But you should also notice….I will draw your attention to this.

The numbers here are sort of up to 9mm, 10mm, 11mm per day.

The numbers here are up to 1.

So there was a moistening of the winter, if you like, from human influence.

But compared to the overall event it was fairly small,

which tells you it’s going to be quite a subtle signal that we are trying to find here.

These are the most technical plots, but I want to tell you the answer.

So here it is. These plots show you the depth of the low….

there are three takes on this weather event.

First of all there’s the depth of the pressure low, south of Iceland.

That was a key feature of this event.

I will go on to talk about precipitation.

And then for the punch line we will get to the wind storm risk,

the high wind risk in the west of Ireland.

So bear with me, I will explain to you how these plots work.

The red line there shows each of these dots….

and there’s many dots in there.

You can see there’s little tiny dots on that red line.

Each of these is a simulation,

sent back to us by a member of the public,

performed on somebody’s PC, somewhere out in the world -

a simulation of January 2014.

We rank them all and so there’s one in a hundred of our simulations….

there’s many thousands of these things…tens of thousands.

One in a hundred of them show an average pressure below 982 hPa.

So below this level. OK?

One in 200 below that level.

One in 50 below that level.

You see how this basically works?

It just shows you more or less how much…

what the odds within our ensemble of possible worlds…

how the odds of a pressure low change ….

and how the size of the pressure load changes

depends on the odds of it occurring.

And then the blue lines show lots of different estimates

of how the world would have been without human influence on climate.

And crucially you can see in this plot,

the blue lines are to the right of the red one,

which means that human influence on climate

has shifted these risk return lines towards higher levels of risk.

So if all we are interested in is the depth of the pressure low south of Iceland

the risk of a low as deep as we got has increased by almost 50%

as a result of human influence on climate to date,

with a broad range of uncertainty.

But that’s our best estimate of that increase in the risk.

Now nobody is really affected by the depth of the average pressure low south of Iceland,

except maybe a few cod fishermen.

Well they are not that affected by the pressure itself either.

So let’s get on to the quantities that really matter.

First of all, precipitation, in southern England.

Now we are looking at the risk of high precipitation.

So the lines are going upwards.

And you see here that again the blue lines are to the right of the red one,

fairly consistently….there’s a certain chance of no change in the risk.

And again we are seeing roughly 40% increase in the risk

of an event of a given magnitude of roughly one in a hundred year event happening.

Or to put that another way, what was a one in a hundred year event

has now become something more like a one in 70 year event.

Finally, to get to what you have come for,

to find out what the role of climate change was,

in the high wind events in the west of Ireland, this is the result.

And I should stress, we had already done this study for the southern England UK rainfall.

So we had this data available,

we had never really looked at wind speeds within it.

Nobody, to my knowledge, has ever actually done a quantitative study

of the role of human influence on climate on wind speed.

So I had no idea when we embarked on this what the result would be.

And so you are seeing this now for the first time.

That’s what it looks like. It’s a hazy picture.

The risk is smaller than the change in risk on rainfall,

only about a 25% increase in risk.

But it’s pretty systematic.

We are seeing pretty much in the region of a 50 to a 100 year return time event.

That’s an event which has a one in 50 to one in a hundred chance of occurring every year.

We are seeing roughly a 25% increase in risk.

What that means is, that what was a one in a hundred year event

has now become closer to a one in 80 year event.

So that’s in a nutshell what if somebody asked me now,

what was the role of climate change….

or if Robert Devoy was asked now,

what was the role of climate change in the storms that clobbered Lahinch in 2014,

we would be able to say, well human influence on climate

so far has increased the risk of a storm of that magnitude by roughly 25%.

That’s not nothing. It’s also not enormous.

I mean a one in a hundred year event becoming a one in 80 year event,

for most practical purposes it’s still one in a lifetime.

Most people perhaps would think that’s a fairly subtle effect.

And that would be a good message to take away from this.

We are seeing real but still quite subtle impacts of global climate change on our weather,

in this part of the world.

In other parts of the world for other kinds of weather events the influence may be more obvious.

So let’s just sort of list some of the implications of this

before we get on to the last part of the talk

where we start to talk about the implications for the big picture.

Large ensemble simulation is what we are doing here,

simulating the system many many times

to look at how the odds of different events are changing.

It provides a nice transparent way of seeing how we are loading the dice

or doping the horses towards certain events happening and away from others.

It’s important we do this,

because most of the damage from climate change

arises from events which are in this category.

We don’t see events that could not have happened without climate change,

certainly not in this part of the world.

Our weather is so variable that

whenever a weather event happens there’s always some possibility

that it might have happened without climate change.

But that doesn’t mean climate change is not playing a role.

Climate change is affecting the probabilities of these events happening.

And quantifying these changes in probabilities is a relatively subtle task,

but it’s absolutely essential if we are actually going to develop

an objective inventory if you like of the impacts of human influence on climate,

if we are actually going to quantify what human influence is doing to …

what global climate change is doing to us, at a local level.

And of course just to wrap up,

the real important question which everybody gets very agitated about –

OK, so you have shown that human influence on climate had played some role in this flood.

Who can I sue? No, that’s an American.

But people tend to say, immediately….

OK…so is there a blame issue here?

Well actually here the answer is actually depressingly fairly straightforward.

I am going to remind you of another result from the IPCC assessment,

one which you would have heard about from Thomas Stocker last year,

which is one of the sort of key conclusions of the IPCC 5th Assessment

is this nice simple straight line relationship

between the total amount of carbon we dump in the atmosphere

and the overall level of warming.

So this simple relationship….

so you can see that’s the total amount of carbon released from the mid 19th century,

against expected warming.

And you see we are sort of moving up this salmon plume in a nice….

these are lots of different scenarios for what might happen in the future.

But they all lie on this nice straight line.

There’s a very nice simple relationship

between the accumulation of carbon in the atmosphere

and the total climate change we get.

This really simplifies the blame issue.

Because it doesn’t really matter when carbon was dumped in the atmosphere

it’s still affecting the climate today.

The carbon released by one set of very great grandparents in Lancashire

is still affecting the climate today,

and affecting therefore in principle the great grandchildren

of another set of great grandparents living in County Mayo.

Here’s where it has come from.

This is a nice graphic of how different countries

have contributed to the accumulation of carbon dioxide emissions since pre-industrial times.

And notice here, this is a rather different picture

to the one you normally get about who is to blame for emissions today.

Normally you are told about who are the big emitters.

And we are told that’s the USA and China.

Those are the 2006 emissions there.

USA and China are the two big blobs,

and all the other blobs are very small.

But in fact if we look at the picture as a whole

the accumulation of carbon since 1751,

yes, the United Kingdom actually shows up as quite a substantial contributor.

And I want to end by giving you a rather sobering animation,

which you can find on the web by the way if you look around for it.

I think you just have to Google carbon emissions by country.

I will show you briefly how carbon dioxide emissions

have evolved over the past 250 years.

It’s sobering from the point of view of

this issue of blame for what’s already happened to our weather.

If we go back to the middle of the 18th century, 1761,

you can see there’s only really one part of the world that’s coloured at all.

And for almost …for 50 years all of the emissions

are coming from this little part of the world here.

It’s almost a 100 years since the industrial revolution started

and still it’s monopolised.

Whereas it’s only now in the mid 19th century

that it starts to spread around into Germany

and perhaps a little bit across into the US.

Again it’s another 50 years before it starts to build up.

Now in a few minutes we will start to see Japan developing.

You can actually see here, there’s Japan coming in,

the US taking off,

you can actually see the impacts of the world wars here.

But only by the mid 20th century do we actually see emissions spreading out

and coming from other parts of the world.

Each point here, the colour is just the amount of carbon that’s being produced

by that point on the earth’s surface,

and vented into the atmosphere.

And we get to the present day, and you see all the familiar pattern of the continents emerging.

But I do find it sobering, looking at that animation,

that for a good 100 years it was all coming from one country,

which is why when it comes to the issue of who is to blame

there’s only really one place to look, to start with at least.

So in a nutshell, you keep being told to do something about climate.

I am sure the EPA is telling you about this all the time.

The point of this talk,

and for you to feed back to your government

and your met services,

it’s time that you were told on a regular basis…

how strange….OK. It’s disappeared.

I can tell you….you keep being told to do something about climate.

It’s time we all knew what climate was doing to us….

what climate change is doing to us.

And that’s the point of this talk really.

It is possible to quantify how much climate change is affecting our lives.

We have to do it through understanding the impact of climate change on extreme weather.

And in my view by the time my children grow up I think an assessment,

at the end of a winter,

this is the winter we had and this is how it would have been

if we hadn’t interfered with the climate,

should be part of the standard offering of climate services

from Met Eireann or the Met Office or our standard met services.

We are doing this at an experimental level now.

But I hope soon it will become just part of the standard services we get

in understanding the weather and the climate around us.

But I would welcome your thoughts on how that would work

and what the implications would be when we go on to the discussion.

Thank you.

PictAural