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rC3 preroll music
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Herald: It is with much pleasure that I[br]can now introduce our next speaker, so
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it's just started raining outside, but[br]this heavy rain is not at all probably the
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extreme weather effects that we will hear[br]about right now. The weather, the talk
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that we are being presented next will deal[br]with extreme weather effects and how they
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are linked with climate change and how we[br]even know about that. Our speaker today
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is Fredi Otto. She's associate director of[br]the Environmental Change Institute of the
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University of Oxford, and she's also the[br]lead author of the upcoming IPCC
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assessment report, AR6. And without with[br]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.[br]It's stopped raining here in Oxford, just
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about, but it's definitely flooded, so[br]that might actually be something to come
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back to and talk about with respect to[br]climate change. So. Whenever we hear or
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whenever today an extreme weather event[br]happens, we hear about hurricanes,
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wildfires, droughts, etc., the question[br]that is immediately asked is, was this,
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what is the role of climate change? And to[br]answer that, for quite a long time,
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scientists gave an answer that we cannot[br]attribute individual weather events to
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climate change. But… Sorry, OK. But this…[br]Because the first, the one answer that
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people were giving were that, well, you[br]can't attribute individual weather events
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or they were saying in a world where[br]climate change happens, of course, every
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extreme weather event is somewhat affected[br]by climate change. And the latter is
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attributed too, but that does not[br]obviously provide much information,
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because it doesn't say anything about[br]whether the event was made more likely or
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less likely or what the role of climate[br]change was. And the first answer that you
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can't attribute individual events is not[br]true any longer. And this is... why that has
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changed and how that has changed. And what[br]we can say is what the content of this
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talk will be. So ultimately, every weather[br]event, extreme or not, is if you
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absolutely boil down to it is unique and[br]they all have many different causes. So
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there is always the role of just the[br]natural chaotic variability of the climate
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system and climate and weather system that[br]plays a role. There's always a causal
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factor in where the event[br]happens, whether it's over land, over a
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desert, over a city or a forest, but also[br]man-made climate change can have an
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influence on the likelihood and intensity[br]of extreme weather events to occur. And so
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what we can say now, and what we mean when[br]we talk about attribution of extreme
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weather events to climate change is how[br]the magnitude and likelihood of an event
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to occur has changed because of man-made[br]climate change. And in order to do that,
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we first of all need to know, what is[br]possible weather in the world we live in
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today? So say we have a flooding event in[br]Oxford and the question is, was this
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climate change or not? So the first[br]question is we need to find out what type
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or what kind of event is the heavy[br]rainfall event that leads to the flooding.
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So is it a 1 in 10 year event? Is it a 1[br]in 100 year event? And in order to do
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that, you can't just look at the observed[br]weather records because that will tell you
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what the actual weather that occurred is.[br]But it doesn't tell you what the possible
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weather under the same current climate[br]conditions are. And so we need to find out
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what is possible weather. And to do that,[br]we use different climate models. So we
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simulate under the same climate conditions[br]that we have today, possible rainfall
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events in December in Oxford. And we might[br]find out that the event that we have
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observed today is a one in 10 year event.[br]And so if you do this, look at all the
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possible weather events, you get a[br]distribution of possible weather under
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certain conditions, which is shown in the[br]schematic on the slide here in the red
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curve. And then you know that when it[br]rains above, say, 30 millimeters a day in
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Oxford, then you have a real problem with[br]flooding. So you define that this is your
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threshold from when you speak about an[br]extreme event. And so you have a
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probability of this event to occur in the[br]world we live in today. Of course, that
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does not tell you the role of climate[br]change, because in order to know that, you
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would also you will also need to know what[br]would the likelihood of this event to
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occur have been without man-made climate[br]change, and so. But because we know very
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well how many greenhouse gases have been[br]introduced into the atmosphere since the
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beginning of the industrial revolution, we[br]can actually remove these additional
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greenhouse gases from the climate models[br]atmospheres that we use and simulate a
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world that would have been exactly as it[br]is today, but without the greenhouse gases
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from the burning of fossil fuels. And in[br]that world, we can then also ask the
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question, what are possible heavy rainfall[br]events in December in Oxford? And we might
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find that the event that we are interested[br]in is in that world, not a one in 10 year
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event, but a one in 20 year event. And[br]because everything else is held the same,
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we can then attribute the difference[br]between these two likelihoods of
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occurrence of the extreme event in[br]question to man-made climate change. And
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so with this fake example that I've just[br]used, we would then say climate change has
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doubled the likelihood of the event to[br]occur because one that was one in 20 year
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event is now one in 10 years. So that is[br]basically the whole theoretical idea
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behind attributing extreme events and this[br]method can be used. And so, for example,
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with our initiative that's called World[br]Weather Attribution, we have looked this
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year at the extreme heat in Siberia, the[br]beginning of this year that, among other
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things, led to temperatures above 38[br]degrees in the city of Verkhoyansk, but
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also let to permafrost thawing and large[br]wildfires. And that event was made so much
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more likely because of climate change that[br]it's almost would have been impossible
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without climate change. So when we did the[br]experiments that the models it's a one in
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80 million year event in a world without[br]climate change. And it's still a
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relatively extreme event in today's world,[br]but it is possible. So this is the type of
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event where climate change really is a[br]game changer. Another event that we have
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looked at is Hurricane Harvey that hit the[br]Houston and Texas in 2017 and caused huge
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amounts of damage with the rainfall[br]amounts it brought. And several attribution
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studies doing exactly what I've just[br]described found that this type of, so this
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extreme rainfall associated with a[br]hurricane like Harvey has been made three
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times more likely because of climate[br]change. And colleagues of mine, Dave Frame
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and his team, have then used these studies[br]to figure out how much of the economic
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costs this hurricane can be attributed to[br]climate change, and found that of the 90
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billion US dollars that were associated,[br]that were associated with the flood damage
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from Harvey, 67 billion can be attributed[br]to climate change, which is in particular
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interesting when you compare that to the[br]state of the art economic cost estimations
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of climate change in general, which had[br]estimated only 20 billion US dollars for
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2017 in the US from climate change. And of[br]course, not every year is an event like
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Harvey, but it shows that when you look at[br]the impact of climate change in a more
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bottom up approach, so looking at the[br]extreme events, which are how climate
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change manifests and affect people, you get[br]very different numbers, as if you just
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look at large scale changes in temperature[br]and precipitation. But of course, not
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every extreme event that occurs today has[br]been made worse because of climate change.
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So this is an example of a drought in[br]southeast Brazil that happened in 2014,
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2015, where we found that Climate change[br]did not change the likelihood of this
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drought to occur, so it was a one in 10[br]year event in 2014, 2015, and also without
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climate change, it has a very similar[br]likelihood of occurrence. However, what we
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did find when we looked at, OK, what else[br]has changed? Why has this drought that has
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occurred in a very similar way earlier in[br]the 2000s and also in the 1970s with much
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less impacts. We looked at other factors[br]and found that the population has
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increased a lot over the last or over the[br]beginning of the 21st century, but in
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particular, the water consumption in in[br]the area and the water usage has increased
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almost exponentially. And that explains[br]why the impacts were so large. So this is
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what I've just said is sort of basically[br]the the very basic idea and and how in
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theory these studies work and how and some[br]results that we find. In practice, it is
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usually not quite as straightforward,[br]because while the idea is still the
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same, we need to use climate models and[br]statistical models for observational data
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to simulate possible weather in the world[br]we live in and possible weather in the
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world that might have been. That is, in[br]theory, straight forward, in practice,
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it's often relatively difficult, and what[br]you see here is how the results of these
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studies look when you don't use schematic[br]and if you're not a hydrologist, this
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might be a bit of an unfriendly plot. But[br]it's it's basically the same as the
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schematic that I've showed at the[br]beginning, but just plotted in a way that
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you can see the tails of the distribution[br]particularly well, so where the extreme
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events are. So on the X-axis, we have the[br]return time of the event in years on a
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logarithmic scale and on the Y-axis, you[br]see the magnitude of the event and that
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defines what our extreme event is. And[br]this is actually a real example from heavy
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rainfall in the south of the U.K. And you[br]can see here in red, each of these red
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dots that that you see on the red curve is[br]a simulation of one possible rainfall
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event in the South of the U.K. in the year[br]2015 in the world we live in today with
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climate change and the dashed line[br]indicates the threshold that led to to
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flooding in in that year. And on the[br]X-axis, when you go down from the dashed
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line, you can then see that this is[br]roughly a one in 20 year event in the
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world we live in today. And all the blue[br]dots on the blue curve are simulations of
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possible heavy rainfall in the South of[br]the U.K. in 2015, in a world without man-
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made climate change. And you can see that[br]these two curves are different and
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significantly different, but they are[br]still relatively close together. And so
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the event in the world without climate[br]change would have been a bit less likely,
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so we have roughly a 40 percent increase[br]in the likelihood. But still other factors
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like, yeah, just the chaotic variability[br]of the weather and also, of course, than
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other factors on the ground where houses[br]build in floodplains and so on play an
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important role. So this is the[br]actual attribution step. So when we find
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out what the role of climate change is,[br]but of course, in order to do that, there
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are a few steps before that are crucially[br]important and absolutely determine the
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outcome. And the first step, the first[br]thing to find out is what has actually
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happened, because usually when we read[br]about extreme weather events or when we
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hear about extreme weather events, you see[br]pictures in newspapers of flooded parts of
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the world. And so you don't usually have[br]observed weather recordings reported in
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the media. And the same actually is[br]true for us. So when we are, so we work a
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lot with the Red Cross and they ask us:[br]OK, we have this large flooding event, can
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you do an attribution study? Can you tell[br]us what the role of climate change is?
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Then we also just know: OK, there is[br]flooding. And so the first step is we need
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to find out what is the weather event that[br]actually caused that flooding. And that is
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not always that straightforward. And this[br]is what you see here on this map, on this
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slide is a relatively stark example, but[br]not an untypical. So it's of an extreme
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rainfall event on the 10th of November[br]2018 in Kenya. And on the left hand side
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is one data product of observational data,[br]of observational rainfall data that is
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available and on the right hand side is[br]another showing the same event. And the
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scale which I failed to to say on the[br]slide in millimeters per day. And so on
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the left hand side, you have extreme[br]rainfall of above 50 millimeters per day,
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which is considering that, for example, in[br]in my home town of Kiel in Schleswig-
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Holstein, there is about 700 millimeters[br]of rainfall per year. You can see that 50
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millimeters in a single day is very heavy[br]rainfall, whereas in the other data
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product, you don't see as much rain. You[br]still see large rain, but it's not in
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the same magnitude, and it's also not[br]exactly in the same place. And so given
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that most countries in the world do not[br]have an open data policy, so you can't
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actually get access to the observed[br]station data, but you have to use
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available, publicly available products[br]like the two have shown here, you have to
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know and you have to work with experts in[br]the region to actually know who hopefully
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has access to the data to actually find[br]out what has happened in the first place.
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But of course, if you don't know that or[br]there is not always a perfect answer, then
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if you don't know what event that is. It's[br]very difficult to do an attribution study.
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Assuming though you have found a data[br]product that you trust, the next question
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then is what is actually the right[br]threshold to use for the event? So if you
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have flooding that was pretty obviously[br]caused by one day extreme rainfall event,
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then that would be your definition of the[br]event. But it could also be that the
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flooding has been caused by a very soggy,[br]rainy season. So actually, the really the
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real event you would want to look at is[br]over a much longer time scale or if the
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flooding occurred mainly because of some[br]water management in the rivers and has
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actually flooded further upstream, your[br]spatial definition of the event would be
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very different. And so and what you see[br]here on this plot is an example of a heat
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wave in Europe in 2019. And there, what[br]usually makes the headlines is the maximum
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daily temperature. So if records are[br]broken, so you could use that as a
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definition of the event that you're[br]interested in. But of course, what really
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causes the losses and damages from extreme[br]events is not necessarily the one day
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maximum temperature, but it is when heat[br]waves last for longer, and especially when
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the night temperatures are also high and[br]not just the daytime temperatures. So you
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might want to look at an event over five[br]day period instead of just the maximum
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daily temperatures. Or, and this is sort[br]of why I have shown the pressure plot on
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the right hand side, which is really just[br]an illustration, it's not terribly
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important what's on there. But there are,[br]of course, different weather systems that
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can cause heat waves, especially in the[br]area here in the south of France. It could
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be a relatively short lived high[br]pressure system bringing hot air from the
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Mediterranean. Or it could be something[br]that is caused from a long time stationary
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high pressure system over all of Europe.[br]If you want to take that into account,
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obviously also your event is different.[br]And there is no right or wrong way to
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define the event because there are[br]legitimate interests in the maximum
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daily temperatures, legitimate interest in[br]just a specific type of pressure system
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and interest in what actually causes more[br]excess mortality on people, what would be
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the three day or longer type of heat[br]waves. But whichever definition you
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choose, it will determine the outcome of[br]the study. And here are some typical
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results of attribution studies when you[br]look at them in a slightly more scientific
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way and slightly less just the headline[br]way as the ones that I've shown earlier.
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Because, of course, what also is important[br]is not only how you define the event,
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depending on the impacts and depending on[br]what you're interested in. The extreme
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event and what observational data you have[br]available. But of course, there's also
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then the question of what models, what[br]climate models do we have available? And
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there's always some tradeoff between what[br]exactly caused the event and what we can
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meaningfully simulate in a climate model.[br]And then all climate models are good for
0:22:04.600,0:22:10.740
something and bad for other things. So[br]there always need to be a model evaluation
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stage. So where you test if the models[br]that you have available are actually able
0:22:15.130,0:22:20.690
to simulate in a reliable way the event[br]that you're interested in. But even if you
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have done all this, it can sometimes be[br]that the models and the observations that
0:22:26.980,0:22:34.191
you have show very different things. And[br]so the heat wave in Germany in 2019, which
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was also on the slide before,[br]is an example of that. When we
0:22:39.520,0:22:48.310
look at the long term observations of[br]extreme, of high temperatures and see how
0:22:48.310,0:22:55.190
they have changed over time, we find that,[br]because of the change in climate, we have
0:22:55.190,0:23:02.680
observed, the likelihood of this type of[br]heat wave has increased more, yeah, about
0:23:02.680,0:23:10.410
300 times. So you see this[br]in the black bar, the black bar in the
0:23:10.410,0:23:14.900
middle of the blue bar, on the left hand[br]side, at the very top where it says DWD
0:23:14.900,0:23:19.770
obs, that's the Deutscher Wetterdienst[br]observations and we see that where this
0:23:19.770,0:23:25.240
black bar is about, again, a logarithmic[br]scale, about 300 hundred times more
0:23:25.240,0:23:30.800
likely. But of course, because we have[br]only 100 years worth of
0:23:30.800,0:23:38.510
observations and summer temperatures[br]are extremely variable, there is a large
0:23:38.510,0:23:43.820
uncertainty around this change. And so we[br]cannot, from the observations alone, we
0:23:43.820,0:23:50.300
cannot exclude 100.000 times change in the[br]likelihood of this heat wave. But
0:23:50.300,0:23:55.910
similarly, also not a 20 times heat wave.[br]But what the main point is, that in all
0:23:55.910,0:24:01.760
the climate models and all the red bars[br]that you see on there are the same
0:24:01.760,0:24:08.330
results, but for climate models where we[br]have compared today's likelihood of the
0:24:08.330,0:24:13.200
event to occur with the likelihood in the[br]world without climate change, and you see
0:24:13.200,0:24:18.180
that the change is much lower. And of[br]course, climate change is not the only
0:24:18.180,0:24:23.940
thing that has changed and that has[br]affected observed temperatures. But other
0:24:23.940,0:24:30.820
factors like land use change and things[br]like that are much smaller in the size
0:24:30.820,0:24:36.260
than the climate signal. So they cannot[br]explain this discrepancy. So this means
0:24:36.260,0:24:42.860
that the climate models we have available[br]for this type of study have obviously a
0:24:42.860,0:24:51.390
problem with the extreme temperatures in a[br]small scale. And there are effects that we
0:24:51.390,0:24:56.320
don't yet understand. And so we can't say:[br]OK, this heat wave was made 10 times more
0:24:56.320,0:25:03.530
likely. But we can only say, that with our[br]current knowledge and understanding, we
0:25:03.530,0:25:07.280
can say that climate change was an[br]absolute game changer for this type of
0:25:07.280,0:25:14.350
heat wave, but we can't really quantify[br]it. On the right hand side is a much nicer
0:25:14.350,0:25:21.110
result on the top one, which is for[br]extreme rainfall, in Texas 2019 and nicer
0:25:21.110,0:25:27.660
result I mean now for a scientist and[br]in a scientific way. So we have in blue
0:25:27.660,0:25:35.530
two different types of observations from[br]the heavy rainfall event, and they both
0:25:35.530,0:25:43.650
show pretty much exactly the same result.[br]And also the two climate models that we
0:25:43.650,0:25:51.500
had available that passed the model[br]evaluation tests show an increase in the
0:25:51.500,0:25:56.640
likelihood of this event to occur. That is[br]very similar to that in the observations
0:25:56.640,0:26:04.190
in terms of order of magnitude. And so in[br]that case, we can just synthesize the
0:26:04.190,0:26:09.760
results and give an overarching answer,[br]which is that the likelihood of this event
0:26:09.760,0:26:18.250
to occur has about doubled because of man-[br]made climate change. And the last example
0:26:18.250,0:26:27.080
that I, that is here is for drought[br]in Somalia in 2010, where not only the
0:26:27.080,0:26:32.850
observations are extremely uncertain. So[br]from the observations, you could say we
0:26:32.850,0:26:37.540
could have both an increase in likelihood[br]or a decrease in likelihood by a factor of
0:26:37.540,0:26:45.330
10. But also the climate models show a[br]very, very mixed picture where you can't
0:26:45.330,0:26:51.720
even see a sign that that is conclusive.[br]So in that case, you can say, we can
0:26:51.720,0:26:59.740
exclude that climate change made this[br]event more or less than 10 times, more
0:26:59.740,0:27:05.720
than 10 times or less than 10 times more[br]likely. But we can't say anything more. So
0:27:05.720,0:27:09.560
we can exclude that it's a complete game-[br]changer like we have for heat waves, for
0:27:09.560,0:27:14.030
example. But that's about the only[br]thing that you can say for a result
0:27:14.030,0:27:24.050
like this. So this was sort of the[br]most detailed scientific stuff that I
0:27:24.050,0:27:29.780
would like to show, because I think it's[br]important to get some background behind
0:27:29.780,0:27:35.310
the headline results that would just[br]be climate change doubled the likelihood
0:27:35.310,0:27:42.840
of this event. So there are always four[br]possible outcomes of an attribution study
0:27:42.840,0:27:51.780
a priori. And that is because[br]climate change affects extreme weather in
0:27:51.780,0:27:58.381
two ways basically. One way is what we[br]would call the thermodynamic way, which
0:27:58.381,0:28:02.170
means that because we have more greenhouse[br]gases in the atmosphere, the atmosphere
0:28:02.170,0:28:07.160
overall gets warmer. So you have, on[br]average, an increase in the likelihood of
0:28:07.160,0:28:12.380
heat waves decrease in the likelihood of[br]cold waves. A warmer atmosphere can also
0:28:12.380,0:28:17.550
hold more water vapor that needs[br]to get out of the atmosphere as rainfall.
0:28:17.550,0:28:24.270
So on average, from the warming alone, we[br]would also have more extreme rainfall. But
0:28:24.270,0:28:28.240
then there's the second effect, which I[br]call the dynamic effect, and that is
0:28:28.240,0:28:33.500
because we've changed the composition of the[br]atmosphere, that affects the atmospheric
0:28:33.500,0:28:38.780
circulation. So where weather systems[br]develop, how they develop and and how they
0:28:38.780,0:28:44.230
move. And this effect can either be in the[br]same direction as the warming effect. So it
0:28:44.230,0:28:51.990
can be that we expect more extreme rainfall,[br]but we also get more low pressure systems
0:28:51.990,0:28:57.350
bring rain to get even more extreme[br]rainfall. But these two effects can also
0:28:57.350,0:29:03.380
counteract each other. And so you[br]can expect more rainfall on
0:29:03.380,0:29:07.860
average. But if you don't get the weather[br]systems that bring rain, you either have
0:29:07.860,0:29:13.580
no change in likelihood and intensity or,[br]if the dynamics win, you have actually
0:29:13.580,0:29:19.450
decrease in the likelihood of extreme[br]rainfall in a particular season or region.
0:29:19.450,0:29:24.550
And so this is why a priori, that can[br]always be four outcomes: It can be that
0:29:24.550,0:29:29.010
the event was made more likely. It can be[br]that it was made less likely. It can be
0:29:29.010,0:29:34.330
there's no change. Or it can be that with[br]our current understanding and tools, we
0:29:34.330,0:29:46.760
can't actually answer the question. And so[br]this has been possible to do now for
0:29:46.760,0:29:52.860
about a decade, but only in the last five[br]years really have many, many people or
0:29:52.860,0:29:57.380
many scientists started to do these[br]studies. And so there is actually a
0:29:57.380,0:30:05.370
relatively large, there are[br]lots of attribution studies on different
0:30:05.370,0:30:12.150
kinds of extreme events. And what you can[br]see on this map here is what the news and
0:30:12.150,0:30:17.510
energy outlet CarbonBrief has put all[br]these studies together. And you can see in
0:30:17.510,0:30:22.401
red where climate change played an[br]important role, and blue where climate
0:30:22.401,0:30:33.930
change did not play a role. And in gray,[br]that was an inconclusive result. This is
0:30:33.930,0:30:39.750
very important, though, that this is not[br]representative of the extreme events that
0:30:39.750,0:30:46.580
have happened. This is just represents the[br]studies that have been done by scientists
0:30:46.580,0:30:59.559
and they are, of course biased towards[br]where scientists live
0:30:59.559,0:31:05.280
and also towards extreme events that are[br]relatively easy to simulate with climate
0:31:05.280,0:31:12.780
models. So there are lots of heat waves in[br]Europe, Australia and North America
0:31:12.780,0:31:21.309
because that is where people live. And on[br]this next map, I have tried to
0:31:21.309,0:31:26.390
show the discrepancy between the extreme[br]events that have happened and those for
0:31:26.390,0:31:33.850
which we actually do know the role of[br]climate change. So here in red are deaths
0:31:33.850,0:31:39.570
associated with extreme events since 2003.[br]So since the first event attribution
0:31:39.570,0:31:49.340
study. And it's death from heat waves,[br]storms, heavy rainfall events and droughts
0:31:49.340,0:31:54.940
primarily in different parts of the world,[br]the bubble is always on the capital of the
0:31:54.940,0:31:59.820
country. And the larger the bubble, the[br]more deaths due to extreme events in those
0:31:59.820,0:32:07.600
years. And in black overlaying that are[br]those deaths for which we know the role of
0:32:07.600,0:32:11.300
climate change. So that doesn't mean that[br]the deaths are attributed to
0:32:11.300,0:32:17.250
climate change, but it means that there[br]we do know whether or not to what
0:32:17.250,0:32:23.381
extent climate change played a role. And[br]you can see that most of the European
0:32:23.381,0:32:28.780
countries, the black circle is almost as[br]large as the red one. So for most of the
0:32:28.780,0:32:32.440
extremes or most of the deaths associated[br]with extreme events, we do know the role
0:32:32.440,0:32:39.740
of climate change. But for many[br]other parts of the world,
0:32:39.740,0:32:44.470
there are no or very small black circles.[br]So for most of the events and the deaths
0:32:44.470,0:32:49.090
associated with them, we don't know what[br]the role of climate change is. And I've
0:32:49.090,0:32:52.951
used death here not because I'm[br]particularly morbid, but because it's
0:32:52.951,0:32:58.640
an indicator of the impacts of[br]extreme weather that is relatively good
0:32:58.640,0:33:05.990
comparable between countries. So this[br]means that with event attribution methods
0:33:05.990,0:33:12.130
that we have developed over the last[br]decade, we now have the tools available to
0:33:12.130,0:33:19.950
do, to provide an inventory of the impacts[br]of climate change on our livelihoods. But
0:33:19.950,0:33:25.680
we are very far from having such an[br]inventory at the moment because most of
0:33:25.680,0:33:30.000
the events that have happened, we actually[br]don't know what the role of climate change
0:33:30.000,0:33:37.960
is. And so we don't know in detail on[br]country scale and on the scale where
0:33:37.960,0:33:46.710
people live and make decisions, what the[br]role of climate change is today. There's
0:33:46.710,0:33:56.530
another slightly related issue with that[br]is, that the extreme events that I've used
0:33:56.530,0:34:01.510
to create the map are shown before with[br]the death of climate change, with the
0:34:01.510,0:34:07.670
death of extreme weather events. They are[br]from a database called EM-DAT, which is a
0:34:07.670,0:34:16.290
publicly available database where losses[br]and damages associated with disasters
0:34:16.290,0:34:20.310
technological disasters, but also[br]disasters associated with weather are
0:34:20.310,0:34:31.290
recorded. But, of course, they only can[br]record losses and damages if these losses
0:34:31.290,0:34:36.590
and damages are recorded in the first[br]place. And so what you see on this map is
0:34:36.590,0:34:44.640
in grey and then overlayed with different[br]with different circles are heat waves that
0:34:44.640,0:34:50.580
have occurred, they have occurred between[br]1986 and 2015 on this map. But you could
0:34:50.580,0:34:56.330
draw a map from 1900 to today, and it[br]would look very similar. And that shows
0:34:56.330,0:35:03.510
lots and lots of heat waves reported in[br]Europe and in the US, India, but there are
0:35:03.510,0:35:09.170
no heat waves reported in most of sub-[br]Saharan Africa. However, when you look at
0:35:09.170,0:35:17.420
observations, and also we see that extreme[br]heat has increased quite dramatically in
0:35:17.420,0:35:24.320
most parts of the world and a particular[br]hotspot is sub-Saharan Africa. So, we know
0:35:24.320,0:35:29.400
from when we look at the weather that heat[br]waves are happening, but it's not
0:35:29.400,0:35:35.200
registered and it's not recorded. So we[br]have no idea how many people are actually
0:35:35.200,0:35:41.060
affected by these heat waves. And so we[br]then, of course, don't do attribution
0:35:41.060,0:35:46.080
studies and don't find out what the role[br]of climate change in these heat waves is.
0:35:46.080,0:35:52.750
So in order to really understand the[br]whole picture, we would also need to start
0:35:52.750,0:36:01.820
recording these type of events in other[br]parts of the world. And so my very last
0:36:01.820,0:36:08.700
point, before, I hope that you have[br]questions for me, is: Of course,
0:36:08.700,0:36:14.270
everything I've said so far was talking[br]about the hazards, so talking about the
0:36:14.270,0:36:19.760
weather event and how climate change[br]affects the hazard. But of course that is
0:36:19.760,0:36:25.790
not the same or translates immediately[br]into losses and damages, because whether
0:36:25.790,0:36:32.119
or not a weather event actually has any[br]impact at all is completely driven by
0:36:32.119,0:36:38.440
exposure and vulnerability. So who and[br]what is in harm's way. And I've already
0:36:38.440,0:36:46.160
shown, I've already mentioned the example[br]early on with the drought in Brazil, where
0:36:46.160,0:36:52.110
the huge losses and damages were to a[br]large degree attributable to the increase
0:36:52.110,0:37:01.630
in water consumption. And thus,[br]therefore, in order to really find out how
0:37:01.630,0:37:07.950
climate change is affecting us today, we[br]not only need to define the extreme events
0:37:07.950,0:37:14.930
so that it connects to the impacts, but[br]also look into vulnerability and exposure:
0:37:14.930,0:37:21.520
What is changing, what's there and what[br]are the important factors. But we can
0:37:21.520,0:37:28.330
do that. And so we have really made a lot[br]of progress in understanding of how
0:37:28.330,0:37:35.010
climate change not only affects global[br]mean temperature, which we have known for
0:37:35.010,0:37:41.830
centuries, and how it affects large[br]scale changes in temperature and
0:37:41.830,0:37:46.869
precipitation, which we have also known[br]for a very long time. But we now have
0:37:46.869,0:37:52.110
actually all the puzzle pieces together to[br]really understand what climate change
0:37:52.110,0:37:58.740
means on the scale where people live and[br]where decisions are made. We just need to
0:37:58.740,0:38:07.190
put them together. And one lens or one way[br]of where they are currently put together
0:38:07.190,0:38:16.619
is, for example, in courts. And so because[br]it's obviously people who experience
0:38:16.619,0:38:23.010
losses and damages from climate change.[br]And so one way to address that is going
0:38:23.010,0:38:28.910
through national governments, local[br]governments, hoping for adaptation
0:38:28.910,0:38:35.070
measures to be put in place. But if that's[br]not forthcoming quickly enough, there is
0:38:35.070,0:38:40.510
the option to sue. And so this is one[br]example which is currently happening in
0:38:40.510,0:38:53.820
Germany where a peruvian farmer is suing[br]RWE to basically pay their share of a
0:38:53.820,0:39:01.100
adaptation because of largely increased[br]flood risk from glacier melt in the area.
0:39:01.100,0:39:09.360
And they want RWE to pay from their[br]contribution to climate change, where
0:39:09.360,0:39:16.260
their emissions and then have some funding[br]for the adaptation measures from them. And
0:39:16.260,0:39:21.970
that is one example of where these kind of[br]attribution studies can be used in a very
0:39:21.970,0:39:29.220
direct way to hopefully change[br]something in the real world. And with
0:39:29.220,0:39:36.350
this, I would like to end and yeah, leave[br]you with some references, and hope you
0:39:36.350,0:39:39.010
have some questions for me.
0:40:01.132,0:40:14.912
Herald: Sind wir durch? So, ja. Herzlichen[br]Dank für den Vortrag. Ich hab, bevor wir
0:40:14.912,0:40:20.782
zum Q&A kommen muss ich einmal mich im[br]Namen der Produktion bei den Zuschauern
0:40:20.782,0:40:25.382
entschuldigen, ich glaube ihr hattet etwas[br]Produktionssound auf den Ohren, das sollte
0:40:25.382,0:40:34.201
natürlich nicht so sein. Gut, wir haben[br]jetzt keine Fragen aus dem Chat bisher.
0:40:42.141,0:40:50.941
Aber vielleicht eine Frage von mir, das[br]letzte Beispiel war ja ein Fall
0:40:50.941,0:41:00.661
einer Klage über Ländergrenzen hinaus[br]quasi, ist das ein Ansatz, den man, den
0:41:00.661,0:41:06.560
wir in Zukunft öfter sehen würden, das[br]heißt, dass über Ländergrenzen hinweg
0:41:06.560,0:41:13.540
Menschen oder Organisationen sich[br]gegenseitig versuchen quasi über den
0:41:13.540,0:41:20.490
Klageweg auf den richtigen Weg zu bringen.[br]FO: Also es ist tatsächlich ein, eine
0:41:20.490,0:41:31.940
Ausnahme, dass das im Fall RWE und Lliuya[br]funktioniert, denn das deutsche Recht
0:41:31.940,0:41:36.330
sieht vor, dass Firmen, die in Deutschland[br]ansässig sind auch verschieden
0:41:36.330,0:41:39.340
verantworlich sind, die nicht in[br]Deutschland stattfinden.
0:41:39.340,0:41:44.750
H: So sorry to interrupt. I just realized[br]that we are still in English talk. Sorry
0:41:44.750,0:41:48.810
for that.[br]FO: OK. No worries. So your question was
0:41:48.810,0:41:56.119
if we're going to see more[br]international court cases where across
0:41:56.119,0:42:03.040
countries, across nation states we have[br]climate litigation. And this type of
0:42:03.040,0:42:07.369
litigation that I've just shown as[br]the example is in so far an
0:42:07.369,0:42:13.869
exception, as in German law, a company is[br]also responsible for the damages caused
0:42:13.869,0:42:20.060
outside of Germany. Which is not the case,[br]for example, for companies in the US
0:42:20.060,0:42:30.150
or so. So, and this is why Lliuya sued RWE[br]and not, for example, ExxonMobil. But
0:42:30.150,0:42:40.780
these type of cases, where this[br]Lliuya case is an example. We see a lot of
0:42:40.780,0:42:48.380
a lot of them, an increasing number of[br]them each year. And they are difficult to
0:42:48.380,0:42:57.940
do across nations because this, the German[br]law is exceptional on that case. But there
0:42:57.940,0:43:03.340
are other ways, like, for example, why are[br]human rights courts that can be done
0:43:03.340,0:43:11.230
across nation states and that is also[br]happening. So it's at the moment, it is
0:43:11.230,0:43:18.560
still legally not super straightforward to[br]to actually win these cases, but
0:43:18.560,0:43:24.320
increasingly a lot of lawyers working on[br]that so that we will see a lot of
0:43:24.320,0:43:31.580
change in that in the coming years.[br]H: OK, thank you. In the meantime, there
0:43:31.580,0:43:37.860
appeared some questions from the chat and[br]from the Internet. I will go through them.
0:43:37.860,0:43:42.910
First question is: are the results of the[br]individual attribution studies published
0:43:42.910,0:43:50.450
as open data in a machine readable format?[br]FO: laughter So all the studies that
0:43:50.450,0:43:57.620
we do that that I've done with my[br]team, with world weather attribution. So
0:43:57.620,0:44:03.020
there all the data is[br]available, and it's available on a
0:44:03.020,0:44:11.000
platform that's called Climate Explorer.[br]So that should be machine readable. So and
0:44:11.000,0:44:17.790
this is deliberately because yeah, because[br]we want to make it as transparent as
0:44:17.790,0:44:23.760
possible so everyone can go away, use our[br]data, and redo our studies, and find out
0:44:23.760,0:44:29.450
if we made any mistakes. But this is not[br]the case for all the studies that exist,
0:44:29.450,0:44:34.830
because most of them or many of them are[br]published in peer reviewed journals and
0:44:34.830,0:44:39.070
not all peer reviewed journals have[br]open data and open access policies.
0:44:39.070,0:44:45.950
But increasingly, journals have.[br]So if you, for example, go to the
0:44:45.950,0:44:51.410
CarbonBrief website and look at the map of[br]studies, there you have links to all
0:44:51.410,0:44:56.330
the studies. And a lot of them have the[br]data available.
0:44:56.330,0:45:05.000
H: OK, maybe a follow up to this one. The[br]next question is, are the models somehow
0:45:05.000,0:45:11.910
available or usable for a wider interest[br]public or is APC required? I'm not quite
0:45:11.910,0:45:18.020
sure what APC means.[br]FO: So the model data is publicly
0:45:18.020,0:45:25.780
available from–and this is one reason why[br]we have been able to do these studies
0:45:25.780,0:45:31.280
because until relatively recently, model[br]data was not publicly available and only
0:45:31.280,0:45:36.390
scientist working in a specific country[br]could use the model developed in that
0:45:36.390,0:45:44.810
country–but now all the model data is[br]shared publicly and people can use it. So
0:45:44.810,0:45:50.830
it's definitely there and usable. It just[br]requires some expertise to make sense of
0:45:50.830,0:46:00.000
it. But it's, yeah, people can use it.[br]H: OK, the next question is: to what
0:46:00.000,0:46:05.450
certainty can you set up counterfactual[br]models, which are an important reference
0:46:05.450,0:46:12.915
to your percentage value, and what[br]data are the basis for these models?
0:46:12.915,0:46:19.760
FO: So the counterfactual simulations are-[br]the climate models we use are basically the
0:46:19.760,0:46:23.970
same models that are used also for the[br]weather forecast. They are just run in
0:46:23.970,0:46:30.520
lower resolution. So, which I guess most[br]of this audience knows what that means. So
0:46:30.520,0:46:36.670
the data points for the part, so that it's[br]not so computing intensive. And these
0:46:36.670,0:46:43.390
models, they are tested against observed[br]data. And so that is how we do the model
0:46:43.390,0:46:48.600
evaluation. So that is some simulations of[br]the present day. And for the
0:46:48.600,0:46:57.430
counterfactual, we know extremely well how[br]many greenhouse gases have been included
0:46:57.430,0:47:02.010
into the atmosphere since the beginning of[br]the Industrial Revolution, so that there
0:47:02.010,0:47:07.740
is some very large certainty with that[br]number and we remove that from the models'
0:47:07.740,0:47:13.080
atmospheres. So the models have exactly[br]the same set up, but the lower
0:47:13.080,0:47:16.720
greenhouse gases, lower amount of[br]greenhouse gases in the atmosphere, and
0:47:16.720,0:47:24.580
then are spun up and run in exactly the[br]same way. So, they, but of course, we
0:47:24.580,0:47:33.620
can't test the counterfactual. And so that[br]means that we assume that the sort of the
0:47:33.620,0:47:40.510
the weather was still the same, physics[br]will still hold in the counterfactual. And
0:47:40.510,0:47:45.800
that the models that are developed[br]using present day represent the
0:47:45.800,0:47:48.880
counterfactual. Which is, which is an[br]assumption.
0:47:48.880,0:47:51.820
But it is not a completely[br]unreasonable assumption, because of
0:47:51.820,0:48:00.740
course, we have now decades of model[br]development and have seen that, in fact,
0:48:00.740,0:48:05.990
that indeed climate model projections that[br]have been made 30 years ago have actually
0:48:05.990,0:48:13.110
come… come to… have been realized, and[br]pretty much the same way on a large scale
0:48:13.110,0:48:18.980
that they have, as they had been predicted[br]30 years ago. And so that assumption
0:48:18.980,0:48:24.890
is not, yeah, it's not a big assumption.[br]So the counterfactual itself is not a
0:48:24.890,0:48:29.700
problem. But of course, also the present[br]day model simulations, they are
0:48:29.700,0:48:34.990
not… they are very far from perfect. And[br]there are some types of events which state
0:48:34.990,0:48:41.040
of the art climate models just can't[br]simulate. And so, where we can- what
0:48:41.040,0:48:46.560
we can say very little. So well, for[br]example, for hurricanes, we can say
0:48:46.560,0:48:51.730
with high certainty about the[br]rainfall associated with hurricanes, the
0:48:51.730,0:48:56.670
hurricane strength itself and the[br]frequency of hurricanes is something
0:48:56.670,0:49:01.970
which is very difficult to simulate with[br]state of the art models. So our
0:49:01.970,0:49:12.640
uncertainty there is much higher.[br]H: OK. And then, well, some, one question
0:49:12.640,0:49:20.170
that emerges from all of this is,[br]of course, if we know this much and way
0:49:20.170,0:49:26.720
more than in the past, how are[br]politicians still ignoring that
0:49:26.720,0:49:34.944
information? And how can we[br]convey that into their minds?
0:49:34.944,0:49:39.880
FO: Well, if I knew the answer to that, I[br]would probably not be standing here,
0:49:39.880,0:49:49.480
but actually doing politics. But I[br]think it takes a frustratingly long time
0:49:49.480,0:49:56.849
for things to change and things should[br]change much faster. But we actually- the
0:49:56.849,0:50:03.510
last two years have shown huge progress, I[br]think, in terms of also putting climate
0:50:03.510,0:50:11.830
change on the agenda of every politician.[br]Because, and that's largely due to the
0:50:11.830,0:50:17.740
Fridays For Future movement, but also to a[br]degree, I think, due to the fact that we
0:50:17.740,0:50:23.800
now actually know that the weather that[br]people experience in their backyard–and
0:50:23.800,0:50:29.400
pretty much independent of where their[br]backyard is–is not the same as it used to
0:50:29.400,0:50:37.430
be. And so people do experience today[br]climate change. And I think that
0:50:37.430,0:50:42.700
does help to bring a bit more urgency.[br]And, of course, I would have said everyone
0:50:42.700,0:50:47.630
has climate change on their agenda, which[br]was very different even two years ago,
0:50:47.630,0:50:52.140
where there were lots of people who[br]would never talk about climate change and
0:50:52.140,0:50:57.880
their political agendas has played no[br]role. It doesn't mean that it
0:50:57.880,0:51:05.790
has the right priority on that agenda,[br]but it's still a huge step forward that
0:51:05.790,0:51:18.710
has been made. And so I think we do know[br]some things that do work, but we just have
0:51:18.710,0:51:28.080
to just keep doing that. Yeah, I don't[br]think I can say more. I don't have a magic
0:51:28.080,0:51:35.200
wand to change it otherwise.[br]H: Maybe some other point of impact.
0:51:35.200,0:51:40.140
One of the question is, is it possible to[br]turn the results of attribution studies
0:51:40.140,0:51:47.920
into recommendations for farmers and[br]people who are affected in a financial way
0:51:47.920,0:51:53.359
by extreme weather and how to change[br]agriculture to reduce losses from extreme
0:51:53.359,0:51:56.580
weather effects?[br]FO: Yes, absolutely. So that is
0:51:56.580,0:52:03.730
one of the most useful things of these[br]studies is well, on the one hand, to raise
0:52:03.730,0:52:07.960
awareness. But on the other hand, if you[br]know that a drought that you have
0:52:07.960,0:52:16.930
experienced that has led to losses is a[br]harbinger of what is to come, then that is
0:52:16.930,0:52:22.630
incredibly helpful to know how[br]agricultural practices might need to be
0:52:22.630,0:52:29.880
changed. Or that insurance for losses from[br]agriculture might need to be changed. And
0:52:29.880,0:52:36.009
so this is exactly why we do these[br]attribution studies. Because not
0:52:36.009,0:52:42.770
every extreme event has always[br]shows the fingerprints of
0:52:42.770,0:52:47.990
climate change. And if you know[br]which of the events are the ones where
0:52:47.990,0:52:53.920
climate change is a real game changer, you[br]also do know where to put your efforts and
0:52:53.920,0:53:00.491
resources to be more resilient in the[br]future. And for financial losses, it
0:53:00.491,0:53:06.070
is on the one hand, yeah, you can use[br]these studies to find out what your
0:53:06.070,0:53:12.930
physical risks are for your assets. And[br]how they, and of course, everything that
0:53:12.930,0:53:17.710
I've said, comparing the counterfactual[br]with the present we can do, and we do this
0:53:17.710,0:53:23.950
also with the future. So you can also see[br]how in a two degree world, the events,
0:53:23.950,0:53:29.220
the likelihood and intensities are[br]changing. And of course, you can then
0:53:29.220,0:53:35.250
also, in a less direct way, use this kind[br]of information to see, to assess what
0:53:35.250,0:53:42.880
might be other risks from- where might be[br]stranded assets, what are other risks
0:53:42.880,0:53:48.910
for the financial sector,[br]for the financial planning.
0:53:48.910,0:53:57.190
Where could liability risks be and how[br]could they look like. So there is, because
0:53:57.190,0:54:02.450
extreme weather events and their changes[br]in intensity and magnitude is how climate
0:54:02.450,0:54:10.360
change is manifesting, it really connects[br]all these aspects of where the
0:54:10.360,0:54:21.920
impacts of climate change are.[br]H: OK, last question for today. I hope I
0:54:21.920,0:54:30.020
can get that right. I think the question[br]is if there are study, if there are
0:54:30.020,0:54:40.520
studies on how we cultivates fields[br]and agriculture. How does this impact the
0:54:40.520,0:54:48.950
overall climate in that area? The example[br]here is that only an increase in water
0:54:48.950,0:54:57.849
consumption was directed to São Paulo. Or[br]might there also be a warm world created
0:54:57.849,0:55:06.440
by monoculture in central Brazil?[br]FO: So, yeah, I don't know details, but
0:55:06.440,0:55:13.460
there are, but land use changes and land[br]use does play a role. On the one hand, it
0:55:13.460,0:55:20.210
affects the climate. So if you have, if[br]you have a rainforest, you have a very
0:55:20.210,0:55:26.940
different climate in that location as if[br]there is a savanna or plantation. And
0:55:26.940,0:55:35.230
also, of course, if you have monocultures,[br]you are much more, your losses are
0:55:35.230,0:55:42.100
larger usually as if you have different[br]types of agriculture. Because
0:55:42.100,0:55:46.830
in a monoculture everything is in[br]exactly the same way vulnerable and
0:55:46.830,0:55:52.030
so that, yeah. So that does,[br]land use change plays a hugely important
0:55:52.030,0:55:59.390
role with respect to the impacts of[br]extreme weather. And that is one thing to
0:55:59.390,0:56:03.570
look at. When I was saying, talking about[br]looking at vulnerability and exposure, and
0:56:03.570,0:56:08.520
of course also changes in the hazard are[br]not just because of climate change, but
0:56:08.520,0:56:12.610
also because of land use change. And you[br]can use exactly the same methods, but
0:56:12.610,0:56:17.040
instead of changing the CO2 or the[br]greenhouse gases in the atmosphere of your
0:56:17.040,0:56:22.790
model, you can change the land use and[br]then disentangle these different drivers
0:56:22.790,0:56:29.870
in and hazards.[br]H: OK, Fredi Otto thank you very much for
0:56:29.870,0:56:37.290
your presentation and for the Q&A. It was[br]a pleasure to have you with us. And yeah,
0:56:37.290,0:56:43.800
if you have any questions, any more[br]questions, I guess there are ways to
0:56:43.800,0:56:47.250
contact you.[br]FO: laughter
0:56:47.250,0:56:52.540
H: I think your email address and contact[br]details are in the Fahrplan for all the
0:56:52.540,0:56:58.930
viewers that have way more questions. And,[br]I don't know, do you have access to the 2D
0:56:58.930,0:57:05.520
world and do you explore that?[br]FO: Given that I don't know what you mean,
0:57:05.520,0:57:07.350
probably not, but…[br]laughter
0:57:07.350,0:57:12.070
H: OK.[br]FO: That can also be changed.
0:57:12.070,0:57:20.950
H: Yeah, it's the the replacement for[br]the congress place itself. But anyway,
0:57:20.950,0:57:26.700
if you viewers and you people out there[br]have any more questions, contact Fredi
0:57:26.700,0:57:32.930
Otto. And thank you again very much for[br]your talk. And, yeah. Have a
0:57:32.930,0:57:35.330
nice congress, all of you.
0:57:35.330,0:57:39.080
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0:57:39.080,0:58:13.960
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