rC3 preroll music Herald: It is with much pleasure that I can now introduce our next speaker, so it's just started raining outside, but this heavy rain is not at all probably the extreme weather effects that we will hear about right now. The weather, the talk that we are being presented next will deal with extreme weather effects and how they are linked with climate change and how we even know about that. Our speaker today is Fredi Otto. She's associate director of the Environmental Change Institute of the University of Oxford, and she's also the lead author of the upcoming IPCC assessment report, AR6. And without with no further ado, I give you the stage Fredi, please. Frederike Otto: OK, thank you. Yeah. Hi. It's stopped raining here in Oxford, just about, but it's definitely flooded, so that might actually be something to come back to and talk about with respect to climate change. So. Whenever we hear or whenever today an extreme weather event happens, we hear about hurricanes, wildfires, droughts, etc., the question that is immediately asked is, was this, what is the role of climate change? And to answer that, for quite a long time, scientists gave an answer that we cannot attribute individual weather events to climate change. But… Sorry, OK. But this… Because the first, the one answer that people were giving were that, well, you can't attribute individual weather events or they were saying in a world where climate change happens, of course, every extreme weather event is somewhat affected by climate change. And the latter is attributed too, but that does not obviously provide much information, because it doesn't say anything about whether the event was made more likely or less likely or what the role of climate change was. And the first answer that you can't attribute individual events is not true any longer. And this is... why that has changed and how that has changed. And what we can say is what the content of this talk will be. So ultimately, every weather event, extreme or not, is if you absolutely boil down to it is unique and they all have many different causes. So there is always the role of just the natural chaotic variability of the climate system and climate and weather system that plays a role. There's always a causal factor in where the event happens, whether it's over land, over a desert, over a city or a forest, but also man-made climate change can have an influence on the likelihood and intensity of extreme weather events to occur. And so what we can say now, and what we mean when we talk about attribution of extreme weather events to climate change is how the magnitude and likelihood of an event to occur has changed because of man-made climate change. And in order to do that, we first of all need to know, what is possible weather in the world we live in today? So say we have a flooding event in Oxford and the question is, was this climate change or not? So the first question is we need to find out what type or what kind of event is the heavy rainfall event that leads to the flooding. So is it a 1 in 10 year event? Is it a 1 in 100 year event? And in order to do that, you can't just look at the observed weather records because that will tell you what the actual weather that occurred is. But it doesn't tell you what the possible weather under the same current climate conditions are. And so we need to find out what is possible weather. And to do that, we use different climate models. So we simulate under the same climate conditions that we have today, possible rainfall events in December in Oxford. And we might find out that the event that we have observed today is a one in 10 year event. And so if you do this, look at all the possible weather events, you get a distribution of possible weather under certain conditions, which is shown in the schematic on the slide here in the red curve. And then you know that when it rains above, say, 30 millimeters a day in Oxford, then you have a real problem with flooding. So you define that this is your threshold from when you speak about an extreme event. And so you have a probability of this event to occur in the world we live in today. Of course, that does not tell you the role of climate change, because in order to know that, you would also you will also need to know what would the likelihood of this event to occur have been without man-made climate change, and so. But because we know very well how many greenhouse gases have been introduced into the atmosphere since the beginning of the industrial revolution, we can actually remove these additional greenhouse gases from the climate models atmospheres that we use and simulate a world that would have been exactly as it is today, but without the greenhouse gases from the burning of fossil fuels. And in that world, we can then also ask the question, what are possible heavy rainfall events in December in Oxford? And we might find that the event that we are interested in is in that world, not a one in 10 year event, but a one in 20 year event. And because everything else is held the same, we can then attribute the difference between these two likelihoods of occurrence of the extreme event in question to man-made climate change. And so with this fake example that I've just used, we would then say climate change has doubled the likelihood of the event to occur because one that was one in 20 year event is now one in 10 years. So that is basically the whole theoretical idea behind attributing extreme events and this method can be used. And so, for example, with our initiative that's called World Weather Attribution, we have looked this year at the extreme heat in Siberia, the beginning of this year that, among other things, led to temperatures above 38 degrees in the city of Verkhoyansk, but also let to permafrost thawing and large wildfires. And that event was made so much more likely because of climate change that it's almost would have been impossible without climate change. So when we did the experiments that the models it's a one in 80 million year event in a world without climate change. And it's still a relatively extreme event in today's world, but it is possible. So this is the type of event where climate change really is a game changer. Another event that we have looked at is Hurricane Harvey that hit the Houston and Texas in 2017 and caused huge amounts of damage with the rainfall amounts it brought. And several attribution studies doing exactly what I've just described found that this type of, so this extreme rainfall associated with a hurricane like Harvey has been made three times more likely because of climate change. And colleagues of mine, Dave Frame and his team, have then used these studies to figure out how much of the economic costs this hurricane can be attributed to climate change, and found that of the 90 billion US dollars that were associated, that were associated with the flood damage from Harvey, 67 billion can be attributed to climate change, which is in particular interesting when you compare that to the state of the art economic cost estimations of climate change in general, which had estimated only 20 billion US dollars for 2017 in the US from climate change. And of course, not every year is an event like Harvey, but it shows that when you look at the impact of climate change in a more bottom up approach, so looking at the extreme events, which are how climate change manifests and affect people, you get very different numbers, as if you just look at large scale changes in temperature and precipitation. But of course, not every extreme event that occurs today has been made worse because of climate change. So this is an example of a drought in southeast Brazil that happened in 2014, 2015, where we found that Climate change did not change the likelihood of this drought to occur, so it was a one in 10 year event in 2014, 2015, and also without climate change, it has a very similar likelihood of occurrence. However, what we did find when we looked at, OK, what else has changed? Why has this drought that has occurred in a very similar way earlier in the 2000s and also in the 1970s with much less impacts. We looked at other factors and found that the population has increased a lot over the last or over the beginning of the 21st century, but in particular, the water consumption in in the area and the water usage has increased almost exponentially. And that explains why the impacts were so large. So this is what I've just said is sort of basically the the very basic idea and and how in theory these studies work and how and some results that we find. In practice, it is usually not quite as straightforward, because while the idea is still the same, we need to use climate models and statistical models for observational data to simulate possible weather in the world we live in and possible weather in the world that might have been. That is, in theory, straight forward, in practice, it's often relatively difficult, and what you see here is how the results of these studies look when you don't use schematic and if you're not a hydrologist, this might be a bit of an unfriendly plot. But it's it's basically the same as the schematic that I've showed at the beginning, but just plotted in a way that you can see the tails of the distribution particularly well, so where the extreme events are. So on the X-axis, we have the return time of the event in years on a logarithmic scale and on the Y-axis, you see the magnitude of the event and that defines what our extreme event is. And this is actually a real example from heavy rainfall in the south of the U.K. And you can see here in red, each of these red dots that that you see on the red curve is a simulation of one possible rainfall event in the South of the U.K. in the year 2015 in the world we live in today with climate change and the dashed line indicates the threshold that led to to flooding in in that year. And on the X-axis, when you go down from the dashed line, you can then see that this is roughly a one in 20 year event in the world we live in today. And all the blue dots on the blue curve are simulations of possible heavy rainfall in the South of the U.K. in 2015, in a world without man- made climate change. And you can see that these two curves are different and significantly different, but they are still relatively close together. And so the event in the world without climate change would have been a bit less likely, so we have roughly a 40 percent increase in the likelihood. But still other factors like, yeah, just the chaotic variability of the weather and also, of course, than other factors on the ground where houses build in floodplains and so on play an important role. So this is the actual attribution step. So when we find out what the role of climate change is, but of course, in order to do that, there are a few steps before that are crucially important and absolutely determine the outcome. And the first step, the first thing to find out is what has actually happened, because usually when we read about extreme weather events or when we hear about extreme weather events, you see pictures in newspapers of flooded parts of the world. And so you don't usually have observed weather recordings reported in the media. And the same actually is true for us. So when we are, so we work a lot with the Red Cross and they ask us: OK, we have this large flooding event, can you do an attribution study? Can you tell us what the role of climate change is? Then we also just know: OK, there is flooding. And so the first step is we need to find out what is the weather event that actually caused that flooding. And that is not always that straightforward. And this is what you see here on this map, on this slide is a relatively stark example, but not an untypical. So it's of an extreme rainfall event on the 10th of November 2018 in Kenya. And on the left hand side is one data product of observational data, of observational rainfall data that is available and on the right hand side is another showing the same event. And the scale which I failed to to say on the slide in millimeters per day. And so on the left hand side, you have extreme rainfall of above 50 millimeters per day, which is considering that, for example, in in my home town of Kiel in Schleswig- Holstein, there is about 700 millimeters of rainfall per year. You can see that 50 millimeters in a single day is very heavy rainfall, whereas in the other data product, you don't see as much rain. You still see large rain, but it's not in the same magnitude, and it's also not exactly in the same place. And so given that most countries in the world do not have an open data policy, so you can't actually get access to the observed station data, but you have to use available, publicly available products like the two have shown here, you have to know and you have to work with experts in the region to actually know who hopefully has access to the data to actually find out what has happened in the first place. But of course, if you don't know that or there is not always a perfect answer, then if you don't know what event that is. It's very difficult to do an attribution study. Assuming though you have found a data product that you trust, the next question then is what is actually the right threshold to use for the event? So if you have flooding that was pretty obviously caused by one day extreme rainfall event, then that would be your definition of the event. But it could also be that the flooding has been caused by a very soggy, rainy season. So actually, the really the real event you would want to look at is over a much longer time scale or if the flooding occurred mainly because of some water management in the rivers and has actually flooded further upstream, your spatial definition of the event would be very different. And so and what you see here on this plot is an example of a heat wave in Europe in 2019. And there, what usually makes the headlines is the maximum daily temperature. So if records are broken, so you could use that as a definition of the event that you're interested in. But of course, what really causes the losses and damages from extreme events is not necessarily the one day maximum temperature, but it is when heat waves last for longer, and especially when the night temperatures are also high and not just the daytime temperatures. So you might want to look at an event over five day period instead of just the maximum daily temperatures. Or, and this is sort of why I have shown the pressure plot on the right hand side, which is really just an illustration, it's not terribly important what's on there. But there are, of course, different weather systems that can cause heat waves, especially in the area here in the south of France. It could be a relatively short lived high pressure system bringing hot air from the Mediterranean. Or it could be something that is caused from a long time stationary high pressure system over all of Europe. If you want to take that into account, obviously also your event is different. And there is no right or wrong way to define the event because there are legitimate interests in the maximum daily temperatures, legitimate interest in just a specific type of pressure system and interest in what actually causes more excess mortality on people, what would be the three day or longer type of heat waves. But whichever definition you choose, it will determine the outcome of the study. And here are some typical results of attribution studies when you look at them in a slightly more scientific way and slightly less just the headline way as the ones that I've shown earlier. Because, of course, what also is important is not only how you define the event, depending on the impacts and depending on what you're interested in. The extreme event and what observational data you have available. But of course, there's also then the question of what models, what climate models do we have available? And there's always some tradeoff between what exactly caused the event and what we can meaningfully simulate in a climate model. And then all climate models are good for something and bad for other things. So there always need to be a model evaluation stage. So where you test if the models that you have available are actually able to simulate in a reliable way the event that you're interested in. But even if you have done all this, it can sometimes be that the models and the observations that you have show very different things. And so the heat wave in Germany in 2019, which was also on the slide before, is an example of that. When we look at the long term observations of extreme, of high temperatures and see how they have changed over time, we find that, because of the change in climate, we have observed, the likelihood of this type of heat wave has increased more, yeah, about 300 times. So you see this in the black bar, the black bar in the middle of the blue bar, on the left hand side, at the very top where it says DWD obs, that's the Deutscher Wetterdienst observations and we see that where this black bar is about, again, a logarithmic scale, about 300 hundred times more likely. But of course, because we have only 100 years worth of observations and summer temperatures are extremely variable, there is a large uncertainty around this change. And so we cannot, from the observations alone, we cannot exclude 100.000 times change in the likelihood of this heat wave. But similarly, also not a 20 times heat wave. But what the main point is, that in all the climate models and all the red bars that you see on there are the same results, but for climate models where we have compared today's likelihood of the event to occur with the likelihood in the world without climate change, and you see that the change is much lower. And of course, climate change is not the only thing that has changed and that has affected observed temperatures. But other factors like land use change and things like that are much smaller in the size than the climate signal. So they cannot explain this discrepancy. So this means that the climate models we have available for this type of study have obviously a problem with the extreme temperatures in a small scale. And there are effects that we don't yet understand. And so we can't say: OK, this heat wave was made 10 times more likely. But we can only say, that with our current knowledge and understanding, we can say that climate change was an absolute game changer for this type of heat wave, but we can't really quantify it. On the right hand side is a much nicer result on the top one, which is for extreme rainfall, in Texas 2019 and nicer result I mean now for a scientist and in a scientific way. So we have in blue two different types of observations from the heavy rainfall event, and they both show pretty much exactly the same result. And also the two climate models that we had available that passed the model evaluation tests show an increase in the likelihood of this event to occur. That is very similar to that in the observations in terms of order of magnitude. And so in that case, we can just synthesize the results and give an overarching answer, which is that the likelihood of this event to occur has about doubled because of man- made climate change. And the last example that I, that is here is for drought in Somalia in 2010, where not only the observations are extremely uncertain. So from the observations, you could say we could have both an increase in likelihood or a decrease in likelihood by a factor of 10. But also the climate models show a very, very mixed picture where you can't even see a sign that that is conclusive. So in that case, you can say, we can exclude that climate change made this event more or less than 10 times, more than 10 times or less than 10 times more likely. But we can't say anything more. So we can exclude that it's a complete game- changer like we have for heat waves, for example. But that's about the only thing that you can say for a result like this. So this was sort of the most detailed scientific stuff that I would like to show, because I think it's important to get some background behind the headline results that would just be climate change doubled the likelihood of this event. So there are always four possible outcomes of an attribution study a priori. And that is because climate change affects extreme weather in two ways basically. One way is what we would call the thermodynamic way, which means that because we have more greenhouse gases in the atmosphere, the atmosphere overall gets warmer. So you have, on average, an increase in the likelihood of heat waves decrease in the likelihood of cold waves. A warmer atmosphere can also hold more water vapor that needs to get out of the atmosphere as rainfall. So on average, from the warming alone, we would also have more extreme rainfall. But then there's the second effect, which I call the dynamic effect, and that is because we've changed the composition of the atmosphere, that affects the atmospheric circulation. So where weather systems develop, how they develop and and how they move. And this effect can either be in the same direction as the warming effect. So it can be that we expect more extreme rainfall, but we also get more low pressure systems bring rain to get even more extreme rainfall. But these two effects can also counteract each other. And so you can expect more rainfall on average. But if you don't get the weather systems that bring rain, you either have no change in likelihood and intensity or, if the dynamics win, you have actually decrease in the likelihood of extreme rainfall in a particular season or region. And so this is why a priori, that can always be four outcomes: It can be that the event was made more likely. It can be that it was made less likely. It can be there's no change. Or it can be that with our current understanding and tools, we can't actually answer the question. And so this has been possible to do now for about a decade, but only in the last five years really have many, many people or many scientists started to do these studies. And so there is actually a relatively large, there are lots of attribution studies on different kinds of extreme events. And what you can see on this map here is what the news and energy outlet CarbonBrief has put all these studies together. And you can see in red where climate change played an important role, and blue where climate change did not play a role. And in gray, that was an inconclusive result. This is very important, though, that this is not representative of the extreme events that have happened. This is just represents the studies that have been done by scientists and they are, of course biased towards where scientists live and also towards extreme events that are relatively easy to simulate with climate models. So there are lots of heat waves in Europe, Australia and North America because that is where people live. And on this next map, I have tried to show the discrepancy between the extreme events that have happened and those for which we actually do know the role of climate change. So here in red are deaths associated with extreme events since 2003. So since the first event attribution study. And it's death from heat waves, storms, heavy rainfall events and droughts primarily in different parts of the world, the bubble is always on the capital of the country. And the larger the bubble, the more deaths due to extreme events in those years. And in black overlaying that are those deaths for which we know the role of climate change. So that doesn't mean that the deaths are attributed to climate change, but it means that there we do know whether or not to what extent climate change played a role. And you can see that most of the European countries, the black circle is almost as large as the red one. So for most of the extremes or most of the deaths associated with extreme events, we do know the role of climate change. But for many other parts of the world, there are no or very small black circles. So for most of the events and the deaths associated with them, we don't know what the role of climate change is. And I've used death here not because I'm particularly morbid, but because it's an indicator of the impacts of extreme weather that is relatively good comparable between countries. So this means that with event attribution methods that we have developed over the last decade, we now have the tools available to do, to provide an inventory of the impacts of climate change on our livelihoods. But we are very far from having such an inventory at the moment because most of the events that have happened, we actually don't know what the role of climate change is. And so we don't know in detail on country scale and on the scale where people live and make decisions, what the role of climate change is today. There's another slightly related issue with that is, that the extreme events that I've used to create the map are shown before with the death of climate change, with the death of extreme weather events. They are from a database called EM-DAT, which is a publicly available database where losses and damages associated with disasters technological disasters, but also disasters associated with weather are recorded. But, of course, they only can record losses and damages if these losses and damages are recorded in the first place. And so what you see on this map is in grey and then overlayed with different with different circles are heat waves that have occurred, they have occurred between 1986 and 2015 on this map. But you could draw a map from 1900 to today, and it would look very similar. And that shows lots and lots of heat waves reported in Europe and in the US, India, but there are no heat waves reported in most of sub- Saharan Africa. However, when you look at observations, and also we see that extreme heat has increased quite dramatically in most parts of the world and a particular hotspot is sub-Saharan Africa. So, we know from when we look at the weather that heat waves are happening, but it's not registered and it's not recorded. So we have no idea how many people are actually affected by these heat waves. And so we then, of course, don't do attribution studies and don't find out what the role of climate change in these heat waves is. So in order to really understand the whole picture, we would also need to start recording these type of events in other parts of the world. And so my very last point, before, I hope that you have questions for me, is: Of course, everything I've said so far was talking about the hazards, so talking about the weather event and how climate change affects the hazard. But of course that is not the same or translates immediately into losses and damages, because whether or not a weather event actually has any impact at all is completely driven by exposure and vulnerability. So who and what is in harm's way. And I've already shown, I've already mentioned the example early on with the drought in Brazil, where the huge losses and damages were to a large degree attributable to the increase in water consumption. And thus, therefore, in order to really find out how climate change is affecting us today, we not only need to define the extreme events so that it connects to the impacts, but also look into vulnerability and exposure: What is changing, what's there and what are the important factors. But we can do that. And so we have really made a lot of progress in understanding of how climate change not only affects global mean temperature, which we have known for centuries, and how it affects large scale changes in temperature and precipitation, which we have also known for a very long time. But we now have actually all the puzzle pieces together to really understand what climate change means on the scale where people live and where decisions are made. We just need to put them together. And one lens or one way of where they are currently put together is, for example, in courts. And so because it's obviously people who experience losses and damages from climate change. And so one way to address that is going through national governments, local governments, hoping for adaptation measures to be put in place. But if that's not forthcoming quickly enough, there is the option to sue. And so this is one example which is currently happening in Germany where a peruvian farmer is suing RWE to basically pay their share of a adaptation because of largely increased flood risk from glacier melt in the area. And they want RWE to pay from their contribution to climate change, where their emissions and then have some funding for the adaptation measures from them. And that is one example of where these kind of attribution studies can be used in a very direct way to hopefully change something in the real world. And with this, I would like to end and yeah, leave you with some references, and hope you have some questions for me. Herald: Sind wir durch? So, ja. Herzlichen Dank für den Vortrag. Ich hab, bevor wir zum Q&A kommen muss ich einmal mich im Namen der Produktion bei den Zuschauern entschuldigen, ich glaube ihr hattet etwas Produktionssound auf den Ohren, das sollte natürlich nicht so sein. Gut, wir haben jetzt keine Fragen aus dem Chat bisher. Aber vielleicht eine Frage von mir, das letzte Beispiel war ja ein Fall einer Klage über Ländergrenzen hinaus quasi, ist das ein Ansatz, den man, den wir in Zukunft öfter sehen würden, das heißt, dass über Ländergrenzen hinweg Menschen oder Organisationen sich gegenseitig versuchen quasi über den Klageweg auf den richtigen Weg zu bringen. FO: Also es ist tatsächlich ein, eine Ausnahme, dass das im Fall RWE und Lliuya funktioniert, denn das deutsche Recht sieht vor, dass Firmen, die in Deutschland ansässig sind auch verschieden verantworlich sind, die nicht in Deutschland stattfinden. H: So sorry to interrupt. I just realized that we are still in English talk. Sorry for that. FO: OK. No worries. So your question was if we're going to see more international court cases where across countries, across nation states we have climate litigation. And this type of litigation that I've just shown as the example is in so far an exception, as in German law, a company is also responsible for the damages caused outside of Germany. Which is not the case, for example, for companies in the US or so. So, and this is why Lliuya sued RWE and not, for example, ExxonMobil. But these type of cases, where this Lliuya case is an example. We see a lot of a lot of them, an increasing number of them each year. And they are difficult to do across nations because this, the German law is exceptional on that case. But there are other ways, like, for example, why are human rights courts that can be done across nation states and that is also happening. So it's at the moment, it is still legally not super straightforward to to actually win these cases, but increasingly a lot of lawyers working on that so that we will see a lot of change in that in the coming years. H: OK, thank you. In the meantime, there appeared some questions from the chat and from the Internet. I will go through them. First question is: are the results of the individual attribution studies published as open data in a machine readable format? FO: laughter So all the studies that we do that that I've done with my team, with world weather attribution. So there all the data is available, and it's available on a platform that's called Climate Explorer. So that should be machine readable. So and this is deliberately because yeah, because we want to make it as transparent as possible so everyone can go away, use our data, and redo our studies, and find out if we made any mistakes. But this is not the case for all the studies that exist, because most of them or many of them are published in peer reviewed journals and not all peer reviewed journals have open data and open access policies. But increasingly, journals have. So if you, for example, go to the CarbonBrief website and look at the map of studies, there you have links to all the studies. And a lot of them have the data available. H: OK, maybe a follow up to this one. The next question is, are the models somehow available or usable for a wider interest public or is APC required? I'm not quite sure what APC means. FO: So the model data is publicly available from–and this is one reason why we have been able to do these studies because until relatively recently, model data was not publicly available and only scientist working in a specific country could use the model developed in that country–but now all the model data is shared publicly and people can use it. So it's definitely there and usable. It just requires some expertise to make sense of it. But it's, yeah, people can use it. H: OK, the next question is: to what certainty can you set up counterfactual models, which are an important reference to your percentage value, and what data are the basis for these models? FO: So the counterfactual simulations are- the climate models we use are basically the same models that are used also for the weather forecast. They are just run in lower resolution. So, which I guess most of this audience knows what that means. So the data points for the part, so that it's not so computing intensive. And these models, they are tested against observed data. And so that is how we do the model evaluation. So that is some simulations of the present day. And for the counterfactual, we know extremely well how many greenhouse gases have been included into the atmosphere since the beginning of the Industrial Revolution, so that there is some very large certainty with that number and we remove that from the models' atmospheres. So the models have exactly the same set up, but the lower greenhouse gases, lower amount of greenhouse gases in the atmosphere, and then are spun up and run in exactly the same way. So, they, but of course, we can't test the counterfactual. And so that means that we assume that the sort of the the weather was still the same, physics will still hold in the counterfactual. And that the models that are developed using present day represent the counterfactual. Which is, which is an assumption. But it is not a completely unreasonable assumption, because of course, we have now decades of model development and have seen that, in fact, that indeed climate model projections that have been made 30 years ago have actually come… come to… have been realized, and pretty much the same way on a large scale that they have, as they had been predicted 30 years ago. And so that assumption is not, yeah, it's not a big assumption. So the counterfactual itself is not a problem. But of course, also the present day model simulations, they are not… they are very far from perfect. And there are some types of events which state of the art climate models just can't simulate. And so, where we can- what we can say very little. So well, for example, for hurricanes, we can say with high certainty about the rainfall associated with hurricanes, the hurricane strength itself and the frequency of hurricanes is something which is very difficult to simulate with state of the art models. So our uncertainty there is much higher. H: OK. And then, well, some, one question that emerges from all of this is, of course, if we know this much and way more than in the past, how are politicians still ignoring that information? And how can we convey that into their minds? FO: Well, if I knew the answer to that, I would probably not be standing here, but actually doing politics. But I think it takes a frustratingly long time for things to change and things should change much faster. But we actually- the last two years have shown huge progress, I think, in terms of also putting climate change on the agenda of every politician. Because, and that's largely due to the Fridays For Future movement, but also to a degree, I think, due to the fact that we now actually know that the weather that people experience in their backyard–and pretty much independent of where their backyard is–is not the same as it used to be. And so people do experience today climate change. And I think that does help to bring a bit more urgency. And, of course, I would have said everyone has climate change on their agenda, which was very different even two years ago, where there were lots of people who would never talk about climate change and their political agendas has played no role. It doesn't mean that it has the right priority on that agenda, but it's still a huge step forward that has been made. And so I think we do know some things that do work, but we just have to just keep doing that. Yeah, I don't think I can say more. I don't have a magic wand to change it otherwise. H: Maybe some other point of impact. One of the question is, is it possible to turn the results of attribution studies into recommendations for farmers and people who are affected in a financial way by extreme weather and how to change agriculture to reduce losses from extreme weather effects? FO: Yes, absolutely. So that is one of the most useful things of these studies is well, on the one hand, to raise awareness. But on the other hand, if you know that a drought that you have experienced that has led to losses is a harbinger of what is to come, then that is incredibly helpful to know how agricultural practices might need to be changed. Or that insurance for losses from agriculture might need to be changed. And so this is exactly why we do these attribution studies. Because not every extreme event has always shows the fingerprints of climate change. And if you know which of the events are the ones where climate change is a real game changer, you also do know where to put your efforts and resources to be more resilient in the future. And for financial losses, it is on the one hand, yeah, you can use these studies to find out what your physical risks are for your assets. And how they, and of course, everything that I've said, comparing the counterfactual with the present we can do, and we do this also with the future. So you can also see how in a two degree world, the events, the likelihood and intensities are changing. And of course, you can then also, in a less direct way, use this kind of information to see, to assess what might be other risks from- where might be stranded assets, what are other risks for the financial sector, for the financial planning. Where could liability risks be and how could they look like. So there is, because extreme weather events and their changes in intensity and magnitude is how climate change is manifesting, it really connects all these aspects of where the impacts of climate change are. H: OK, last question for today. I hope I can get that right. I think the question is if there are study, if there are studies on how we cultivates fields and agriculture. How does this impact the overall climate in that area? The example here is that only an increase in water consumption was directed to São Paulo. Or might there also be a warm world created by monoculture in central Brazil? FO: So, yeah, I don't know details, but there are, but land use changes and land use does play a role. On the one hand, it affects the climate. So if you have, if you have a rainforest, you have a very different climate in that location as if there is a savanna or plantation. And also, of course, if you have monocultures, you are much more, your losses are larger usually as if you have different types of agriculture. Because in a monoculture everything is in exactly the same way vulnerable and so that, yeah. So that does, land use change plays a hugely important role with respect to the impacts of extreme weather. And that is one thing to look at. When I was saying, talking about looking at vulnerability and exposure, and of course also changes in the hazard are not just because of climate change, but also because of land use change. And you can use exactly the same methods, but instead of changing the CO2 or the greenhouse gases in the atmosphere of your model, you can change the land use and then disentangle these different drivers in and hazards. H: OK, Fredi Otto thank you very much for your presentation and for the Q&A. It was a pleasure to have you with us. And yeah, if you have any questions, any more questions, I guess there are ways to contact you. FO: laughter H: I think your email address and contact details are in the Fahrplan for all the viewers that have way more questions. And, I don't know, do you have access to the 2D world and do you explore that? FO: Given that I don't know what you mean, probably not, but… laughter H: OK. FO: That can also be changed. H: Yeah, it's the the replacement for the congress place itself. But anyway, if you viewers and you people out there have any more questions, contact Fredi Otto. And thank you again very much for your talk. And, yeah. Have a nice congress, all of you. rc3 postroll music Subtitles created by c3subtitles.de in the year 2020. Join, and help us!