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rC3 preroll music
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Herald: It is with much pleasure that I
can now introduce our next speaker, so
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it's just started raining outside, but
this heavy rain is not at all probably the
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extreme weather effects that we will hear
about right now. The weather, the talk
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that we are being presented next will deal
with extreme weather effects and how they
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are linked with climate change and how we
even know about that. Our speaker today
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is Fredi Otto. She's associate director of
the Environmental Change Institute of the
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University of Oxford, and she's also the
lead author of the upcoming IPCC
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assessment report, AR6. And without with
no further ado, I give you the stage
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Fredi, please.
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Frederike Otto: OK, thank you. Yeah. Hi.
It's stopped raining here in Oxford, just
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about, but it's definitely flooded, so
that might actually be something to come
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back to and talk about with respect to
climate change. So. Whenever we hear or
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whenever today an extreme weather event
happens, we hear about hurricanes,
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wildfires, droughts, etc., the question
that is immediately asked is, was this,
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what is the role of climate change? And to
answer that, for quite a long time,
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scientists gave an answer that we cannot
attribute individual weather events to
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climate change. But… Sorry, OK. But this…
Because the first, the one answer that
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people were giving were that, well, you
can't attribute individual weather events
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or they were saying in a world where
climate change happens, of course, every
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extreme weather event is somewhat affected
by climate change. And the latter is
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attributed too, but that does not
obviously provide much information,
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because it doesn't say anything about
whether the event was made more likely or
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less likely or what the role of climate
change was. And the first answer that you
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can't attribute individual events is not
true any longer. And this is... why that has
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changed and how that has changed. And what
we can say is what the content of this
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talk will be. So ultimately, every weather
event, extreme or not, is if you
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absolutely boil down to it is unique and
they all have many different causes. So
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there is always the role of just the
natural chaotic variability of the climate
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system and climate and weather system that
plays a role. There's always a causal
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factor in where the event
happens, whether it's over land, over a
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desert, over a city or a forest, but also
man-made climate change can have an
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influence on the likelihood and intensity
of extreme weather events to occur. And so
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what we can say now, and what we mean when
we talk about attribution of extreme
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weather events to climate change is how
the magnitude and likelihood of an event
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to occur has changed because of man-made
climate change. And in order to do that,
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we first of all need to know, what is
possible weather in the world we live in
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today? So say we have a flooding event in
Oxford and the question is, was this
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climate change or not? So the first
question is we need to find out what type
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or what kind of event is the heavy
rainfall event that leads to the flooding.
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So is it a 1 in 10 year event? Is it a 1
in 100 year event? And in order to do
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that, you can't just look at the observed
weather records because that will tell you
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what the actual weather that occurred is.
But it doesn't tell you what the possible
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weather under the same current climate
conditions are. And so we need to find out
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what is possible weather. And to do that,
we use different climate models. So we
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simulate under the same climate conditions
that we have today, possible rainfall
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events in December in Oxford. And we might
find out that the event that we have
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observed today is a one in 10 year event.
And so if you do this, look at all the
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possible weather events, you get a
distribution of possible weather under
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certain conditions, which is shown in the
schematic on the slide here in the red
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curve. And then you know that when it
rains above, say, 30 millimeters a day in
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Oxford, then you have a real problem with
flooding. So you define that this is your
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threshold from when you speak about an
extreme event. And so you have a
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probability of this event to occur in the
world we live in today. Of course, that
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does not tell you the role of climate
change, because in order to know that, you
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would also you will also need to know what
would the likelihood of this event to
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occur have been without man-made climate
change, and so. But because we know very
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well how many greenhouse gases have been
introduced into the atmosphere since the
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beginning of the industrial revolution, we
can actually remove these additional
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greenhouse gases from the climate models
atmospheres that we use and simulate a
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world that would have been exactly as it
is today, but without the greenhouse gases
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from the burning of fossil fuels. And in
that world, we can then also ask the
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question, what are possible heavy rainfall
events in December in Oxford? And we might
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find that the event that we are interested
in is in that world, not a one in 10 year
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event, but a one in 20 year event. And
because everything else is held the same,
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we can then attribute the difference
between these two likelihoods of
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occurrence of the extreme event in
question to man-made climate change. And
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so with this fake example that I've just
used, we would then say climate change has
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doubled the likelihood of the event to
occur because one that was one in 20 year
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event is now one in 10 years. So that is
basically the whole theoretical idea
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behind attributing extreme events and this
method can be used. And so, for example,
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with our initiative that's called World
Weather Attribution, we have looked this
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year at the extreme heat in Siberia, the
beginning of this year that, among other
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things, led to temperatures above 38
degrees in the city of Verkhoyansk, but
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also let to permafrost thawing and large
wildfires. And that event was made so much
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more likely because of climate change that
it's almost would have been impossible
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without climate change. So when we did the
experiments that the models it's a one in
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80 million year event in a world without
climate change. And it's still a
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relatively extreme event in today's world,
but it is possible. So this is the type of
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event where climate change really is a
game changer. Another event that we have
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looked at is Hurricane Harvey that hit the
Houston and Texas in 2017 and caused huge
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amounts of damage with the rainfall
amounts it brought. And several attribution
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studies doing exactly what I've just
described found that this type of, so this
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extreme rainfall associated with a
hurricane like Harvey has been made three
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times more likely because of climate
change. And colleagues of mine, Dave Frame
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and his team, have then used these studies
to figure out how much of the economic
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costs this hurricane can be attributed to
climate change, and found that of the 90
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billion US dollars that were associated,
that were associated with the flood damage
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from Harvey, 67 billion can be attributed
to climate change, which is in particular
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interesting when you compare that to the
state of the art economic cost estimations
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of climate change in general, which had
estimated only 20 billion US dollars for
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2017 in the US from climate change. And of
course, not every year is an event like
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Harvey, but it shows that when you look at
the impact of climate change in a more
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bottom up approach, so looking at the
extreme events, which are how climate
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change manifests and affect people, you get
very different numbers, as if you just
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look at large scale changes in temperature
and precipitation. But of course, not
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every extreme event that occurs today has
been made worse because of climate change.
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So this is an example of a drought in
southeast Brazil that happened in 2014,
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2015, where we found that Climate change
did not change the likelihood of this
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drought to occur, so it was a one in 10
year event in 2014, 2015, and also without
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climate change, it has a very similar
likelihood of occurrence. However, what we
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did find when we looked at, OK, what else
has changed? Why has this drought that has
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occurred in a very similar way earlier in
the 2000s and also in the 1970s with much
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less impacts. We looked at other factors
and found that the population has
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increased a lot over the last or over the
beginning of the 21st century, but in
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particular, the water consumption in in
the area and the water usage has increased
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almost exponentially. And that explains
why the impacts were so large. So this is
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what I've just said is sort of basically
the the very basic idea and and how in
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theory these studies work and how and some
results that we find. In practice, it is
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usually not quite as straightforward,
because while the idea is still the
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same, we need to use climate models and
statistical models for observational data
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to simulate possible weather in the world
we live in and possible weather in the
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world that might have been. That is, in
theory, straight forward, in practice,
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it's often relatively difficult, and what
you see here is how the results of these
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studies look when you don't use schematic
and if you're not a hydrologist, this
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might be a bit of an unfriendly plot. But
it's it's basically the same as the
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schematic that I've showed at the
beginning, but just plotted in a way that
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you can see the tails of the distribution
particularly well, so where the extreme
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events are. So on the X-axis, we have the
return time of the event in years on a
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logarithmic scale and on the Y-axis, you
see the magnitude of the event and that
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defines what our extreme event is. And
this is actually a real example from heavy
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rainfall in the south of the U.K. And you
can see here in red, each of these red
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dots that that you see on the red curve is
a simulation of one possible rainfall
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event in the South of the U.K. in the year
2015 in the world we live in today with
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climate change and the dashed line
indicates the threshold that led to to
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flooding in in that year. And on the
X-axis, when you go down from the dashed
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line, you can then see that this is
roughly a one in 20 year event in the
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world we live in today. And all the blue
dots on the blue curve are simulations of
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possible heavy rainfall in the South of
the U.K. in 2015, in a world without man-
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made climate change. And you can see that
these two curves are different and
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significantly different, but they are
still relatively close together. And so
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the event in the world without climate
change would have been a bit less likely,
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so we have roughly a 40 percent increase
in the likelihood. But still other factors
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like, yeah, just the chaotic variability
of the weather and also, of course, than
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other factors on the ground where houses
build in floodplains and so on play an
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important role. So this is the
actual attribution step. So when we find
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out what the role of climate change is,
but of course, in order to do that, there
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are a few steps before that are crucially
important and absolutely determine the
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outcome. And the first step, the first
thing to find out is what has actually
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happened, because usually when we read
about extreme weather events or when we
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hear about extreme weather events, you see
pictures in newspapers of flooded parts of
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the world. And so you don't usually have
observed weather recordings reported in
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the media. And the same actually is
true for us. So when we are, so we work a
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lot with the Red Cross and they ask us:
OK, we have this large flooding event, can
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you do an attribution study? Can you tell
us what the role of climate change is?
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Then we also just know: OK, there is
flooding. And so the first step is we need
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to find out what is the weather event that
actually caused that flooding. And that is
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not always that straightforward. And this
is what you see here on this map, on this
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slide is a relatively stark example, but
not an untypical. So it's of an extreme
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rainfall event on the 10th of November
2018 in Kenya. And on the left hand side
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is one data product of observational data,
of observational rainfall data that is
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available and on the right hand side is
another showing the same event. And the
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scale which I failed to to say on the
slide in millimeters per day. And so on
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the left hand side, you have extreme
rainfall of above 50 millimeters per day,
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which is considering that, for example, in
in my home town of Kiel in Schleswig-
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Holstein, there is about 700 millimeters
of rainfall per year. You can see that 50
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millimeters in a single day is very heavy
rainfall, whereas in the other data
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product, you don't see as much rain. You
still see large rain, but it's not in
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the same magnitude, and it's also not
exactly in the same place. And so given
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that most countries in the world do not
have an open data policy, so you can't
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actually get access to the observed
station data, but you have to use
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available, publicly available products
like the two have shown here, you have to
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know and you have to work with experts in
the region to actually know who hopefully
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has access to the data to actually find
out what has happened in the first place.
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But of course, if you don't know that or
there is not always a perfect answer, then
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if you don't know what event that is. It's
very difficult to do an attribution study.
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Assuming though you have found a data
product that you trust, the next question
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then is what is actually the right
threshold to use for the event? So if you
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have flooding that was pretty obviously
caused by one day extreme rainfall event,
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then that would be your definition of the
event. But it could also be that the
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flooding has been caused by a very soggy,
rainy season. So actually, the really the
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real event you would want to look at is
over a much longer time scale or if the
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flooding occurred mainly because of some
water management in the rivers and has
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actually flooded further upstream, your
spatial definition of the event would be
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very different. And so and what you see
here on this plot is an example of a heat
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wave in Europe in 2019. And there, what
usually makes the headlines is the maximum
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daily temperature. So if records are
broken, so you could use that as a
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definition of the event that you're
interested in. But of course, what really
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causes the losses and damages from extreme
events is not necessarily the one day
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maximum temperature, but it is when heat
waves last for longer, and especially when
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the night temperatures are also high and
not just the daytime temperatures. So you
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might want to look at an event over five
day period instead of just the maximum
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daily temperatures. Or, and this is sort
of why I have shown the pressure plot on
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the right hand side, which is really just
an illustration, it's not terribly
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important what's on there. But there are,
of course, different weather systems that
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can cause heat waves, especially in the
area here in the south of France. It could
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be a relatively short lived high
pressure system bringing hot air from the
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Mediterranean. Or it could be something
that is caused from a long time stationary
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high pressure system over all of Europe.
If you want to take that into account,
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obviously also your event is different.
And there is no right or wrong way to
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define the event because there are
legitimate interests in the maximum
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daily temperatures, legitimate interest in
just a specific type of pressure system
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and interest in what actually causes more
excess mortality on people, what would be
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the three day or longer type of heat
waves. But whichever definition you
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choose, it will determine the outcome of
the study. And here are some typical
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results of attribution studies when you
look at them in a slightly more scientific
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way and slightly less just the headline
way as the ones that I've shown earlier.
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Because, of course, what also is important
is not only how you define the event,
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depending on the impacts and depending on
what you're interested in. The extreme
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event and what observational data you have
available. But of course, there's also
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then the question of what models, what
climate models do we have available? And
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there's always some tradeoff between what
exactly caused the event and what we can
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meaningfully simulate in a climate model.
And then all climate models are good for
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something and bad for other things. So
there always need to be a model evaluation
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stage. So where you test if the models
that you have available are actually able
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to simulate in a reliable way the event
that you're interested in. But even if you
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have done all this, it can sometimes be
that the models and the observations that
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you have show very different things. And
so the heat wave in Germany in 2019, which
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was also on the slide before,
is an example of that. When we
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look at the long term observations of
extreme, of high temperatures and see how
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they have changed over time, we find that,
because of the change in climate, we have
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observed, the likelihood of this type of
heat wave has increased more, yeah, about
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300 times. So you see this
in the black bar, the black bar in the
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middle of the blue bar, on the left hand
side, at the very top where it says DWD
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obs, that's the Deutscher Wetterdienst
observations and we see that where this
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black bar is about, again, a logarithmic
scale, about 300 hundred times more
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likely. But of course, because we have
only 100 years worth of
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observations and summer temperatures
are extremely variable, there is a large
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uncertainty around this change. And so we
cannot, from the observations alone, we
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cannot exclude 100.000 times change in the
likelihood of this heat wave. But
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similarly, also not a 20 times heat wave.
But what the main point is, that in all
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the climate models and all the red bars
that you see on there are the same
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results, but for climate models where we
have compared today's likelihood of the
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event to occur with the likelihood in the
world without climate change, and you see
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that the change is much lower. And of
course, climate change is not the only
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thing that has changed and that has
affected observed temperatures. But other
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factors like land use change and things
like that are much smaller in the size
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than the climate signal. So they cannot
explain this discrepancy. So this means
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that the climate models we have available
for this type of study have obviously a
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problem with the extreme temperatures in a
small scale. And there are effects that we
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don't yet understand. And so we can't say:
OK, this heat wave was made 10 times more
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likely. But we can only say, that with our
current knowledge and understanding, we
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can say that climate change was an
absolute game changer for this type of
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heat wave, but we can't really quantify
it. On the right hand side is a much nicer
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result on the top one, which is for
extreme rainfall, in Texas 2019 and nicer
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result I mean now for a scientist and
in a scientific way. So we have in blue
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two different types of observations from
the heavy rainfall event, and they both
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show pretty much exactly the same result.
And also the two climate models that we
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had available that passed the model
evaluation tests show an increase in the
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likelihood of this event to occur. That is
very similar to that in the observations
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in terms of order of magnitude. And so in
that case, we can just synthesize the
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results and give an overarching answer,
which is that the likelihood of this event
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to occur has about doubled because of man-
made climate change. And the last example
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that I, that is here is for drought
in Somalia in 2010, where not only the
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observations are extremely uncertain. So
from the observations, you could say we
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could have both an increase in likelihood
or a decrease in likelihood by a factor of
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10. But also the climate models show a
very, very mixed picture where you can't
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even see a sign that that is conclusive.
So in that case, you can say, we can
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exclude that climate change made this
event more or less than 10 times, more
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than 10 times or less than 10 times more
likely. But we can't say anything more. So
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we can exclude that it's a complete game-
changer like we have for heat waves, for
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example. But that's about the only
thing that you can say for a result
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like this. So this was sort of the
most detailed scientific stuff that I
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would like to show, because I think it's
important to get some background behind
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the headline results that would just
be climate change doubled the likelihood
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of this event. So there are always four
possible outcomes of an attribution study
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a priori. And that is because
climate change affects extreme weather in
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two ways basically. One way is what we
would call the thermodynamic way, which
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means that because we have more greenhouse
gases in the atmosphere, the atmosphere
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overall gets warmer. So you have, on
average, an increase in the likelihood of
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heat waves decrease in the likelihood of
cold waves. A warmer atmosphere can also
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hold more water vapor that needs
to get out of the atmosphere as rainfall.
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So on average, from the warming alone, we
would also have more extreme rainfall. But
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then there's the second effect, which I
call the dynamic effect, and that is
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because we've changed the composition of the
atmosphere, that affects the atmospheric
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circulation. So where weather systems
develop, how they develop and and how they
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move. And this effect can either be in the
same direction as the warming effect. So it
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can be that we expect more extreme rainfall,
but we also get more low pressure systems
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bring rain to get even more extreme
rainfall. But these two effects can also
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counteract each other. And so you
can expect more rainfall on
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average. But if you don't get the weather
systems that bring rain, you either have
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no change in likelihood and intensity or,
if the dynamics win, you have actually
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decrease in the likelihood of extreme
rainfall in a particular season or region.
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And so this is why a priori, that can
always be four outcomes: It can be that
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the event was made more likely. It can be
that it was made less likely. It can be
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there's no change. Or it can be that with
our current understanding and tools, we
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can't actually answer the question. And so
this has been possible to do now for
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about a decade, but only in the last five
years really have many, many people or
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many scientists started to do these
studies. And so there is actually a
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relatively large, there are
lots of attribution studies on different
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kinds of extreme events. And what you can
see on this map here is what the news and
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energy outlet CarbonBrief has put all
these studies together. And you can see in
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red where climate change played an
important role, and blue where climate
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change did not play a role. And in gray,
that was an inconclusive result. This is
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very important, though, that this is not
representative of the extreme events that
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have happened. This is just represents the
studies that have been done by scientists
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and they are, of course biased towards
where scientists live
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and also towards extreme events that are
relatively easy to simulate with climate
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models. So there are lots of heat waves in
Europe, Australia and North America
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because that is where people live. And on
this next map, I have tried to
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show the discrepancy between the extreme
events that have happened and those for
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which we actually do know the role of
climate change. So here in red are deaths
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associated with extreme events since 2003.
So since the first event attribution
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study. And it's death from heat waves,
storms, heavy rainfall events and droughts
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primarily in different parts of the world,
the bubble is always on the capital of the
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country. And the larger the bubble, the
more deaths due to extreme events in those
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years. And in black overlaying that are
those deaths for which we know the role of
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climate change. So that doesn't mean that
the deaths are attributed to
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climate change, but it means that there
we do know whether or not to what
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extent climate change played a role. And
you can see that most of the European
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countries, the black circle is almost as
large as the red one. So for most of the
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00:32:28,780 --> 00:32:32,440
extremes or most of the deaths associated
with extreme events, we do know the role
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of climate change. But for many
other parts of the world,
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there are no or very small black circles.
So for most of the events and the deaths
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associated with them, we don't know what
the role of climate change is. And I've
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used death here not because I'm
particularly morbid, but because it's
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an indicator of the impacts of
extreme weather that is relatively good
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comparable between countries. So this
means that with event attribution methods
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that we have developed over the last
decade, we now have the tools available to
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do, to provide an inventory of the impacts
of climate change on our livelihoods. But
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we are very far from having such an
inventory at the moment because most of
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the events that have happened, we actually
don't know what the role of climate change
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is. And so we don't know in detail on
country scale and on the scale where
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people live and make decisions, what the
role of climate change is today. There's
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another slightly related issue with that
is, that the extreme events that I've used
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to create the map are shown before with
the death of climate change, with the
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death of extreme weather events. They are
from a database called EM-DAT, which is a
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publicly available database where losses
and damages associated with disasters
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technological disasters, but also
disasters associated with weather are
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recorded. But, of course, they only can
record losses and damages if these losses
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and damages are recorded in the first
place. And so what you see on this map is
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in grey and then overlayed with different
with different circles are heat waves that
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have occurred, they have occurred between
1986 and 2015 on this map. But you could
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draw a map from 1900 to today, and it
would look very similar. And that shows
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lots and lots of heat waves reported in
Europe and in the US, India, but there are
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no heat waves reported in most of sub-
Saharan Africa. However, when you look at
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observations, and also we see that extreme
heat has increased quite dramatically in
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00:35:17,420 --> 00:35:24,320
most parts of the world and a particular
hotspot is sub-Saharan Africa. So, we know
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00:35:24,320 --> 00:35:29,400
from when we look at the weather that heat
waves are happening, but it's not
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00:35:29,400 --> 00:35:35,200
registered and it's not recorded. So we
have no idea how many people are actually
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affected by these heat waves. And so we
then, of course, don't do attribution
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00:35:41,060 --> 00:35:46,080
studies and don't find out what the role
of climate change in these heat waves is.
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So in order to really understand the
whole picture, we would also need to start
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recording these type of events in other
parts of the world. And so my very last
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00:36:01,820 --> 00:36:08,700
point, before, I hope that you have
questions for me, is: Of course,
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everything I've said so far was talking
about the hazards, so talking about the
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weather event and how climate change
affects the hazard. But of course that is
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not the same or translates immediately
into losses and damages, because whether
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00:36:25,790 --> 00:36:32,119
or not a weather event actually has any
impact at all is completely driven by
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exposure and vulnerability. So who and
what is in harm's way. And I've already
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shown, I've already mentioned the example
early on with the drought in Brazil, where
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the huge losses and damages were to a
large degree attributable to the increase
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in water consumption. And thus,
therefore, in order to really find out how
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climate change is affecting us today, we
not only need to define the extreme events
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00:37:07,950 --> 00:37:14,930
so that it connects to the impacts, but
also look into vulnerability and exposure:
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What is changing, what's there and what
are the important factors. But we can
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00:37:21,520 --> 00:37:28,330
do that. And so we have really made a lot
of progress in understanding of how
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climate change not only affects global
mean temperature, which we have known for
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centuries, and how it affects large
scale changes in temperature and
350
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precipitation, which we have also known
for a very long time. But we now have
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actually all the puzzle pieces together to
really understand what climate change
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means on the scale where people live and
where decisions are made. We just need to
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put them together. And one lens or one way
of where they are currently put together
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is, for example, in courts. And so because
it's obviously people who experience
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losses and damages from climate change.
And so one way to address that is going
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through national governments, local
governments, hoping for adaptation
357
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measures to be put in place. But if that's
not forthcoming quickly enough, there is
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the option to sue. And so this is one
example which is currently happening in
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Germany where a peruvian farmer is suing
RWE to basically pay their share of a
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adaptation because of largely increased
flood risk from glacier melt in the area.
361
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And they want RWE to pay from their
contribution to climate change, where
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their emissions and then have some funding
for the adaptation measures from them. And
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that is one example of where these kind of
attribution studies can be used in a very
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direct way to hopefully change
something in the real world. And with
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this, I would like to end and yeah, leave
you with some references, and hope you
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have some questions for me.
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Herald: Sind wir durch? So, ja. Herzlichen
Dank für den Vortrag. Ich hab, bevor wir
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zum Q&A kommen muss ich einmal mich im
Namen der Produktion bei den Zuschauern
369
00:40:20,782 --> 00:40:25,382
entschuldigen, ich glaube ihr hattet etwas
Produktionssound auf den Ohren, das sollte
370
00:40:25,382 --> 00:40:34,201
natürlich nicht so sein. Gut, wir haben
jetzt keine Fragen aus dem Chat bisher.
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Aber vielleicht eine Frage von mir, das
letzte Beispiel war ja ein Fall
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einer Klage über Ländergrenzen hinaus
quasi, ist das ein Ansatz, den man, den
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wir in Zukunft öfter sehen würden, das
heißt, dass über Ländergrenzen hinweg
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Menschen oder Organisationen sich
gegenseitig versuchen quasi über den
375
00:41:13,540 --> 00:41:20,490
Klageweg auf den richtigen Weg zu bringen.
FO: Also es ist tatsächlich ein, eine
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Ausnahme, dass das im Fall RWE und Lliuya
funktioniert, denn das deutsche Recht
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sieht vor, dass Firmen, die in Deutschland
ansässig sind auch verschieden
378
00:41:36,330 --> 00:41:39,340
verantworlich sind, die nicht in
Deutschland stattfinden.
379
00:41:39,340 --> 00:41:44,750
H: So sorry to interrupt. I just realized
that we are still in English talk. Sorry
380
00:41:44,750 --> 00:41:48,810
for that.
FO: OK. No worries. So your question was
381
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if we're going to see more
international court cases where across
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countries, across nation states we have
climate litigation. And this type of
383
00:42:03,040 --> 00:42:07,369
litigation that I've just shown as
the example is in so far an
384
00:42:07,369 --> 00:42:13,869
exception, as in German law, a company is
also responsible for the damages caused
385
00:42:13,869 --> 00:42:20,060
outside of Germany. Which is not the case,
for example, for companies in the US
386
00:42:20,060 --> 00:42:30,150
or so. So, and this is why Lliuya sued RWE
and not, for example, ExxonMobil. But
387
00:42:30,150 --> 00:42:40,780
these type of cases, where this
Lliuya case is an example. We see a lot of
388
00:42:40,780 --> 00:42:48,380
a lot of them, an increasing number of
them each year. And they are difficult to
389
00:42:48,380 --> 00:42:57,940
do across nations because this, the German
law is exceptional on that case. But there
390
00:42:57,940 --> 00:43:03,340
are other ways, like, for example, why are
human rights courts that can be done
391
00:43:03,340 --> 00:43:11,230
across nation states and that is also
happening. So it's at the moment, it is
392
00:43:11,230 --> 00:43:18,560
still legally not super straightforward to
to actually win these cases, but
393
00:43:18,560 --> 00:43:24,320
increasingly a lot of lawyers working on
that so that we will see a lot of
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change in that in the coming years.
H: OK, thank you. In the meantime, there
395
00:43:31,580 --> 00:43:37,860
appeared some questions from the chat and
from the Internet. I will go through them.
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00:43:37,860 --> 00:43:42,910
First question is: are the results of the
individual attribution studies published
397
00:43:42,910 --> 00:43:50,450
as open data in a machine readable format?
FO: laughter So all the studies that
398
00:43:50,450 --> 00:43:57,620
we do that that I've done with my
team, with world weather attribution. So
399
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there all the data is
available, and it's available on a
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platform that's called Climate Explorer.
So that should be machine readable. So and
401
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this is deliberately because yeah, because
we want to make it as transparent as
402
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possible so everyone can go away, use our
data, and redo our studies, and find out
403
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if we made any mistakes. But this is not
the case for all the studies that exist,
404
00:44:29,450 --> 00:44:34,830
because most of them or many of them are
published in peer reviewed journals and
405
00:44:34,830 --> 00:44:39,070
not all peer reviewed journals have
open data and open access policies.
406
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But increasingly, journals have.
So if you, for example, go to the
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00:44:45,950 --> 00:44:51,410
CarbonBrief website and look at the map of
studies, there you have links to all
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00:44:51,410 --> 00:44:56,330
the studies. And a lot of them have the
data available.
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H: OK, maybe a follow up to this one. The
next question is, are the models somehow
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available or usable for a wider interest
public or is APC required? I'm not quite
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sure what APC means.
FO: So the model data is publicly
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available from–and this is one reason why
we have been able to do these studies
413
00:45:25,780 --> 00:45:31,280
because until relatively recently, model
data was not publicly available and only
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00:45:31,280 --> 00:45:36,390
scientist working in a specific country
could use the model developed in that
415
00:45:36,390 --> 00:45:44,810
country–but now all the model data is
shared publicly and people can use it. So
416
00:45:44,810 --> 00:45:50,830
it's definitely there and usable. It just
requires some expertise to make sense of
417
00:45:50,830 --> 00:46:00,000
it. But it's, yeah, people can use it.
H: OK, the next question is: to what
418
00:46:00,000 --> 00:46:05,450
certainty can you set up counterfactual
models, which are an important reference
419
00:46:05,450 --> 00:46:12,915
to your percentage value, and what
data are the basis for these models?
420
00:46:12,915 --> 00:46:19,760
FO: So the counterfactual simulations are-
the climate models we use are basically the
421
00:46:19,760 --> 00:46:23,970
same models that are used also for the
weather forecast. They are just run in
422
00:46:23,970 --> 00:46:30,520
lower resolution. So, which I guess most
of this audience knows what that means. So
423
00:46:30,520 --> 00:46:36,670
the data points for the part, so that it's
not so computing intensive. And these
424
00:46:36,670 --> 00:46:43,390
models, they are tested against observed
data. And so that is how we do the model
425
00:46:43,390 --> 00:46:48,600
evaluation. So that is some simulations of
the present day. And for the
426
00:46:48,600 --> 00:46:57,430
counterfactual, we know extremely well how
many greenhouse gases have been included
427
00:46:57,430 --> 00:47:02,010
into the atmosphere since the beginning of
the Industrial Revolution, so that there
428
00:47:02,010 --> 00:47:07,740
is some very large certainty with that
number and we remove that from the models'
429
00:47:07,740 --> 00:47:13,080
atmospheres. So the models have exactly
the same set up, but the lower
430
00:47:13,080 --> 00:47:16,720
greenhouse gases, lower amount of
greenhouse gases in the atmosphere, and
431
00:47:16,720 --> 00:47:24,580
then are spun up and run in exactly the
same way. So, they, but of course, we
432
00:47:24,580 --> 00:47:33,620
can't test the counterfactual. And so that
means that we assume that the sort of the
433
00:47:33,620 --> 00:47:40,510
the weather was still the same, physics
will still hold in the counterfactual. And
434
00:47:40,510 --> 00:47:45,800
that the models that are developed
using present day represent the
435
00:47:45,800 --> 00:47:48,880
counterfactual. Which is, which is an
assumption.
436
00:47:48,880 --> 00:47:51,820
But it is not a completely
unreasonable assumption, because of
437
00:47:51,820 --> 00:48:00,740
course, we have now decades of model
development and have seen that, in fact,
438
00:48:00,740 --> 00:48:05,990
that indeed climate model projections that
have been made 30 years ago have actually
439
00:48:05,990 --> 00:48:13,110
come… come to… have been realized, and
pretty much the same way on a large scale
440
00:48:13,110 --> 00:48:18,980
that they have, as they had been predicted
30 years ago. And so that assumption
441
00:48:18,980 --> 00:48:24,890
is not, yeah, it's not a big assumption.
So the counterfactual itself is not a
442
00:48:24,890 --> 00:48:29,700
problem. But of course, also the present
day model simulations, they are
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00:48:29,700 --> 00:48:34,990
not… they are very far from perfect. And
there are some types of events which state
444
00:48:34,990 --> 00:48:41,040
of the art climate models just can't
simulate. And so, where we can- what
445
00:48:41,040 --> 00:48:46,560
we can say very little. So well, for
example, for hurricanes, we can say
446
00:48:46,560 --> 00:48:51,730
with high certainty about the
rainfall associated with hurricanes, the
447
00:48:51,730 --> 00:48:56,670
hurricane strength itself and the
frequency of hurricanes is something
448
00:48:56,670 --> 00:49:01,970
which is very difficult to simulate with
state of the art models. So our
449
00:49:01,970 --> 00:49:12,640
uncertainty there is much higher.
H: OK. And then, well, some, one question
450
00:49:12,640 --> 00:49:20,170
that emerges from all of this is,
of course, if we know this much and way
451
00:49:20,170 --> 00:49:26,720
more than in the past, how are
politicians still ignoring that
452
00:49:26,720 --> 00:49:34,944
information? And how can we
convey that into their minds?
453
00:49:34,944 --> 00:49:39,880
FO: Well, if I knew the answer to that, I
would probably not be standing here,
454
00:49:39,880 --> 00:49:49,480
but actually doing politics. But I
think it takes a frustratingly long time
455
00:49:49,480 --> 00:49:56,849
for things to change and things should
change much faster. But we actually- the
456
00:49:56,849 --> 00:50:03,510
last two years have shown huge progress, I
think, in terms of also putting climate
457
00:50:03,510 --> 00:50:11,830
change on the agenda of every politician.
Because, and that's largely due to the
458
00:50:11,830 --> 00:50:17,740
Fridays For Future movement, but also to a
degree, I think, due to the fact that we
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00:50:17,740 --> 00:50:23,800
now actually know that the weather that
people experience in their backyard–and
460
00:50:23,800 --> 00:50:29,400
pretty much independent of where their
backyard is–is not the same as it used to
461
00:50:29,400 --> 00:50:37,430
be. And so people do experience today
climate change. And I think that
462
00:50:37,430 --> 00:50:42,700
does help to bring a bit more urgency.
And, of course, I would have said everyone
463
00:50:42,700 --> 00:50:47,630
has climate change on their agenda, which
was very different even two years ago,
464
00:50:47,630 --> 00:50:52,140
where there were lots of people who
would never talk about climate change and
465
00:50:52,140 --> 00:50:57,880
their political agendas has played no
role. It doesn't mean that it
466
00:50:57,880 --> 00:51:05,790
has the right priority on that agenda,
but it's still a huge step forward that
467
00:51:05,790 --> 00:51:18,710
has been made. And so I think we do know
some things that do work, but we just have
468
00:51:18,710 --> 00:51:28,080
to just keep doing that. Yeah, I don't
think I can say more. I don't have a magic
469
00:51:28,080 --> 00:51:35,200
wand to change it otherwise.
H: Maybe some other point of impact.
470
00:51:35,200 --> 00:51:40,140
One of the question is, is it possible to
turn the results of attribution studies
471
00:51:40,140 --> 00:51:47,920
into recommendations for farmers and
people who are affected in a financial way
472
00:51:47,920 --> 00:51:53,359
by extreme weather and how to change
agriculture to reduce losses from extreme
473
00:51:53,359 --> 00:51:56,580
weather effects?
FO: Yes, absolutely. So that is
474
00:51:56,580 --> 00:52:03,730
one of the most useful things of these
studies is well, on the one hand, to raise
475
00:52:03,730 --> 00:52:07,960
awareness. But on the other hand, if you
know that a drought that you have
476
00:52:07,960 --> 00:52:16,930
experienced that has led to losses is a
harbinger of what is to come, then that is
477
00:52:16,930 --> 00:52:22,630
incredibly helpful to know how
agricultural practices might need to be
478
00:52:22,630 --> 00:52:29,880
changed. Or that insurance for losses from
agriculture might need to be changed. And
479
00:52:29,880 --> 00:52:36,009
so this is exactly why we do these
attribution studies. Because not
480
00:52:36,009 --> 00:52:42,770
every extreme event has always
shows the fingerprints of
481
00:52:42,770 --> 00:52:47,990
climate change. And if you know
which of the events are the ones where
482
00:52:47,990 --> 00:52:53,920
climate change is a real game changer, you
also do know where to put your efforts and
483
00:52:53,920 --> 00:53:00,491
resources to be more resilient in the
future. And for financial losses, it
484
00:53:00,491 --> 00:53:06,070
is on the one hand, yeah, you can use
these studies to find out what your
485
00:53:06,070 --> 00:53:12,930
physical risks are for your assets. And
how they, and of course, everything that
486
00:53:12,930 --> 00:53:17,710
I've said, comparing the counterfactual
with the present we can do, and we do this
487
00:53:17,710 --> 00:53:23,950
also with the future. So you can also see
how in a two degree world, the events,
488
00:53:23,950 --> 00:53:29,220
the likelihood and intensities are
changing. And of course, you can then
489
00:53:29,220 --> 00:53:35,250
also, in a less direct way, use this kind
of information to see, to assess what
490
00:53:35,250 --> 00:53:42,880
might be other risks from- where might be
stranded assets, what are other risks
491
00:53:42,880 --> 00:53:48,910
for the financial sector,
for the financial planning.
492
00:53:48,910 --> 00:53:57,190
Where could liability risks be and how
could they look like. So there is, because
493
00:53:57,190 --> 00:54:02,450
extreme weather events and their changes
in intensity and magnitude is how climate
494
00:54:02,450 --> 00:54:10,360
change is manifesting, it really connects
all these aspects of where the
495
00:54:10,360 --> 00:54:21,920
impacts of climate change are.
H: OK, last question for today. I hope I
496
00:54:21,920 --> 00:54:30,020
can get that right. I think the question
is if there are study, if there are
497
00:54:30,020 --> 00:54:40,520
studies on how we cultivates fields
and agriculture. How does this impact the
498
00:54:40,520 --> 00:54:48,950
overall climate in that area? The example
here is that only an increase in water
499
00:54:48,950 --> 00:54:57,849
consumption was directed to São Paulo. Or
might there also be a warm world created
500
00:54:57,849 --> 00:55:06,440
by monoculture in central Brazil?
FO: So, yeah, I don't know details, but
501
00:55:06,440 --> 00:55:13,460
there are, but land use changes and land
use does play a role. On the one hand, it
502
00:55:13,460 --> 00:55:20,210
affects the climate. So if you have, if
you have a rainforest, you have a very
503
00:55:20,210 --> 00:55:26,940
different climate in that location as if
there is a savanna or plantation. And
504
00:55:26,940 --> 00:55:35,230
also, of course, if you have monocultures,
you are much more, your losses are
505
00:55:35,230 --> 00:55:42,100
larger usually as if you have different
types of agriculture. Because
506
00:55:42,100 --> 00:55:46,830
in a monoculture everything is in
exactly the same way vulnerable and
507
00:55:46,830 --> 00:55:52,030
so that, yeah. So that does,
land use change plays a hugely important
508
00:55:52,030 --> 00:55:59,390
role with respect to the impacts of
extreme weather. And that is one thing to
509
00:55:59,390 --> 00:56:03,570
look at. When I was saying, talking about
looking at vulnerability and exposure, and
510
00:56:03,570 --> 00:56:08,520
of course also changes in the hazard are
not just because of climate change, but
511
00:56:08,520 --> 00:56:12,610
also because of land use change. And you
can use exactly the same methods, but
512
00:56:12,610 --> 00:56:17,040
instead of changing the CO2 or the
greenhouse gases in the atmosphere of your
513
00:56:17,040 --> 00:56:22,790
model, you can change the land use and
then disentangle these different drivers
514
00:56:22,790 --> 00:56:29,870
in and hazards.
H: OK, Fredi Otto thank you very much for
515
00:56:29,870 --> 00:56:37,290
your presentation and for the Q&A. It was
a pleasure to have you with us. And yeah,
516
00:56:37,290 --> 00:56:43,800
if you have any questions, any more
questions, I guess there are ways to
517
00:56:43,800 --> 00:56:47,250
contact you.
FO: laughter
518
00:56:47,250 --> 00:56:52,540
H: I think your email address and contact
details are in the Fahrplan for all the
519
00:56:52,540 --> 00:56:58,930
viewers that have way more questions. And,
I don't know, do you have access to the 2D
520
00:56:58,930 --> 00:57:05,520
world and do you explore that?
FO: Given that I don't know what you mean,
521
00:57:05,520 --> 00:57:07,350
probably not, but…
laughter
522
00:57:07,350 --> 00:57:12,070
H: OK.
FO: That can also be changed.
523
00:57:12,070 --> 00:57:20,950
H: Yeah, it's the the replacement for
the congress place itself. But anyway,
524
00:57:20,950 --> 00:57:26,700
if you viewers and you people out there
have any more questions, contact Fredi
525
00:57:26,700 --> 00:57:32,930
Otto. And thank you again very much for
your talk. And, yeah. Have a
526
00:57:32,930 --> 00:57:35,330
nice congress, all of you.
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00:57:35,330 --> 00:57:39,080
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00:57:39,080 --> 00:58:13,960
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