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#rC3 - Angry weather ?

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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
  • 25:57 - 26:04
    in terms of order of magnitude. And so in
    that case, we can just synthesize the
  • 26:04 - 26:10
    results and give an overarching answer,
    which is that the likelihood of this event
  • 26:10 - 26:18
    to occur has about doubled because of man-
    made climate change. And the last example
  • 26:18 - 26:27
    that I, that is here is for drought
    in Somalia in 2010, where not only the
  • 26:27 - 26:33
    observations are extremely uncertain. So
    from the observations, you could say we
  • 26:33 - 26:38
    could have both an increase in likelihood
    or a decrease in likelihood by a factor of
  • 26:38 - 26:45
    10. But also the climate models show a
    very, very mixed picture where you can't
  • 26:45 - 26:52
    even see a sign that that is conclusive.
    So in that case, you can say, we can
  • 26:52 - 27:00
    exclude that climate change made this
    event more or less than 10 times, more
  • 27:00 - 27:06
    than 10 times or less than 10 times more
    likely. But we can't say anything more. So
  • 27:06 - 27:10
    we can exclude that it's a complete game-
    changer like we have for heat waves, for
  • 27:10 - 27:14
    example. But that's about the only
    thing that you can say for a result
  • 27:14 - 27:24
    like this. So this was sort of the
    most detailed scientific stuff that I
  • 27:24 - 27:30
    would like to show, because I think it's
    important to get some background behind
  • 27:30 - 27:35
    the headline results that would just
    be climate change doubled the likelihood
  • 27:35 - 27:43
    of this event. So there are always four
    possible outcomes of an attribution study
  • 27:43 - 27:52
    a priori. And that is because
    climate change affects extreme weather in
  • 27:52 - 27:58
    two ways basically. One way is what we
    would call the thermodynamic way, which
  • 27:58 - 28:02
    means that because we have more greenhouse
    gases in the atmosphere, the atmosphere
  • 28:02 - 28:07
    overall gets warmer. So you have, on
    average, an increase in the likelihood of
  • 28:07 - 28:12
    heat waves decrease in the likelihood of
    cold waves. A warmer atmosphere can also
  • 28:12 - 28:18
    hold more water vapor that needs
    to get out of the atmosphere as rainfall.
  • 28:18 - 28:24
    So on average, from the warming alone, we
    would also have more extreme rainfall. But
  • 28:24 - 28:28
    then there's the second effect, which I
    call the dynamic effect, and that is
  • 28:28 - 28:34
    because we've changed the composition of the
    atmosphere, that affects the atmospheric
  • 28:34 - 28:39
    circulation. So where weather systems
    develop, how they develop and and how they
  • 28:39 - 28:44
    move. And this effect can either be in the
    same direction as the warming effect. So it
  • 28:44 - 28:52
    can be that we expect more extreme rainfall,
    but we also get more low pressure systems
  • 28:52 - 28:57
    bring rain to get even more extreme
    rainfall. But these two effects can also
  • 28:57 - 29:03
    counteract each other. And so you
    can expect more rainfall on
  • 29:03 - 29:08
    average. But if you don't get the weather
    systems that bring rain, you either have
  • 29:08 - 29:14
    no change in likelihood and intensity or,
    if the dynamics win, you have actually
  • 29:14 - 29:19
    decrease in the likelihood of extreme
    rainfall in a particular season or region.
  • 29:19 - 29:25
    And so this is why a priori, that can
    always be four outcomes: It can be that
  • 29:25 - 29:29
    the event was made more likely. It can be
    that it was made less likely. It can be
  • 29:29 - 29:34
    there's no change. Or it can be that with
    our current understanding and tools, we
  • 29:34 - 29:47
    can't actually answer the question. And so
    this has been possible to do now for
  • 29:47 - 29:53
    about a decade, but only in the last five
    years really have many, many people or
  • 29:53 - 29:57
    many scientists started to do these
    studies. And so there is actually a
  • 29:57 - 30:05
    relatively large, there are
    lots of attribution studies on different
  • 30:05 - 30:12
    kinds of extreme events. And what you can
    see on this map here is what the news and
  • 30:12 - 30:18
    energy outlet CarbonBrief has put all
    these studies together. And you can see in
  • 30:18 - 30:22
    red where climate change played an
    important role, and blue where climate
  • 30:22 - 30:34
    change did not play a role. And in gray,
    that was an inconclusive result. This is
  • 30:34 - 30:40
    very important, though, that this is not
    representative of the extreme events that
  • 30:40 - 30:47
    have happened. This is just represents the
    studies that have been done by scientists
  • 30:47 - 31:00
    and they are, of course biased towards
    where scientists live
  • 31:00 - 31:05
    and also towards extreme events that are
    relatively easy to simulate with climate
  • 31:05 - 31:13
    models. So there are lots of heat waves in
    Europe, Australia and North America
  • 31:13 - 31:21
    because that is where people live. And on
    this next map, I have tried to
  • 31:21 - 31:26
    show the discrepancy between the extreme
    events that have happened and those for
  • 31:26 - 31:34
    which we actually do know the role of
    climate change. So here in red are deaths
  • 31:34 - 31:40
    associated with extreme events since 2003.
    So since the first event attribution
  • 31:40 - 31:49
    study. And it's death from heat waves,
    storms, heavy rainfall events and droughts
  • 31:49 - 31:55
    primarily in different parts of the world,
    the bubble is always on the capital of the
  • 31:55 - 32:00
    country. And the larger the bubble, the
    more deaths due to extreme events in those
  • 32:00 - 32:08
    years. And in black overlaying that are
    those deaths for which we know the role of
  • 32:08 - 32:11
    climate change. So that doesn't mean that
    the deaths are attributed to
  • 32:11 - 32:17
    climate change, but it means that there
    we do know whether or not to what
  • 32:17 - 32:23
    extent climate change played a role. And
    you can see that most of the European
  • 32:23 - 32:29
    countries, the black circle is almost as
    large as the red one. So for most of the
  • 32:29 - 32:32
    extremes or most of the deaths associated
    with extreme events, we do know the role
  • 32:32 - 32:40
    of climate change. But for many
    other parts of the world,
  • 32:40 - 32:44
    there are no or very small black circles.
    So for most of the events and the deaths
  • 32:44 - 32:49
    associated with them, we don't know what
    the role of climate change is. And I've
  • 32:49 - 32:53
    used death here not because I'm
    particularly morbid, but because it's
  • 32:53 - 32:59
    an indicator of the impacts of
    extreme weather that is relatively good
  • 32:59 - 33:06
    comparable between countries. So this
    means that with event attribution methods
  • 33:06 - 33:12
    that we have developed over the last
    decade, we now have the tools available to
  • 33:12 - 33:20
    do, to provide an inventory of the impacts
    of climate change on our livelihoods. But
  • 33:20 - 33:26
    we are very far from having such an
    inventory at the moment because most of
  • 33:26 - 33:30
    the events that have happened, we actually
    don't know what the role of climate change
  • 33:30 - 33:38
    is. And so we don't know in detail on
    country scale and on the scale where
  • 33:38 - 33:47
    people live and make decisions, what the
    role of climate change is today. There's
  • 33:47 - 33:57
    another slightly related issue with that
    is, that the extreme events that I've used
  • 33:57 - 34:02
    to create the map are shown before with
    the death of climate change, with the
  • 34:02 - 34:08
    death of extreme weather events. They are
    from a database called EM-DAT, which is a
  • 34:08 - 34:16
    publicly available database where losses
    and damages associated with disasters
  • 34:16 - 34:20
    technological disasters, but also
    disasters associated with weather are
  • 34:20 - 34:31
    recorded. But, of course, they only can
    record losses and damages if these losses
  • 34:31 - 34:37
    and damages are recorded in the first
    place. And so what you see on this map is
  • 34:37 - 34:45
    in grey and then overlayed with different
    with different circles are heat waves that
  • 34:45 - 34:51
    have occurred, they have occurred between
    1986 and 2015 on this map. But you could
  • 34:51 - 34:56
    draw a map from 1900 to today, and it
    would look very similar. And that shows
  • 34:56 - 35:04
    lots and lots of heat waves reported in
    Europe and in the US, India, but there are
  • 35:04 - 35:09
    no heat waves reported in most of sub-
    Saharan Africa. However, when you look at
  • 35:09 - 35:17
    observations, and also we see that extreme
    heat has increased quite dramatically in
  • 35:17 - 35:24
    most parts of the world and a particular
    hotspot is sub-Saharan Africa. So, we know
  • 35:24 - 35:29
    from when we look at the weather that heat
    waves are happening, but it's not
  • 35:29 - 35:35
    registered and it's not recorded. So we
    have no idea how many people are actually
  • 35:35 - 35:41
    affected by these heat waves. And so we
    then, of course, don't do attribution
  • 35:41 - 35:46
    studies and don't find out what the role
    of climate change in these heat waves is.
  • 35:46 - 35:53
    So in order to really understand the
    whole picture, we would also need to start
  • 35:53 - 36:02
    recording these type of events in other
    parts of the world. And so my very last
  • 36:02 - 36:09
    point, before, I hope that you have
    questions for me, is: Of course,
  • 36:09 - 36:14
    everything I've said so far was talking
    about the hazards, so talking about the
  • 36:14 - 36:20
    weather event and how climate change
    affects the hazard. But of course that is
  • 36:20 - 36:26
    not the same or translates immediately
    into losses and damages, because whether
  • 36:26 - 36:32
    or not a weather event actually has any
    impact at all is completely driven by
  • 36:32 - 36:38
    exposure and vulnerability. So who and
    what is in harm's way. And I've already
  • 36:38 - 36:46
    shown, I've already mentioned the example
    early on with the drought in Brazil, where
  • 36:46 - 36:52
    the huge losses and damages were to a
    large degree attributable to the increase
  • 36:52 - 37:02
    in water consumption. And thus,
    therefore, in order to really find out how
  • 37:02 - 37:08
    climate change is affecting us today, we
    not only need to define the extreme events
  • 37:08 - 37:15
    so that it connects to the impacts, but
    also look into vulnerability and exposure:
  • 37:15 - 37:22
    What is changing, what's there and what
    are the important factors. But we can
  • 37:22 - 37:28
    do that. And so we have really made a lot
    of progress in understanding of how
  • 37:28 - 37:35
    climate change not only affects global
    mean temperature, which we have known for
  • 37:35 - 37:42
    centuries, and how it affects large
    scale changes in temperature and
  • 37:42 - 37:47
    precipitation, which we have also known
    for a very long time. But we now have
  • 37:47 - 37:52
    actually all the puzzle pieces together to
    really understand what climate change
  • 37:52 - 37:59
    means on the scale where people live and
    where decisions are made. We just need to
  • 37:59 - 38:07
    put them together. And one lens or one way
    of where they are currently put together
  • 38:07 - 38:17
    is, for example, in courts. And so because
    it's obviously people who experience
  • 38:17 - 38:23
    losses and damages from climate change.
    And so one way to address that is going
  • 38:23 - 38:29
    through national governments, local
    governments, hoping for adaptation
  • 38:29 - 38:35
    measures to be put in place. But if that's
    not forthcoming quickly enough, there is
  • 38:35 - 38:41
    the option to sue. And so this is one
    example which is currently happening in
  • 38:41 - 38:54
    Germany where a peruvian farmer is suing
    RWE to basically pay their share of a
  • 38:54 - 39:01
    adaptation because of largely increased
    flood risk from glacier melt in the area.
  • 39:01 - 39:09
    And they want RWE to pay from their
    contribution to climate change, where
  • 39:09 - 39:16
    their emissions and then have some funding
    for the adaptation measures from them. And
  • 39:16 - 39:22
    that is one example of where these kind of
    attribution studies can be used in a very
  • 39:22 - 39:29
    direct way to hopefully change
    something in the real world. And with
  • 39:29 - 39:36
    this, I would like to end and yeah, leave
    you with some references, and hope you
  • 39:36 - 39:39
    have some questions for me.
  • 40:01 - 40:15
    Herald: Sind wir durch? So, ja. Herzlichen
    Dank für den Vortrag. Ich hab, bevor wir
  • 40:15 - 40:21
    zum Q&A kommen muss ich einmal mich im
    Namen der Produktion bei den Zuschauern
  • 40:21 - 40:25
    entschuldigen, ich glaube ihr hattet etwas
    Produktionssound auf den Ohren, das sollte
  • 40:25 - 40:34
    natürlich nicht so sein. Gut, wir haben
    jetzt keine Fragen aus dem Chat bisher.
  • 40:42 - 40:51
    Aber vielleicht eine Frage von mir, das
    letzte Beispiel war ja ein Fall
  • 40:51 - 41:01
    einer Klage über Ländergrenzen hinaus
    quasi, ist das ein Ansatz, den man, den
  • 41:01 - 41:07
    wir in Zukunft öfter sehen würden, das
    heißt, dass über Ländergrenzen hinweg
  • 41:07 - 41:14
    Menschen oder Organisationen sich
    gegenseitig versuchen quasi über den
  • 41:14 - 41:20
    Klageweg auf den richtigen Weg zu bringen.
    FO: Also es ist tatsächlich ein, eine
  • 41:20 - 41:32
    Ausnahme, dass das im Fall RWE und Lliuya
    funktioniert, denn das deutsche Recht
  • 41:32 - 41:36
    sieht vor, dass Firmen, die in Deutschland
    ansässig sind auch verschieden
  • 41:36 - 41:39
    verantworlich sind, die nicht in
    Deutschland stattfinden.
  • 41:39 - 41:45
    H: So sorry to interrupt. I just realized
    that we are still in English talk. Sorry
  • 41:45 - 41:49
    for that.
    FO: OK. No worries. So your question was
  • 41:49 - 41:56
    if we're going to see more
    international court cases where across
  • 41:56 - 42:03
    countries, across nation states we have
    climate litigation. And this type of
  • 42:03 - 42:07
    litigation that I've just shown as
    the example is in so far an
  • 42:07 - 42:14
    exception, as in German law, a company is
    also responsible for the damages caused
  • 42:14 - 42:20
    outside of Germany. Which is not the case,
    for example, for companies in the US
  • 42:20 - 42:30
    or so. So, and this is why Lliuya sued RWE
    and not, for example, ExxonMobil. But
  • 42:30 - 42:41
    these type of cases, where this
    Lliuya case is an example. We see a lot of
  • 42:41 - 42:48
    a lot of them, an increasing number of
    them each year. And they are difficult to
  • 42:48 - 42:58
    do across nations because this, the German
    law is exceptional on that case. But there
  • 42:58 - 43:03
    are other ways, like, for example, why are
    human rights courts that can be done
  • 43:03 - 43:11
    across nation states and that is also
    happening. So it's at the moment, it is
  • 43:11 - 43:19
    still legally not super straightforward to
    to actually win these cases, but
  • 43:19 - 43:24
    increasingly a lot of lawyers working on
    that so that we will see a lot of
  • 43:24 - 43:32
    change in that in the coming years.
    H: OK, thank you. In the meantime, there
  • 43:32 - 43:38
    appeared some questions from the chat and
    from the Internet. I will go through them.
  • 43:38 - 43:43
    First question is: are the results of the
    individual attribution studies published
  • 43:43 - 43:50
    as open data in a machine readable format?
    FO: laughter So all the studies that
  • 43:50 - 43:58
    we do that that I've done with my
    team, with world weather attribution. So
  • 43:58 - 44:03
    there all the data is
    available, and it's available on a
  • 44:03 - 44:11
    platform that's called Climate Explorer.
    So that should be machine readable. So and
  • 44:11 - 44:18
    this is deliberately because yeah, because
    we want to make it as transparent as
  • 44:18 - 44:24
    possible so everyone can go away, use our
    data, and redo our studies, and find out
  • 44:24 - 44:29
    if we made any mistakes. But this is not
    the case for all the studies that exist,
  • 44:29 - 44:35
    because most of them or many of them are
    published in peer reviewed journals and
  • 44:35 - 44:39
    not all peer reviewed journals have
    open data and open access policies.
  • 44:39 - 44:46
    But increasingly, journals have.
    So if you, for example, go to the
  • 44:46 - 44:51
    CarbonBrief website and look at the map of
    studies, there you have links to all
  • 44:51 - 44:56
    the studies. And a lot of them have the
    data available.
  • 44:56 - 45:05
    H: OK, maybe a follow up to this one. The
    next question is, are the models somehow
  • 45:05 - 45:12
    available or usable for a wider interest
    public or is APC required? I'm not quite
  • 45:12 - 45:18
    sure what APC means.
    FO: So the model data is publicly
  • 45:18 - 45:26
    available from–and this is one reason why
    we have been able to do these studies
  • 45:26 - 45:31
    because until relatively recently, model
    data was not publicly available and only
  • 45:31 - 45:36
    scientist working in a specific country
    could use the model developed in that
  • 45:36 - 45:45
    country–but now all the model data is
    shared publicly and people can use it. So
  • 45:45 - 45:51
    it's definitely there and usable. It just
    requires some expertise to make sense of
  • 45:51 - 46:00
    it. But it's, yeah, people can use it.
    H: OK, the next question is: to what
  • 46:00 - 46:05
    certainty can you set up counterfactual
    models, which are an important reference
  • 46:05 - 46:13
    to your percentage value, and what
    data are the basis for these models?
  • 46:13 - 46:20
    FO: So the counterfactual simulations are-
    the climate models we use are basically the
  • 46:20 - 46:24
    same models that are used also for the
    weather forecast. They are just run in
  • 46:24 - 46:31
    lower resolution. So, which I guess most
    of this audience knows what that means. So
  • 46:31 - 46:37
    the data points for the part, so that it's
    not so computing intensive. And these
  • 46:37 - 46:43
    models, they are tested against observed
    data. And so that is how we do the model
  • 46:43 - 46:49
    evaluation. So that is some simulations of
    the present day. And for the
  • 46:49 - 46:57
    counterfactual, we know extremely well how
    many greenhouse gases have been included
  • 46:57 - 47:02
    into the atmosphere since the beginning of
    the Industrial Revolution, so that there
  • 47:02 - 47:08
    is some very large certainty with that
    number and we remove that from the models'
  • 47:08 - 47:13
    atmospheres. So the models have exactly
    the same set up, but the lower
  • 47:13 - 47:17
    greenhouse gases, lower amount of
    greenhouse gases in the atmosphere, and
  • 47:17 - 47:25
    then are spun up and run in exactly the
    same way. So, they, but of course, we
  • 47:25 - 47:34
    can't test the counterfactual. And so that
    means that we assume that the sort of the
  • 47:34 - 47:41
    the weather was still the same, physics
    will still hold in the counterfactual. And
  • 47:41 - 47:46
    that the models that are developed
    using present day represent the
  • 47:46 - 47:49
    counterfactual. Which is, which is an
    assumption.
  • 47:49 - 47:52
    But it is not a completely
    unreasonable assumption, because of
  • 47:52 - 48:01
    course, we have now decades of model
    development and have seen that, in fact,
  • 48:01 - 48:06
    that indeed climate model projections that
    have been made 30 years ago have actually
  • 48:06 - 48:13
    come… come to… have been realized, and
    pretty much the same way on a large scale
  • 48:13 - 48:19
    that they have, as they had been predicted
    30 years ago. And so that assumption
  • 48:19 - 48:25
    is not, yeah, it's not a big assumption.
    So the counterfactual itself is not a
  • 48:25 - 48:30
    problem. But of course, also the present
    day model simulations, they are
  • 48:30 - 48:35
    not… they are very far from perfect. And
    there are some types of events which state
  • 48:35 - 48:41
    of the art climate models just can't
    simulate. And so, where we can- what
  • 48:41 - 48:47
    we can say very little. So well, for
    example, for hurricanes, we can say
  • 48:47 - 48:52
    with high certainty about the
    rainfall associated with hurricanes, the
  • 48:52 - 48:57
    hurricane strength itself and the
    frequency of hurricanes is something
  • 48:57 - 49:02
    which is very difficult to simulate with
    state of the art models. So our
  • 49:02 - 49:13
    uncertainty there is much higher.
    H: OK. And then, well, some, one question
  • 49:13 - 49:20
    that emerges from all of this is,
    of course, if we know this much and way
  • 49:20 - 49:27
    more than in the past, how are
    politicians still ignoring that
  • 49:27 - 49:35
    information? And how can we
    convey that into their minds?
  • 49:35 - 49:40
    FO: Well, if I knew the answer to that, I
    would probably not be standing here,
  • 49:40 - 49:49
    but actually doing politics. But I
    think it takes a frustratingly long time
  • 49:49 - 49:57
    for things to change and things should
    change much faster. But we actually- the
  • 49:57 - 50:04
    last two years have shown huge progress, I
    think, in terms of also putting climate
  • 50:04 - 50:12
    change on the agenda of every politician.
    Because, and that's largely due to the
  • 50:12 - 50:18
    Fridays For Future movement, but also to a
    degree, I think, due to the fact that we
  • 50:18 - 50:24
    now actually know that the weather that
    people experience in their backyard–and
  • 50:24 - 50:29
    pretty much independent of where their
    backyard is–is not the same as it used to
  • 50:29 - 50:37
    be. And so people do experience today
    climate change. And I think that
  • 50:37 - 50:43
    does help to bring a bit more urgency.
    And, of course, I would have said everyone
  • 50:43 - 50:48
    has climate change on their agenda, which
    was very different even two years ago,
  • 50:48 - 50:52
    where there were lots of people who
    would never talk about climate change and
  • 50:52 - 50:58
    their political agendas has played no
    role. It doesn't mean that it
  • 50:58 - 51:06
    has the right priority on that agenda,
    but it's still a huge step forward that
  • 51:06 - 51:19
    has been made. And so I think we do know
    some things that do work, but we just have
  • 51:19 - 51:28
    to just keep doing that. Yeah, I don't
    think I can say more. I don't have a magic
  • 51:28 - 51:35
    wand to change it otherwise.
    H: Maybe some other point of impact.
  • 51:35 - 51:40
    One of the question is, is it possible to
    turn the results of attribution studies
  • 51:40 - 51:48
    into recommendations for farmers and
    people who are affected in a financial way
  • 51:48 - 51:53
    by extreme weather and how to change
    agriculture to reduce losses from extreme
  • 51:53 - 51:57
    weather effects?
    FO: Yes, absolutely. So that is
  • 51:57 - 52:04
    one of the most useful things of these
    studies is well, on the one hand, to raise
  • 52:04 - 52:08
    awareness. But on the other hand, if you
    know that a drought that you have
  • 52:08 - 52:17
    experienced that has led to losses is a
    harbinger of what is to come, then that is
  • 52:17 - 52:23
    incredibly helpful to know how
    agricultural practices might need to be
  • 52:23 - 52:30
    changed. Or that insurance for losses from
    agriculture might need to be changed. And
  • 52:30 - 52:36
    so this is exactly why we do these
    attribution studies. Because not
  • 52:36 - 52:43
    every extreme event has always
    shows the fingerprints of
  • 52:43 - 52:48
    climate change. And if you know
    which of the events are the ones where
  • 52:48 - 52:54
    climate change is a real game changer, you
    also do know where to put your efforts and
  • 52:54 - 53:00
    resources to be more resilient in the
    future. And for financial losses, it
  • 53:00 - 53:06
    is on the one hand, yeah, you can use
    these studies to find out what your
  • 53:06 - 53:13
    physical risks are for your assets. And
    how they, and of course, everything that
  • 53:13 - 53:18
    I've said, comparing the counterfactual
    with the present we can do, and we do this
  • 53:18 - 53:24
    also with the future. So you can also see
    how in a two degree world, the events,
  • 53:24 - 53:29
    the likelihood and intensities are
    changing. And of course, you can then
  • 53:29 - 53:35
    also, in a less direct way, use this kind
    of information to see, to assess what
  • 53:35 - 53:43
    might be other risks from- where might be
    stranded assets, what are other risks
  • 53:43 - 53:49
    for the financial sector,
    for the financial planning.
  • 53:49 - 53:57
    Where could liability risks be and how
    could they look like. So there is, because
  • 53:57 - 54:02
    extreme weather events and their changes
    in intensity and magnitude is how climate
  • 54:02 - 54:10
    change is manifesting, it really connects
    all these aspects of where the
  • 54:10 - 54:22
    impacts of climate change are.
    H: OK, last question for today. I hope I
  • 54:22 - 54:30
    can get that right. I think the question
    is if there are study, if there are
  • 54:30 - 54:41
    studies on how we cultivates fields
    and agriculture. How does this impact the
  • 54:41 - 54:49
    overall climate in that area? The example
    here is that only an increase in water
  • 54:49 - 54:58
    consumption was directed to São Paulo. Or
    might there also be a warm world created
  • 54:58 - 55:06
    by monoculture in central Brazil?
    FO: So, yeah, I don't know details, but
  • 55:06 - 55:13
    there are, but land use changes and land
    use does play a role. On the one hand, it
  • 55:13 - 55:20
    affects the climate. So if you have, if
    you have a rainforest, you have a very
  • 55:20 - 55:27
    different climate in that location as if
    there is a savanna or plantation. And
  • 55:27 - 55:35
    also, of course, if you have monocultures,
    you are much more, your losses are
  • 55:35 - 55:42
    larger usually as if you have different
    types of agriculture. Because
  • 55:42 - 55:47
    in a monoculture everything is in
    exactly the same way vulnerable and
  • 55:47 - 55:52
    so that, yeah. So that does,
    land use change plays a hugely important
  • 55:52 - 55:59
    role with respect to the impacts of
    extreme weather. And that is one thing to
  • 55:59 - 56:04
    look at. When I was saying, talking about
    looking at vulnerability and exposure, and
  • 56:04 - 56:09
    of course also changes in the hazard are
    not just because of climate change, but
  • 56:09 - 56:13
    also because of land use change. And you
    can use exactly the same methods, but
  • 56:13 - 56:17
    instead of changing the CO2 or the
    greenhouse gases in the atmosphere of your
  • 56:17 - 56:23
    model, you can change the land use and
    then disentangle these different drivers
  • 56:23 - 56:30
    in and hazards.
    H: OK, Fredi Otto thank you very much for
  • 56:30 - 56:37
    your presentation and for the Q&A. It was
    a pleasure to have you with us. And yeah,
  • 56:37 - 56:44
    if you have any questions, any more
    questions, I guess there are ways to
  • 56:44 - 56:47
    contact you.
    FO: laughter
  • 56:47 - 56:53
    H: I think your email address and contact
    details are in the Fahrplan for all the
  • 56:53 - 56:59
    viewers that have way more questions. And,
    I don't know, do you have access to the 2D
  • 56:59 - 57:06
    world and do you explore that?
    FO: Given that I don't know what you mean,
  • 57:06 - 57:07
    probably not, but…
    laughter
  • 57:07 - 57:12
    H: OK.
    FO: That can also be changed.
  • 57:12 - 57:21
    H: Yeah, it's the the replacement for
    the congress place itself. But anyway,
  • 57:21 - 57:27
    if you viewers and you people out there
    have any more questions, contact Fredi
  • 57:27 - 57:33
    Otto. And thank you again very much for
    your talk. And, yeah. Have a
  • 57:33 - 57:35
    nice congress, all of you.
  • 57:35 - 57:39
    rc3 postroll music
  • 57:39 - 58:14
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Title:
#rC3 - Angry weather ?
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Video Language:
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Duration:
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